Centralize logging (#6434)

* Logging

* Style

* hf_logging > utils.logging

* Address @thomwolf's comments

* Update test

* Update src/transformers/benchmark/benchmark_utils.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Revert bad change

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

src/transformers/__init__.py

@@ -17,8 +17,6 @@ else:
     absl.logging.set_stderrthreshold("info")
     absl.logging._warn_preinit_stderr = False
 
-import logging
-
 # Configurations
 from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig
 from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, AutoConfig
@@ -184,9 +182,10 @@ from .tokenization_xlnet import SPIECE_UNDERLINE, XLNetTokenizer
 from .trainer_utils import EvalPrediction, set_seed
 from .training_args import TrainingArguments
 from .training_args_tf import TFTrainingArguments
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
 if is_sklearn_available():

src/transformers/activations.py

@@ -1,11 +1,12 @@
-import logging
 import math
 
 import torch
 import torch.nn.functional as F
 
+from .utils import logging
 
-logger = logging.getLogger(__name__)
+
+logger = logging.get_logger(__name__)
 
 
 def swish(x):

src/transformers/benchmark/benchmark.py

@@ -18,13 +18,13 @@
 """
 
 
-import logging
 import timeit
 from typing import Callable, Optional
 
 from ..configuration_utils import PretrainedConfig
 from ..file_utils import is_py3nvml_available, is_torch_available
 from ..modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
+from ..utils import logging
 from .benchmark_utils import (
     Benchmark,
     Memory,
@@ -45,7 +45,7 @@ if is_py3nvml_available():
     import py3nvml.py3nvml as nvml
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 
 class PyTorchBenchmark(Benchmark):

src/transformers/benchmark/benchmark_args.py

@@ -14,11 +14,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import logging
 from dataclasses import dataclass, field
 from typing import Tuple
 
 from ..file_utils import cached_property, is_torch_available, is_torch_tpu_available, torch_required
+from ..utils import logging
 from .benchmark_args_utils import BenchmarkArguments
 
 
@@ -29,7 +29,7 @@ if is_torch_tpu_available():
     import torch_xla.core.xla_model as xm
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 
 @dataclass

src/transformers/benchmark/benchmark_args_tf.py

@@ -14,11 +14,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import logging
 from dataclasses import dataclass, field
 from typing import Tuple
 
 from ..file_utils import cached_property, is_tf_available, tf_required
+from ..utils import logging
 from .benchmark_args_utils import BenchmarkArguments
 
 
@@ -26,7 +26,7 @@ if is_tf_available():
     import tensorflow as tf
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 
 @dataclass

src/transformers/benchmark/benchmark_args_utils.py

@@ -16,13 +16,14 @@
 
 import dataclasses
 import json
-import logging
 from dataclasses import dataclass, field
 from time import time
 from typing import List
 
+from ..utils import logging
 
-logger = logging.getLogger(__name__)
+
+logger = logging.get_logger(__name__)
 
 
 def list_field(default=None, metadata=None):

src/transformers/benchmark/benchmark_tf.py

@@ -18,7 +18,6 @@
 """
 
 
-import logging
 import random
 import timeit
 from functools import wraps
@@ -27,6 +26,7 @@ from typing import Callable, Optional
 from ..configuration_utils import PretrainedConfig
 from ..file_utils import is_py3nvml_available, is_tf_available
 from ..modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
+from ..utils import logging
 from .benchmark_utils import (
     Benchmark,
     Memory,
@@ -46,7 +46,7 @@ if is_tf_available():
 if is_py3nvml_available():
     import py3nvml.py3nvml as nvml
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 
 def run_with_tf_optimizations(do_eager_mode: bool, use_xla: bool):

src/transformers/benchmark/benchmark_utils.py

@@ -7,7 +7,6 @@ Copyright by the AllenNLP authors.
 import copy
 import csv
 import linecache
-import logging
 import os
 import platform
 import sys
@@ -22,6 +21,7 @@ from transformers import AutoConfig, PretrainedConfig
 from transformers import __version__ as version
 
 from ..file_utils import is_psutil_available, is_py3nvml_available, is_tf_available, is_torch_available
+from ..utils import logging
 from .benchmark_args_utils import BenchmarkArguments
 
 
@@ -43,7 +43,7 @@ else:
     from signal import SIGKILL
 
 
-logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
 _is_memory_tracing_enabled = False
@@ -94,7 +94,7 @@ def separate_process_wrapper_fn(func: Callable[[], None], do_multi_processing: b
         return result
 
     if do_multi_processing:
-        logging.info("fFunction {func} is executed in its own process...")
+        logger.info(f"Function {func} is executed in its own process...")
         return multi_process_func
     else:
         return func

src/transformers/commands/convert.py

@@ -1,8 +1,9 @@
 from argparse import ArgumentParser, Namespace
-from logging import getLogger
 
 from transformers.commands import BaseTransformersCLICommand
 
+from ..utils import logging
+
 
 def convert_command_factory(args: Namespace):
     """
@@ -52,7 +53,7 @@ class ConvertCommand(BaseTransformersCLICommand):
         finetuning_task_name: str,
         *args
     ):
-        self._logger = getLogger("transformers-cli/converting")
+        self._logger = logging.get_logger("transformers-cli/converting")
 
         self._logger.info("Loading model {}".format(model_type))
         self._model_type = model_type

src/transformers/commands/run.py

@@ -1,11 +1,12 @@
-import logging
 from argparse import ArgumentParser
 
 from transformers.commands import BaseTransformersCLICommand
 from transformers.pipelines import SUPPORTED_TASKS, Pipeline, PipelineDataFormat, pipeline
 
+from ..utils import logging
 
-logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
 def try_infer_format_from_ext(path: str):

src/transformers/commands/serving.py

@@ -1,4 +1,3 @@
-import logging
 from argparse import ArgumentParser, Namespace
 from typing import Any, List, Optional
 
@@ -6,6 +5,8 @@ from transformers import Pipeline
 from transformers.commands import BaseTransformersCLICommand
 from transformers.pipelines import SUPPORTED_TASKS, pipeline
 
+from ..utils import logging
+
 
 try:
     from fastapi import Body, FastAPI, HTTPException

src/transformers/commands/train.py

@@ -1,11 +1,12 @@
 import os
 from argparse import ArgumentParser, Namespace
-from logging import getLogger
 
 from transformers import SingleSentenceClassificationProcessor as Processor
 from transformers import TextClassificationPipeline, is_tf_available, is_torch_available
 from transformers.commands import BaseTransformersCLICommand
 
+from ..utils import logging
+
 
 if not is_tf_available() and not is_torch_available():
     raise RuntimeError("At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training")
@@ -76,7 +77,7 @@ class TrainCommand(BaseTransformersCLICommand):
         train_parser.set_defaults(func=train_command_factory)
 
     def __init__(self, args: Namespace):
-        self.logger = getLogger("transformers-cli/training")
+        self.logger = logging.get_logger("transformers-cli/training")
 
         self.framework = "tf" if is_tf_available() else "torch"
 

src/transformers/configuration_auto.py

@@ -15,7 +15,6 @@
 """ Auto Config class. """
 
 
-import logging
 from collections import OrderedDict
 
 from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig
@@ -45,9 +44,6 @@ from .configuration_xlm_roberta import XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP
 from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig
 
 
-logger = logging.getLogger(__name__)
-
-
 ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = dict(
     (key, value)
     for pretrained_map in [

src/transformers/configuration_bart.py

@@ -14,14 +14,12 @@
 # limitations under the License.
 """ BART configuration """
 
-
-import logging
-
 from .configuration_utils import PretrainedConfig
 from .file_utils import add_start_docstrings_to_callable
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 BART_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     "facebook/bart-base": "https://s3.amazonaws.com/models.huggingface.co/bert/facebook/bart-base/config.json",

src/transformers/configuration_bert.py

@@ -15,13 +15,11 @@
 # limitations under the License.
 """ BERT model configuration """
 
-
-import logging
-
 from .configuration_utils import PretrainedConfig
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 BERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     "bert-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json",

src/transformers/configuration_camembert.py

@@ -15,13 +15,11 @@
 # limitations under the License.
 """ CamemBERT configuration """
 
-
-import logging
-
 from .configuration_roberta import RobertaConfig
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     "camembert-base": "https://s3.amazonaws.com/models.huggingface.co/bert/camembert-base-config.json",

src/transformers/configuration_ctrl.py

@@ -14,13 +14,11 @@
 # limitations under the License.
 """ Salesforce CTRL configuration """
 
-
-import logging
-
 from .configuration_utils import PretrainedConfig
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP = {"ctrl": "https://s3.amazonaws.com/models.huggingface.co/bert/ctrl-config.json"}
 

src/transformers/configuration_distilbert.py

@@ -14,13 +14,11 @@
 # limitations under the License.
 """ DistilBERT model configuration """
 
-
-import logging
-
 from .configuration_utils import PretrainedConfig
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     "distilbert-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-config.json",

src/transformers/configuration_dpr.py

@@ -14,13 +14,11 @@
 # limitations under the License.
 """ DPR model configuration """
 
-
-import logging
-
 from .configuration_bert import BertConfig
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 DPR_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     "facebook/dpr-ctx_encoder-single-nq-base": "https://s3.amazonaws.com/models.huggingface.co/bert/facebook/dpr-ctx_encoder-single-nq-base/config.json",

src/transformers/configuration_electra.py

@@ -15,13 +15,11 @@
 # limitations under the License.
 """ ELECTRA model configuration """
 
-
-import logging
-
 from .configuration_utils import PretrainedConfig
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     "google/electra-small-generator": "https://s3.amazonaws.com/models.huggingface.co/bert/google/electra-small-generator/config.json",

src/transformers/configuration_encoder_decoder.py

@@ -15,12 +15,12 @@
 # limitations under the License.
 
 import copy
-import logging
 
 from .configuration_utils import PretrainedConfig
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 
 class EncoderDecoderConfig(PretrainedConfig):

src/transformers/configuration_flaubert.py

@@ -14,13 +14,11 @@
 # limitations under the License.
 """ Flaubert configuration, based on XLM. """
 
-
-import logging
-
 from .configuration_xlm import XLMConfig
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     "flaubert/flaubert_small_cased": "https://s3.amazonaws.com/models.huggingface.co/bert/flaubert/flaubert_small_cased/config.json",

src/transformers/configuration_gpt2.py

@@ -15,13 +15,11 @@
 # limitations under the License.
 """ OpenAI GPT-2 configuration """
 
-
-import logging
-
 from .configuration_utils import PretrainedConfig
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     "gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-config.json",

src/transformers/configuration_longformer.py

@@ -14,13 +14,13 @@
 # limitations under the License.
 """ Longformer configuration """
 
-import logging
 from typing import List, Union
 
 from .configuration_roberta import RobertaConfig
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     "allenai/longformer-base-4096": "https://s3.amazonaws.com/models.huggingface.co/bert/allenai/longformer-base-4096/config.json",

src/transformers/configuration_mbart.py

@@ -14,12 +14,11 @@
 # limitations under the License.
 """ MBART configuration """
 
-import logging
-
 from .configuration_bart import BartConfig
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 MBART_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     "facebook/mbart-large-en-ro": "https://s3.amazonaws.com/models.huggingface.co/bert/facebook/mbart-large-en-ro/config.json",

src/transformers/configuration_mmbt.py

@@ -15,11 +15,10 @@
 # limitations under the License.
 """ MMBT configuration """
 
-
+from .utils import logging
-import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 
 class MMBTConfig(object):

src/transformers/configuration_mobilebert.py

@@ -12,12 +12,11 @@
 # limitations under the License.
 """ MobileBERT model configuration """
 
-import logging
-
 from .configuration_utils import PretrainedConfig
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     "mobilebert-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/google/mobilebert-uncased/config.json"

src/transformers/configuration_openai.py

@@ -15,13 +15,11 @@
 # limitations under the License.
 """ OpenAI GPT configuration """
 
-
-import logging
-
 from .configuration_utils import PretrainedConfig
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     "openai-gpt": "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-config.json"

src/transformers/configuration_pegasus.py

@@ -14,13 +14,12 @@
 # limitations under the License.
 """ PEGASUS model configuration """
 
-import logging
-
 from .configuration_bart import BART_CONFIG_ARGS_DOC, BartConfig
 from .file_utils import add_start_docstrings_to_callable
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 # These config values do not vary between checkpoints
 DEFAULTS = dict(

src/transformers/configuration_reformer.py

@@ -15,13 +15,11 @@
 # limitations under the License.
 """ Reformer model configuration """
 
-
-import logging
-
 from .configuration_utils import PretrainedConfig
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     "google/reformer-crime-and-punishment": "https://cdn.huggingface.co/google/reformer-crime-and-punishment/config.json",

src/transformers/configuration_retribert.py

@@ -14,13 +14,11 @@
 # limitations under the License.
 """ RetriBERT model configuration """
 
-
-import logging
-
 from .configuration_utils import PretrainedConfig
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 # TODO: uploadto AWS
 RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {

src/transformers/configuration_roberta.py

@@ -15,13 +15,11 @@
 # limitations under the License.
 """ RoBERTa configuration """
 
-
-import logging
-
 from .configuration_bert import BertConfig
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     "roberta-base": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-config.json",

src/transformers/configuration_t5.py

@@ -14,13 +14,11 @@
 # limitations under the License.
 """ T5 model configuration """
 
-
-import logging
-
 from .configuration_utils import PretrainedConfig
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 T5_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     "t5-small": "https://s3.amazonaws.com/models.huggingface.co/bert/t5-small-config.json",

src/transformers/configuration_transfo_xl.py

@@ -16,13 +16,12 @@
 """ Transformer XL configuration """
 
 
-import logging
 import warnings
-
 from .configuration_utils import PretrainedConfig
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     "transfo-xl-wt103": "https://s3.amazonaws.com/models.huggingface.co/bert/transfo-xl-wt103-config.json",

src/transformers/configuration_utils.py

@@ -18,14 +18,14 @@
 
 import copy
 import json
-import logging
 import os
 from typing import Any, Dict, Tuple
 
 from .file_utils import CONFIG_NAME, cached_path, hf_bucket_url, is_remote_url
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 
 class PretrainedConfig(object):

src/transformers/configuration_xlm.py

@@ -14,13 +14,11 @@
 # limitations under the License.
 """ XLM configuration """
 
-
-import logging
-
 from .configuration_utils import PretrainedConfig
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 XLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     "xlm-mlm-en-2048": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-en-2048-config.json",

src/transformers/configuration_xlm_roberta.py

@@ -15,13 +15,11 @@
 # limitations under the License.
 """ XLM-RoBERTa configuration """
 
-
-import logging
-
 from .configuration_roberta import RobertaConfig
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     "xlm-roberta-base": "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-roberta-base-config.json",

src/transformers/configuration_xlnet.py

@@ -15,13 +15,13 @@
 # limitations under the License.
 """ XLNet configuration """
 
-import logging
 import warnings
 
 from .configuration_utils import PretrainedConfig
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     "xlnet-base-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-base-cased-config.json",

src/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py

@@ -16,14 +16,15 @@
 
 
 import argparse
-import logging
 
 import torch
 
 from transformers import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
 
+from .utils import logging
 
-logging.basicConfig(level=logging.INFO)
+
+logging.set_verbosity_info()
 
 
 def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, albert_config_file, pytorch_dump_path):

src/transformers/convert_bart_original_pytorch_checkpoint_to_pytorch.py

@@ -16,7 +16,6 @@
 
 
 import argparse
-import logging
 import os
 from pathlib import Path
 
@@ -33,6 +32,8 @@ from transformers import (
 )
 from transformers.modeling_bart import _make_linear_from_emb
 
+from .utils import logging
+
 
 FAIRSEQ_MODELS = ["bart.large", "bart.large.mnli", "bart.large.cnn", "bart_xsum/model.pt"]
 extra_arch = {"bart.large": BartModel, "bart.large.mnli": BartForSequenceClassification}
@@ -40,8 +41,8 @@ if version.parse(fairseq.__version__) < version.parse("0.9.0"):
     raise Exception("requires fairseq >= 0.9.0")
 
 
-logging.basicConfig(level=logging.INFO)
+logging.set_verbosity_info()
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 SAMPLE_TEXT = " Hello world! cécé herlolip"
 

src/transformers/convert_bert_original_tf2_checkpoint_to_pytorch.py

@@ -8,7 +8,6 @@ The script re-maps the TF2.x Bert weight names to the original names, so the mod
 You may adapt this script to include classification/MLM/NSP/etc. heads.
 """
 import argparse
-import logging
 import os
 import re
 
@@ -17,9 +16,11 @@ import torch
 
 from transformers import BertConfig, BertModel
 
+from .utils import logging
 
-logging.basicConfig(level=logging.INFO)
+
-logger = logging.getLogger(__name__)
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
 
 
 def load_tf2_weights_in_bert(model, tf_checkpoint_path, config):

src/transformers/convert_bert_original_tf_checkpoint_to_pytorch.py

@@ -16,14 +16,15 @@
 
 
 import argparse
-import logging
 
 import torch
 
 from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
 
+from .utils import logging
 
-logging.basicConfig(level=logging.INFO)
+
+logging.set_verbosity_info()
 
 
 def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path):

src/transformers/convert_electra_original_tf_checkpoint_to_pytorch.py

@@ -16,14 +16,15 @@
 
 
 import argparse
-import logging
 
 import torch
 
 from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
 
+from .utils import logging
 
-logging.basicConfig(level=logging.INFO)
+
+logging.set_verbosity_info()
 
 
 def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, config_file, pytorch_dump_path, discriminator_or_generator):

src/transformers/convert_gpt2_original_tf_checkpoint_to_pytorch.py

@@ -16,14 +16,15 @@
 
 
 import argparse
-import logging
 
 import torch
 
 from transformers import CONFIG_NAME, WEIGHTS_NAME, GPT2Config, GPT2Model, load_tf_weights_in_gpt2
 
+from .utils import logging
 
-logging.basicConfig(level=logging.INFO)
+
+logging.set_verbosity_info()
 
 
 def convert_gpt2_checkpoint_to_pytorch(gpt2_checkpoint_path, gpt2_config_file, pytorch_dump_folder_path):

src/transformers/convert_mobilebert_original_tf_checkpoint_to_pytorch.py

@@ -1,12 +1,13 @@
 import argparse
-import logging
 
 import torch
 
 from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
 
+from .utils import logging
 
-logging.basicConfig(level=logging.INFO)
+
+logging.set_verbosity_info()
 
 
 def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, mobilebert_config_file, pytorch_dump_path):

src/transformers/convert_openai_original_tf_checkpoint_to_pytorch.py

@@ -16,14 +16,15 @@
 
 
 import argparse
-import logging
 
 import torch
 
 from transformers import CONFIG_NAME, WEIGHTS_NAME, OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
 
+from .utils import logging
 
-logging.basicConfig(level=logging.INFO)
+
+logging.set_verbosity_info()
 
 
 def convert_openai_checkpoint_to_pytorch(openai_checkpoint_folder_path, openai_config_file, pytorch_dump_folder_path):

src/transformers/convert_pytorch_checkpoint_to_tf2.py

@@ -16,7 +16,6 @@
 
 
 import argparse
-import logging
 import os
 
 from transformers import (
@@ -76,6 +75,8 @@ from transformers import (
 )
 from transformers.file_utils import hf_bucket_url
 
+from .utils import logging
+
 
 if is_torch_available():
     import numpy as np
@@ -104,7 +105,7 @@ if is_torch_available():
     )
 
 
-logging.basicConfig(level=logging.INFO)
+logging.set_verbosity_info()
 
 MODEL_CLASSES = {
     "bert": (BertConfig, TFBertForPreTraining, BertForPreTraining, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,),

src/transformers/convert_reformer_trax_checkpoint_to_pytorch.py

@@ -16,7 +16,6 @@
 
 
 import argparse
-import logging
 import pickle
 
 import numpy as np
@@ -24,8 +23,10 @@ import torch
 
 from transformers import ReformerConfig, ReformerModelWithLMHead
 
+from .utils import logging
 
-logging.basicConfig(level=logging.INFO)
+
+logging.set_verbosity_info()
 
 
 def set_param(torch_layer, weight, bias=None):

src/transformers/convert_roberta_original_pytorch_checkpoint_to_pytorch.py

@@ -16,7 +16,6 @@
 
 
 import argparse
-import logging
 import pathlib
 
 import fairseq
@@ -28,13 +27,15 @@ from packaging import version
 from transformers.modeling_bert import BertIntermediate, BertLayer, BertOutput, BertSelfAttention, BertSelfOutput
 from transformers.modeling_roberta import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
 
+from .utils import logging
+
 
 if version.parse(fairseq.__version__) < version.parse("0.9.0"):
     raise Exception("requires fairseq >= 0.9.0")
 
 
-logging.basicConfig(level=logging.INFO)
+logging.set_verbosity_info()
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 SAMPLE_TEXT = "Hello world! cécé herlolip"
 

src/transformers/convert_t5_original_tf_checkpoint_to_pytorch.py

@@ -16,14 +16,15 @@
 
 
 import argparse
-import logging
 
 import torch
 
 from transformers import T5Config, T5Model, load_tf_weights_in_t5
 
+from .utils import logging
 
-logging.basicConfig(level=logging.INFO)
+
+logging.set_verbosity_info()
 
 
 def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, config_file, pytorch_dump_path):

src/transformers/convert_transfo_xl_original_tf_checkpoint_to_pytorch.py

@@ -16,7 +16,6 @@
 
 
 import argparse
-import logging
 import os
 import pickle
 import sys
@@ -33,8 +32,10 @@ from transformers import (
 )
 from transformers.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
 
+from .utils import logging
 
-logging.basicConfig(level=logging.INFO)
+
+logging.set_verbosity_info()
 
 # We do this to be able to load python 2 datasets pickles
 # See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918

src/transformers/convert_xlm_original_pytorch_checkpoint_to_pytorch.py

@@ -17,7 +17,6 @@
 
 import argparse
 import json
-import logging
 
 import numpy
 import torch
@@ -25,8 +24,10 @@ import torch
 from transformers import CONFIG_NAME, WEIGHTS_NAME
 from transformers.tokenization_xlm import VOCAB_FILES_NAMES
 
+from .utils import logging
 
-logging.basicConfig(level=logging.INFO)
+
+logging.set_verbosity_info()
 
 
 def convert_xlm_checkpoint_to_pytorch(xlm_checkpoint_path, pytorch_dump_folder_path):

src/transformers/convert_xlnet_original_tf_checkpoint_to_pytorch.py

@@ -16,7 +16,6 @@
 
 
 import argparse
-import logging
 import os
 
 import torch
@@ -31,6 +30,8 @@ from transformers import (
     load_tf_weights_in_xlnet,
 )
 
+from .utils import logging
+
 
 GLUE_TASKS_NUM_LABELS = {
     "cola": 2,
@@ -45,7 +46,7 @@ GLUE_TASKS_NUM_LABELS = {
 }
 
 
-logging.basicConfig(level=logging.INFO)
+logging.set_verbosity_info()
 
 
 def convert_xlnet_checkpoint_to_pytorch(

src/transformers/data/datasets/glue.py

@@ -1,4 +1,3 @@
-import logging
 import os
 import time
 from dataclasses import dataclass, field
@@ -14,11 +13,12 @@ from ...tokenization_bart import BartTokenizer, BartTokenizerFast
 from ...tokenization_roberta import RobertaTokenizer, RobertaTokenizerFast
 from ...tokenization_utils import PreTrainedTokenizer
 from ...tokenization_xlm_roberta import XLMRobertaTokenizer
+from ...utils import logging
 from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
 from ..processors.utils import InputFeatures
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 
 @dataclass

src/transformers/data/datasets/language_modeling.py

@@ -1,4 +1,3 @@
-import logging
 import os
 import pickle
 import time
@@ -9,9 +8,10 @@ from torch.utils.data.dataset import Dataset
 from filelock import FileLock
 
 from ...tokenization_utils import PreTrainedTokenizer
+from ...utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 
 class TextDataset(Dataset):

src/transformers/data/datasets/squad.py

@@ -1,4 +1,3 @@
-import logging
 import os
 import time
 from dataclasses import dataclass, field
@@ -12,10 +11,11 @@ from filelock import FileLock
 
 from ...modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
 from ...tokenization_utils import PreTrainedTokenizer
+from ...utils import logging
 from ..processors.squad import SquadFeatures, SquadV1Processor, SquadV2Processor, squad_convert_examples_to_features
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 MODEL_CONFIG_CLASSES = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
 MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

src/transformers/data/metrics/squad_metrics.py

@@ -10,15 +10,16 @@ that a question is unanswerable.
 
 import collections
 import json
-import logging
 import math
 import re
 import string
 
 from transformers.tokenization_bert import BasicTokenizer
 
+from ...utils import logging
 
-logger = logging.getLogger(__name__)
+
+logger = logging.get_logger(__name__)
 
 
 def normalize_answer(s):

src/transformers/data/processors/glue.py

@@ -15,7 +15,6 @@
 # limitations under the License.
 """ GLUE processors and helpers """
 
-import logging
 import os
 from dataclasses import asdict
 from enum import Enum
@@ -23,13 +22,14 @@ from typing import List, Optional, Union
 
 from ...file_utils import is_tf_available
 from ...tokenization_utils import PreTrainedTokenizer
+from ...utils import logging
 from .utils import DataProcessor, InputExample, InputFeatures
 
 
 if is_tf_available():
     import tensorflow as tf
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 
 def glue_convert_examples_to_features(

src/transformers/data/processors/squad.py

@@ -1,5 +1,4 @@
 import json
-import logging
 import os
 from functools import partial
 from multiprocessing import Pool, cpu_count
@@ -10,6 +9,7 @@ from tqdm import tqdm
 from ...file_utils import is_tf_available, is_torch_available
 from ...tokenization_bert import whitespace_tokenize
 from ...tokenization_utils_base import TruncationStrategy
+from ...utils import logging
 from .utils import DataProcessor
 
 
@@ -24,7 +24,7 @@ if is_torch_available():
 if is_tf_available():
     import tensorflow as tf
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 
 def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, orig_answer_text):

src/transformers/data/processors/utils.py

@@ -17,14 +17,14 @@
 import csv
 import dataclasses
 import json
-import logging
 from dataclasses import dataclass
 from typing import List, Optional, Union
 
 from ...file_utils import is_tf_available, is_torch_available
+from ...utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 
 @dataclass

src/transformers/data/processors/xnli.py

@@ -16,13 +16,13 @@
 """ XNLI utils (dataset loading and evaluation) """
 
 
-import logging
 import os
 
+from ...utils import logging
 from .utils import DataProcessor, InputExample
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 
 class XnliProcessor(DataProcessor):

src/transformers/file_utils.py

@@ -6,7 +6,6 @@ Copyright by the AllenNLP authors.
 
 import fnmatch
 import json
-import logging
 import os
 import re
 import shutil
@@ -30,9 +29,10 @@ import requests
 from filelock import FileLock
 
 from . import __version__
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 try:
     USE_TF = os.environ.get("USE_TF", "AUTO").upper()
@@ -757,7 +757,7 @@ def http_get(url, temp_file, proxies=None, resume_size=0, user_agent: Union[Dict
         total=total,
         initial=resume_size,
         desc="Downloading",
-        disable=bool(logger.getEffectiveLevel() == logging.NOTSET),
+        disable=bool(logging.get_verbosity() > logging.NOTSET),
     )
     for chunk in response.iter_content(chunk_size=1024):
         if chunk:  # filter out keep-alive new chunks

src/transformers/generation_tf_utils.py

@@ -14,13 +14,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import logging
-
 import numpy as np
 import tensorflow as tf
 
+from .utils import logging
 
-logger = logging.getLogger(__name__)
+
+logger = logging.get_logger(__name__)
 
 
 class TFGenerationMixin:

src/transformers/generation_utils.py

@@ -14,15 +14,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import logging
 from typing import Iterable, List, Optional, Tuple
 
 import torch
 from torch import Tensor
 from torch.nn import functional as F
 
+from .utils import logging
 
-logger = logging.getLogger(__name__)
+
+logger = logging.get_logger(__name__)
 
 
 class GenerationMixin:

src/transformers/modelcard.py

@@ -17,7 +17,6 @@
 
 import copy
 import json
-import logging
 import os
 
 from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP
@@ -30,9 +29,10 @@ from .file_utils import (
     hf_bucket_url,
     is_remote_url,
 )
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 
 class ModelCard:

src/transformers/modeling_albert.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 """PyTorch ALBERT model. """
 
-import logging
 import math
 import os
 import warnings
@@ -44,9 +43,10 @@ from .modeling_outputs import (
     TokenClassifierOutput,
 )
 from .modeling_utils import PreTrainedModel, apply_chunking_to_forward, find_pruneable_heads_and_indices
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "AlbertConfig"
 _TOKENIZER_FOR_DOC = "AlbertTokenizer"

src/transformers/modeling_auto.py

@@ -15,7 +15,6 @@
 """ Auto Model class. """
 
 
-import logging
 import warnings
 from collections import OrderedDict
 
@@ -172,9 +171,10 @@ from .modeling_xlnet import (
     XLNetLMHeadModel,
     XLNetModel,
 )
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 
 MODEL_MAPPING = OrderedDict(

src/transformers/modeling_bart.py

@@ -13,7 +13,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """PyTorch BART model, ported from the fairseq repo."""
-import logging
 import math
 import random
 import warnings
@@ -43,9 +42,10 @@ from .modeling_outputs import (
     Seq2SeqSequenceClassifierOutput,
 )
 from .modeling_utils import PreTrainedModel
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "BartConfig"
 _TOKENIZER_FOR_DOC = "BartTokenizer"

src/transformers/modeling_bert.py

@@ -16,7 +16,6 @@
 """PyTorch BERT model. """
 
 
-import logging
 import math
 import os
 import warnings
@@ -54,9 +53,10 @@ from .modeling_utils import (
     find_pruneable_heads_and_indices,
     prune_linear_layer,
 )
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "BertConfig"
 _TOKENIZER_FOR_DOC = "BertTokenizer"

src/transformers/modeling_camembert.py

@@ -15,8 +15,6 @@
 # limitations under the License.
 """PyTorch CamemBERT model. """
 
-import logging
-
 from .configuration_camembert import CamembertConfig
 from .file_utils import add_start_docstrings
 from .modeling_roberta import (
@@ -28,9 +26,10 @@ from .modeling_roberta import (
     RobertaForTokenClassification,
     RobertaModel,
 )
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _TOKENIZER_FOR_DOC = "CamembertTokenizer"
 

src/transformers/modeling_ctrl.py

@@ -16,7 +16,6 @@
 """ PyTorch CTRL model."""
 
 
-import logging
 import warnings
 
 import numpy as np
@@ -28,9 +27,10 @@ from .configuration_ctrl import CTRLConfig
 from .file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_callable
 from .modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
 from .modeling_utils import Conv1D, PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "CTRLConfig"
 _TOKENIZER_FOR_DOC = "CTRLTokenizer"

src/transformers/modeling_distilbert.py

@@ -19,7 +19,6 @@
 
 
 import copy
-import logging
 import math
 import warnings
 
@@ -50,9 +49,10 @@ from .modeling_utils import (
     find_pruneable_heads_and_indices,
     prune_linear_layer,
 )
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "DistilBertConfig"
 _TOKENIZER_FOR_DOC = "DistilBertTokenizer"

src/transformers/modeling_dpr.py

@@ -15,7 +15,6 @@
 """ PyTorch DPR model for Open Domain Question Answering."""
 
 
-import logging
 from dataclasses import dataclass
 from typing import Optional, Tuple, Union
 
@@ -27,9 +26,10 @@ from .file_utils import ModelOutput, add_start_docstrings, add_start_docstrings_
 from .modeling_bert import BertModel
 from .modeling_outputs import BaseModelOutputWithPooling
 from .modeling_utils import PreTrainedModel
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "DPRConfig"
 

src/transformers/modeling_electra.py

@@ -1,4 +1,3 @@
-import logging
 import os
 import warnings
 from dataclasses import dataclass
@@ -27,9 +26,10 @@ from .modeling_outputs import (
     TokenClassifierOutput,
 )
 from .modeling_utils import SequenceSummary
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "ElectraConfig"
 _TOKENIZER_FOR_DOC = "ElectraTokenizer"

src/transformers/modeling_encoder_decoder.py

@@ -15,15 +15,15 @@
 """ Classes to support Encoder-Decoder architectures """
 
 
-import logging
 from typing import Optional
 
 from .configuration_encoder_decoder import EncoderDecoderConfig
 from .configuration_utils import PretrainedConfig
 from .modeling_utils import PreTrainedModel
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 
 class EncoderDecoderModel(PreTrainedModel):

src/transformers/modeling_flaubert.py

@@ -15,7 +15,6 @@
 """ PyTorch Flaubert model, based on XLM. """
 
 
-import logging
 import random
 
 import torch
@@ -34,9 +33,10 @@ from .modeling_xlm import (
     XLMWithLMHeadModel,
     get_masks,
 )
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "FlaubertConfig"
 _TOKENIZER_FOR_DOC = "FlaubertTokenizer"

src/transformers/modeling_gpt2.py

@@ -16,7 +16,6 @@
 """PyTorch OpenAI GPT-2 model."""
 
 
-import logging
 import os
 import warnings
 from dataclasses import dataclass
@@ -43,9 +42,10 @@ from .modeling_utils import (
     find_pruneable_heads_and_indices,
     prune_conv1d_layer,
 )
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "GPT2Config"
 _TOKENIZER_FOR_DOC = "GPT2Tokenizer"

src/transformers/modeling_longformer.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 """PyTorch Longformer model. """
 
-import logging
 import math
 import warnings
 
@@ -47,9 +46,10 @@ from .modeling_utils import (
     find_pruneable_heads_and_indices,
     prune_linear_layer,
 )
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "LongformerConfig"
 _TOKENIZER_FOR_DOC = "LongformerTokenizer"

src/transformers/modeling_mmbt.py

@@ -16,8 +16,6 @@
 """PyTorch MMBT model. """
 
 
-import logging
-
 import torch
 import torch.nn as nn
 from torch.nn import CrossEntropyLoss, MSELoss
@@ -25,9 +23,10 @@ from torch.nn import CrossEntropyLoss, MSELoss
 from .file_utils import add_start_docstrings, add_start_docstrings_to_callable, replace_return_docstrings
 from .modeling_outputs import BaseModelOutputWithPooling, SequenceClassifierOutput
 from .modeling_utils import ModuleUtilsMixin
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "MMBTConfig"
 

src/transformers/modeling_mobilebert.py

@@ -20,7 +20,6 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 
-import logging
 import math
 import os
 import warnings
@@ -53,9 +52,10 @@ from .modeling_outputs import (
     TokenClassifierOutput,
 )
 from .modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "MobileBertConfig"
 _TOKENIZER_FOR_DOC = "MobileBertTokenizer"

src/transformers/modeling_openai.py

@@ -17,7 +17,6 @@
 
 
 import json
-import logging
 import math
 import os
 import warnings
@@ -45,9 +44,10 @@ from .modeling_utils import (
     find_pruneable_heads_and_indices,
     prune_conv1d_layer,
 )
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "OpenAIGPTConfig"
 _TOKENIZER_FOR_DOC = "OpenAIGPTTokenizer"

src/transformers/modeling_reformer.py

@@ -15,7 +15,6 @@
 # limitations under the License.
 """PyTorch REFORMER model. """
 
-import logging
 import sys
 from collections import namedtuple
 from dataclasses import dataclass
@@ -41,9 +40,10 @@ from .file_utils import (
 )
 from .modeling_outputs import CausalLMOutput, MaskedLMOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput
 from .modeling_utils import PreTrainedModel, apply_chunking_to_forward
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "ReformerConfig"
 _TOKENIZER_FOR_DOC = "ReformerTokenizer"

src/transformers/modeling_retribert.py

@@ -17,7 +17,6 @@ RetriBERT model
 """
 
 
-import logging
 import math
 
 import torch
@@ -28,9 +27,10 @@ from .configuration_retribert import RetriBertConfig
 from .file_utils import add_start_docstrings
 from .modeling_bert import BertLayerNorm, BertModel
 from .modeling_utils import PreTrainedModel
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
     "yjernite/retribert-base-uncased",

src/transformers/modeling_roberta.py

@@ -16,7 +16,6 @@
 """PyTorch RoBERTa model. """
 
 
-import logging
 import warnings
 
 import torch
@@ -39,9 +38,10 @@ from .modeling_outputs import (
     SequenceClassifierOutput,
     TokenClassifierOutput,
 )
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "RobertaConfig"
 _TOKENIZER_FOR_DOC = "RobertaTokenizer"

src/transformers/modeling_t5.py

@@ -16,7 +16,6 @@
 
 
 import copy
-import logging
 import math
 import os
 import warnings
@@ -36,9 +35,10 @@ from .file_utils import (
 )
 from .modeling_outputs import BaseModelOutput, BaseModelOutputWithPast, Seq2SeqLMOutput, Seq2SeqModelOutput
 from .modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "T5Config"
 _TOKENIZER_FOR_DOC = "T5Tokenizer"

src/transformers/modeling_tf_albert.py

@@ -16,7 +16,6 @@
 """ TF 2.0 ALBERT model. """
 
 
-import logging
 from dataclasses import dataclass
 from typing import Optional, Tuple
 
@@ -53,9 +52,10 @@ from .modeling_tf_utils import (
     shape_list,
 )
 from .tokenization_utils import BatchEncoding
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "AlbertConfig"
 _TOKENIZER_FOR_DOC = "AlbertTokenizer"

src/transformers/modeling_tf_auto.py

@@ -15,7 +15,6 @@
 """ Auto Model class. """
 
 
-import logging
 import warnings
 from collections import OrderedDict
 
@@ -139,9 +138,10 @@ from .modeling_tf_xlnet import (
     TFXLNetLMHeadModel,
     TFXLNetModel,
 )
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 
 TF_MODEL_MAPPING = OrderedDict(

src/transformers/modeling_tf_bert.py

@@ -16,7 +16,6 @@
 """ TF 2.0 BERT model. """
 
 
-import logging
 from dataclasses import dataclass
 from typing import Optional, Tuple
 
@@ -56,9 +55,10 @@ from .modeling_tf_utils import (
     shape_list,
 )
 from .tokenization_utils import BatchEncoding
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "BertConfig"
 _TOKENIZER_FOR_DOC = "BertTokenizer"

src/transformers/modeling_tf_camembert.py

@@ -15,9 +15,6 @@
 # limitations under the License.
 """ TF 2.0 CamemBERT model. """
 
-
-import logging
-
 from .configuration_camembert import CamembertConfig
 from .file_utils import add_start_docstrings
 from .modeling_tf_roberta import (
@@ -28,9 +25,10 @@ from .modeling_tf_roberta import (
     TFRobertaForTokenClassification,
     TFRobertaModel,
 )
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
     # See all CamemBERT models at https://huggingface.co/models?filter=camembert

src/transformers/modeling_tf_ctrl.py

@@ -16,8 +16,6 @@
 """ TF 2.0 CTRL model."""
 
 
-import logging
-
 import numpy as np
 import tensorflow as tf
 
@@ -32,9 +30,10 @@ from .modeling_tf_utils import (
     shape_list,
 )
 from .tokenization_utils import BatchEncoding
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "CTRLConfig"
 _TOKENIZER_FOR_DOC = "CTRLTokenizer"

src/transformers/modeling_tf_distilbert.py

@@ -16,7 +16,6 @@
 """
 
 
-import logging
 import math
 
 import numpy as np
@@ -50,9 +49,10 @@ from .modeling_tf_utils import (
     shape_list,
 )
 from .tokenization_utils import BatchEncoding
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "DistilBertConfig"
 _TOKENIZER_FOR_DOC = "DistilBertTokenizer"

src/transformers/modeling_tf_electra.py

@@ -1,4 +1,3 @@
-import logging
 from dataclasses import dataclass
 from typing import Optional, Tuple
 
@@ -35,9 +34,10 @@ from .modeling_tf_utils import (
     shape_list,
 )
 from .tokenization_utils import BatchEncoding
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "ElectraConfig"
 _TOKENIZER_FOR_DOC = "ElectraTokenizer"

src/transformers/modeling_tf_flaubert.py

@@ -15,7 +15,6 @@
 """ TF 2.0 Flaubert model.
 """
 
-import logging
 import random
 
 import tensorflow as tf
@@ -36,9 +35,10 @@ from .modeling_tf_xlm import (
     get_masks,
 )
 from .tokenization_utils import BatchEncoding
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
     # See all Flaubert models at https://huggingface.co/models?filter=flaubert

src/transformers/modeling_tf_gpt2.py

@@ -16,7 +16,6 @@
 """ TF 2.0 OpenAI GPT-2 model. """
 
 
-import logging
 from dataclasses import dataclass
 from typing import List, Optional, Tuple
 
@@ -43,9 +42,10 @@ from .modeling_tf_utils import (
     shape_list,
 )
 from .tokenization_utils import BatchEncoding
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "GPT2Config"
 _TOKENIZER_FOR_DOC = "GPT2Tokenizer"

src/transformers/modeling_tf_longformer.py

@@ -14,8 +14,6 @@
 # limitations under the License.
 """Tensorflow Longformer model. """
 
-import logging
-
 import tensorflow as tf
 
 from .configuration_longformer import LongformerConfig
@@ -37,9 +35,10 @@ from .modeling_tf_utils import (
     shape_list,
 )
 from .tokenization_utils import BatchEncoding
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "LongformerConfig"
 _TOKENIZER_FOR_DOC = "LongformerTokenizer"

src/transformers/modeling_tf_mobilebert.py

@@ -16,7 +16,6 @@
 """ TF 2.0 MobileBERT model. """
 
 
-import logging
 from dataclasses import dataclass
 from typing import Optional, Tuple
 
@@ -54,9 +53,10 @@ from .modeling_tf_utils import (
     shape_list,
 )
 from .tokenization_utils import BatchEncoding
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "MobileBertConfig"
 _TOKENIZER_FOR_DOC = "MobileBertTokenizer"

src/transformers/modeling_tf_openai.py

@@ -16,7 +16,6 @@
 """ TF 2.0 OpenAI GPT model."""
 
 
-import logging
 from dataclasses import dataclass
 from typing import Optional, Tuple
 
@@ -43,9 +42,10 @@ from .modeling_tf_utils import (
     shape_list,
 )
 from .tokenization_utils import BatchEncoding
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "OpenAIGPTConfig"
 _TOKENIZER_FOR_DOC = "OpenAIGPTTokenizer"

src/transformers/modeling_tf_pytorch_utils.py

@@ -16,14 +16,15 @@
 """ PyTorch - TF 2.0 general utilities."""
 
 
-import logging
 import os
 import re
 
 import numpy
 
+from .utils import logging
 
-logger = logging.getLogger(__name__)
+
+logger = logging.get_logger(__name__)
 
 
 def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove=""):

src/transformers/modeling_tf_roberta.py

@@ -16,8 +16,6 @@
 """ TF 2.0 RoBERTa model. """
 
 
-import logging
-
 import tensorflow as tf
 
 from .configuration_roberta import RobertaConfig
@@ -48,9 +46,10 @@ from .modeling_tf_utils import (
     shape_list,
 )
 from .tokenization_utils_base import BatchEncoding
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "RobertaConfig"
 _TOKENIZER_FOR_DOC = "RobertaTokenizer"

src/transformers/modeling_tf_t5.py

@@ -18,7 +18,6 @@
 
 import copy
 import itertools
-import logging
 import math
 import warnings
 
@@ -42,9 +41,10 @@ from .modeling_tf_utils import (
     shape_list,
 )
 from .tokenization_utils import BatchEncoding
+from .utils import logging
 
 
-logger = logging.getLogger(__name__)
+logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "T5Config"
 _TOKENIZER_FOR_DOC = "T5Tokenizer"