Apply quality and style requirements

src/transformers/__init__.py

@@ -29,10 +29,8 @@ from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config
 from .configuration_mmbt import MMBTConfig
 from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
 from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig
-from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig
 from .configuration_t5 import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, T5Config
 from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
-
 # Configurations
 from .configuration_utils import PretrainedConfig
 from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig
@@ -57,7 +55,6 @@ from .data import (
     xnli_processors,
     xnli_tasks_num_labels,
 )
-
 # Files and general utilities
 from .file_utils import (
     CONFIG_NAME,
@@ -74,10 +71,8 @@ from .file_utils import (
     is_tf_available,
     is_torch_available,
 )
-
 # Model Cards
 from .modelcard import ModelCard
-
 # TF 2.0 <=> PyTorch conversion utilities
 from .modeling_tf_pytorch_utils import (
     convert_tf_weight_name_to_pt_weight_name,
@@ -88,7 +83,6 @@ from .modeling_tf_pytorch_utils import (
     load_tf2_model_in_pytorch_model,
     load_tf2_weights_in_pytorch_model,
 )
-
 # Pipelines
 from .pipelines import (
     CsvPipelineDataFormat,
@@ -114,7 +108,6 @@ from .tokenization_openai import OpenAIGPTTokenizer
 from .tokenization_roberta import RobertaTokenizer
 from .tokenization_t5 import T5Tokenizer
 from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer
-
 # Tokenizers
 from .tokenization_utils import PreTrainedTokenizer
 from .tokenization_xlm import XLMTokenizer

src/transformers/convert_pytorch_checkpoint_to_tf2.py

@@ -22,12 +22,12 @@ import os
 from transformers import (
     ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
+    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
     TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
     XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
@@ -35,17 +35,18 @@ from transformers import (
     XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
     AlbertConfig,
     BertConfig,
+    CamembertConfig,
     CTRLConfig,
     DistilBertConfig,
     GPT2Config,
     OpenAIGPTConfig,
     RobertaConfig,
-    CamembertConfig,
     T5Config,
     TFAlbertForMaskedLM,
     TFBertForPreTraining,
     TFBertForQuestionAnswering,
     TFBertForSequenceClassification,
+    TFCamembertForMaskedLM,
     TFCTRLLMHeadModel,
     TFDistilBertForMaskedLM,
     TFDistilBertForQuestionAnswering,
@@ -53,8 +54,6 @@ from transformers import (
     TFOpenAIGPTLMHeadModel,
     TFRobertaForMaskedLM,
     TFRobertaForSequenceClassification,
-    TFCamembertForMaskedLM,
-    TFCamembertForSequenceClassification,
     TFT5WithLMHeadModel,
     TFTransfoXLLMHeadModel,
     TFXLMRobertaForMaskedLM,

src/transformers/modeling_tf_camembert.py

@@ -18,8 +18,6 @@
 
 import logging
 
-import tensorflow as tf
-
 from .configuration_camembert import CamembertConfig
 from .file_utils import add_start_docstrings
 from .modeling_tf_roberta import (
@@ -29,6 +27,7 @@ from .modeling_tf_roberta import (
     TFRobertaModel,
 )
 
+
 logger = logging.getLogger(__name__)
 
 TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {

templates/adding_a_new_example_script/run_xxx.py

@@ -52,7 +52,6 @@ from utils_squad import (
     write_predictions,
     write_predictions_extended,
 )
-
 # The follwing import is the official SQuAD evaluation script (2.0).
 # You can remove it from the dependencies if you are using this script outside of the library
 # We've added it here for automated tests (see examples/test_examples.py file)
@@ -333,7 +332,8 @@ def evaluate(args, model, tokenizer, prefix=""):
 
 def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False):
     if args.local_rank not in [-1, 0] and not evaluate:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
+        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset,
+        # and the others will use the cache
 
     # Load data features from cache or dataset file
     input_file = args.predict_file if evaluate else args.train_file
@@ -366,7 +366,8 @@ def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=Fal
             torch.save(features, cached_features_file)
 
     if args.local_rank == 0 and not evaluate:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
+        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset,
+        # and the others will use the cache
 
     # Convert to Tensors and build dataset
     all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
@@ -620,7 +621,8 @@ def main():
 
     # Load pretrained model and tokenizer
     if args.local_rank not in [-1, 0]:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
+        torch.distributed.barrier()  # Make sure only the first process in distributed training will
+        # download model & vocab
 
     args.model_type = args.model_type.lower()
     config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
@@ -641,15 +643,16 @@ def main():
     )
 
     if args.local_rank == 0:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
+        torch.distributed.barrier()  # Make sure only the first process in distributed training will
+        # download model & vocab
 
     model.to(args.device)
 
     logger.info("Training/evaluation parameters %s", args)
 
-    # Before we do anything with models, we want to ensure that we get fp16 execution of torch.einsum if args.fp16 is set.
+    # Before we do anything with models, we want to ensure that we get fp16 execution of torch.einsum
-    # Otherwise it'll default to "promote" mode, and we'll get fp32 operations. Note that running `--fp16_opt_level="O2"` will
+    # if args.fp16 is set. Otherwise it'll default to "promote" mode, and we'll get fp32 operations.
-    # remove the need for this code, but it is still valid.
+    # Note that running `--fp16_opt_level="O2"` will remove the need for this code, but it is still valid.
     if args.fp16:
         try:
             import apex

templates/adding_a_new_example_script/utils_xxx.py

@@ -21,7 +21,6 @@ import logging
 import math
 
 from transformers.tokenization_bert import BasicTokenizer, whitespace_tokenize
-
 # Required by XLNet evaluation method to compute optimal threshold (see write_predictions_extended() method)
 from utils_squad_evaluate import find_all_best_thresh_v2, get_raw_scores, make_qid_to_has_ans