196 lines
12 KiB
Python
196 lines
12 KiB
Python
__version__ = "2.2.1"
|
|
|
|
# Work around to update TensorFlow's absl.logging threshold which alters the
|
|
# default Python logging output behavior when present.
|
|
# see: https://github.com/abseil/abseil-py/issues/99
|
|
# and: https://github.com/tensorflow/tensorflow/issues/26691#issuecomment-500369493
|
|
try:
|
|
import absl.logging
|
|
absl.logging.set_verbosity('info')
|
|
absl.logging.set_stderrthreshold('info')
|
|
absl.logging._warn_preinit_stderr = False
|
|
except:
|
|
pass
|
|
|
|
import logging
|
|
|
|
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
|
|
|
|
# Files and general utilities
|
|
from .file_utils import (TRANSFORMERS_CACHE, PYTORCH_TRANSFORMERS_CACHE, PYTORCH_PRETRAINED_BERT_CACHE,
|
|
cached_path, add_start_docstrings, add_end_docstrings,
|
|
WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, CONFIG_NAME,
|
|
is_tf_available, is_torch_available)
|
|
|
|
from .data import (is_sklearn_available,
|
|
InputExample, InputFeatures, DataProcessor,
|
|
glue_output_modes, glue_convert_examples_to_features,
|
|
glue_processors, glue_tasks_num_labels,
|
|
xnli_output_modes, xnli_processors, xnli_tasks_num_labels,
|
|
squad_convert_examples_to_features, SquadFeatures,
|
|
SquadExample, SquadV1Processor, SquadV2Processor)
|
|
|
|
if is_sklearn_available():
|
|
from .data import glue_compute_metrics, xnli_compute_metrics
|
|
|
|
# Tokenizers
|
|
from .tokenization_utils import (PreTrainedTokenizer)
|
|
from .tokenization_auto import AutoTokenizer
|
|
from .tokenization_bert import BertTokenizer, BasicTokenizer, WordpieceTokenizer
|
|
from .tokenization_openai import OpenAIGPTTokenizer
|
|
from .tokenization_transfo_xl import (TransfoXLTokenizer, TransfoXLCorpus)
|
|
from .tokenization_gpt2 import GPT2Tokenizer
|
|
from .tokenization_ctrl import CTRLTokenizer
|
|
from .tokenization_xlnet import XLNetTokenizer, SPIECE_UNDERLINE
|
|
from .tokenization_xlm import XLMTokenizer
|
|
from .tokenization_roberta import RobertaTokenizer
|
|
from .tokenization_distilbert import DistilBertTokenizer
|
|
from .tokenization_albert import AlbertTokenizer
|
|
from .tokenization_camembert import CamembertTokenizer
|
|
|
|
# Configurations
|
|
from .configuration_utils import PretrainedConfig
|
|
from .configuration_auto import AutoConfig
|
|
from .configuration_bert import BertConfig, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP
|
|
from .configuration_openai import OpenAIGPTConfig, OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP
|
|
from .configuration_transfo_xl import TransfoXLConfig, TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP
|
|
from .configuration_gpt2 import GPT2Config, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP
|
|
from .configuration_ctrl import CTRLConfig, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP
|
|
from .configuration_xlnet import XLNetConfig, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP
|
|
from .configuration_ctrl import CTRLConfig, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP
|
|
from .configuration_xlm import XLMConfig, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP
|
|
from .configuration_roberta import RobertaConfig, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP
|
|
from .configuration_distilbert import DistilBertConfig, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
|
|
from .configuration_albert import AlbertConfig, ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
|
|
from .configuration_camembert import CamembertConfig, CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
|
|
|
|
# Modeling
|
|
if is_torch_available():
|
|
from .modeling_utils import (PreTrainedModel, prune_layer, Conv1D)
|
|
from .modeling_auto import (AutoModel, AutoModelForSequenceClassification, AutoModelForQuestionAnswering,
|
|
AutoModelWithLMHead)
|
|
|
|
from .modeling_bert import (BertPreTrainedModel, BertModel, BertForPreTraining,
|
|
BertForMaskedLM, BertForNextSentencePrediction,
|
|
BertForSequenceClassification, BertForMultipleChoice,
|
|
BertForTokenClassification, BertForQuestionAnswering,
|
|
load_tf_weights_in_bert, BERT_PRETRAINED_MODEL_ARCHIVE_MAP)
|
|
from .modeling_openai import (OpenAIGPTPreTrainedModel, OpenAIGPTModel,
|
|
OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel,
|
|
load_tf_weights_in_openai_gpt, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP)
|
|
from .modeling_transfo_xl import (TransfoXLPreTrainedModel, TransfoXLModel, TransfoXLLMHeadModel,
|
|
AdaptiveEmbedding,
|
|
load_tf_weights_in_transfo_xl, TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP)
|
|
from .modeling_gpt2 import (GPT2PreTrainedModel, GPT2Model,
|
|
GPT2LMHeadModel, GPT2DoubleHeadsModel,
|
|
load_tf_weights_in_gpt2, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
|
|
from .modeling_ctrl import (CTRLPreTrainedModel, CTRLModel,
|
|
CTRLLMHeadModel,
|
|
CTRL_PRETRAINED_MODEL_ARCHIVE_MAP)
|
|
from .modeling_xlnet import (XLNetPreTrainedModel, XLNetModel, XLNetLMHeadModel,
|
|
XLNetForSequenceClassification, XLNetForTokenClassification,
|
|
XLNetForMultipleChoice, XLNetForQuestionAnsweringSimple,
|
|
XLNetForQuestionAnswering, load_tf_weights_in_xlnet,
|
|
XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
|
|
from .modeling_xlm import (XLMPreTrainedModel , XLMModel,
|
|
XLMWithLMHeadModel, XLMForSequenceClassification,
|
|
XLMForQuestionAnswering, XLMForQuestionAnsweringSimple,
|
|
XLM_PRETRAINED_MODEL_ARCHIVE_MAP)
|
|
from .modeling_roberta import (RobertaForMaskedLM, RobertaModel,
|
|
RobertaForSequenceClassification, RobertaForMultipleChoice,
|
|
RobertaForTokenClassification,
|
|
ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
|
|
from .modeling_distilbert import (DistilBertPreTrainedModel, DistilBertForMaskedLM, DistilBertModel,
|
|
DistilBertForSequenceClassification, DistilBertForQuestionAnswering,
|
|
DistilBertForTokenClassification,
|
|
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
|
|
from .modeling_camembert import (CamembertForMaskedLM, CamembertModel,
|
|
CamembertForSequenceClassification, CamembertForMultipleChoice,
|
|
CamembertForTokenClassification,
|
|
CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
|
|
from .modeling_encoder_decoder import PreTrainedEncoderDecoder, Model2Model
|
|
|
|
from .modeling_albert import (AlbertPreTrainedModel, AlbertModel, AlbertForMaskedLM, AlbertForSequenceClassification,
|
|
AlbertForQuestionAnswering,
|
|
load_tf_weights_in_albert, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
|
|
|
|
# Optimization
|
|
from .optimization import (AdamW, get_constant_schedule, get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup,
|
|
get_cosine_with_hard_restarts_schedule_with_warmup, get_linear_schedule_with_warmup)
|
|
|
|
|
|
# TensorFlow
|
|
if is_tf_available():
|
|
from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, TFSequenceSummary, shape_list
|
|
from .modeling_tf_auto import (TFAutoModel, TFAutoModelForSequenceClassification, TFAutoModelForQuestionAnswering,
|
|
TFAutoModelWithLMHead)
|
|
|
|
from .modeling_tf_bert import (TFBertPreTrainedModel, TFBertMainLayer, TFBertEmbeddings,
|
|
TFBertModel, TFBertForPreTraining,
|
|
TFBertForMaskedLM, TFBertForNextSentencePrediction,
|
|
TFBertForSequenceClassification, TFBertForMultipleChoice,
|
|
TFBertForTokenClassification, TFBertForQuestionAnswering,
|
|
TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP)
|
|
|
|
from .modeling_tf_gpt2 import (TFGPT2PreTrainedModel, TFGPT2MainLayer,
|
|
TFGPT2Model, TFGPT2LMHeadModel, TFGPT2DoubleHeadsModel,
|
|
TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
|
|
|
|
from .modeling_tf_openai import (TFOpenAIGPTPreTrainedModel, TFOpenAIGPTMainLayer,
|
|
TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel,
|
|
TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP)
|
|
|
|
from .modeling_tf_transfo_xl import (TFTransfoXLPreTrainedModel, TFTransfoXLMainLayer,
|
|
TFTransfoXLModel, TFTransfoXLLMHeadModel,
|
|
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP)
|
|
|
|
from .modeling_tf_xlnet import (TFXLNetPreTrainedModel, TFXLNetMainLayer,
|
|
TFXLNetModel, TFXLNetLMHeadModel,
|
|
TFXLNetForSequenceClassification,
|
|
TFXLNetForTokenClassification,
|
|
TFXLNetForQuestionAnsweringSimple,
|
|
TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
|
|
|
|
from .modeling_tf_xlm import (TFXLMPreTrainedModel, TFXLMMainLayer,
|
|
TFXLMModel, TFXLMWithLMHeadModel,
|
|
TFXLMForSequenceClassification,
|
|
TFXLMForQuestionAnsweringSimple,
|
|
TF_XLM_PRETRAINED_MODEL_ARCHIVE_MAP)
|
|
|
|
from .modeling_tf_roberta import (TFRobertaPreTrainedModel, TFRobertaMainLayer,
|
|
TFRobertaModel, TFRobertaForMaskedLM,
|
|
TFRobertaForSequenceClassification,
|
|
TFRobertaForTokenClassification,
|
|
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
|
|
|
|
from .modeling_tf_distilbert import (TFDistilBertPreTrainedModel, TFDistilBertMainLayer,
|
|
TFDistilBertModel, TFDistilBertForMaskedLM,
|
|
TFDistilBertForSequenceClassification,
|
|
TFDistilBertForTokenClassification,
|
|
TFDistilBertForQuestionAnswering,
|
|
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
|
|
|
|
from .modeling_tf_ctrl import (TFCTRLPreTrainedModel, TFCTRLModel,
|
|
TFCTRLLMHeadModel,
|
|
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP)
|
|
|
|
from .modeling_tf_albert import (TFAlbertPreTrainedModel, TFAlbertModel, TFAlbertForMaskedLM,
|
|
TFAlbertForSequenceClassification,
|
|
TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
|
|
# Optimization
|
|
from .optimization_tf import (WarmUp, create_optimizer, AdamWeightDecay, GradientAccumulator)
|
|
|
|
# TF 2.0 <=> PyTorch conversion utilities
|
|
from .modeling_tf_pytorch_utils import (convert_tf_weight_name_to_pt_weight_name,
|
|
load_pytorch_checkpoint_in_tf2_model,
|
|
load_pytorch_weights_in_tf2_model,
|
|
load_pytorch_model_in_tf2_model,
|
|
load_tf2_checkpoint_in_pytorch_model,
|
|
load_tf2_weights_in_pytorch_model,
|
|
load_tf2_model_in_pytorch_model)
|
|
|
|
if not is_tf_available() and not is_torch_available():
|
|
logger.warning("Neither PyTorch nor TensorFlow >= 2.0 have been found."
|
|
"Models won't be available and only tokenizers, configuration"
|
|
"and file/data utilities can be used.")
|