


import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()




def       a__						(		snake_case__				:       List[Any]	,      snake_case__				:       int	,      snake_case__				:       Tuple    ):
  # Initialise PyTorch model
  _UpperCAmelCase		:       Tuple       =    TaConfig.from_json_file(snake_case__    )
  print(f'''Building PyTorch model from configuration: {config}'''    )
  _UpperCAmelCase		:       Dict       =    TaForConditionalGeneration(snake_case__    )

  # Load weights from tf checkpoint
  load_tf_weights_in_ta(snake_case__	,      snake_case__	,      snake_case__    )

  # Save pytorch-model
  print(f'''Save PyTorch model to {pytorch_dump_path}'''    )
  model.save_pretrained(snake_case__    )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__				:    Tuple         =    argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
 )
 parser.add_argument(
     '--config_file',
     default=None,
     type=str,
     required=True,
     help=(
         'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
     ),
 )
 parser.add_argument(
     '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 SCREAMING_SNAKE_CASE__				:    Union[str, Any]         =    parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE__				:    List[str]         =    {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__				:    int         =    [
     'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'FlaubertForMultipleChoice',
     'FlaubertForQuestionAnswering',
     'FlaubertForQuestionAnsweringSimple',
     'FlaubertForSequenceClassification',
     'FlaubertForTokenClassification',
     'FlaubertModel',
     'FlaubertWithLMHeadModel',
     'FlaubertPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__				:    Dict         =    [
     'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFFlaubertForMultipleChoice',
     'TFFlaubertForQuestionAnsweringSimple',
     'TFFlaubertForSequenceClassification',
     'TFFlaubertForTokenClassification',
     'TFFlaubertModel',
     'TFFlaubertPreTrainedModel',
     'TFFlaubertWithLMHeadModel',
 ]


if TYPE_CHECKING:
 from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
 from .tokenization_flaubert import FlaubertTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flaubert import (
      FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      FlaubertForMultipleChoice,
      FlaubertForQuestionAnswering,
      FlaubertForQuestionAnsweringSimple,
      FlaubertForSequenceClassification,
      FlaubertForTokenClassification,
      FlaubertModel,
      FlaubertPreTrainedModel,
      FlaubertWithLMHeadModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_flaubert import (
      TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFFlaubertForMultipleChoice,
      TFFlaubertForQuestionAnsweringSimple,
      TFFlaubertForSequenceClassification,
      TFFlaubertForTokenClassification,
      TFFlaubertModel,
      TFFlaubertPreTrainedModel,
      TFFlaubertWithLMHeadModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE__				:    List[Any]         =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





