


"""simple docstring"""






import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()



def 			A__		(					_UpperCAmelCase    :      str				,    _UpperCAmelCase    :      Optional[Any]	)							->	str:

	'''simple docstring'''


	snake_case__  :						Tuple		=      CanineConfig()
	snake_case__  :						str		=      CanineModel(_UpperCAmelCase	)
	model.eval()

	print(F"""Building PyTorch model from configuration: {config}"""	)

	# Load weights from tf checkpoint
	load_tf_weights_in_canine(_UpperCAmelCase				,    _UpperCAmelCase				,    _UpperCAmelCase	)

	# Save pytorch-model (weights and configuration)
	print(F"""Save PyTorch model to {pytorch_dump_path}"""	)
	model.save_pretrained(_UpperCAmelCase	)

	# Save tokenizer files
	snake_case__  :						int		=      CanineTokenizer()
	print(F"""Save tokenizer files to {pytorch_dump_path}"""	)
	tokenizer.save_pretrained(_UpperCAmelCase	)


if __name__ == "__main__":
					lowercase  		=			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_checkpoint_path""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Path to the TensorFlow checkpoint. Should end with model.ckpt""",
					)
					parser.add_argument(
					    """--pytorch_dump_path""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Path to a folder where the PyTorch model will be placed.""",
					)
					lowercase  		=			parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)









"""simple docstring"""






from typing import TYPE_CHECKING

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


lowercase  		=			{
    """configuration_flaubert""": ["""FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FlaubertConfig""", """FlaubertOnnxConfig"""],
    """tokenization_flaubert""": ["""FlaubertTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  		=			[
					    """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:
					lowercase  		=			[
					    """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

					lowercase  		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





