


'''simple docstring'''



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase    :    int			     =  {
    """configuration_xmod""": [
        """XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XmodConfig""",
        """XmodOnnxConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    str			     =  [
	    """XMOD_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XmodForCausalLM""",
	    """XmodForMaskedLM""",
	    """XmodForMultipleChoice""",
	    """XmodForQuestionAnswering""",
	    """XmodForSequenceClassification""",
	    """XmodForTokenClassification""",
	    """XmodModel""",
	    """XmodPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xmod import (
		    XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XmodForCausalLM,
		    XmodForMaskedLM,
		    XmodForMultipleChoice,
		    XmodForQuestionAnswering,
		    XmodForSequenceClassification,
		    XmodForTokenClassification,
		    XmodModel,
		    XmodPreTrainedModel,
		)

else:
	import sys

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









'''simple docstring'''



import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()


def    lowerCamelCase__   ( A     :    List[Any]   ,     A     :    int   ,     A     :    List[str]   ,     A     :    Optional[int]  ):




						'''simple docstring'''






						UpperCAmelCase         =			BigBirdConfig.from_json_file(A  )
						print(f"""Building PyTorch model from configuration: {config}"""  )

						if is_trivia_qa:
												UpperCAmelCase         =			BigBirdForQuestionAnswering(A  )
						else:
												UpperCAmelCase         =			BigBirdForPreTraining(A  )

						# Load weights from tf checkpoint
						load_tf_weights_in_big_bird(A   ,     A   ,     is_trivia_qa=A  )

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


if __name__ == "__main__":
	_lowercase    :    List[str]			     =  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(
	    """--big_bird_config_file""",
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        """The config json file corresponding to the pre-trained BERT model. \n"""
	        """This specifies the model architecture."""
	    ),
	)
	parser.add_argument(
	    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	parser.add_argument(
	    """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
	)
	_lowercase    :    Any			     =  parser.parse_args()
	convert_tf_checkpoint_to_pytorch(
	    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
	)





