






import argparse

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


logging.set_verbosity_info()

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:List[str]   =   BigBirdConfig.from_json_file(snake_case			)
				print(f'''Building PyTorch model from configuration: {config}'''			)

				if is_trivia_qa:
								__magic_name__		:List[Any]   =   BigBirdForQuestionAnswering(snake_case			)
				else:
								__magic_name__		:List[Any]   =   BigBirdForPreTraining(snake_case			)

				# Load weights from tf checkpoint
				load_tf_weights_in_big_bird(snake_case,	snake_case,	is_trivia_qa=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__   :      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."""
					)
					SCREAMING_SNAKE_CASE__   :      Dict				=		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
					)














from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__   :      Dict				=		{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :      str				=		[
					    """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """CanineForMultipleChoice""",
					    """CanineForQuestionAnswering""",
					    """CanineForSequenceClassification""",
					    """CanineForTokenClassification""",
					    """CanineLayer""",
					    """CanineModel""",
					    """CaninePreTrainedModel""",
					    """load_tf_weights_in_canine""",
					]


if TYPE_CHECKING:
					from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
					from .tokenization_canine import CanineTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_canine import (
										    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CanineForMultipleChoice,
										    CanineForQuestionAnswering,
										    CanineForSequenceClassification,
										    CanineForTokenClassification,
										    CanineLayer,
										    CanineModel,
										    CaninePreTrainedModel,
										    load_tf_weights_in_canine,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






