"""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   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				# Initialise PyTorch model
				UpperCAmelCase__             =							BigBirdConfig.from_json_file(lowerCamelCase       )
				print(f'''Building PyTorch model from configuration: {config}'''       )

				if is_trivia_qa:
								UpperCAmelCase__             =							BigBirdForQuestionAnswering(lowerCamelCase       )
				else:
								UpperCAmelCase__             =							BigBirdForPreTraining(lowerCamelCase       )

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

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


if __name__ == "__main__":
						lowerCAmelCase__  :  int						     =	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.'
						)
						lowerCAmelCase__  :  List[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
						)










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase						:   Any			  =     {
    '''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   Any			  =     [
				    '''FALCON_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''FalconForCausalLM''',
				    '''FalconModel''',
				    '''FalconPreTrainedModel''',
				    '''FalconForSequenceClassification''',
				    '''FalconForTokenClassification''',
				    '''FalconForQuestionAnswering''',
				]


if TYPE_CHECKING:
				from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_falcon import (
								    FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
								    FalconForCausalLM,
								    FalconForQuestionAnswering,
								    FalconForSequenceClassification,
								    FalconForTokenClassification,
								    FalconModel,
								    FalconPreTrainedModel,
								)


else:
				import sys

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




