
"""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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# Initialise PyTorch model
						__lowerCAmelCase     =  BigBirdConfig.from_json_file(lowerCAmelCase_						)
						print(f"""Building PyTorch model from configuration: {config}"""						)

						if is_trivia_qa:
												__lowerCAmelCase     =  BigBirdForQuestionAnswering(lowerCAmelCase_						)
						else:
												__lowerCAmelCase     =  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__":
			SCREAMING_SNAKE_CASE_					     =	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_					     =	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
			)





'''simple docstring'''

from typing import TYPE_CHECKING

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


__lowerCAmelCase		     =  {
    '''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		     =  [
							    '''ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''ErnieForCausalLM''',
							    '''ErnieForMaskedLM''',
							    '''ErnieForMultipleChoice''',
							    '''ErnieForNextSentencePrediction''',
							    '''ErnieForPreTraining''',
							    '''ErnieForQuestionAnswering''',
							    '''ErnieForSequenceClassification''',
							    '''ErnieForTokenClassification''',
							    '''ErnieModel''',
							    '''ErniePreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_ernie import (
														    ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ErnieForCausalLM,
														    ErnieForMaskedLM,
														    ErnieForMultipleChoice,
														    ErnieForNextSentencePrediction,
														    ErnieForPreTraining,
														    ErnieForQuestionAnswering,
														    ErnieForSequenceClassification,
														    ErnieForTokenClassification,
														    ErnieModel,
														    ErniePreTrainedModel,
														)

else:
							import sys

							__lowerCAmelCase		     =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




