
'''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 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int      ,      __SCREAMING_SNAKE_CASE					: Any      ,      __SCREAMING_SNAKE_CASE					: Optional[int]      ,      __SCREAMING_SNAKE_CASE					: Optional[int]							):



						"""simple docstring"""





						lowercase_	:     List[Any]													=       BigBirdConfig.from_json_file(__SCREAMING_SNAKE_CASE							)
						print(F'''Building PyTorch model from configuration: {config}'''							)

						if is_trivia_qa:
												lowercase_	:     Tuple													=       BigBirdForQuestionAnswering(__SCREAMING_SNAKE_CASE							)
						else:
												lowercase_	:     Optional[Any]													=       BigBirdForPreTraining(__SCREAMING_SNAKE_CASE							)

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

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


if __name__ == "__main__":
							_lowercase :  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."
							)
							_lowercase :  Union[str, 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
							)





'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
							from transformers import AutoModelForSeqaSeqLM, AutoTokenizer






@require_torch
@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase__   (  unittest.TestCase     ):






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Dict													=       AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''				,							return_dict=__SCREAMING_SNAKE_CASE		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Union[str, Any]													=       AutoTokenizer.from_pretrained('''google/mt5-small'''		)

											lowercase_	:     int													=       tokenizer('''Hello there'''				,							return_tensors='''pt'''		).input_ids
											lowercase_	:     Union[str, Any]													=       tokenizer('''Hi I am'''				,							return_tensors='''pt'''		).input_ids

											lowercase_	:     Union[str, Any]													=       model(input_ids.to(__SCREAMING_SNAKE_CASE		)				,							labels=labels.to(__SCREAMING_SNAKE_CASE		)		).loss
											lowercase_	:     int													=       -(labels.shape[-1] * loss.item())

											lowercase_	:     Any													=       -84.9_127
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)



