

'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__						:       Dict										=						logging.get_logger(__name__)

lowerCAmelCase__						:       str										=						{
    """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json""",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}




class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    """gpt_neox"""







						def __init__(							self					:      Optional[Any]			,			snake_case_					:      List[str]=5_0_4_3_2			,			snake_case_					:      Any=6_1_4_4			,			snake_case_					:      Tuple=4_4			,			snake_case_					:      Tuple=6_4			,			snake_case_					:      Dict=2_4_5_7_6			,			snake_case_					:      List[str]="gelu"			,			snake_case_					:      Tuple=0.2_5			,			snake_case_					:      Union[str, Any]=1_0_0_0_0			,			snake_case_					:      List[str]=0.0			,			snake_case_					:      List[str]=0.0			,			snake_case_					:      str=0.1			,			snake_case_					:      Optional[Any]=2_0_4_8			,			snake_case_					:      int=0.0_2			,			snake_case_					:      Any=1e-5			,			snake_case_					:      Optional[int]=True			,			snake_case_					:      Dict=0			,			snake_case_					:      List[str]=2			,			snake_case_					:      Dict=False			,			snake_case_					:      Optional[Any]=True			,			snake_case_					:      Optional[Any]=None			,			**snake_case_					:      Dict			,			):



								'''simple docstring'''



								super().__init__(bos_token_id=_UpperCAmelCase			,			eos_token_id=_UpperCAmelCase			,			**_UpperCAmelCase  )
								snake_case__						:       Dict       						=  vocab_size
								snake_case__						:       int       						=  max_position_embeddings
								snake_case__						:       Optional[Any]       						=  hidden_size
								snake_case__						:       Any       						=  num_hidden_layers
								snake_case__						:       Optional[Any]       						=  num_attention_heads
								snake_case__						:       Any       						=  intermediate_size
								snake_case__						:       List[Any]       						=  hidden_act
								snake_case__						:       Any       						=  rotary_pct
								snake_case__						:       List[str]       						=  rotary_emb_base
								snake_case__						:       List[Any]       						=  attention_dropout
								snake_case__						:       Optional[int]       						=  hidden_dropout
								snake_case__						:       str       						=  classifier_dropout
								snake_case__						:       int       						=  initializer_range
								snake_case__						:       List[Any]       						=  layer_norm_eps
								snake_case__						:       Optional[Any]       						=  use_cache
								snake_case__						:       Dict       						=  tie_word_embeddings
								snake_case__						:       List[Any]       						=  use_parallel_residual
								snake_case__						:       int       						=  rope_scaling
								self._rope_scaling_validation()

								if self.hidden_size % self.num_attention_heads != 0:
										raise ValueError(
										    '''The hidden size is not divisble by the number of attention heads! Make sure to update them!'''  )







						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								if self.rope_scaling is None:
										return

								if not isinstance(self.rope_scaling			,			_UpperCAmelCase  ) or len(self.rope_scaling  ) != 2:
										raise ValueError(
										    '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
										    F"""got {self.rope_scaling}"""  )
								snake_case__						:       Optional[Any]       						=  self.rope_scaling.get('''type'''			,			_UpperCAmelCase  )
								snake_case__						:       List[Any]       						=  self.rope_scaling.get('''factor'''			,			_UpperCAmelCase  )
								if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
										raise ValueError(
										    F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""  )
								if rope_scaling_factor is None or not isinstance(_UpperCAmelCase			,			_UpperCAmelCase  ) or rope_scaling_factor <= 1.0:
										raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""  )






'''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		:      Union[str, Any]   ,							__lowerCAmelCase		:      Any   ,							__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      List[Any]     ):

		"""simple docstring"""

		snake_case__						:       Any       						=  BigBirdConfig.from_json_file(__lowerCAmelCase     )
		print(F"""Building PyTorch model from configuration: {config}"""     )

		if is_trivia_qa:
				snake_case__						:       str       						=  BigBirdForQuestionAnswering(__lowerCAmelCase     )
		else:
				snake_case__						:       Tuple       						=  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__						:       Union[str, Any]										=						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__						:       Optional[int]										=						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
					)



