


'''simple docstring'''






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


a_					:    Any          =    logging.get_logger(__name__)

a_					:    Optional[int]          =    {
    "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       ):
 _lowerCAmelCase 					=	"""gpt_neox"""
 def __init__( self      ,				__magic_name__=5_04_32      ,				__magic_name__=61_44      ,				__magic_name__=44      ,				__magic_name__=64      ,				__magic_name__=2_45_76      ,				__magic_name__="gelu"      ,				__magic_name__=0.2_5      ,				__magic_name__=1_00_00      ,				__magic_name__=0.0      ,				__magic_name__=0.0      ,				__magic_name__=0.1      ,				__magic_name__=20_48      ,				__magic_name__=0.0_2      ,				__magic_name__=1e-5      ,				__magic_name__=True      ,				__magic_name__=0      ,				__magic_name__=2      ,				__magic_name__=False      ,				__magic_name__=True      ,				__magic_name__=None      ,				**__magic_name__      ,				)					->							Dict:
      super().__init__(bos_token_id=__magic_name__      ,				eos_token_id=__magic_name__      ,				**__magic_name__						)
      _a			      =							vocab_size
      _a			      =							max_position_embeddings
      _a			      =							hidden_size
      _a			      =							num_hidden_layers
      _a			      =							num_attention_heads
      _a			      =							intermediate_size
      _a			      =							hidden_act
      _a			      =							rotary_pct
      _a			      =							rotary_emb_base
      _a			      =							attention_dropout
      _a			      =							hidden_dropout
      _a			      =							classifier_dropout
      _a			      =							initializer_range
      _a			      =							layer_norm_eps
      _a			      =							use_cache
      _a			      =							tie_word_embeddings
      _a			      =							use_parallel_residual
      _a			      =							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 							__UpperCAmelCase		( self						)					->							Dict:
      if self.rope_scaling is None:
           return

      if not isinstance(self.rope_scaling      ,				__magic_name__						) 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}'						)
      _a			      =							self.rope_scaling.get('type'      ,				__magic_name__						)
      _a			      =							self.rope_scaling.get('factor'      ,				__magic_name__						)
      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(__magic_name__      ,				__magic_name__						) 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

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()






def 	_A				(lowerCAmelCase__  :str				,						lowerCAmelCase__  :int				,						lowerCAmelCase__  :Any		)   ->  str:




     '''simple docstring'''



     _a			      =							BertConfig.from_json_file(lowerCAmelCase__		)
     print(f'Building PyTorch model from configuration: {config}'		)
     _a			      =							BertForPreTraining(lowerCAmelCase__		)

     # Load weights from tf checkpoint
     load_tf_weights_in_bert(lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__		)

     # Save pytorch-model
     print(f'Save PyTorch model to {pytorch_dump_path}'		)
     torch.save(model.state_dict()				,						lowerCAmelCase__		)


if __name__ == "__main__":
 a_					:    Optional[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(
     "--bert_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."
 )
 a_					:    Optional[int]          =    parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)

