






"""simple docstring"""







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       # Initialise PyTorch model
       UpperCAmelCase					=	RoFormerConfig.from_json_file(lowerCAmelCase						)
       print(F'''Building PyTorch model from configuration: {config}'''						)
       UpperCAmelCase					=	RoFormerForMaskedLM(lowerCAmelCase						)

       # Load weights from tf checkpoint
       load_tf_weights_in_roformer(lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						)

       # Save pytorch-model
       print(F'''Save PyTorch model to {pytorch_dump_path}'''						)
       torch.save(model.state_dict()		,					lowerCAmelCase		,					_use_new_zipfile_serialization=lowerCAmelCase						)


if __name__ == "__main__":
 lowerCAmelCase_		:			Optional[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(
     '''--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.'''
 )
 lowerCAmelCase_		:			List[str]				   =					parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)








"""simple docstring"""







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


lowerCAmelCase_		:			str				   =					logging.get_logger(__name__)

lowerCAmelCase_		:			Optional[int]				   =					{
    '''google/reformer-crime-and-punishment''': (
        '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json'''
    ),
    '''google/reformer-enwik8''': '''https://huggingface.co/google/reformer-enwik8/resolve/main/config.json''',
}





class      UpperCamelCase_       (				a_     ):
  _A				:    int =		'reformer'
  _A				:    Any =		['past_buckets_states']
  _A				:    Optional[Any] =		{}



  def __init__(					self       ,						snake_case__=64       ,						snake_case__=["local", "lsh", "local", "lsh", "local", "lsh"]       ,						snake_case__=1.0       ,						snake_case__=True       ,						snake_case__=[64, 64]       ,						snake_case__=[64, 1_92]       ,						snake_case__=0       ,						snake_case__=2       ,						snake_case__=5_12       ,						snake_case__=None       ,						snake_case__="relu"       ,						snake_case__=0.05       ,						snake_case__=2_56       ,						snake_case__=0.02       ,						snake_case__=False       ,						snake_case__=1e-12       ,						snake_case__=1       ,						snake_case__=0       ,						snake_case__=0.05       ,						snake_case__=64       ,						snake_case__=64       ,						snake_case__=0.0       ,						snake_case__=1       ,						snake_case__=0       ,						snake_case__=40_96       ,						snake_case__=12       ,						snake_case__=None       ,						snake_case__=1       ,						snake_case__=0       ,						snake_case__=3_20       ,						snake_case__=False       ,						snake_case__=True       ,						snake_case__=None       ,						**snake_case__       ,						)	->	List[Any]:



         """simple docstring"""





         UpperCAmelCase					=	hash_seed
         UpperCAmelCase					=	vocab_size
         UpperCAmelCase					=	attention_head_size
         UpperCAmelCase					=	hidden_size
         UpperCAmelCase					=	num_attention_heads
         UpperCAmelCase					=	num_hashes
         UpperCAmelCase					=	len(snake_case__ )
         UpperCAmelCase					=	tuple(snake_case__ ) if isinstance(snake_case__       ,						snake_case__ ) else num_buckets
         UpperCAmelCase					=	lsh_attn_chunk_length
         UpperCAmelCase					=	local_attn_chunk_length
         UpperCAmelCase					=	lsh_num_chunks_after
         UpperCAmelCase					=	lsh_num_chunks_before
         UpperCAmelCase					=	local_num_chunks_after
         UpperCAmelCase					=	local_num_chunks_before
         UpperCAmelCase					=	hidden_act
         UpperCAmelCase					=	feed_forward_size
         UpperCAmelCase					=	hidden_dropout_prob
         UpperCAmelCase					=	lsh_attention_probs_dropout_prob
         UpperCAmelCase					=	local_attention_probs_dropout_prob
         UpperCAmelCase					=	max_position_embeddings
         UpperCAmelCase					=	initializer_range
         UpperCAmelCase					=	layer_norm_eps
         UpperCAmelCase					=	axial_pos_embds
         UpperCAmelCase					=	tuple(snake_case__ )
         UpperCAmelCase					=	tuple(snake_case__ )
         UpperCAmelCase					=	axial_norm_std
         UpperCAmelCase					=	chunk_size_lm_head
         UpperCAmelCase					=	attn_layers
         UpperCAmelCase					=	use_cache
         UpperCAmelCase					=	classifier_dropout
         super().__init__(
             pad_token_id=snake_case__       ,						eos_token_id=snake_case__       ,						is_decoder=snake_case__       ,						tie_word_embeddings=snake_case__       ,						**snake_case__       ,						)
