'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A		:			Tuple	      =logging.get_logger(__name__)

_A		:			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 _lowerCAmelCase (  snake_case				):
      UpperCAmelCase_  :	Any     =					"reformer"
      UpperCAmelCase_  :	int     =					["past_buckets_states"]
      UpperCAmelCase_  :	Any     =					{}







      def __init__(     self     ,						lowercase=64     ,						lowercase=["local", "lsh", "local", "lsh", "local", "lsh"]     ,						lowercase=1.0     ,						lowercase=True     ,						lowercase=[64, 64]     ,						lowercase=[64, 192]     ,						lowercase=0     ,						lowercase=2     ,						lowercase=512     ,						lowercase=None     ,						lowercase="relu"     ,						lowercase=0.05     ,						lowercase=256     ,						lowercase=0.02     ,						lowercase=False     ,						lowercase=1e-12     ,						lowercase=1     ,						lowercase=0     ,						lowercase=0.05     ,						lowercase=64     ,						lowercase=64     ,						lowercase=0.0     ,						lowercase=1     ,						lowercase=0     ,						lowercase=4096     ,						lowercase=12     ,						lowercase=None     ,						lowercase=1     ,						lowercase=0     ,						lowercase=320     ,						lowercase=False     ,						lowercase=True     ,						lowercase=None     ,						**lowercase     ,						):
        lowercase							:       List[Any]											=      hash_seed
        lowercase							:       Tuple											=      vocab_size
        lowercase							:       int											=      attention_head_size
        lowercase							:       Any											=      hidden_size
        lowercase							:       List[Any]											=      num_attention_heads
        lowercase							:       Dict											=      num_hashes
        lowercase							:       Tuple											=      len(lowercase    )
        lowercase							:       Any											=      tuple(lowercase    ) if isinstance(lowercase     ,						lowercase    ) else num_buckets
        lowercase							:       Optional[int]											=      lsh_attn_chunk_length
        lowercase							:       Optional[int]											=      local_attn_chunk_length
        lowercase							:       List[str]											=      lsh_num_chunks_after
        lowercase							:       Optional[Any]											=      lsh_num_chunks_before
        lowercase							:       Optional[int]											=      local_num_chunks_after
        lowercase							:       Dict											=      local_num_chunks_before
        lowercase							:       Tuple											=      hidden_act
        lowercase							:       int											=      feed_forward_size
        lowercase							:       Any											=      hidden_dropout_prob
        lowercase							:       Any											=      lsh_attention_probs_dropout_prob
        lowercase							:       Dict											=      local_attention_probs_dropout_prob
        lowercase							:       int											=      max_position_embeddings
        lowercase							:       List[str]											=      initializer_range
        lowercase							:       List[Any]											=      layer_norm_eps
        lowercase							:       str											=      axial_pos_embds
        lowercase							:       Dict											=      tuple(lowercase    )
        lowercase							:       Dict											=      tuple(lowercase    )
        lowercase							:       Tuple											=      axial_norm_std
        lowercase							:       Optional[int]											=      chunk_size_lm_head
        lowercase							:       str											=      attn_layers
        lowercase							:       List[Any]											=      use_cache
        lowercase							:       Union[str, Any]											=      classifier_dropout
        super().__init__(
            pad_token_id=lowercase     ,						eos_token_id=lowercase     ,						is_decoder=lowercase     ,						tie_word_embeddings=lowercase     ,						**lowercase     ,						)
'''simple docstring'''
import argparse

import torch

from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging


logging.set_verbosity_info()







def 							__UpperCamelCase   (				__UpperCAmelCase     :				Optional[Any]   ,      __UpperCAmelCase     :				Tuple   ,      __UpperCAmelCase     :				Union[str, Any]   ,      __UpperCAmelCase     :				List[str] )       ->  List[str]:
  # Initialise PyTorch model
  lowercase							:       Any											=      ElectraConfig.from_json_file(__UpperCAmelCase )
  print(f"""Building PyTorch model from configuration: {config}""" )

  if discriminator_or_generator == "discriminator":
    lowercase							:       Union[str, Any]											=      ElectraForPreTraining(__UpperCAmelCase )
  elif discriminator_or_generator == "generator":
    lowercase							:       Tuple											=      ElectraForMaskedLM(__UpperCAmelCase )
  else:
    raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\'''' )

  # Load weights from tf checkpoint
  load_tf_weights_in_electra(
      __UpperCAmelCase   ,      __UpperCAmelCase   ,      __UpperCAmelCase   ,      discriminator_or_generator=__UpperCAmelCase )

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


if __name__ == "__main__":
 _A		:			str	      =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(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the pre-trained model. \nThis 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(
     '''--discriminator_or_generator''',
     default=None,
     type=str,
     required=True,
     help=(
         '''Whether to export the generator or the discriminator. Should be a string, either \'discriminator\' or '''
         '''\'generator\'.'''
     ),
 )
 _A		:			Any	      =parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
 )