
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


__lowerCAmelCase			:     Any           =   logging.get_logger(__name__)

__lowerCAmelCase			:     Any           =   {
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}




class        UpperCAmelCase_			( a__     ):


  '''simple docstring'''







  a__           =		"gpt_neo"
  a__           =		["past_key_values"]
  a__           =		{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
  def __init__( self			: int			,							UpperCamelCase__			: Dict=5_0257			,							UpperCamelCase__			: List[Any]=2048			,							UpperCamelCase__			: List[Any]=2048			,							UpperCamelCase__			: List[str]=24			,							UpperCamelCase__			: Tuple=[[["global", "local"], 12]]			,							UpperCamelCase__			: Any=16			,							UpperCamelCase__			: Union[str, Any]=None			,							UpperCamelCase__			: int=256			,							UpperCamelCase__			: Union[str, Any]="gelu_new"			,							UpperCamelCase__			: Optional[int]=0.0			,							UpperCamelCase__			: List[Any]=0.0			,							UpperCamelCase__			: Dict=0.0			,							UpperCamelCase__			: Tuple=0.1			,							UpperCamelCase__			: List[Any]=1E-5			,							UpperCamelCase__			: Optional[int]=0.02			,							UpperCamelCase__			: str=True			,							UpperCamelCase__			: Union[str, Any]=5_0256			,							UpperCamelCase__			: Optional[int]=5_0256			,							**UpperCamelCase__			: Optional[Any]			,							)			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       vocab_size
    __magic_name__				     =       max_position_embeddings
    __magic_name__				     =       hidden_size
    __magic_name__				     =       num_layers
    __magic_name__				     =       num_heads
    __magic_name__				     =       intermediate_size
    __magic_name__				     =       window_size
    __magic_name__				     =       activation_function
    __magic_name__				     =       resid_dropout
    __magic_name__				     =       embed_dropout
    __magic_name__				     =       attention_dropout
    __magic_name__				     =       classifier_dropout
    __magic_name__				     =       layer_norm_epsilon
    __magic_name__				     =       initializer_range
    __magic_name__				     =       use_cache

    __magic_name__				     =       bos_token_id
    __magic_name__				     =       eos_token_id

    __magic_name__				     =       attention_types
    __magic_name__				     =       self.expand_attention_types_params(SCREAMING_SNAKE_CASE_    )

    if len(self.attention_layers    ) != self.num_layers:
      raise ValueError(
          """Configuration for convolutional module is incorrect. """
          """It is required that `len(config.attention_layers)` == `config.num_layers` """
          F'''but is `len(config.attention_layers) = {len(self.attention_layers    )}`, '''
          F'''`config.num_layers = {self.num_layers}`. '''
          """`config.attention_layers` is prepared using `config.attention_types`. """
          """Please verify the value of `config.attention_types` argument."""    )

    super().__init__(bos_token_id=SCREAMING_SNAKE_CASE_			,							eos_token_id=SCREAMING_SNAKE_CASE_			,							**SCREAMING_SNAKE_CASE_    )





  @staticmethod
  def 							_lowercase							( UpperCamelCase__			: Any    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       []
    for item in attention_types:
      for _ in range(item[1]    ):
        attentions.extend(item[0]    )
    return attentions







def a__    (       A_,     A_,     A_,     A_					):






  '''simple docstring'''





  import torch

  __magic_name__				     =       input.size()
  __magic_name__				     =       len(snake_case__					)
  __magic_name__				     =       shape[dimension]

  __magic_name__				     =       torch.arange(0,     snake_case__,     snake_case__					)
  __magic_name__				     =       torch.div(sizedim - size,     snake_case__,     rounding_mode="""floor"""					) + 1
  __magic_name__				     =       torch.arange(snake_case__					) + low_indices[:min_length][:, None]

  __magic_name__				     =       [slice(snake_case__					)] * rank
  __magic_name__				     =       indices
  __magic_name__				     =       input[s]

  __magic_name__				     =       list(range(0,     rank + 1					)					)
  perm.append(perm.pop(dimension + 1					)					)

  return sliced.permute(snake_case__					)

def a__    (       A_,     A_					):






  '''simple docstring'''





  import torch

  __magic_name__				     =       torch.arange(1,     snake_case__					)
  __magic_name__				     =       torch.remainder(snake_case__,     snake_case__					)
  __magic_name__				     =       remainders == 0
  __magic_name__				     =       candidates[divisor_indices]
  __magic_name__				     =       torch.max(snake_case__					)
  return largest_divisor, torch.div(snake_case__,     snake_case__,     rounding_mode="""floor"""					)





class        UpperCAmelCase_			( a__     ):


  '''simple docstring'''







  @property
  def 							_lowercase							( self			: Dict    )			->	Mapping[str, Mapping[int, str]]:







    """simple docstring"""






    __magic_name__				     =       OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}    )
    if self.use_past:
      self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_			,							direction="""inputs"""    )
      __magic_name__				     =       {0: 'batch', 1: 'past_sequence + sequence'}
    else:
      __magic_name__				     =       {0: 'batch', 1: 'sequence'}

    return common_inputs
  @property
  def 							_lowercase							( self			: Dict    )			->	int:







    """simple docstring"""






    return self._config.num_heads
  def 							_lowercase							( self			: Any			,							UpperCamelCase__			: Union[str, Any]			,							UpperCamelCase__			: Any = -1			,							UpperCamelCase__			: Tuple = -1			,							UpperCamelCase__			: Any = False			,							UpperCamelCase__			: Tuple = None			,							)			->	Mapping[str, Any]:







    """simple docstring"""






    __magic_name__				     =       super(SCREAMING_SNAKE_CASE_			,							self    ).generate_dummy_inputs(
        SCREAMING_SNAKE_CASE_			,							batch_size=SCREAMING_SNAKE_CASE_			,							seq_length=SCREAMING_SNAKE_CASE_			,							is_pair=SCREAMING_SNAKE_CASE_			,							framework=SCREAMING_SNAKE_CASE_    )

    # We need to order the input in the way they appears in the forward()
    __magic_name__				     =       OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}    )

    # Need to add the past_keys
    if self.use_past:
      if not is_torch_available():
        raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""    )
      else:
        import torch

        __magic_name__				     =       common_inputs['input_ids'].shape
        # Not using the same length for past_key_values
        __magic_name__				     =       seqlen + 2
        __magic_name__				     =       (
            batch,
            self.num_attention_heads,
            past_key_values_length,
            self._config.hidden_size // self.num_attention_heads,
        )
        __magic_name__				     =       [
            (torch.zeros(SCREAMING_SNAKE_CASE_    ), torch.zeros(SCREAMING_SNAKE_CASE_    )) for _ in range(self.num_layers    )
        ]

    __magic_name__				     =       common_inputs['attention_mask']
    if self.use_past:
      __magic_name__				     =       ordered_inputs['attention_mask'].dtype
      __magic_name__				     =       torch.cat(
          [ordered_inputs["""attention_mask"""], torch.ones(SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_			,							dtype=SCREAMING_SNAKE_CASE_    )]			,							dim=1    )

    return ordered_inputs





  @property
  def 							_lowercase							( self			: Union[str, Any]    )			->	int:







    """simple docstring"""






    return 13






import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()
def 		UpperCamelCase	(   snake_case__       :    List[str]       ,     snake_case__       :    Union[str, Any]       ,     snake_case__       :    Union[str, Any]	)	->     int:
   # Initialise PyTorch model
   UpperCamelCase    :  Any							=	MobileBertConfig.from_json_file(snake_case__	)
   print(F"""Building PyTorch model from configuration: {config}"""	)
   UpperCamelCase    :  Tuple							=	MobileBertForPreTraining(snake_case__	)
   # Load weights from tf checkpoint
   UpperCamelCase    :  int							=	load_tf_weights_in_mobilebert(snake_case__       ,     snake_case__       ,     snake_case__	)
   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}"""	)
   torch.save(model.state_dict()       ,     snake_case__	)


if __name__ == "__main__":
    __UpperCAmelCase          =       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(
        '''--mobilebert_config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained MobileBERT 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.'''
    )
    __UpperCAmelCase          =       parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



