

def 	a (     A__      :  str	)       -> int:





      """simple docstring"""



      _lowercase      			=hex_num.strip()
      if not hex_num:
            raise ValueError('No value was passed to the function'	)

      _lowercase      			=hex_num[0] == '-'
      if is_negative:
            _lowercase      			=hex_num[1:]

      try:
            _lowercase      			=int(A__ ,  16	)
      except ValueError:
            raise ValueError('Invalid value was passed to the function'	)

      _lowercase      			=''
      while int_num > 0:
            _lowercase      			=str(int_num % 2	) + bin_str
            int_num >>= 1

      return int(('-' + bin_str) if is_negative else bin_str	)


if __name__ == "__main__":
  import doctest

  doctest.testmod()





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

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


lowercase_   							=  logging.get_logger(__name__)

lowercase_   							=  {
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    """gptj"""
  _a		     =    {
      """max_position_embeddings""": """n_positions""",
      """hidden_size""": """n_embd""",
      """num_attention_heads""": """n_head""",
      """num_hidden_layers""": """n_layer""",
  }




  def __init__(							self				,  lowerCAmelCase=50_400				,  lowerCAmelCase=2_048				,  lowerCAmelCase=4_096				,  lowerCAmelCase=28				,  lowerCAmelCase=16				,  lowerCAmelCase=64				,  lowerCAmelCase=None				,  lowerCAmelCase="gelu_new"				,  lowerCAmelCase=0.0				,  lowerCAmelCase=0.0				,  lowerCAmelCase=0.0				,  lowerCAmelCase=1e-5				,  lowerCAmelCase=0.02				,  lowerCAmelCase=True				,  lowerCAmelCase=50_256				,  lowerCAmelCase=50_256				,  lowerCAmelCase=False				,  **lowerCAmelCase				,  )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			=vocab_size
        _lowercase      			=n_positions
        _lowercase      			=n_embd
        _lowercase      			=n_layer
        _lowercase      			=n_head
        _lowercase      			=n_inner
        _lowercase      			=rotary_dim
        _lowercase      			=activation_function
        _lowercase      			=resid_pdrop
        _lowercase      			=embd_pdrop
        _lowercase      			=attn_pdrop
        _lowercase      			=layer_norm_epsilon
        _lowercase      			=initializer_range
        _lowercase      			=use_cache

        _lowercase      			=bos_token_id
        _lowercase      			=eos_token_id

        super().__init__(
            bos_token_id=lowerCAmelCase				,  eos_token_id=lowerCAmelCase				,  tie_word_embeddings=lowerCAmelCase				,  **lowerCAmelCase   )





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):




  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase = "default"				,  lowerCAmelCase = None				,  lowerCAmelCase = False				,  )    ->					Optional[int]:
        '''simple docstring'''






        super().__init__(lowerCAmelCase				,  task=lowerCAmelCase				,  patching_specs=lowerCAmelCase				,  use_past=lowerCAmelCase   )
        if not getattr(self._config				,  'pad_token_id'				,  lowerCAmelCase   ):
              # TODO: how to do that better?
              _lowercase      			=0




  @property
  def     A__	(							self   )    ->					Mapping[str, Mapping[int, str]]:
        '''simple docstring'''






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

        return common_inputs




  @property
  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        return self._config.n_layer




  @property
  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        return self._config.n_head




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase = -1				,  lowerCAmelCase = -1				,  lowerCAmelCase = False				,  lowerCAmelCase = None				,  )    ->					Mapping[str, Any]:
        '''simple docstring'''






        _lowercase      			=super(lowerCAmelCase				,  self   ).generate_dummy_inputs(
            lowerCAmelCase				,  batch_size=lowerCAmelCase				,  seq_length=lowerCAmelCase				,  is_pair=lowerCAmelCase				,  framework=lowerCAmelCase   )

        # We need to order the input in the way they appears in the forward()
        _lowercase      			=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

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

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

        return ordered_inputs




  @property
  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        return 13


