





def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Optional[Any]			,							snake_case__  :Union[str, Any]     )      ->						Any:
  _lowercase							= 0
  _lowercase							= len(snake_case__     ) - 1

  while left <= right:
    # avoid divided by 0 during interpolation
    if sorted_collection[left] == sorted_collection[right]:
      if sorted_collection[left] == item:
        return left
      else:
        return None

    _lowercase							= left + ((item - sorted_collection[left]) * (right - left)) // (
        sorted_collection[right] - sorted_collection[left]
    )

    # out of range check
    if point < 0 or point >= len(snake_case__     ):
      return None

    _lowercase							= sorted_collection[point]
    if current_item == item:
      return point
    else:
      if point < left:
        _lowercase							= left
        _lowercase							= point
      elif point > right:
        _lowercase							= right
        _lowercase							= point
      else:
        if item < current_item:
          _lowercase							= point - 1
        else:
          _lowercase							= point + 1
  return None

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[Any]			,							snake_case__  :Dict			,							snake_case__  :int			,							snake_case__  :Optional[Any]     )      ->						Dict:

  # avoid divided by 0 during interpolation
  if sorted_collection[left] == sorted_collection[right]:
    if sorted_collection[left] == item:
      return left
    else:
      return None

  _lowercase							= left + ((item - sorted_collection[left]) * (right - left)) // (
      sorted_collection[right] - sorted_collection[left]
  )

  # out of range check
  if point < 0 or point >= len(snake_case__     ):
    return None

  if sorted_collection[point] == item:
    return point
  elif point < left:
    return interpolation_search_by_recursion(snake_case__			,							snake_case__			,							snake_case__			,							snake_case__     )
  elif point > right:
    return interpolation_search_by_recursion(snake_case__			,							snake_case__			,							snake_case__			,							snake_case__     )
  else:
    if sorted_collection[point] > item:
      return interpolation_search_by_recursion(
          snake_case__			,							snake_case__			,							snake_case__			,							point - 1     )
    else:
      return interpolation_search_by_recursion(
          snake_case__			,							snake_case__			,							point + 1			,							snake_case__     )

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Dict     )      ->						int:
  if collection != sorted(snake_case__     ):
    raise ValueError('Collection must be ascending sorted'     )
  return True


if __name__ == "__main__":
      import sys
      snake_case 				=       0
      if debug == 1:
            snake_case 				=       [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
            try:
                  __assert_sorted(collection)
            except ValueError:
                  sys.exit('Sequence must be ascending sorted to apply interpolation search')
            snake_case 				=       6_7

      snake_case 				=       interpolation_search(collection, target)
      if result is not None:
            print(F"""{target} found at positions: {result}""")
      else:
            print('Not found')






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


snake_case 				=       logging.get_logger(__name__)

snake_case 				=       {
    """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 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : str      =  '''gptj'''
       SCREAMING_SNAKE_CASE_      : int      =  {
           '''max_position_embeddings''': '''n_positions''',
           '''hidden_size''': '''n_embd''',
           '''num_attention_heads''': '''n_head''',
           '''num_hidden_layers''': '''n_layer''',
       }







       def __init__(    self	:	str						,__A	:	List[str]=5_0400						,__A	:	Dict=2048						,__A	:	List[str]=4096						,__A	:	Dict=28						,__A	:	Optional[int]=16						,__A	:	Tuple=64						,__A	:	Optional[int]=None						,__A	:	Optional[int]="gelu_new"						,__A	:	Dict=0.0						,__A	:	List[str]=0.0						,__A	:	Optional[int]=0.0						,__A	:	str=1e-5						,__A	:	List[Any]=0.02						,__A	:	str=True						,__A	:	int=5_0256						,__A	:	Union[str, Any]=5_0256						,__A	:	int=False						,**__A	:	List[Any]						,)						->					str:
         _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=__A						,eos_token_id=__A						,tie_word_embeddings=__A						,**__A  )





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       def __init__(    self	:	Tuple						,__A	:	PretrainedConfig						,__A	:	str = "default"						,__A	:	List[PatchingSpec] = None						,__A	:	bool = False						,)						->					Any:
         super().__init__(__A						,task=__A						,patching_specs=__A						,use_past=__A  )
         if not getattr(self._config						,'pad_token_id'						,__A  ):
           # TODO: how to do that better?
           _lowercase							= 0







       @property
       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					Mapping[str, Mapping[int, str]]:
         _lowercase							= OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}  )
         if self.use_past:
           self.fill_with_past_key_values_(__A						,direction='inputs'  )
           _lowercase							= {0: 'batch', 1: 'past_sequence + sequence'}
         else:
           _lowercase							= {0: 'batch', 1: 'sequence'}

         return common_inputs







       @property
       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					int:
         return self._config.n_layer







       @property
       def    __UpperCAmelCase (    self	:	Tuple  )						->					int:
         return self._config.n_head







       def    __UpperCAmelCase (    self	:	Any						,__A	:	PreTrainedTokenizer						,__A	:	int = -1						,__A	:	int = -1						,__A	:	bool = False						,__A	:	Optional[TensorType] = None						,)						->					Mapping[str, Any]:
         _lowercase							= super(__A						,self  ).generate_dummy_inputs(
             __A						,batch_size=__A						,seq_length=__A						,is_pair=__A						,framework=__A  )

         # 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(__A  ), torch.zeros(__A  )) 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(__A						,__A						,dtype=__A  )]						,dim=1  )

         return ordered_inputs







       @property
       def    __UpperCAmelCase (    self	:	str  )						->					int:
         return 13