'''simple docstring'''


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


__snake_case						  =		logging.get_logger(__name__)

__snake_case						  =		{
    """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       _a    (		__a      ):


  """simple docstring"""

  A_    							=						'''gpt_neo'''
  A_    							=						['''past_key_values''']
  A_    							=						{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}




  def __init__(		self      :       Any   ,				lowercase_      :       int=50_257   ,				lowercase_      :       str=2_048   ,				lowercase_      :       List[Any]=2_048   ,				lowercase_      :       List[Any]=24   ,				lowercase_      :       Optional[Any]=[[["global", "local"], 12]]   ,				lowercase_      :       List[Any]=16   ,				lowercase_      :       str=None   ,				lowercase_      :       str=256   ,				lowercase_      :       str="gelu_new"   ,				lowercase_      :       Any=0.0   ,				lowercase_      :       Tuple=0.0   ,				lowercase_      :       int=0.0   ,				lowercase_      :       Optional[int]=0.1   ,				lowercase_      :       Tuple=1e-5   ,				lowercase_      :       Union[str, Any]=0.0_2   ,				lowercase_      :       Optional[Any]=True   ,				lowercase_      :       Tuple=50_256   ,				lowercase_      :       Any=50_256   ,				**lowercase_      :       List[Any]   ,				):







    '''simple docstring'''


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

    lowercase_     =       bos_token_id
    lowercase_     =       eos_token_id

    lowercase_     =       attention_types
    lowercase_     =       self.expand_attention_types_params(lowercase_      )

    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=lowercase_   ,				eos_token_id=lowercase_   ,				**lowercase_      )





  @staticmethod
  def 		lowerCamelCase__  (		lowercase_      :       str      ):







    '''simple docstring'''


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






def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Any:
  import torch

  lowercase_     =       input.size()
  lowercase_     =       len(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       shape[dimension]

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

  lowercase_     =       [slice(SCREAMING_SNAKE_CASE_						)] * rank
  lowercase_     =       indices
  lowercase_     =       input[s]

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

  return sliced.permute(SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Optional[Any]:
  import torch

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





class       _a    (		__a      ):


  """simple docstring"""

  @property
  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


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

    return common_inputs




  @property
  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    return self._config.num_heads




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       PreTrainedTokenizer   ,				lowercase_      :       int = -1   ,				lowercase_      :       int = -1   ,				lowercase_      :       bool = False   ,				lowercase_      :       Optional[TensorType] = None   ,				):







    '''simple docstring'''


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

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

    return ordered_inputs





  @property
  def 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


    return 13


'''simple docstring'''


from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__snake_case						  =		10

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->int:
  for i in range(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						):
    if array[i] == target:
      return i
  return -1

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->int:
  lowercase_     =       0
  lowercase_     =       len(SCREAMING_SNAKE_CASE_						)
  while left <= right:
    if right - left < precision:
      return lin_search(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

    lowercase_     =       (left + right) // 3 + 1
    lowercase_     =       2 * (left + right) // 3 + 1

    if array[one_third] == target:
      return one_third
    elif array[two_third] == target:
      return two_third

    elif target < array[one_third]:
      lowercase_     =       one_third - 1
    elif array[two_third] < target:
      lowercase_     =       two_third + 1

    else:
      lowercase_     =       one_third + 1
      lowercase_     =       two_third - 1
  else:
    return -1

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->int:
  if left < right:
    if right - left < precision:
      return lin_search(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    lowercase_     =       (left + right) // 3 + 1
    lowercase_     =       2 * (left + right) // 3 + 1

    if array[one_third] == target:
      return one_third
    elif array[two_third] == target:
      return two_third

    elif target < array[one_third]:
      return rec_ternary_search(SCREAMING_SNAKE_CASE_ ,	one_third - 1 ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    elif array[two_third] < target:
      return rec_ternary_search(two_third + 1 ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    else:
      return rec_ternary_search(one_third + 1 ,	two_third - 1 ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
  else:
    return -1


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __snake_case						  =		input("""Enter numbers separated by comma:\n""").strip()
       __snake_case						  =		[int(item.strip()) for item in user_input.split(""",""")]
       assert collection == sorted(collection), f"List must be ordered.\n{collection}."
       __snake_case						  =		int(input("""Enter the number to be found in the list:\n""").strip())
       __snake_case						  =		ite_ternary_search(collection, target)
       __snake_case						  =		rec_ternary_search(0, len(collection) - 1, collection, target)
       if resulta != -1:
              print(f'''Iterative search: {target} found at positions: {resulta}''')
              print(f'''Recursive search: {target} found at positions: {resulta}''')
       else:
              print("""Not found""")

