






'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
  from ... import FeatureExtractionMixin, TensorType

a_					= logging.get_logger(__name__)

a_					= {
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}






class      __SCREAMING_SNAKE_CASE      (			__A				):

 '''simple docstring'''





 snake_case_  							=							"""imagegpt"""
 snake_case_  							=							["""past_key_values"""]
 snake_case_  							=							{
     """hidden_size""": """n_embd""",
     """max_position_embeddings""": """n_positions""",
     """num_attention_heads""": """n_head""",
     """num_hidden_layers""": """n_layer""",
 }




 def __init__(      self	:							List[str]					,      __lowercase	:							Union[str, Any]=5_12 + 1					,      __lowercase	:							str=32 * 32					,      __lowercase	:							Union[str, Any]=5_12					,      __lowercase	:							Optional[int]=24					,      __lowercase	:							List[str]=8					,      __lowercase	:							Dict=None					,      __lowercase	:							Dict="quick_gelu"					,      __lowercase	:							List[Any]=0.1					,      __lowercase	:							Tuple=0.1					,      __lowercase	:							Any=0.1					,      __lowercase	:							Union[str, Any]=1e-5					,      __lowercase	:							Tuple=0.02					,      __lowercase	:							Optional[Any]=True					,      __lowercase	:							Union[str, Any]=True					,      __lowercase	:							int=False					,      __lowercase	:							Any=False					,      __lowercase	:							Any=False					,      **__lowercase	:							int					,      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=vocab_size
       SCREAMING_SNAKE_CASE__     :				str									=n_positions
       SCREAMING_SNAKE_CASE__     :				Dict									=n_embd
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=n_layer
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=n_head
       SCREAMING_SNAKE_CASE__     :				List[str]									=n_inner
       SCREAMING_SNAKE_CASE__     :				Dict									=activation_function
       SCREAMING_SNAKE_CASE__     :				List[Any]									=resid_pdrop
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=embd_pdrop
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=attn_pdrop
       SCREAMING_SNAKE_CASE__     :				Any									=layer_norm_epsilon
       SCREAMING_SNAKE_CASE__     :				List[str]									=initializer_range
       SCREAMING_SNAKE_CASE__     :				int									=scale_attn_weights
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=use_cache
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=scale_attn_by_inverse_layer_idx
       SCREAMING_SNAKE_CASE__     :				List[str]									=reorder_and_upcast_attn
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=tie_word_embeddings

       super().__init__(tie_word_embeddings=__lowercase					,      **__lowercase      )






class      __SCREAMING_SNAKE_CASE      (			__A				):

 '''simple docstring'''





 @property
 def      __magic_name__	(      self	:							Any      )	->						Mapping[str, Mapping[int, str]]:
       return OrderedDict(
           [
               ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
           ]      )




 def      __magic_name__	(      self	:							Union[str, Any]					,      __lowercase	:							"FeatureExtractionMixin"					,      __lowercase	:							int = 1					,      __lowercase	:							int = -1					,      __lowercase	:							bool = False					,      __lowercase	:							Optional["TensorType"] = None					,      __lowercase	:							int = 3					,      __lowercase	:							int = 32					,      __lowercase	:							int = 32					,      )	->						Mapping[str, Any]:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=self._generate_dummy_images(__lowercase					,      __lowercase					,      __lowercase					,      __lowercase      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									=dict(preprocessor(images=__lowercase					,      return_tensors=__lowercase      )      )

       return inputs







'''simple docstring'''
def  _a(  UpperCamelCase__				:						Optional[int],	UpperCamelCase__				:						int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Optional[int]									=0
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=len(UpperCamelCase__							) - 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

            SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=left + ((item - sorted_collection[left]) * (right - left)) // (
                sorted_collection[right] - sorted_collection[left]
            )

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

            SCREAMING_SNAKE_CASE__     :				Optional[int]									=sorted_collection[point]
            if current_item == item:
                  return point
            else:
                  if point < left:
                        SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=left
                        SCREAMING_SNAKE_CASE__     :				Optional[Any]									=point
                  elif point > right:
                        SCREAMING_SNAKE_CASE__     :				Optional[int]									=right
                        SCREAMING_SNAKE_CASE__     :				Tuple									=point
                  else:
                        if item < current_item:
                              SCREAMING_SNAKE_CASE__     :				str									=point - 1
                        else:
                              SCREAMING_SNAKE_CASE__     :				Tuple									=point + 1
      return None





def  _a(  UpperCamelCase__				:						List[str],	UpperCamelCase__				:						Optional[Any],	UpperCamelCase__				:						Union[str, Any],	UpperCamelCase__				:						Union[str, Any]							):




      '''simple docstring'''






      if sorted_collection[left] == sorted_collection[right]:
            if sorted_collection[left] == item:
                  return left
            else:
                  return None

      SCREAMING_SNAKE_CASE__     :				Dict									=left + ((item - sorted_collection[left]) * (right - left)) // (
          sorted_collection[right] - sorted_collection[left]
      )

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

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





def  _a(  UpperCamelCase__				:						Dict							):




      '''simple docstring'''






      if collection != sorted(UpperCamelCase__							):
            raise ValueError('''Collection must be ascending sorted'''							)
      return True


if __name__ == "__main__":
  import sys
  a_					= 0
  if debug == 1:
    a_					= [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')
    a_					= 6_7

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