





import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class   _lowercase						(							a		,					a		,					a				):


   @register_to_config
   def __init__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = False    ,   ):
       super().__init__()

       A       :       Dict							=						nn.Embedding(_UpperCAmelCase    ,   _UpperCAmelCase					)

       A       :       str							=						nn.Embedding(_UpperCAmelCase    ,   _UpperCAmelCase					)
       A       :       Union[str, Any]							=						False

       A       :       List[str]							=						nn.Dropout(p=_UpperCAmelCase					)

       A       :       str							=						TaConfig(
           vocab_size=_UpperCAmelCase    ,   d_model=_UpperCAmelCase    ,   num_heads=_UpperCAmelCase    ,   d_kv=_UpperCAmelCase    ,   d_ff=_UpperCAmelCase    ,   dropout_rate=_UpperCAmelCase    ,   feed_forward_proj=_UpperCAmelCase    ,   is_decoder=_UpperCAmelCase    ,   is_encoder_decoder=_UpperCAmelCase    ,   )

       A       :       Union[str, Any]							=						nn.ModuleList()
       for lyr_num in range(_UpperCAmelCase					):
           A       :       Tuple							=						TaBlock(_UpperCAmelCase					)
           self.encoders.append(_UpperCAmelCase					)

       A       :       str							=						TaLayerNorm(_UpperCAmelCase					)
       A       :       Any							=						nn.Dropout(p=_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       Dict							=						self.token_embedder(_UpperCAmelCase					)

       A       :       Dict							=						encoder_input_tokens.shape[1]
       A       :       Union[str, Any]							=						torch.arange(_UpperCAmelCase    ,   device=encoder_input_tokens.device					)
       x += self.position_encoding(_UpperCAmelCase					)

       A       :       Union[str, Any]							=						self.dropout_pre(_UpperCAmelCase					)

       # inverted the attention mask
       A       :       Optional[int]							=						encoder_input_tokens.size()
       A       :       str							=						self.get_extended_attention_mask(_UpperCAmelCase    ,   _UpperCAmelCase					)

       for lyr in self.encoders:
           A       :       Optional[Any]							=						lyr(_UpperCAmelCase    ,   _UpperCAmelCase					)[0]
       A       :       int							=						self.layer_norm(_UpperCAmelCase					)

       return self.dropout_post(_UpperCAmelCase					), encoder_inputs_mask







'''simple docstring'''






def 							_lowerCamelCase(  UpperCamelCase__    : Optional[int]    ,       UpperCamelCase__    : str		)		->  Optional[int]:
    A       :       Optional[int]							=						0
    A       :       str							=						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

        A       :       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

        A       :       Union[str, Any]							=						sorted_collection[point]
        if current_item == item:
            return point
        else:
            if point < left:
                A       :       Tuple							=						left
                A       :       Tuple							=						point
            elif point > right:
                A       :       Optional[Any]							=						right
                A       :       str							=						point
            else:
                if item < current_item:
                    A       :       Tuple							=						point - 1
                else:
                    A       :       Optional[Any]							=						point + 1
    return None
def 							_lowerCamelCase(  UpperCamelCase__    : Dict    ,       UpperCamelCase__    : str    ,       UpperCamelCase__    : Dict    ,       UpperCamelCase__    : List[str]		)		->  List[Any]:

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

    A       :       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 							_lowerCamelCase(  UpperCamelCase__    : Optional[int]		)		->  List[Any]:
    if collection != sorted(UpperCamelCase__		):
        raise ValueError('''Collection must be ascending sorted'''		)
    return True


if __name__ == "__main__":
     import sys
     snake_case_     =	0
     if debug == 1:
          snake_case_     =	[10, 30, 40, 45, 50, 66, 77, 93]
          try:
               __assert_sorted(collection)
          except ValueError:
               sys.exit("""Sequence must be ascending sorted to apply interpolation search""")
          snake_case_     =	67

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