


'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


a_  :				Union[str, Any]						= logging.get_logger(__name__)





class      snake_case    (    lowercase			):





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  feature_size
   lowerCamelCase_      =  sampling_rate
   lowerCamelCase_      =  padding_value

   lowerCamelCase_      =  kwargs.pop("padding_side"			,  "right"     )
   lowerCamelCase_      =  kwargs.pop("return_attention_mask"			,  UpperCamelCase     )

   super().__init__(**UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = True			,  UpperCamelCase = None			,  UpperCamelCase = False			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  ):







   """simple docstring"""
   # If we have a list of dicts, let's convert it in a dict of lists
   # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
   if isinstance(UpperCamelCase			,  (list, tuple)     ) and isinstance(processed_features[0]			,  (dict, BatchFeature)     ):
    lowerCamelCase_      =  {
        key: [example[key] for example in processed_features] for key in processed_features[0].keys()
    }

   # The model's main input name, usually `input_values`, has be passed for padding
   if self.model_input_names[0] not in processed_features:
    raise ValueError(
        "You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
        f''' to this method that includes {self.model_input_names[0]}, but you provided'''
        f''' {list(processed_features.keys()     )}'''     )

   lowerCamelCase_      =  processed_features[self.model_input_names[0]]
   lowerCamelCase_      =  (
       return_attention_mask if return_attention_mask is not None else self.return_attention_mask
   )

   if len(UpperCamelCase     ) == 0:
    if return_attention_mask:
     lowerCamelCase_      =  []
    return processed_features

   # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
   # and rebuild them afterwards if no return_tensors is specified
   # Note that we lose the specific device the tensor may be on for PyTorch

   lowerCamelCase_      =  required_input[0]
   if isinstance(UpperCamelCase			,  (list, tuple)     ):
    # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
    lowerCamelCase_      =  0
    while len(required_input[index]     ) == 0:
     index += 1
    if index < len(UpperCamelCase     ):
     lowerCamelCase_      =  required_input[index][0]

   if return_tensors is None:
    if is_tf_tensor(UpperCamelCase     ):
     lowerCamelCase_      =  "tf"
    elif is_torch_tensor(UpperCamelCase     ):
     lowerCamelCase_      =  "pt"
    elif isinstance(UpperCamelCase			,  (int, float, list, tuple, np.ndarray)     ):
     lowerCamelCase_      =  "np"
    else:
     raise ValueError(
         f'''type of {first_element} unknown: {type(UpperCamelCase     )}. '''
         "Should be one of a python, numpy, pytorch or tensorflow object."     )

   for key, value in processed_features.items():
    if isinstance(value[0]			,  (int, float)     ):
     lowerCamelCase_      =  to_numpy(UpperCamelCase     )
    else:
     lowerCamelCase_      =  [to_numpy(UpperCamelCase     ) for v in value]

        # Convert padding_strategy in PaddingStrategy
   lowerCamelCase_      =  self._get_padding_strategies(padding=UpperCamelCase			,  max_length=UpperCamelCase     )

   lowerCamelCase_      =  processed_features[self.model_input_names[0]]

   lowerCamelCase_      =  len(UpperCamelCase     )
   if not all(len(UpperCamelCase     ) == batch_size for v in processed_features.values()     ):
    raise ValueError("Some items in the output dictionary have a different batch size than others."     )

   lowerCamelCase_      =  []
   for i in range(UpperCamelCase     ):
    lowerCamelCase_      =  {k: v[i] for k, v in processed_features.items()}
    # truncation
    lowerCamelCase_      =  self._truncate(
        UpperCamelCase			,  max_length=UpperCamelCase			,  pad_to_multiple_of=UpperCamelCase			,  truncation=UpperCamelCase			,  )
    truncated_inputs.append(UpperCamelCase     )

   if padding_strategy == PaddingStrategy.LONGEST:
    # make sure that `max_length` cannot be longer than the longest truncated length
    lowerCamelCase_      =  max(len(input_slice[self.model_input_names[0]]     ) for input_slice in truncated_inputs     )
    lowerCamelCase_      =  PaddingStrategy.MAX_LENGTH

   lowerCamelCase_      =  {}
   for i in range(UpperCamelCase     ):
    # padding
    lowerCamelCase_      =  self._pad(
        truncated_inputs[i]			,  max_length=UpperCamelCase			,  padding_strategy=UpperCamelCase			,  pad_to_multiple_of=UpperCamelCase			,  return_attention_mask=UpperCamelCase			,  )

    for key, value in outputs.items():
     if key not in batch_outputs:
      lowerCamelCase_      =  []
     if value.dtype is np.dtype(np.floataa     ):
      lowerCamelCase_      =  value.astype(np.floataa     )
     batch_outputs[key].append(UpperCamelCase     )

   return BatchFeature(UpperCamelCase			,  tensor_type=UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None			,  UpperCamelCase = PaddingStrategy.DO_NOT_PAD			,  UpperCamelCase = None			,  UpperCamelCase = None			,  ):







   """simple docstring"""
   lowerCamelCase_      =  processed_features[self.model_input_names[0]]

   if padding_strategy == PaddingStrategy.LONGEST:
    lowerCamelCase_      =  len(UpperCamelCase     )

   if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
    lowerCamelCase_      =  ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

   lowerCamelCase_      =  padding_strategy != PaddingStrategy.DO_NOT_PAD and len(UpperCamelCase     ) < max_length

   if return_attention_mask and "attention_mask" not in processed_features:
    lowerCamelCase_      =  np.ones(len(UpperCamelCase     )			,  dtype=np.intaa     )

   if needs_to_be_padded:
    lowerCamelCase_      =  max_length - len(UpperCamelCase     )
    if self.padding_side == "right":
     if return_attention_mask:
      lowerCamelCase_      =  np.pad(
          processed_features["attention_mask"]			,  (0, difference)     )
     lowerCamelCase_      =  ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
     lowerCamelCase_      =  np.pad(
         UpperCamelCase			,  UpperCamelCase			,  "constant"			,  constant_values=self.padding_value     )
    elif self.padding_side == "left":
     if return_attention_mask:
      lowerCamelCase_      =  np.pad(
          processed_features["attention_mask"]			,  (difference, 0)     )
     lowerCamelCase_      =  ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
     lowerCamelCase_      =  np.pad(
         UpperCamelCase			,  UpperCamelCase			,  "constant"			,  constant_values=self.padding_value     )
    else:
     raise ValueError("Invalid padding strategy:" + str(self.padding_side     )     )

   return processed_features






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  ):







   """simple docstring"""
   if not truncation:
    return processed_features
   elif truncation and max_length is None:
    raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined."     )

   lowerCamelCase_      =  processed_features[self.model_input_names[0]]

   # find `max_length` that fits `pad_to_multiple_of`
   if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
    lowerCamelCase_      =  ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

   lowerCamelCase_      =  len(UpperCamelCase     ) > max_length

   if needs_to_be_truncated:
    lowerCamelCase_      =  processed_features[self.model_input_names[0]][:max_length]
    if "attention_mask" in processed_features:
     lowerCamelCase_      =  processed_features["attention_mask"][:max_length]

   return processed_features







  def   snake_case		(    self			,  UpperCamelCase=False			,  UpperCamelCase=None     ):







   """simple docstring"""

   # Get padding strategy
   if padding is not False:
    if padding is True:
     lowerCamelCase_      =  PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
    elif not isinstance(UpperCamelCase			,  UpperCamelCase     ):
     lowerCamelCase_      =  PaddingStrategy(UpperCamelCase     )
    elif isinstance(UpperCamelCase			,  UpperCamelCase     ):
     lowerCamelCase_      =  padding
   else:
    lowerCamelCase_      =  PaddingStrategy.DO_NOT_PAD

   # Set max length if needed
   if max_length is None:
    if padding_strategy == PaddingStrategy.MAX_LENGTH:
     raise ValueError(
         f'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'''     )

        # Test if we have a padding value
   if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
    raise ValueError(
        "Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
        " as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."     )

   return padding_strategy








"""simple docstring"""







from __future__ import annotations

def 				UpperCamelCase_    (    lowerCAmelCase__      :				list[int]    ,   lowerCAmelCase__      :				int						)						->   bool:

  """simple docstring"""




  if len(lowerCAmelCase__						) == 0:
    return False
  lowerCAmelCase_   :				Union[str, Any]								= len(lowerCAmelCase__						) // 2
  if a_list[midpoint] == item:
    return True
  if item < a_list[midpoint]:
    return binary_search(a_list[:midpoint]    ,   lowerCAmelCase__						)
  else:
    return binary_search(a_list[midpoint + 1 :]    ,   lowerCAmelCase__						)


if __name__ == "__main__":
     lowercase__		:				str          =	input("""Enter numbers separated by comma:\n""").strip()
     lowercase__		:				Optional[int]          =	[int(item.strip()) for item in user_input.split(""",""")]
     lowercase__		:				Optional[Any]          =	int(input("""Enter the number to be found in the list:\n""").strip())
     lowercase__		:				int          =	"""""" if binary_search(sequence, target) else """not """
     print(f'{target} was {not_str}found in {sequence}')





