



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


snake_case__					:     Dict    		=   logging.get_logger(__name__)

class   _A   ( _lowercase	):


						'''simple docstring'''





						def __init__(	self	:							List[Any]							,      lowerCamelCase	:							int							,      lowerCamelCase	:							int							,      lowerCamelCase	:							float							,      **lowerCamelCase	:							Any	):






							'''simple docstring'''



							__lowercase		       =			feature_size
							__lowercase		       =			sampling_rate
							__lowercase		       =			padding_value

							__lowercase		       =			kwargs.pop("padding_side"							,      "right"	)
							__lowercase		       =			kwargs.pop("return_attention_mask"							,      lowerCamelCase	)

							super().__init__(**lowerCamelCase	)



						def     _snake_case	(	self	:							Any							,      lowerCamelCase	:							Union[
						        BatchFeature,
						        List[BatchFeature],
						        Dict[str, BatchFeature],
						        Dict[str, List[BatchFeature]],
						        List[Dict[str, BatchFeature]],
						    ]							,      lowerCamelCase	:							Union[bool, str, PaddingStrategy] = True							,      lowerCamelCase	:							Optional[int] = None							,      lowerCamelCase	:							bool = False							,      lowerCamelCase	:							Optional[int] = None							,      lowerCamelCase	:							Optional[bool] = None							,      lowerCamelCase	:							Optional[Union[str, TensorType]] = None							,      ):






							'''simple docstring'''



							if isinstance(lowerCamelCase							,      (list, tuple)	) and isinstance(processed_features[0]							,      (dict, BatchFeature)	):
								__lowercase		       =			{
								    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()	)}"""	)

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

							if len(lowerCamelCase	) == 0:
								if return_attention_mask:
									__lowercase		       =			[]
								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

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

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

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

        # Convert padding_strategy in PaddingStrategy
							__lowercase		       =			self._get_padding_strategies(padding=lowerCamelCase							,      max_length=lowerCamelCase	)

							__lowercase		       =			processed_features[self.model_input_names[0]]

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

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

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

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

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

							return BatchFeature(lowerCamelCase							,      tensor_type=lowerCamelCase	)



						def     _snake_case	(	self	:							Optional[int]							,      lowerCamelCase	:							Union[Dict[str, np.ndarray], BatchFeature]							,      lowerCamelCase	:							Optional[int] = None							,      lowerCamelCase	:							PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,      lowerCamelCase	:							Optional[int] = None							,      lowerCamelCase	:							Optional[bool] = None							,      ):






							'''simple docstring'''



							__lowercase		       =			processed_features[self.model_input_names[0]]

							if padding_strategy == PaddingStrategy.LONGEST:
								__lowercase		       =			len(lowerCamelCase	)

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

							__lowercase		       =			padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowerCamelCase	) < max_length

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

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

							return processed_features



						def     _snake_case	(	self	:							int							,      lowerCamelCase	:							Union[Dict[str, np.ndarray], BatchFeature]							,      lowerCamelCase	:							Optional[int] = None							,      lowerCamelCase	:							Optional[int] = None							,      lowerCamelCase	:							Optional[bool] = 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."	)

							__lowercase		       =			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):
								__lowercase		       =			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

							__lowercase		       =			len(lowerCamelCase	) > max_length

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

							return processed_features



						def     _snake_case	(	self	:							List[str]							,      lowerCamelCase	:							Tuple=False							,      lowerCamelCase	:							str=None	):






							'''simple docstring'''



							if padding is not False:
								if padding is True:
									__lowercase		       =			PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
								elif not isinstance(lowerCamelCase							,      lowerCamelCase	):
									__lowercase		       =			PaddingStrategy(lowerCamelCase	)
								elif isinstance(lowerCamelCase							,      lowerCamelCase	):
									__lowercase		       =			padding
							else:
								__lowercase		       =			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








from __future__ import annotations

import bisect



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 0  ,	_SCREAMING_SNAKE_CASE = -1   ):
	if hi < 0:
		__lowercase		       =			len(_SCREAMING_SNAKE_CASE   )

	while lo < hi:
		__lowercase		       =			lo + (hi - lo) // 2
		if sorted_collection[mid] < item:
			__lowercase		       =			mid + 1
		else:
			__lowercase		       =			mid

	return lo



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 0  ,	_SCREAMING_SNAKE_CASE = -1   ):
	if hi < 0:
		__lowercase		       =			len(_SCREAMING_SNAKE_CASE   )

	while lo < hi:
		__lowercase		       =			lo + (hi - lo) // 2
		if sorted_collection[mid] <= item:
			__lowercase		       =			mid + 1
		else:
			__lowercase		       =			mid

	return lo



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 0  ,	_SCREAMING_SNAKE_CASE = -1   ):
	sorted_collection.insert(bisect_left(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )  ,	_SCREAMING_SNAKE_CASE   )



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 0  ,	_SCREAMING_SNAKE_CASE = -1   ):
	sorted_collection.insert(bisect_right(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )  ,	_SCREAMING_SNAKE_CASE   )



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			0
	__lowercase		       =			len(_SCREAMING_SNAKE_CASE   ) - 1

	while left <= right:
		__lowercase		       =			left + (right - left) // 2
		__lowercase		       =			sorted_collection[midpoint]
		if current_item == item:
			return midpoint
		elif item < current_item:
			__lowercase		       =			midpoint - 1
		else:
			__lowercase		       =			midpoint + 1
	return None



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			bisect.bisect_left(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
	if index != len(_SCREAMING_SNAKE_CASE   ) and sorted_collection[index] == item:
		return index
	return None



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	if right < left:
		return None

	__lowercase		       =			left + (right - left) // 2

	if sorted_collection[midpoint] == item:
		return midpoint
	elif sorted_collection[midpoint] > item:
		return binary_search_by_recursion(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	midpoint - 1   )
	else:
		return binary_search_by_recursion(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	midpoint + 1  ,	_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	snake_case__					:     Optional[Any]    		=   input("""Enter numbers separated by comma:\n""").strip()
	snake_case__					:     Any    		=   sorted(int(item) for item in user_input.split(""","""))
	snake_case__					:     Any    		=   int(input("""Enter a single number to be found in the list:\n"""))
	snake_case__					:     List[Any]    		=   binary_search(collection, target)
	if result is None:
		print(F'''{target} was not found in {collection}.''')
	else:
		print(F'''{target} was found at position {result} in {collection}.''')



