



import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
						import torch



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		'char'
							lowerCamelCase__   			=		'bpe'
							lowerCamelCase__   			=		'wp'



_snake_case		=  (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		['image_processor', 'char_tokenizer']
							lowerCamelCase__   			=		'ViTImageProcessor'
							lowerCamelCase__   			=		'MgpstrTokenizer'







							def __init__(   self,			__a=None,			__a=None,			**__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    None
														if "feature_extractor" in kwargs:
																					warnings.warn(
																					    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																					    " instead.",			__a,			)
																					_lowerCAmelCase     :							List[Any]    =    kwargs.pop("feature_extractor")

														_lowerCAmelCase     :							List[Any]    =    image_processor if image_processor is not None else feature_extractor
														if image_processor is None:
																					raise ValueError("You need to specify an `image_processor`.")
														if tokenizer is None:
																					raise ValueError("You need to specify a `tokenizer`.")

														_lowerCAmelCase     :							int    =    tokenizer
														_lowerCAmelCase     :							Union[str, Any]    =    AutoTokenizer.from_pretrained("gpt2")
														_lowerCAmelCase     :							Optional[Any]    =    AutoTokenizer.from_pretrained("bert-base-uncased")

														super().__init__(__a,			__a)







							def __call__(   self,			__a=None,			__a=None,			__a=None,			**__a):

														'''simple docstring'''

														if images is None and text is None:
																					raise ValueError("You need to specify either an `images` or `text` input to process.")

														if images is not None:
																					_lowerCAmelCase     :							int    =    self.image_processor(__a,			return_tensors=__a,			**__a)
														if text is not None:
																					_lowerCAmelCase     :							List[Any]    =    self.char_tokenizer(__a,			return_tensors=__a,			**__a)

														if text is None:
																					return inputs
														elif images is None:
																					return encodings
														else:
																					_lowerCAmelCase     :							Tuple    =    encodings["input_ids"]
																					return inputs







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[int]    =    sequences
														_lowerCAmelCase     :							Optional[int]    =    char_preds.size(0)

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[Any]    =    self._decode_helper(__a,			"char")
														_lowerCAmelCase   ,    _lowerCAmelCase     :							Dict    =    self._decode_helper(__a,			"bpe")
														_lowerCAmelCase   ,    _lowerCAmelCase     :							Tuple    =    self._decode_helper(__a,			"wp")

														_lowerCAmelCase     :							Dict    =    []
														_lowerCAmelCase     :							int    =    []
														for i in range(__a):
																					_lowerCAmelCase     :							Dict    =    [char_scores[i], bpe_scores[i], wp_scores[i]]
																					_lowerCAmelCase     :							List[str]    =    [char_strs[i], bpe_strs[i], wp_strs[i]]
																					_lowerCAmelCase     :							Optional[int]    =    scores.index(max(__a))
																					final_strs.append(strs[max_score_index])
																					final_scores.append(scores[max_score_index])

														_lowerCAmelCase     :							Optional[int]    =    {}
														_lowerCAmelCase     :							str    =    final_strs
														_lowerCAmelCase     :							Any    =    final_scores
														_lowerCAmelCase     :							List[Any]    =    char_strs
														_lowerCAmelCase     :							List[str]    =    bpe_strs
														_lowerCAmelCase     :							Dict    =    wp_strs
														return out







							def    snake_case__      (   self,			__a,			__a):

														'''simple docstring'''

														if format == DecodeType.CHARACTER:
																					_lowerCAmelCase     :							int    =    self.char_decode
																					_lowerCAmelCase     :							Union[str, Any]    =    1
																					_lowerCAmelCase     :							int    =    "[s]"
														elif format == DecodeType.BPE:
																					_lowerCAmelCase     :							int    =    self.bpe_decode
																					_lowerCAmelCase     :							Tuple    =    2
																					_lowerCAmelCase     :							Dict    =    "#"
														elif format == DecodeType.WORDPIECE:
																					_lowerCAmelCase     :							Any    =    self.wp_decode
																					_lowerCAmelCase     :							Union[str, Any]    =    102
																					_lowerCAmelCase     :							Optional[Any]    =    "[SEP]"
														else:
																					raise ValueError(f"Format {format} is not supported.")

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Union[str, Any]    =    [], []
														_lowerCAmelCase     :							Dict    =    pred_logits.size(0)
														_lowerCAmelCase     :							int    =    pred_logits.size(1)
														_lowerCAmelCase   ,    _lowerCAmelCase     :							Dict    =    pred_logits.topk(1,			dim=-1,			largest=__a,			sorted=__a)
														_lowerCAmelCase     :							Tuple    =    preds_index.view(-1,			__a)[:, 1:]
														_lowerCAmelCase     :							List[Any]    =    decoder(__a)
														_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[int]    =    torch.nn.functional.softmax(__a,			dim=2).max(dim=2)
														_lowerCAmelCase     :							str    =    preds_max_prob[:, 1:]

														for index in range(__a):
																					_lowerCAmelCase     :							List[Any]    =    preds_str[index].find(__a)
																					_lowerCAmelCase     :							int    =    preds_str[index][:pred_eos]
																					_lowerCAmelCase     :							Tuple    =    preds_index[index].cpu().tolist()
																					_lowerCAmelCase     :							List[Any]    =    pred_index.index(__a) if eos_token in pred_index else -1
																					_lowerCAmelCase     :							Dict    =    preds_max_prob[index][: pred_eos_index + 1]
																					_lowerCAmelCase     :							int    =    pred_max_prob.cumprod(dim=0)[-1] if pred_max_prob.nelement() != 0 else 0.0
																					dec_strs.append(__a)
																					conf_scores.append(__a)

														return dec_strs, conf_scores







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    [seq.replace(" ",			"") for seq in self.char_tokenizer.batch_decode(__a)]
														return decode_strs







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														return self.bpe_tokenizer.batch_decode(__a)







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    [seq.replace(" ",			"") for seq in self.wp_tokenizer.batch_decode(__a)]
														return decode_strs





from __future__ import annotations

import bisect






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase = 0				, _lowerCamelCase = -1  ):





							'''simple docstring'''




							if hi < 0:
														_lowerCAmelCase     :							int    =    len(_lowerCamelCase  )

							while lo < hi:
														_lowerCAmelCase     :							Optional[Any]    =    lo + (hi - lo) // 2
														if sorted_collection[mid] < item:
																					_lowerCAmelCase     :							Union[str, Any]    =    mid + 1
														else:
																					_lowerCAmelCase     :							str    =    mid

							return lo






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase = 0				, _lowerCamelCase = -1  ):





							'''simple docstring'''




							if hi < 0:
														_lowerCAmelCase     :							str    =    len(_lowerCamelCase  )

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

							return lo






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase = 0				, _lowerCamelCase = -1  ):





							'''simple docstring'''




							sorted_collection.insert(bisect_left(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  )				, _lowerCamelCase  )






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase = 0				, _lowerCamelCase = -1  ):





							'''simple docstring'''




							sorted_collection.insert(bisect_right(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  )				, _lowerCamelCase  )






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							int    =    0
							_lowerCAmelCase     :							Union[str, Any]    =    len(_lowerCamelCase  ) - 1

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






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Tuple    =    bisect.bisect_left(_lowerCamelCase				, _lowerCamelCase  )
							if index != len(_lowerCamelCase  ) and sorted_collection[index] == item:
														return index
							return None






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							if right < left:
														return None

							_lowerCAmelCase     :							Optional[int]    =    left + (right - left) // 2

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


if __name__ == "__main__":
						_snake_case		=  input("Enter numbers separated by comma:\n").strip()
						_snake_case		=  sorted(int(item) for item in user_input.split(","))
						_snake_case		=  int(input("Enter a single number to be found in the list:\n"))
						_snake_case		=  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}.''')
