

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 					a	(						__A					):
						_lowercase         =      "char"
						_lowercase         =      "bpe"
						_lowercase         =      "wp"







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

class 					a	(						__A					):
						_lowercase         =      ["image_processor", "char_tokenizer"]
						_lowercase         =      "ViTImageProcessor"
						_lowercase         =      "MgpstrTokenizer"


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







										'''simple docstring'''


										_UpperCAmelCase							:		str					=     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_    ,       )
														_UpperCAmelCase							:		Dict					=     kwargs.pop("feature_extractor"						)

										_UpperCAmelCase							:		int					=     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`."						)

										_UpperCAmelCase							:		str					=     tokenizer
										_UpperCAmelCase							:		List[str]					=     AutoTokenizer.from_pretrained("gpt2"						)
										_UpperCAmelCase							:		List[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:
														_UpperCAmelCase							:		Tuple					=     self.image_processor(A_    ,       return_tensors=A_    ,       **A_						)
										if text is not None:
														_UpperCAmelCase							:		List[str]					=     self.char_tokenizer(A_    ,       return_tensors=A_    ,       **A_						)

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


						def 							_UpperCAmelCase					(							self    ,       A_						):







										'''simple docstring'''


										_UpperCAmelCase							:		List[str]					=     sequences
										_UpperCAmelCase							:		Optional[int]					=     char_preds.size(0						)

										_UpperCAmelCase							:		Tuple					=     self._decode_helper(A_    ,       "char"						)
										_UpperCAmelCase							:		Optional[int]					=     self._decode_helper(A_    ,       "bpe"						)
										_UpperCAmelCase							:		Union[str, Any]					=     self._decode_helper(A_    ,       "wp"						)

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

										_UpperCAmelCase							:		int					=     {}
										_UpperCAmelCase							:		List[str]					=     final_strs
										_UpperCAmelCase							:		Union[str, Any]					=     final_scores
										_UpperCAmelCase							:		Any					=     char_strs
										_UpperCAmelCase							:		Tuple					=     bpe_strs
										_UpperCAmelCase							:		Union[str, Any]					=     wp_strs
										return out


						def 							_UpperCAmelCase					(							self    ,       A_    ,       A_						):







										'''simple docstring'''


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

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

										for index in range(A_						):
														_UpperCAmelCase							:		List[Any]					=     preds_str[index].find(A_						)
														_UpperCAmelCase							:		Tuple					=     preds_str[index][:pred_eos]
														_UpperCAmelCase							:		int					=     preds_index[index].cpu().tolist()
														_UpperCAmelCase							:		Union[str, Any]					=     pred_index.index(A_						) if eos_token in pred_index else -1
														_UpperCAmelCase							:		int					=     preds_max_prob[index][: pred_eos_index + 1]
														_UpperCAmelCase							:		List[Any]					=     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 							_UpperCAmelCase					(							self    ,       A_						):







										'''simple docstring'''


										_UpperCAmelCase							:		Union[str, Any]					=     [seq.replace(" "    ,       ""						) for seq in self.char_tokenizer.batch_decode(A_						)]
										return decode_strs


						def 							_UpperCAmelCase					(							self    ,       A_						):







										'''simple docstring'''


										return self.bpe_tokenizer.batch_decode(A_						)





						def 							_UpperCAmelCase					(							self    ,       A_						):







										'''simple docstring'''


										_UpperCAmelCase							:		Optional[Any]					=     [seq.replace(" "    ,       ""						) for seq in self.wp_tokenizer.batch_decode(A_						)]
										return decode_strs





from __future__ import annotations

def 			lowerCamelCase__			(       _a):
	if len(_a) == 0:
		return []
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Tuple					  =							min(_a), max(_a)
	SCREAMING_SNAKE_CASE :   Dict					  =							int(max_value - min_value) + 1
	SCREAMING_SNAKE_CASE :   list[list]					  =							[[] for _ in range(_a)]

	for i in my_list:
		buckets[int(i - min_value)].append(_a)

	return [v for bucket in buckets for v in sorted(_a)]


if __name__ == "__main__":
			from doctest import testmod

			testmod()
			assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
			assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]