
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  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     'char'
	_A								=     'bpe'
	_A								=     'wp'
lowercase  :					int													=       (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     ['image_processor', 'char_tokenizer']
	_A								=     'ViTImageProcessor'
	_A								=     'MgpstrTokenizer'







	def __init__(					self		:Optional[Any]     ,   a		:str=None     ,   a		:List[str]=None     ,   **a		:List[Any]						)		-> int:
								__UpperCamelCase	:   Union[str, 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     ,   )
															__UpperCamelCase	:   List[str]			   =					kwargs.pop("feature_extractor"						)

								__UpperCamelCase	:   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`."						)

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

								super().__init__(a     ,   a						)







	def __call__(					self		:List[str]     ,   a		:Any=None     ,   a		:Optional[int]=None     ,   a		:Tuple=None     ,   **a		:Optional[Any]						)		-> Optional[Any]:
								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	:   int			   =					self.image_processor(a     ,   return_tensors=a     ,   **a						)
								if text is not None:
															__UpperCamelCase	:   Optional[Any]			   =					self.char_tokenizer(a     ,   return_tensors=a     ,   **a						)

								if text is None:
															return inputs
								elif images is None:
															return encodings
								else:
															__UpperCamelCase	:   Union[str, Any]			   =					encodings["input_ids"]
															return inputs







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:Union[str, Any]						)		-> List[Any]:
								__UpperCamelCase   ,						__UpperCamelCase   ,						__UpperCamelCase	:   int			   =					sequences
								__UpperCamelCase	:   Dict			   =					char_preds.size(0						)

								__UpperCamelCase   ,						__UpperCamelCase	:   Tuple			   =					self._decode_helper(a     ,   "char"						)
								__UpperCamelCase   ,						__UpperCamelCase	:   List[Any]			   =					self._decode_helper(a     ,   "bpe"						)
								__UpperCamelCase   ,						__UpperCamelCase	:   int			   =					self._decode_helper(a     ,   "wp"						)

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

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







	def    _lowerCamelCase				(					self		:Optional[Any]     ,   a		:int     ,   a		:Optional[Any]						)		-> Optional[int]:
								if format == DecodeType.CHARACTER:
															__UpperCamelCase	:   str			   =					self.char_decode
															__UpperCamelCase	:   str			   =					1
															__UpperCamelCase	:   int			   =					"[s]"
								elif format == DecodeType.BPE:
															__UpperCamelCase	:   Dict			   =					self.bpe_decode
															__UpperCamelCase	:   Any			   =					2
															__UpperCamelCase	:   str			   =					"#"
								elif format == DecodeType.WORDPIECE:
															__UpperCamelCase	:   Optional[int]			   =					self.wp_decode
															__UpperCamelCase	:   int			   =					1_0_2
															__UpperCamelCase	:   List[Any]			   =					"[SEP]"
								else:
															raise ValueError(f'Format {format} is not supported.'						)

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

								for index in range(a						):
															__UpperCamelCase	:   List[str]			   =					preds_str[index].find(a						)
															__UpperCamelCase	:   List[str]			   =					preds_str[index][:pred_eos]
															__UpperCamelCase	:   Any			   =					preds_index[index].cpu().tolist()
															__UpperCamelCase	:   Optional[int]			   =					pred_index.index(a						) if eos_token in pred_index else -1
															__UpperCamelCase	:   Any			   =					preds_max_prob[index][: pred_eos_index + 1]
															__UpperCamelCase	:   Tuple			   =					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    _lowerCamelCase				(					self		:Optional[int]     ,   a		:Optional[int]						)		-> Dict:
								__UpperCamelCase	:   Tuple			   =					[seq.replace(" "     ,   ""						) for seq in self.char_tokenizer.batch_decode(a						)]
								return decode_strs







	def    _lowerCamelCase				(					self		:Optional[Any]     ,   a		:Union[str, Any]						)		-> Any:
								return self.bpe_tokenizer.batch_decode(a						)







	def    _lowerCamelCase				(					self		:Union[str, Any]     ,   a		:Union[str, Any]						)		-> Dict:
								__UpperCamelCase	:   Optional[int]			   =					[seq.replace(" "     ,   ""						) for seq in self.wp_tokenizer.batch_decode(a						)]
								return decode_strs

from __future__ import annotations




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			list[list[int]])					->    bool:






							'''simple docstring'''





							__UpperCamelCase	:   Any			   =					len(_lowerCamelCase)
							# We need to create solution object to save path.
							__UpperCamelCase	:   List[str]			   =					[[0 for _ in range(_lowerCamelCase)] for _ in range(_lowerCamelCase)]
							__UpperCamelCase	:   Optional[int]			   =					run_maze(_lowerCamelCase						,  0						,  0						,  _lowerCamelCase)
							if solved:
														print("\n".join(str(_lowerCamelCase) for row in solutions))
							else:
														print("No solution exists!")
							return solved




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			list[list[int]]						,  _lowerCamelCase :			int						,  _lowerCamelCase :			int						,  _lowerCamelCase :			list[list[int]])					->    bool:






							'''simple docstring'''





							__UpperCamelCase	:   Tuple			   =					len(_lowerCamelCase)
							# Final check point.
							if i == j == (size - 1):
														__UpperCamelCase	:   Optional[int]			   =					1
														return True

							__UpperCamelCase	:   List[Any]			   =					(not i < 0) and (not j < 0)  # Check lower bounds
							__UpperCamelCase	:   List[str]			   =					(i < size) and (j < size)  # Check upper bounds

							if lower_flag and upper_flag:
														# check for already visited and block points.
														__UpperCamelCase	:   int			   =					(not solutions[i][j]) and (not maze[i][j])
														if block_flag:
																					# check visited
																					__UpperCamelCase	:   Tuple			   =					1

																					# check for directions
																					if (
																					    run_maze(_lowerCamelCase						,  i + 1						,  _lowerCamelCase						,  _lowerCamelCase)
																					    or run_maze(_lowerCamelCase						,  _lowerCamelCase						,  j + 1						,  _lowerCamelCase)
																					    or run_maze(_lowerCamelCase						,  i - 1						,  _lowerCamelCase						,  _lowerCamelCase)
																					    or run_maze(_lowerCamelCase						,  _lowerCamelCase						,  j - 1						,  _lowerCamelCase)
																					):
																												return True

																					__UpperCamelCase	:   Tuple			   =					0
																					return False
							return False


if __name__ == "__main__":
		import doctest

		doctest.testmod()