






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 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	"char"
					a_			=	"bpe"
					a_			=	"wp"
__lowerCamelCase      :    Any   		=   (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	["image_processor", "char_tokenizer"]
					a_			=	"ViTImageProcessor"
					a_			=	"MgpstrTokenizer"



					def __init__(      self  :						Union[str, Any]							,	__A  :						Any=None							,	__A  :						Dict=None							,	**__A  :						Any ):
						snake_case__				:   List[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							,	)
							snake_case__				:   List[str]     =						kwargs.pop("feature_extractor" )

						snake_case__				:   List[str]     =						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`." )

						snake_case__				:   List[Any]     =						tokenizer
						snake_case__				:   List[str]     =						AutoTokenizer.from_pretrained("gpt2" )
						snake_case__				:   Dict     =						AutoTokenizer.from_pretrained("bert-base-uncased" )

						super().__init__(__A							,	__A )



					def __call__(      self  :						List[str]							,	__A  :						str=None							,	__A  :						Optional[Any]=None							,	__A  :						Dict=None							,	**__A  :						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:
							snake_case__				:   Union[str, Any]     =						self.image_processor(__A							,	return_tensors=__A							,	**__A )
						if text is not None:
							snake_case__				:   List[str]     =						self.char_tokenizer(__A							,	return_tensors=__A							,	**__A )

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



					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						Optional[int] ):
						snake_case__,		snake_case__,		snake_case__				:   List[str]     =						sequences
						snake_case__				:   Optional[Any]     =						char_preds.size(0 )

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

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

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



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

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

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



					def  _lowercase  (      self  :						List[Any]							,	__A  :						Any ):
						return self.bpe_tokenizer.batch_decode(__A )






					def  _lowercase  (      self  :						Optional[int]							,	__A  :						List[str] ):
						snake_case__				:   Optional[Any]     =						[seq.replace(" "							,	"" ) for seq in self.wp_tokenizer.batch_decode(__A )]
						return decode_strs











def   SCREAMING_SNAKE_CASE       (				snake_case_      :							str     ):
	snake_case__				:   Any     =						[0] * len(snake_case_     )

	for i in range(1  ,						len(snake_case_     )     ):
		# use last results for better performance - dynamic programming
		snake_case__				:   Union[str, Any]     =						prefix_result[i - 1]
		while j > 0 and input_string[i] != input_string[j]:
			snake_case__				:   str     =						prefix_result[j - 1]

		if input_string[i] == input_string[j]:
			j += 1
		snake_case__				:   int     =						j

	return prefix_result







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							str     ):
	return max(prefix_function(snake_case_     )     )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



