
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      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     "char"
	snake_case_					    =     "bpe"
	snake_case_					    =     "wp"





SCREAMING_SNAKE_CASE							:Optional[int]							 =				(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





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






	def __init__(       self					: Tuple							,A					: Optional[Any]=None							,A					: Optional[Any]=None							,**A					: Optional[Any] ):
					__A   			= 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							,)
									__A   			= kwargs.pop("feature_extractor" )

					__A   			= 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`." )

					__A   			= tokenizer
					__A   			= AutoTokenizer.from_pretrained("gpt2" )
					__A   			= AutoTokenizer.from_pretrained("bert-base-uncased" )

					super().__init__(A							,A )






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

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






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: Optional[Any] ):
					__A			,							__A			,							__A   			= sequences
					__A   			= char_preds.size(0 )

					__A			,							__A   			= self._decode_helper(A							,"char" )
					__A			,							__A   			= self._decode_helper(A							,"bpe" )
					__A			,							__A   			= self._decode_helper(A							,"wp" )

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

					__A   			= {}
					__A   			= final_strs
					__A   			= final_scores
					__A   			= char_strs
					__A   			= bpe_strs
					__A   			= wp_strs
					return out






	def 				UpperCamelCase_  (       self					: Tuple							,A					: str							,A					: Tuple ):
					if format == DecodeType.CHARACTER:
									__A   			= self.char_decode
									__A   			= 1
									__A   			= "[s]"
					elif format == DecodeType.BPE:
									__A   			= self.bpe_decode
									__A   			= 2
									__A   			= "#"
					elif format == DecodeType.WORDPIECE:
									__A   			= self.wp_decode
									__A   			= 1_02
									__A   			= "[SEP]"
					else:
									raise ValueError(f'''Format {format} is not supported.''' )

					__A			,							__A   			= [], []
					__A   			= pred_logits.size(0 )
					__A   			= pred_logits.size(1 )
					__A			,							__A   			= pred_logits.topk(1							,dim=-1							,largest=A							,sorted=A )
					__A   			= preds_index.view(-1							,A )[:, 1:]
					__A   			= decoder(A )
					__A			,							__A   			= torch.nn.functional.softmax(A							,dim=2 ).max(dim=2 )
					__A   			= preds_max_prob[:, 1:]

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






	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Union[str, Any] ):
					return self.bpe_tokenizer.batch_decode(A )







	def 				UpperCamelCase_  (       self					: Tuple							,A					: List[str] ):
					__A   			= [seq.replace(" "							,"" ) for seq in self.wp_tokenizer.batch_decode(A )]
					return decode_strs


def 						UpperCAmelCase			(	a_						)		->	Optional[int]:







				"""simple docstring"""



				__A   			= [0] * len(a_						)
				__A   			= []
				__A   			= [1] * len(a_						)

				for values in graph.values():
								for i in values:
												indegree[i] += 1

				for i in range(len(a_						)						):
								if indegree[i] == 0:
												queue.append(a_						)

				while queue:
								__A   			= queue.pop(0						)
								for x in graph[vertex]:
												indegree[x] -= 1

												if long_dist[vertex] + 1 > long_dist[x]:
																__A   			= long_dist[vertex] + 1

												if indegree[x] == 0:
																queue.append(a_						)

				print(max(a_						)						)


# Adjacency list of Graph
SCREAMING_SNAKE_CASE							:List[Any]							 =				{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
