'''simple docstring'''





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     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							'''char'''
				UpperCAmelCase__							=							'''bpe'''
				UpperCAmelCase__							=							'''wp'''





lowerCAmelCase_	:		Dict			=     (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							['''image_processor''', '''char_tokenizer''']
				UpperCAmelCase__							=							'''ViTImageProcessor'''
				UpperCAmelCase__							=							'''MgpstrTokenizer'''





				def __init__(						self					:						List[str]       ,		lowercase__					:						Optional[int]=None       ,		lowercase__					:						Optional[Any]=None       ,		**lowercase__					:						Any					)							->Any:


										'''simple docstring'''




										_UpperCamelCase			:			Dict   			=						None
										if "feature_extractor" in kwargs:
																warnings.warn(
																    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																    " instead."       ,		lowercase__       ,		)
																_UpperCamelCase			:			int   			=						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			:			List[str]   			=						tokenizer
										_UpperCamelCase			:			Optional[Any]   			=						AutoTokenizer.from_pretrained("gpt2"					)
										_UpperCamelCase			:			Optional[Any]   			=						AutoTokenizer.from_pretrained("bert-base-uncased"					)

										super().__init__(lowercase__       ,		lowercase__					)





				def __call__(						self					:						Optional[int]       ,		lowercase__					:						Tuple=None       ,		lowercase__					:						Optional[Any]=None       ,		lowercase__					:						List[Any]=None       ,		**lowercase__					:						str					)							->Optional[Any]:


										'''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			:			List[Any]   			=						self.image_processor(lowercase__       ,		return_tensors=lowercase__       ,		**lowercase__					)
										if text is not None:
																_UpperCamelCase			:			List[Any]   			=						self.char_tokenizer(lowercase__       ,		return_tensors=lowercase__       ,		**lowercase__					)

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





				def  snake_case__				(						self					:						Dict       ,		lowercase__					:						Union[str, Any]					)							->Optional[int]:


										'''simple docstring'''




										_UpperCamelCase			,       _UpperCamelCase			,       _UpperCamelCase			:			Union[str, Any]   			=						sequences
										_UpperCamelCase			:			Optional[int]   			=						char_preds.size(0					)

										_UpperCamelCase			,       _UpperCamelCase			:			Tuple   			=						self._decode_helper(lowercase__       ,		"char"					)
										_UpperCamelCase			,       _UpperCamelCase			:			Tuple   			=						self._decode_helper(lowercase__       ,		"bpe"					)
										_UpperCamelCase			,       _UpperCamelCase			:			Optional[Any]   			=						self._decode_helper(lowercase__       ,		"wp"					)

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

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





				def  snake_case__				(						self					:						str       ,		lowercase__					:						List[Any]       ,		lowercase__					:						str					)							->Union[str, Any]:


										'''simple docstring'''




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

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

										for index in range(lowercase__					):
																_UpperCamelCase			:			List[str]   			=						preds_str[index].find(lowercase__					)
																_UpperCamelCase			:			List[str]   			=						preds_str[index][:pred_eos]
																_UpperCamelCase			:			List[Any]   			=						preds_index[index].cpu().tolist()
																_UpperCamelCase			:			Tuple   			=						pred_index.index(lowercase__					) if eos_token in pred_index else -1
																_UpperCamelCase			:			str   			=						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(lowercase__					)
																conf_scores.append(lowercase__					)

										return dec_strs, conf_scores





				def  snake_case__				(						self					:						Any       ,		lowercase__					:						List[Any]					)							->Tuple:


										'''simple docstring'''




										_UpperCamelCase			:			Tuple   			=						[seq.replace(" "       ,		""					) for seq in self.char_tokenizer.batch_decode(lowercase__					)]
										return decode_strs





				def  snake_case__				(						self					:						int       ,		lowercase__					:						Dict					)							->Union[str, Any]:


										'''simple docstring'''




										return self.bpe_tokenizer.batch_decode(lowercase__					)







				def  snake_case__				(						self					:						Any       ,		lowercase__					:						str					)							->Tuple:


										'''simple docstring'''




										_UpperCamelCase			:			Union[str, Any]   			=						[seq.replace(" "       ,		""					) for seq in self.wp_tokenizer.batch_decode(lowercase__					)]
										return decode_strs



'''simple docstring'''





def 						__A							(     UpperCAmelCase					)     ->			List[Any]:




						'''simple docstring'''
						_UpperCamelCase			:			str   			=						[]
						_UpperCamelCase			:			Optional[Any]   			=						set({"(", "[", "{"}					)
						_UpperCamelCase			:			int   			=						set({")", "]", "}"}					)
						_UpperCamelCase			:			Dict   			=						{"{": "}", "[": "]", "(": ")"}

						for i in range(len(UpperCAmelCase					)					):
												if s[i] in open_brackets:
																		stack.append(s[i]					)

												elif s[i] in closed_brackets and (
												    len(UpperCAmelCase					) == 0 or (len(UpperCAmelCase					) > 0 and open_to_closed[stack.pop()] != s[i])
												):
																		return False

						return len(UpperCAmelCase					) == 0
def 						__A							(     )     ->			str:




						'''simple docstring'''
						_UpperCamelCase			:			Any   			=						input("Enter sequence of brackets: "					)
						if is_balanced(UpperCAmelCase					):
												print(UpperCAmelCase     ,"is balanced"					)
						else:
												print(UpperCAmelCase     ,"is not balanced"					)


if __name__ == "__main__":
			main()


