

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 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   str									=				"char"
			SCREAMING_SNAKE_CASE     :   Optional[Any]									=				"bpe"
			SCREAMING_SNAKE_CASE     :   List[str]									=				"wp"






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





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["image_processor", "char_tokenizer"]
			SCREAMING_SNAKE_CASE     :   str									=				"ViTImageProcessor"
			SCREAMING_SNAKE_CASE     :   Any									=				"MgpstrTokenizer"







			def __init__(     self :				Tuple							,   _UpperCamelCase :				int=None							,   _UpperCamelCase :				List[str]=None							,   **_UpperCamelCase :				str							)      ->Union[str, Any]:
									snake_case_   =	None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
															    ''' instead.'''							,   _UpperCamelCase							,   )
															snake_case_   =	kwargs.pop('''feature_extractor'''							)

									snake_case_   =	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_   =	tokenizer
									snake_case_   =	AutoTokenizer.from_pretrained('''gpt2'''							)
									snake_case_   =	AutoTokenizer.from_pretrained('''bert-base-uncased'''							)

									super().__init__(_UpperCamelCase							,   _UpperCamelCase							)







			def __call__(     self :				Optional[Any]							,   _UpperCamelCase :				List[Any]=None							,   _UpperCamelCase :				Optional[int]=None							,   _UpperCamelCase :				Union[str, Any]=None							,   **_UpperCamelCase :				Any							)      ->str:
									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_   =	self.image_processor(_UpperCamelCase							,   return_tensors=_UpperCamelCase							,   **_UpperCamelCase							)
									if text is not None:
															snake_case_   =	self.char_tokenizer(_UpperCamelCase							,   return_tensors=_UpperCamelCase							,   **_UpperCamelCase							)

									if text is None:
															return inputs
									elif images is None:
															return encodings
									else:
															snake_case_   =	encodings['''input_ids''']
															return inputs







			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				Any							)      ->Optional[Any]:
									snake_case_,    snake_case_,    snake_case_   =	sequences
									snake_case_   =	char_preds.size(0							)

									snake_case_,    snake_case_   =	self._decode_helper(_UpperCamelCase							,   '''char'''							)
									snake_case_,    snake_case_   =	self._decode_helper(_UpperCamelCase							,   '''bpe'''							)
									snake_case_,    snake_case_   =	self._decode_helper(_UpperCamelCase							,   '''wp'''							)

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

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







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

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

									for index in range(_UpperCamelCase							):
															snake_case_   =	preds_str[index].find(_UpperCamelCase							)
															snake_case_   =	preds_str[index][:pred_eos]
															snake_case_   =	preds_index[index].cpu().tolist()
															snake_case_   =	pred_index.index(_UpperCamelCase							) if eos_token in pred_index else -1
															snake_case_   =	preds_max_prob[index][: pred_eos_index + 1]
															snake_case_   =	pred_max_prob.cumprod(dim=0							)[-1] if pred_max_prob.nelement() != 0 else 0.0
															dec_strs.append(_UpperCamelCase							)
															conf_scores.append(_UpperCamelCase							)

									return dec_strs, conf_scores







			def 				snake_case__(     self :				int							,   _UpperCamelCase :				List[str]							)      ->Any:
									snake_case_   =	[seq.replace(''' '''							,   ''''''							) for seq in self.char_tokenizer.batch_decode(_UpperCamelCase							)]
									return decode_strs







			def 				snake_case__(     self :				Optional[int]							,   _UpperCamelCase :				List[Any]							)      ->List[str]:
									return self.bpe_tokenizer.batch_decode(_UpperCamelCase							)







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				Union[str, Any]							)      ->Dict:
									snake_case_   =	[seq.replace(''' '''							,   ''''''							) for seq in self.wp_tokenizer.batch_decode(_UpperCamelCase							)]
									return decode_strs


import math


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):

						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5  ,		int(math.sqrt(SCREAMING_SNAKE_CASE__       ) + 1       )  ,		6       ):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__ = 10001       ):

						try:
												snake_case_   =	int(SCREAMING_SNAKE_CASE__       )
						except (TypeError, ValueError):
												raise TypeError('''Parameter nth must be int or castable to int.'''       ) from None
						if nth <= 0:
												raise ValueError('''Parameter nth must be greater than or equal to one.'''       )
						snake_case_   =	[]
						snake_case_   =	2
						while len(SCREAMING_SNAKE_CASE__       ) < nth:
												if is_prime(SCREAMING_SNAKE_CASE__       ):
																		primes.append(SCREAMING_SNAKE_CASE__       )
																		num += 1
												else:
																		num += 1
						return primes[len(SCREAMING_SNAKE_CASE__       ) - 1]


if __name__ == "__main__":
					print(f"""{solution() = }""")