


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    (					__a				):

				"""simple docstring"""







				lowerCAmelCase 		= 'char'
				lowerCAmelCase 		= 'bpe'
				lowerCAmelCase 		= 'wp'
_lowercase       :      Tuple	=	(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class       _UpperCamelCase    (					__a				):

				"""simple docstring"""







				lowerCAmelCase 		= ['image_processor', 'char_tokenizer']
				lowerCAmelCase 		= 'ViTImageProcessor'
				lowerCAmelCase 		= 'MgpstrTokenizer'
				def __init__(						self				,   a__=None				,   a__=None				,   **a__   )	->				List[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				,   a__=None				,   a__=None				,   a__=None				,   **a__   )	->				List[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:
						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				,   a__   )	->				Dict:
					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				,   a__				,   a__   )	->				Optional[Any]:
					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								=				102
						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				,   a__   )	->				int:
					A								=				[seq.replace(""" """				,   """"""   ) for seq in self.char_tokenizer.batch_decode(A__   )]
					return decode_strs
				def      _UpperCAmelCase					(						self				,   a__   )	->				List[str]:
					return self.bpe_tokenizer.batch_decode(A__   )

				def      _UpperCAmelCase					(						self				,   a__   )	->				Union[str, Any]:
					A								=				[seq.replace(""" """				,   """"""   ) for seq in self.wp_tokenizer.batch_decode(A__   )]
					return decode_strs







import math





def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   bool:





	"""simple docstring"""







	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(UpperCamelCase__						) + 1						)							,					6						):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True





def   _lowerCAmelCase				(   UpperCamelCase__:     float = 0.1						) ->   int:





	"""simple docstring"""







	A								=				3
	A								=				3

	while primes / (2 * j - 1) >= ratio:
		for i in range(j * j + j + 1							,					(j + 2) * (j + 2)							,					j + 1						):
			primes += is_prime(UpperCamelCase__						)
		j += 2
	return j


if __name__ == "__main__":
				import doctest

				doctest.testmod()



