




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       a_       (     A__     ):
	A    							= '''char'''
	A    							= '''bpe'''
	A    							= '''wp'''




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



class       a_       (     A__     ):
	A    							= ['''image_processor''', '''char_tokenizer''']
	A    							= '''ViTImageProcessor'''
	A    							= '''MgpstrTokenizer'''





	def __init__(							self      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      ,     **SCREAMING_SNAKE_CASE      )  ->					Union[str, Any]:




				"""simple docstring"""



				SCREAMING_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.'      ,     _lowerCamelCase      ,     )
							SCREAMING_SNAKE_CASE_								=						kwargs.pop('feature_extractor'      )

				SCREAMING_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`.'      )

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

				super().__init__(_lowerCamelCase      ,     _lowerCamelCase      )





	def __call__(							self      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      ,     **SCREAMING_SNAKE_CASE      )  ->					Optional[int]:




				"""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:
							SCREAMING_SNAKE_CASE_								=						self.image_processor(_lowerCamelCase      ,     return_tensors=_lowerCamelCase      ,     **_lowerCamelCase      )
				if text is not None:
							SCREAMING_SNAKE_CASE_								=						self.char_tokenizer(_lowerCamelCase      ,     return_tensors=_lowerCamelCase      ,     **_lowerCamelCase      )

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





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						sequences
				SCREAMING_SNAKE_CASE_								=						char_preds.size(0      )

				SCREAMING_SNAKE_CASE_								=						self._decode_helper(_lowerCamelCase      ,     'char'      )
				SCREAMING_SNAKE_CASE_								=						self._decode_helper(_lowerCamelCase      ,     'bpe'      )
				SCREAMING_SNAKE_CASE_								=						self._decode_helper(_lowerCamelCase      ,     'wp'      )

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

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





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					Optional[Any]:




				"""simple docstring"""



				if format == DecodeType.CHARACTER:
							SCREAMING_SNAKE_CASE_								=						self.char_decode
							SCREAMING_SNAKE_CASE_								=						1
							SCREAMING_SNAKE_CASE_								=						'[s]'
				elif format == DecodeType.BPE:
							SCREAMING_SNAKE_CASE_								=						self.bpe_decode
							SCREAMING_SNAKE_CASE_								=						2
							SCREAMING_SNAKE_CASE_								=						'#'
				elif format == DecodeType.WORDPIECE:
							SCREAMING_SNAKE_CASE_								=						self.wp_decode
							SCREAMING_SNAKE_CASE_								=						102
							SCREAMING_SNAKE_CASE_								=						'[SEP]'
				else:
							raise ValueError(f'Format {format} is not supported.'      )

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

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

				return dec_strs, conf_scores





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[seq.replace(' '      ,     ''      ) for seq in self.char_tokenizer.batch_decode(_lowerCamelCase      )]
				return decode_strs





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					int:




				"""simple docstring"""



				return self.bpe_tokenizer.batch_decode(_lowerCamelCase      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Any:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[seq.replace(' '      ,     ''      ) for seq in self.wp_tokenizer.batch_decode(_lowerCamelCase      )]
				return decode_strs


"""simple docstring"""






def       A_  (				__lowercase						,	__lowercase						,	__lowercase	):
 if len(__lowercase	) != len(__lowercase	):
  raise ValueError('The length of profit and weight must be same.'	)
 if max_weight <= 0:
  raise ValueError('max_weight must greater than zero.'	)
 if any(p < 0 for p in profit	):
  raise ValueError('Profit can not be negative.'	)
 if any(w < 0 for w in weight	):
  raise ValueError('Weight can not be negative.'	)

 # List created to store profit gained for the 1kg in case of each weight
 # respectively.  Calculate and append profit/weight for each element.
 UpperCamelCase_     :					int   	=[p / w for p, w in zip(__lowercase						,	__lowercase	)]

 # Creating a copy of the list and sorting profit/weight in ascending order
 UpperCamelCase_     :					Optional[int]   	=sorted(__lowercase	)

 # declaring useful variables
 UpperCamelCase_     :					Optional[int]   	=len(__lowercase	)
 UpperCamelCase_     :					Optional[int]   	=0
 UpperCamelCase_     :					List[Any]   	=0
 UpperCamelCase_     :					Optional[int]   	=0

 # loop till the total weight do not reach max limit e.g. 15 kg and till i<length
 while limit <= max_weight and i < length:
  # flag value for encountered greatest element in sorted_profit_by_weight
  UpperCamelCase_     :					List[str]   	=sorted_profit_by_weight[length - i - 1]
  UpperCamelCase_     :					Optional[Any]   	=profit_by_weight.index(__lowercase	)
  UpperCamelCase_     :					str   	=-1

  # check if the weight encountered is less than the total weight
  # encountered before.
  if max_weight - limit >= weight[index]:
   limit += weight[index]
   # Adding profit gained for the given weight 1 ===
   # weight[index]/weight[index]
   gain += 1 * profit[index]
  else:
   # Since the weight encountered is greater than limit, therefore take the
   # required number of remaining kgs and calculate profit for it.
   # weight remaining / weight[index]
   gain += (max_weight - limit) / weight[index] * profit[index]
   break
  i += 1
 return gain


if __name__ == "__main__":
 print(
     'Input profits, weights, and then max_weight (all positive ints) separated by '
     'spaces.'
 )

 __SCREAMING_SNAKE_CASE							=     [int(x) for x in input('Input profits separated by spaces: ').split()]
 __SCREAMING_SNAKE_CASE							=     [int(x) for x in input('Input weights separated by spaces: ').split()]
 __SCREAMING_SNAKE_CASE							=     int(input('Max weight allowed: '))

 # Function Call
 calc_profit(profit, weight, max_weight)
