




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


SCREAMING_SNAKE_CASE__     :     Tuple		    =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :     str		    =       {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE__     :     int		    =       {
    "vocab_file": {
        "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_char_small": (
            "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_chinese_char_base": (
            "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_discriminator": (
            "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_generator": (
            "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"
        ),
    }
}

SCREAMING_SNAKE_CASE__     :     Dict		    =       {
    "junnyu/roformer_chinese_small": 15_36,
    "junnyu/roformer_chinese_base": 15_36,
    "junnyu/roformer_chinese_char_small": 5_12,
    "junnyu/roformer_chinese_char_base": 5_12,
    "junnyu/roformer_small_discriminator": 1_28,
    "junnyu/roformer_small_generator": 1_28,
}


SCREAMING_SNAKE_CASE__     :     Tuple		    =       {
    "junnyu/roformer_chinese_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_base": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_base": {"do_lower_case": True},
    "junnyu/roformer_small_discriminator": {"do_lower_case": True},
    "junnyu/roformer_small_generator": {"do_lower_case": True},
}



class       a_       (     A__     ):
	A    							= VOCAB_FILES_NAMES
	A    							= PRETRAINED_VOCAB_FILES_MAP
	A    							= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A    							= PRETRAINED_INIT_CONFIGURATION
	A    							= RoFormerTokenizer





	def __init__(							self      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE="[UNK]"      ,     SCREAMING_SNAKE_CASE="[SEP]"      ,     SCREAMING_SNAKE_CASE="[PAD]"      ,     SCREAMING_SNAKE_CASE="[CLS]"      ,     SCREAMING_SNAKE_CASE="[MASK]"      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=None      ,     **SCREAMING_SNAKE_CASE      ,     )  ->					Tuple:




				"""simple docstring"""



				super().__init__(
				    _lowerCamelCase      ,     tokenizer_file=_lowerCamelCase      ,     do_lower_case=_lowerCamelCase      ,     unk_token=_lowerCamelCase      ,     sep_token=_lowerCamelCase      ,     pad_token=_lowerCamelCase      ,     cls_token=_lowerCamelCase      ,     mask_token=_lowerCamelCase      ,     tokenize_chinese_chars=_lowerCamelCase      ,     strip_accents=_lowerCamelCase      ,     **_lowerCamelCase      ,     )

				SCREAMING_SNAKE_CASE_								=						json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
				if (
				    pre_tok_state.get('lowercase'      ,     _lowerCamelCase      ) != do_lower_case
				    or pre_tok_state.get('strip_accents'      ,     _lowerCamelCase      ) != strip_accents
				):
							SCREAMING_SNAKE_CASE_								=						getattr(_lowerCamelCase      ,     pre_tok_state.pop('type'      )      )
							SCREAMING_SNAKE_CASE_								=						do_lower_case
							SCREAMING_SNAKE_CASE_								=						strip_accents
							SCREAMING_SNAKE_CASE_								=						pre_tok_class(**_lowerCamelCase      )

				SCREAMING_SNAKE_CASE_								=						do_lower_case





	def __getstate__(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.__dict__.copy()
				SCREAMING_SNAKE_CASE_								=						BertPreTokenizer()
				return state





	def __setstate__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Any:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						d
				SCREAMING_SNAKE_CASE_								=						self.__dict__['_tokenizer'].get_vocab()
				SCREAMING_SNAKE_CASE_								=						PreTokenizer.custom(JiebaPreTokenizer(_lowerCamelCase      )      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE=None      )  ->					Any:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

				if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

				return output





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = None      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[self.sep_token_id]
				SCREAMING_SNAKE_CASE_								=						[self.cls_token_id]
				if token_ids_a is None:
							return len(cls + token_ids_a + sep      ) * [0]
				return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = None      )  ->					Any:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self._tokenizer.model.save(_lowerCamelCase      ,     name=_lowerCamelCase      )
				return tuple(_lowerCamelCase      )





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




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						BertPreTokenizer()
				return super().save_pretrained(_lowerCamelCase      ,     _lowerCamelCase      ,     _lowerCamelCase      ,     _lowerCamelCase      ,     **_lowerCamelCase      )


"""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)
