

import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
    if is_torch_available():
        import torch
    if is_tf_available():
        import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


lowerCAmelCase     :   List[str]						     =  logging.get_logger(__name__)

lowerCAmelCase     :   Tuple						     =  {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase     :   int						     =  {
    'vocab_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json',
    },
    'merges_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'Salesforce/codegen-350M-mono': (
            'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'
        ),
    },
}

lowerCAmelCase     :   Optional[Any]						     =  {
    'Salesforce/codegen-350M-mono': 20_48,
}
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      int       =  VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE    :      List[Any]       =  PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE    :      Union[str, Any]       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE    :      str       =  ['''input_ids''', '''attention_mask''']
  SCREAMING_SNAKE_CASE    :      Any       =  CodeGenTokenizer

  def __init__(				self ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE="<|endoftext|>" ,					_SCREAMING_SNAKE_CASE="<|endoftext|>" ,					_SCREAMING_SNAKE_CASE="<|endoftext|>" ,					_SCREAMING_SNAKE_CASE=False ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        super().__init__(
            _SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					tokenizer_file=_SCREAMING_SNAKE_CASE ,					unk_token=_SCREAMING_SNAKE_CASE ,					bos_token=_SCREAMING_SNAKE_CASE ,					eos_token=_SCREAMING_SNAKE_CASE ,					add_prefix_space=_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE ,					)

        if kwargs.pop('add_bos_token' ,					_SCREAMING_SNAKE_CASE					):
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     kwargs.pop('name_or_path' ,					''					)
              raise ValueError(
                  'Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'
                  'Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'
                  f"`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"
                  f"`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"
                  'This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'
                  ' so that the fast tokenizer works correctly.'					)

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
        if pre_tok_state.get('add_prefix_space' ,					_SCREAMING_SNAKE_CASE					) != add_prefix_space:
              SCREAMING_SNAKE_CASE_				:  Tuple           =     getattr(_SCREAMING_SNAKE_CASE ,					pre_tok_state.pop('type'					)					)
              SCREAMING_SNAKE_CASE_				:  str           =     add_prefix_space
              SCREAMING_SNAKE_CASE_				:  Dict           =     pre_tok_class(**_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  str           =     add_prefix_space

  def  UpperCAmelCase    (				self ,					*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     kwargs.get('is_split_into_words' ,					_SCREAMING_SNAKE_CASE					)
        assert self.add_prefix_space or not is_split_into_words, (
            f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
            "to use it with pretokenized inputs."
        )

        return super()._batch_encode_plus(*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self ,					*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Tuple           =     kwargs.get('is_split_into_words' ,					_SCREAMING_SNAKE_CASE					)

        assert self.add_prefix_space or not is_split_into_words, (
            f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
            "to use it with pretokenized inputs."
        )

        return super()._encode_plus(*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = None					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self._tokenizer.model.save(_SCREAMING_SNAKE_CASE ,					name=_SCREAMING_SNAKE_CASE					)
        return tuple(_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = False ,					_SCREAMING_SNAKE_CASE = None ,					_SCREAMING_SNAKE_CASE = None ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     super().decode(
            token_ids=_SCREAMING_SNAKE_CASE ,					skip_special_tokens=_SCREAMING_SNAKE_CASE ,					clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE ,					)

        if truncate_before_pattern is not None and len(_SCREAMING_SNAKE_CASE					) > 0:
              SCREAMING_SNAKE_CASE_				:  Dict           =     self.truncate(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

        return decoded_text



  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        def find_re(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):
              SCREAMING_SNAKE_CASE_				:  int           =     pattern.search(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
              return m.start() if m else -1

        SCREAMING_SNAKE_CASE_				:  str           =     [re.compile(_SCREAMING_SNAKE_CASE ,					re.MULTILINE					) for pattern in truncate_before_pattern]

        SCREAMING_SNAKE_CASE_				:  List[str]           =     list(re.finditer('^print' ,					_SCREAMING_SNAKE_CASE ,					re.MULTILINE					)					)

        if len(_SCREAMING_SNAKE_CASE					) > 1:
              SCREAMING_SNAKE_CASE_				:  Tuple           =     completion[: prints[1].start()]

        SCREAMING_SNAKE_CASE_				:  Any           =     list(re.finditer('^def' ,					_SCREAMING_SNAKE_CASE ,					re.MULTILINE					)					)

        if len(_SCREAMING_SNAKE_CASE					) > 1:
              SCREAMING_SNAKE_CASE_				:  int           =     completion[: defs[1].start()]

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     0

        SCREAMING_SNAKE_CASE_				:  List[str]           =     [
            pos for pos in [find_re(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					) for terminal in terminals] if pos != -1
        ]

        if len(_SCREAMING_SNAKE_CASE					) > 0:
              return completion[: min(_SCREAMING_SNAKE_CASE					)]
        else:
              return completion









def    A_  (							a					,   a					,   a  ):

      """simple docstring"""



      if len(a  ) != len(a  ):
            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.
      SCREAMING_SNAKE_CASE_				:  Tuple           =     [p / w for p, w in zip(a					,   a  )]

      # Creating a copy of the list and sorting profit/weight in ascending order
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     sorted(a  )

      # declaring useful variables
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     len(a  )
      SCREAMING_SNAKE_CASE_				:  str           =     0
      SCREAMING_SNAKE_CASE_				:  List[str]           =     0
      SCREAMING_SNAKE_CASE_				:  Any           =     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
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     sorted_profit_by_weight[length - i - 1]
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     profit_by_weight.index(a  )
            SCREAMING_SNAKE_CASE_				:  Dict           =     -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.'
    )

    lowerCAmelCase     :   Tuple						     =  [int(x) for x in input('Input profits separated by spaces: ').split()]
    lowerCAmelCase     :   Union[str, Any]						     =  [int(x) for x in input('Input weights separated by spaces: ').split()]
    lowerCAmelCase     :   Dict						     =  int(input('Max weight allowed: '))

    # Function Call
    calc_profit(profit, weight, max_weight)






