


"""simple docstring"""


import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


_snake_case          =							logging.get_logger(__name__)



class   _a      :
    def __init__( self							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					Any				):
          lowerCamelCase__           =		question_encoder
          lowerCamelCase__           =		generator
          lowerCamelCase__           =		self.question_encoder
    def      _UpperCamelCase       ( self							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					Optional[int]				):
          if os.path.isfile(SCREAMING_SNAKE_CASE__				):
                raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'				)
          os.makedirs(SCREAMING_SNAKE_CASE__							,					exist_ok=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		os.path.join(SCREAMING_SNAKE_CASE__							,					'question_encoder_tokenizer'				)
          lowerCamelCase__           =		os.path.join(SCREAMING_SNAKE_CASE__							,					'generator_tokenizer'				)
          self.question_encoder.save_pretrained(SCREAMING_SNAKE_CASE__				)
          self.generator.save_pretrained(SCREAMING_SNAKE_CASE__				)
    @classmethod
    def      _UpperCamelCase       ( cls							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Dict							,					**SCREAMING_SNAKE_CASE__							:					Optional[Any]				):
          # dynamically import AutoTokenizer
          from ..auto.tokenization_auto import AutoTokenizer

          lowerCamelCase__           =		kwargs.pop('config'							,					SCREAMING_SNAKE_CASE__				)

          if config is None:
                lowerCamelCase__           =		RagConfig.from_pretrained(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		AutoTokenizer.from_pretrained(
              SCREAMING_SNAKE_CASE__							,					config=config.question_encoder							,					subfolder='question_encoder_tokenizer'				)
          lowerCamelCase__           =		AutoTokenizer.from_pretrained(
              SCREAMING_SNAKE_CASE__							,					config=config.generator							,					subfolder='generator_tokenizer'				)
          return cls(question_encoder=SCREAMING_SNAKE_CASE__							,					generator=SCREAMING_SNAKE_CASE__				)
    def __call__( self							:					List[Any]							,					*SCREAMING_SNAKE_CASE__							:					List[str]							,					**SCREAMING_SNAKE_CASE__							:					Optional[Any]				):
          return self.current_tokenizer(*SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					List[str]							,					*SCREAMING_SNAKE_CASE__							:					Tuple							,					**SCREAMING_SNAKE_CASE__							:					int				):
          return self.generator.batch_decode(*SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					List[str]							,					*SCREAMING_SNAKE_CASE__							:					List[str]							,					**SCREAMING_SNAKE_CASE__							:					Tuple				):
          return self.generator.decode(*SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					Dict				):
          lowerCamelCase__           =		self.question_encoder
    def      _UpperCamelCase       ( self							:					Optional[Any]				):
          lowerCamelCase__           =		self.generator


    def      _UpperCamelCase       ( self							:					str							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Optional[List[str]] = None							,					SCREAMING_SNAKE_CASE__							:					Optional[int] = None							,					SCREAMING_SNAKE_CASE__							:					Optional[int] = None							,					SCREAMING_SNAKE_CASE__							:					str = "longest"							,					SCREAMING_SNAKE_CASE__							:					str = None							,					SCREAMING_SNAKE_CASE__							:					bool = True							,					**SCREAMING_SNAKE_CASE__							:					Optional[int]							,					):
          warnings.warn(
              '`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '
              'regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '
              'context manager to prepare your targets. See the documentation of your specific tokenizer for more '
              'details'							,					SCREAMING_SNAKE_CASE__							,					)
          if max_length is None:
                lowerCamelCase__           =		self.current_tokenizer.model_max_length
          lowerCamelCase__           =		self(
              SCREAMING_SNAKE_CASE__							,					add_special_tokens=SCREAMING_SNAKE_CASE__							,					return_tensors=SCREAMING_SNAKE_CASE__							,					max_length=SCREAMING_SNAKE_CASE__							,					padding=SCREAMING_SNAKE_CASE__							,					truncation=SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__							,					)
          if tgt_texts is None:
                return model_inputs
          # Process tgt_texts
          if max_target_length is None:
                lowerCamelCase__           =		self.current_tokenizer.model_max_length
          lowerCamelCase__           =		self(
              text_target=SCREAMING_SNAKE_CASE__							,					add_special_tokens=SCREAMING_SNAKE_CASE__							,					return_tensors=SCREAMING_SNAKE_CASE__							,					padding=SCREAMING_SNAKE_CASE__							,					max_length=SCREAMING_SNAKE_CASE__							,					truncation=SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__							,					)
          lowerCamelCase__           =		labels['input_ids']
          return model_inputs







"""simple docstring"""


def   snake_case						(				_a:     int		,   _a:     list[int]		,   _a:     int							)->		int:
      '''simple docstring'''


      def count_of_possible_combinations(_a:     int							) -> int:
            if target < 0:
                  return 0
            if target == 0:
                  return 1
            return sum(count_of_possible_combinations(target - item							) for item in array							)

      return count_of_possible_combinations(_a							)



def   snake_case						(				_a:     int		,   _a:     list[int]		,   _a:     int							)->		int:
      '''simple docstring'''


      def count_of_possible_combinations_with_dp_array(
          _a:     int		,   _a:     list[int]							) -> int:
            if target < 0:
                  return 0
            if target == 0:
                  return 1
            if dp_array[target] != -1:
                  return dp_array[target]
            lowerCamelCase__           =		sum(
                count_of_possible_combinations_with_dp_array(target - item		,   _a							)
                for item in array							)
            lowerCamelCase__           =		answer
            return answer

      lowerCamelCase__           =		[-1] * (target + 1)
      return count_of_possible_combinations_with_dp_array(_a		,   _a							)



def   snake_case						(				_a:     int		,   _a:     list[int]		,   _a:     int							)->		int:
      '''simple docstring'''


      lowerCamelCase__           =		[0] * (target + 1)
      lowerCamelCase__           =		1

      for i in range(1		,   target + 1							):
            for j in range(_a							):
                  if i - array[j] >= 0:
                        dp_array[i] += dp_array[i - array[j]]

      return dp_array[target]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   _snake_case          =							3
   _snake_case          =							5
   _snake_case          =							[1, 2, 5]
   print(combination_sum_iv(n, array, target))



