


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

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


UpperCAmelCase_      : Optional[Any]  					=       logging.get_logger(__name__)

UpperCAmelCase_      : Optional[int]  					=       {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_      : str  					=       {
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

UpperCAmelCase_      : List[Any]  					=       {
    '''gpt-neox-20b''': 2048,
}
class  SCREAMING_SNAKE_CASE__  (				__UpperCAmelCase			):
      snake_case__				:     List[str]        =						VOCAB_FILES_NAMES
      snake_case__				:     Union[str, Any]        =						PRETRAINED_VOCAB_FILES_MAP
      snake_case__				:     Dict        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case__				:     List[str]        =						['input_ids', 'attention_mask']
      def __init__(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=None     ,							SCREAMING_SNAKE_CASE__				:       Any=None     ,							SCREAMING_SNAKE_CASE__				:       List[str]=None     ,							SCREAMING_SNAKE_CASE__				:       List[str]="<|endoftext|>"     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]="<|endoftext|>"     ,							SCREAMING_SNAKE_CASE__				:       Dict="<|endoftext|>"     ,							SCREAMING_SNAKE_CASE__				:       Tuple=False     ,							**SCREAMING_SNAKE_CASE__				:       Tuple     ,							)		->			str:
             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__     ,							)

             a_					:		Dict							=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
             if pre_tok_state.get('add_prefix_space'     ,							SCREAMING_SNAKE_CASE__     ) != add_prefix_space:
                    a_					:		Union[str, Any]							=  getattr(SCREAMING_SNAKE_CASE__     ,							pre_tok_state.pop('type'     )     )
                    a_					:		Dict							=  add_prefix_space
                    a_					:		Any							=  pre_tok_class(**SCREAMING_SNAKE_CASE__     )

             a_					:		Optional[int]							=  add_prefix_space
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Tuple = None     )		->			Optional[int]:
             a_					:		List[str]							=  self._tokenizer.model.save(SCREAMING_SNAKE_CASE__     ,							name=SCREAMING_SNAKE_CASE__     )
             return tuple(SCREAMING_SNAKE_CASE__     )

      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       str     )		->			List[str]:
             a_					:		Union[str, Any]							=  []
             for is_user, text in conversation.iter_texts():
                    input_ids.extend(self.encode(SCREAMING_SNAKE_CASE__     ,							add_special_tokens=SCREAMING_SNAKE_CASE__     ) + [self.eos_token_id]     )

             if len(SCREAMING_SNAKE_CASE__     ) > self.model_max_length:
                    a_					:		Optional[int]							=  input_ids[-self.model_max_length :]
             return input_ids







'''simple docstring'''




from math import pow



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      ):
			'''simple docstring'''





			if current_sum == needed_sum:
						# If the sum of the powers is equal to needed_sum, then we have a solution.
						solutions_count += 1
						return current_sum, solutions_count

			A_	:						Optional[int]	=     int(pow(lowerCamelCase__						,      lowerCamelCase__    )    )
			if current_sum + i_to_n <= needed_sum:
						# If the sum of the powers is less than needed_sum, then continue adding powers.
						current_sum += i_to_n
						A_,    A_	:						int	=     backtrack(
						    lowerCamelCase__						,      lowerCamelCase__						,      current_number + 1						,      lowerCamelCase__						,      lowerCamelCase__    )
						current_sum -= i_to_n
			if i_to_n < needed_sum:
						# If the power of i is less than needed_sum, then try with the next power.
						A_,    A_	:						int	=     backtrack(
						    lowerCamelCase__						,      lowerCamelCase__						,      current_number + 1						,      lowerCamelCase__						,      lowerCamelCase__    )
			return current_sum, solutions_count



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			if not (1 <= needed_sum <= 10_00 and 2 <= power <= 10):
						raise ValueError(
						    """Invalid input\n"""
						    """needed_sum must be between 1 and 1000, power between 2 and 10."""    )

			return backtrack(lowerCamelCase__						,      lowerCamelCase__						,      1						,      0						,      0    )[1]  # Return the solutions_count


if __name__ == "__main__":
						import doctest

						doctest.testmod()