



"""simple docstring"""




import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


SCREAMING_SNAKE_CASE_     :			Dict	  =			logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_     :			List[str]	  =			r'''
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

'''


class 		_A       (			__a					):





      @add_start_docstrings(SCREAMING_SNAKE_CASE__		)
      def __call__(  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					bool:
         raise NotImplementedError("StoppingCriteria needs to be subclassed"		)






class 		_A       (			__a					):





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None		)   ->					Union[str, Any]:
         lowerCamelCase__						=			max_length
         lowerCamelCase__						=			max_position_embeddings





      @add_start_docstrings(SCREAMING_SNAKE_CASE__		)
      def __call__(  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					bool:
         lowerCamelCase__						=			input_ids.shape[-1]
         lowerCamelCase__						=			cur_len >= self.max_length
         if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
            logger.warning_once(
                "This is a friendly reminder - the current text generation call will exceed the model's predefined "
                f'maximum length ({self.max_position_embeddings}). Depending on the model, you may observe '
                "exceptions, performance degradation, or nothing at all."		)
         return is_done






class 		_A       (			__a					):





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Union[str, Any]:
         warnings.warn(
             "The class `MaxNewTokensCriteria` is deprecated. "
             f'Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` '
             "with `max_length = start_length + max_new_tokens` instead."    ,						SCREAMING_SNAKE_CASE__    ,						)
         lowerCamelCase__						=			start_length
         lowerCamelCase__						=			max_new_tokens
         lowerCamelCase__						=			start_length + max_new_tokens





      @add_start_docstrings(SCREAMING_SNAKE_CASE__		)
      def __call__(  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					bool:
         return input_ids.shape[-1] >= self.max_length






class 		_A       (			__a					):





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None		)   ->					Tuple:
         lowerCamelCase__						=			max_time
         lowerCamelCase__						=			time.time() if initial_timestamp is None else initial_timestamp





      @add_start_docstrings(SCREAMING_SNAKE_CASE__		)
      def __call__(  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					bool:
         return time.time() - self.initial_timestamp > self.max_time






class 		_A       (			__a					):





      @add_start_docstrings(SCREAMING_SNAKE_CASE__		)
      def __call__(  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					bool:
         return any(criteria(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		) for criteria in self		)





      @property
      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         for stopping_criterium in self:
            if isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
               return stopping_criterium.max_length
            elif isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
               return stopping_criterium.max_length
         return None






def 	UpperCAmelCase__   (				A__	,    A__   )      ->      StoppingCriteriaList:




   """simple docstring"""
   lowerCamelCase__						=			stopping_criteria.max_length
   lowerCamelCase__						=			deepcopy(A__   )
   if stopping_max_length is not None and stopping_max_length != max_length:
      warnings.warn("You set different `max_length` for stopping criteria and `max_length` parameter"	,    A__   )
   elif stopping_max_length is None:
      new_stopping_criteria.append(MaxLengthCriteria(max_length=A__   )   )
   return new_stopping_criteria







"""simple docstring"""




def 	UpperCAmelCase__   (				A__   )      ->      list[int]:




   """simple docstring"""

   if length <= 0 or not isinstance(A__	,    A__   ):
      raise ValueError("Length must be a positive integer."   )
   return [n * (2 * n - 1) for n in range(A__   )]


if __name__ == "__main__":
     print(hexagonal_numbers(length=5))
     print(hexagonal_numbers(length=10))




