
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


__lowerCamelCase				=			logging.get_logger(__name__)


__lowerCamelCase				=			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     UpperCAmelCase						(		A_					):



   @add_start_docstrings(snake_case__					)
   def __call__(self						:   List[Any]       ,		snake_case__						:   torch.LongTensor       ,		snake_case__						:   torch.FloatTensor       ,		**snake_case__						:   List[Any]					)						->      bool:


          '''simple docstring'''


          raise NotImplementedError("StoppingCriteria needs to be subclassed"					)






class     UpperCAmelCase						(		A_					):



   def __init__(self						:   str       ,		snake_case__						:   int       ,		snake_case__						:   Optional[int] = None					)						->      int:


          '''simple docstring'''


          snake_case			:  Any     							= max_length
          snake_case			:  int     							= max_position_embeddings



   @add_start_docstrings(snake_case__					)
   def __call__(self						:   int       ,		snake_case__						:   torch.LongTensor       ,		snake_case__						:   torch.FloatTensor       ,		**snake_case__						:   Any					)						->      bool:


          '''simple docstring'''


          snake_case			:  Tuple     							= input_ids.shape[-1]
          snake_case			:  Optional[Any]     							= 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     UpperCAmelCase						(		A_					):



   def __init__(self						:   List[str]       ,		snake_case__						:   int       ,		snake_case__						:   int					)						->      Dict:


          '''simple docstring'''


          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."       ,		snake_case__       ,		)
          snake_case			:  int     							= start_length
          snake_case			:  str     							= max_new_tokens
          snake_case			:  int     							= start_length + max_new_tokens



   @add_start_docstrings(snake_case__					)
   def __call__(self						:   str       ,		snake_case__						:   torch.LongTensor       ,		snake_case__						:   torch.FloatTensor       ,		**snake_case__						:   Dict					)						->      bool:


          '''simple docstring'''


          return input_ids.shape[-1] >= self.max_length






class     UpperCAmelCase						(		A_					):



   def __init__(self						:   List[str]       ,		snake_case__						:   float       ,		snake_case__						:   Optional[float] = None					)						->      List[Any]:


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= max_time
          snake_case			:  List[str]     							= time.time() if initial_timestamp is None else initial_timestamp



   @add_start_docstrings(snake_case__					)
   def __call__(self						:   int       ,		snake_case__						:   torch.LongTensor       ,		snake_case__						:   torch.FloatTensor       ,		**snake_case__						:   Optional[int]					)						->      bool:


          '''simple docstring'''


          return time.time() - self.initial_timestamp > self.max_time






class     UpperCAmelCase						(		A_					):



   @add_start_docstrings(snake_case__					)
   def __call__(self						:   Tuple       ,		snake_case__						:   torch.LongTensor       ,		snake_case__						:   torch.FloatTensor       ,		**snake_case__						:   Union[str, Any]					)						->      bool:


          '''simple docstring'''


          return any(criteria(snake_case__       ,		snake_case__					) for criteria in self					)



   @property
   def 							_SCREAMING_SNAKE_CASE  (self						:   str					)						->      Optional[int]:


          '''simple docstring'''


          for stopping_criterium in self:
                 if isinstance(snake_case__       ,		snake_case__					):
                        return stopping_criterium.max_length
                 elif isinstance(snake_case__       ,		snake_case__					):
                        return stopping_criterium.max_length
          return None



def      UpperCamelCase   (       __lowerCamelCase	:					StoppingCriteriaList							,   __lowerCamelCase	:					int       ):
       snake_case			:  Union[str, Any]     							= stopping_criteria.max_length
       snake_case			:  List[str]     							= deepcopy(__lowerCamelCase       )
       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"							,   __lowerCamelCase       )
       elif stopping_max_length is None:
              new_stopping_criteria.append(MaxLengthCriteria(max_length=__lowerCamelCase       )       )
       return new_stopping_criteria







def      UpperCamelCase   (       __lowerCamelCase	:					int = 1							,   __lowerCamelCase	:					int = 1000       ):
       snake_case			:  int     							= 1
       snake_case			:  int     							= 0

       for divide_by_number in range(__lowerCamelCase							,   digit + 1       ):
              snake_case			:  list[int]     							= []
              snake_case			:  Optional[int]     							= numerator
              for _ in range(1							,   digit + 1       ):
                     if now_divide in has_been_divided:
                            if longest_list_length < len(__lowerCamelCase       ):
                                   snake_case			:  List[Any]     							= len(__lowerCamelCase       )
                                   snake_case			:  List[str]     							= divide_by_number
                     else:
                            has_been_divided.append(__lowerCamelCase       )
                            snake_case			:  Any     							= now_divide * 10 % divide_by_number

       return the_digit


# Tests
if __name__ == "__main__":
   import doctest

   doctest.testmod()





