# flake8: noqa
# Lint as: python3
__UpperCamelCase		:       List[Any]									=     [
    'VerificationMode',
    'Version',
    'disable_progress_bar',
    'enable_progress_bar',
    'is_progress_bar_enabled',
    'experimental',
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental






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


UpperCamelCase  							=						logging.get_logger(__name__)


UpperCamelCase  							=						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   snake_case_       (		__A       ):






   @add_start_docstrings(lowercase_  )
   def __call__(      self				:     Optional[Any]	,						lowercase_				:     torch.LongTensor	,						lowercase_				:     torch.FloatTensor	,						**lowercase_				:     List[str]  )					->						bool:
       raise NotImplementedError("StoppingCriteria needs to be subclassed"  )



class   snake_case_       (		__A       ):






   def __init__(      self				:     Dict	,						lowercase_				:     int	,						lowercase_				:     Optional[int] = None  )					->						List[str]:
       lowercase__ :   str    					=				max_length
       lowercase__ :   Optional[int]    					=				max_position_embeddings







   @add_start_docstrings(lowercase_  )
   def __call__(      self				:     Tuple	,						lowercase_				:     torch.LongTensor	,						lowercase_				:     torch.FloatTensor	,						**lowercase_				:     Union[str, Any]  )					->						bool:
       lowercase__ :   str    					=				input_ids.shape[-1]
       lowercase__ :   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   snake_case_       (		__A       ):






   def __init__(      self				:     Tuple	,						lowercase_				:     int	,						lowercase_				:     int  )					->						List[str]:
       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."	,						lowercase_	,						)
       lowercase__ :   Optional[int]    					=				start_length
       lowercase__ :   str    					=				max_new_tokens
       lowercase__ :   Tuple    					=				start_length + max_new_tokens







   @add_start_docstrings(lowercase_  )
   def __call__(      self				:     List[Any]	,						lowercase_				:     torch.LongTensor	,						lowercase_				:     torch.FloatTensor	,						**lowercase_				:     Dict  )					->						bool:
       return input_ids.shape[-1] >= self.max_length



class   snake_case_       (		__A       ):






   def __init__(      self				:     Tuple	,						lowercase_				:     float	,						lowercase_				:     Optional[float] = None  )					->						Dict:
       lowercase__ :   List[str]    					=				max_time
       lowercase__ :   Tuple    					=				time.time() if initial_timestamp is None else initial_timestamp







   @add_start_docstrings(lowercase_  )
   def __call__(      self				:     int	,						lowercase_				:     torch.LongTensor	,						lowercase_				:     torch.FloatTensor	,						**lowercase_				:     Union[str, Any]  )					->						bool:
       return time.time() - self.initial_timestamp > self.max_time



class   snake_case_       (		__A       ):






   @add_start_docstrings(lowercase_  )
   def __call__(      self				:     str	,						lowercase_				:     torch.LongTensor	,						lowercase_				:     torch.FloatTensor	,						**lowercase_				:     List[str]  )					->						bool:
       return any(criteria(lowercase_	,						lowercase_  ) for criteria in self  )







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



def 			lowercase_				(			_lowerCamelCase							:       StoppingCriteriaList		,	_lowerCamelCase							:       int):
    lowercase__ :   Optional[int]    					=				stopping_criteria.max_length
    lowercase__ :   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



