'''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


_A     : List[str]    =							logging.get_logger(__name__)


_A     : Any    =							R'\n    Args:\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax\n            or scores for each vocabulary token after SoftMax.\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional stopping criteria specific kwargs.\n\n    Return:\n        `bool`. `False` indicates we should continue, `True` indicates we should stop.\n\n'







class 							_lowercase	(							UpperCAmelCase__     ):


 '''simple docstring'''






 @add_start_docstrings(A_				)
 def __call__( self  :      Tuple ,			SCREAMING_SNAKE_CASE__  :      Union[str, Any] ,			SCREAMING_SNAKE_CASE__  :      List[str] ,			**SCREAMING_SNAKE_CASE__  :      Dict				)    ->       Any:
  raise NotImplementedError("""StoppingCriteria needs to be subclassed"""				)







class 							_lowercase	(							UpperCAmelCase__     ):


 '''simple docstring'''






 def __init__( self  :      List[Any] ,			SCREAMING_SNAKE_CASE__  :      Optional[int] ,			SCREAMING_SNAKE_CASE__  :      int = None				)    ->       str:
  __lowerCAmelCase											=		max_length
  __lowerCAmelCase											=		max_position_embeddings





 @add_start_docstrings(A_				)
 def __call__( self  :      Tuple ,			SCREAMING_SNAKE_CASE__  :      List[str] ,			SCREAMING_SNAKE_CASE__  :      int ,			**SCREAMING_SNAKE_CASE__  :      Optional[Any]				)    ->       Dict:
  __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 							_lowercase	(							UpperCAmelCase__     ):


 '''simple docstring'''






 def __init__( self  :      Optional[Any] ,			SCREAMING_SNAKE_CASE__  :      List[str] ,			SCREAMING_SNAKE_CASE__  :      Tuple				)    ->       int:
  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.""" ,			A_ ,			)
  __lowerCAmelCase											=		start_length
  __lowerCAmelCase											=		max_new_tokens
  __lowerCAmelCase											=		start_length + max_new_tokens





 @add_start_docstrings(A_				)
 def __call__( self  :      Optional[int] ,			SCREAMING_SNAKE_CASE__  :      Dict ,			SCREAMING_SNAKE_CASE__  :      Any ,			**SCREAMING_SNAKE_CASE__  :      int				)    ->       Union[str, Any]:
  return input_ids.shape[-1] >= self.max_length







class 							_lowercase	(							UpperCAmelCase__     ):


 '''simple docstring'''






 def __init__( self  :      Optional[Any] ,			SCREAMING_SNAKE_CASE__  :      Dict ,			SCREAMING_SNAKE_CASE__  :      Dict = None				)    ->       Any:
  __lowerCAmelCase											=		max_time
  __lowerCAmelCase											=		time.time() if initial_timestamp is None else initial_timestamp





 @add_start_docstrings(A_				)
 def __call__( self  :      int ,			SCREAMING_SNAKE_CASE__  :      List[str] ,			SCREAMING_SNAKE_CASE__  :      Any ,			**SCREAMING_SNAKE_CASE__  :      Optional[Any]				)    ->       Tuple:
  return time.time() - self.initial_timestamp > self.max_time







class 							_lowercase	(							UpperCAmelCase__     ):


 '''simple docstring'''






 @add_start_docstrings(A_				)
 def __call__( self  :      Dict ,			SCREAMING_SNAKE_CASE__  :      Optional[Any] ,			SCREAMING_SNAKE_CASE__  :      Dict ,			**SCREAMING_SNAKE_CASE__  :      Tuple				)    ->       str:
  return any(criteria(A_ ,			A_				) for criteria in self				)





 @property
 def a   ( self  :      List[str]				)    ->       Dict:
  for stopping_criterium in self:
   if isinstance(A_ ,			A_				):
    return stopping_criterium.max_length
   elif isinstance(A_ ,			A_				):
    return stopping_criterium.max_length
  return None







def 	UpperCamelCase_		(   snake_case_			:			StoppingCriteriaList		,						snake_case_			:			int )   ->	List[Any]:






 '''simple docstring'''

 __lowerCAmelCase											=		stopping_criteria.max_length
 __lowerCAmelCase											=		deepcopy(snake_case__ )
 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"""		,						snake_case__ )
 elif stopping_max_length is None:
  new_stopping_criteria.append(MaxLengthCriteria(max_length=snake_case__ ) )
 return new_stopping_criteria










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__					:     List[str]       =     [
     'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
     'SEWForCTC',
     'SEWForSequenceClassification',
     'SEWModel',
     'SEWPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_sew import (
      SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
      SEWForCTC,
      SEWForSequenceClassification,
      SEWModel,
      SEWPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE__					:     Tuple       =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





