

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'\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 A__    ( A ):



 """simple docstring"""







 @add_start_docstrings(A_       )
 def __call__(		self :	Union[str, Any]     ,						A_ :	torch.LongTensor     ,						A_ :	torch.FloatTensor     ,						**A_ :	List[Any]       ):






      '''simple docstring'''




      raise NotImplementedError("StoppingCriteria needs to be subclassed"       )





class A__    ( A ):



 """simple docstring"""







 def __init__(		self :	List[str]     ,						A_ :	int     ,						A_ :	Optional[int] = None       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    max_length
      _lowerCAmelCase   :					Dict        =    max_position_embeddings


 @add_start_docstrings(A_       )
 def __call__(		self :	List[str]     ,						A_ :	torch.LongTensor     ,						A_ :	torch.FloatTensor     ,						**A_ :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    input_ids.shape[-1]
      _lowerCAmelCase   :					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 A__    ( A ):



 """simple docstring"""







 def __init__(		self :	Any     ,						A_ :	int     ,						A_ :	int       ):






      '''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."     ,						A_     ,						)
      _lowerCAmelCase   :					Union[str, Any]        =    start_length
      _lowerCAmelCase   :					List[str]        =    max_new_tokens
      _lowerCAmelCase   :					Optional[int]        =    start_length + max_new_tokens


 @add_start_docstrings(A_       )
 def __call__(		self :	Tuple     ,						A_ :	torch.LongTensor     ,						A_ :	torch.FloatTensor     ,						**A_ :	List[Any]       ):






      '''simple docstring'''




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





class A__    ( A ):



 """simple docstring"""







 def __init__(		self :	Optional[int]     ,						A_ :	float     ,						A_ :	Optional[float] = None       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    max_time
      _lowerCAmelCase   :					List[str]        =    time.time() if initial_timestamp is None else initial_timestamp


 @add_start_docstrings(A_       )
 def __call__(		self :	Union[str, Any]     ,						A_ :	torch.LongTensor     ,						A_ :	torch.FloatTensor     ,						**A_ :	str       ):






      '''simple docstring'''




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





class A__    ( A ):



 """simple docstring"""







 @add_start_docstrings(A_       )
 def __call__(		self :	Union[str, Any]     ,						A_ :	torch.LongTensor     ,						A_ :	torch.FloatTensor     ,						**A_ :	Optional[Any]       ):






      '''simple docstring'''




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


 @property
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      StoppingCriteriaList:

     """simple docstring"""







     _lowerCAmelCase   :					List[Any]        =    stopping_criteria.max_length
     _lowerCAmelCase   :					Optional[int]        =    deepcopy(SCREAMING_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"		,							SCREAMING_SNAKE_CASE				)
     elif stopping_max_length is None:
          new_stopping_criteria.append(MaxLengthCriteria(max_length=SCREAMING_SNAKE_CASE				)				)
     return new_stopping_criteria









import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


__UpperCAmelCase    				=     logging.get_logger(__name__)





class A__    ( A ):



 """simple docstring"""







 def __init__(		self :	Tuple     ,						*A_ :	Optional[int]     ,						**A_ :	int       ):






      '''simple docstring'''




      warnings.warn(
          "The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
          " Please use ChineseCLIPImageProcessor instead."     ,						A_     ,						)
      super().__init__(*A_     ,						**A_       )






