






"""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


lowercase_				     =     logging.get_logger(__name__)


lowercase_				     =     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 					snake_case		(							__A						):


   '''simple docstring'''

   @add_start_docstrings(__UpperCAmelCase  )
   def __call__(	self :     Union[str, Any],       _lowerCamelCase :     Union[str, Any],       _lowerCamelCase :     int,       **_lowerCamelCase :     Optional[int]  ):

     '''simple docstring'''
     raise NotImplementedError('''StoppingCriteria needs to be subclassed'''  )





class 					snake_case		(							__A						):


   '''simple docstring'''

   def __init__(	self :     Any,       _lowerCamelCase :     List[str],       _lowerCamelCase :     str = None  ):

     '''simple docstring'''
     __A									= max_length
     __A									= max_position_embeddings






   @add_start_docstrings(__UpperCAmelCase  )
   def __call__(	self :     List[Any],       _lowerCamelCase :     Union[str, Any],       _lowerCamelCase :     Dict,       **_lowerCamelCase :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= input_ids.shape[-1]
     __A									= 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						):


   '''simple docstring'''

   def __init__(	self :     Optional[Any],       _lowerCamelCase :     str,       _lowerCamelCase :     Optional[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.''',       __UpperCAmelCase,       )
     __A									= start_length
     __A									= max_new_tokens
     __A									= start_length + max_new_tokens






   @add_start_docstrings(__UpperCAmelCase  )
   def __call__(	self :     Tuple,       _lowerCamelCase :     Optional[Any],       _lowerCamelCase :     int,       **_lowerCamelCase :     Dict  ):

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





class 					snake_case		(							__A						):


   '''simple docstring'''

   def __init__(	self :     List[Any],       _lowerCamelCase :     str,       _lowerCamelCase :     str = None  ):

     '''simple docstring'''
     __A									= max_time
     __A									= time.time() if initial_timestamp is None else initial_timestamp






   @add_start_docstrings(__UpperCAmelCase  )
   def __call__(	self :     Optional[Any],       _lowerCamelCase :     List[str],       _lowerCamelCase :     int,       **_lowerCamelCase :     Dict  ):

     '''simple docstring'''
     return time.time() - self.initial_timestamp > self.max_time





class 					snake_case		(							__A						):


   '''simple docstring'''

   @add_start_docstrings(__UpperCAmelCase  )
   def __call__(	self :     List[Any],       _lowerCamelCase :     Optional[int],       _lowerCamelCase :     List[Any],       **_lowerCamelCase :     Optional[Any]  ):

     '''simple docstring'''
     return any(criteria(__UpperCAmelCase,       __UpperCAmelCase  ) for criteria in self  )






   @property
   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     for stopping_criterium in self:
       if isinstance(__UpperCAmelCase,       __UpperCAmelCase  ):
         return stopping_criterium.max_length
       elif isinstance(__UpperCAmelCase,       __UpperCAmelCase  ):
         return stopping_criterium.max_length
     return None





def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __A									= stopping_criteria.max_length
  __A									= deepcopy(UpperCamelCase__ )
  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'''		,       UpperCamelCase__ )
  elif stopping_max_length is None:
    new_stopping_criteria.append(MaxLengthCriteria(max_length=UpperCamelCase__ ) )
  return new_stopping_criteria





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCamelCase							 =     {
    "configuration_chinese_clip": [
        "CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ChineseCLIPConfig",
        "ChineseCLIPOnnxConfig",
        "ChineseCLIPTextConfig",
        "ChineseCLIPVisionConfig",
    ],
    "processing_chinese_clip": ["ChineseCLIPProcessor"],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     ["ChineseCLIPFeatureExtractor"]
				__lowerCamelCase							 =     ["ChineseCLIPImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     [
				    "CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "ChineseCLIPModel",
				    "ChineseCLIPPreTrainedModel",
				    "ChineseCLIPTextModel",
				    "ChineseCLIPVisionModel",
				]

if TYPE_CHECKING:
				from .configuration_chinese_clip import (
				    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    ChineseCLIPConfig,
				    ChineseCLIPOnnxConfig,
				    ChineseCLIPTextConfig,
				    ChineseCLIPVisionConfig,
				)
				from .processing_chinese_clip import ChineseCLIPProcessor

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_chinese_clip import (
								    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ChineseCLIPModel,
								    ChineseCLIPPreTrainedModel,
								    ChineseCLIPTextModel,
								    ChineseCLIPVisionModel,
								)

else:
				import sys

				__lowerCamelCase							 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





