





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 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       @add_start_docstrings(__lowerCamelCase							)
       def __call__(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						**__lowerCamelCase							)			->     bool:
            raise NotImplementedError("StoppingCriteria needs to be subclassed"							)





class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


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







       @add_start_docstrings(__lowerCamelCase							)
       def __call__(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						**__lowerCamelCase							)			->     bool:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							input_ids.shape[-1]
            _SCREAMING_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 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       def __init__(      self    ,						__lowerCamelCase    ,						__lowerCamelCase							)			->     Optional[Any]:
            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."    ,						__lowerCamelCase    ,						)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							start_length
            _SCREAMING_SNAKE_CASE       :	Any						    =							max_new_tokens
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							start_length + max_new_tokens







       @add_start_docstrings(__lowerCamelCase							)
       def __call__(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						**__lowerCamelCase							)			->     bool:
            return input_ids.shape[-1] >= self.max_length





class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       def __init__(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = None							)			->     str:
            _SCREAMING_SNAKE_CASE       :	str						    =							max_time
            _SCREAMING_SNAKE_CASE       :	Dict						    =							time.time() if initial_timestamp is None else initial_timestamp







       @add_start_docstrings(__lowerCamelCase							)
       def __call__(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						**__lowerCamelCase							)			->     bool:
            return time.time() - self.initial_timestamp > self.max_time





class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       @add_start_docstrings(__lowerCamelCase							)
       def __call__(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						**__lowerCamelCase							)			->     bool:
            return any(criteria(__lowerCamelCase    ,						__lowerCamelCase							) for criteria in self							)







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






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	Any						    =							stopping_criteria.max_length
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__      ={
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__      =[
           'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
           'CLIPSegModel',
           'CLIPSegPreTrainedModel',
           'CLIPSegTextModel',
           'CLIPSegVisionModel',
           'CLIPSegForImageSegmentation',
       ]

if TYPE_CHECKING:
       from .configuration_clipseg import (
           CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
           CLIPSegConfig,
           CLIPSegTextConfig,
           CLIPSegVisionConfig,
       )
       from .processing_clipseg import CLIPSegProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_clipseg import (
                  CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CLIPSegForImageSegmentation,
                  CLIPSegModel,
                  CLIPSegPreTrainedModel,
                  CLIPSegTextModel,
                  CLIPSegVisionModel,
              )

else:
       import sys

       UpperCamelCase__      =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)