


"""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


_snake_case        =				logging.get_logger(__name__)


_snake_case        =				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   (   SCREAMING_SNAKE_CASE_			):
    @add_start_docstrings(_a					)
    def __call__(			self				:       Tuple				,       SCREAMING_SNAKE_CASE__				:       torch.LongTensor				,       SCREAMING_SNAKE_CASE__				:       torch.FloatTensor				,       **SCREAMING_SNAKE_CASE__				:       str					):
          raise NotImplementedError('StoppingCriteria needs to be subclassed'					)





class 	_a   (   SCREAMING_SNAKE_CASE_			):
    def __init__(			self				:       Dict				,       SCREAMING_SNAKE_CASE__				:       int				,       SCREAMING_SNAKE_CASE__				:       Optional[int] = None					):
          lowerCamelCase__							     =		max_length
          lowerCamelCase__							     =		max_position_embeddings


    @add_start_docstrings(_a					)
    def __call__(			self				:       Tuple				,       SCREAMING_SNAKE_CASE__				:       torch.LongTensor				,       SCREAMING_SNAKE_CASE__				:       torch.FloatTensor				,       **SCREAMING_SNAKE_CASE__				:       str					):
          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 	_a   (   SCREAMING_SNAKE_CASE_			):
    def __init__(			self				:       Union[str, Any]				,       SCREAMING_SNAKE_CASE__				:       int				,       SCREAMING_SNAKE_CASE__				:       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				:       Dict				,       SCREAMING_SNAKE_CASE__				:       torch.LongTensor				,       SCREAMING_SNAKE_CASE__				:       torch.FloatTensor				,       **SCREAMING_SNAKE_CASE__				:       Any					):
          return input_ids.shape[-1] >= self.max_length





class 	_a   (   SCREAMING_SNAKE_CASE_			):
    def __init__(			self				:       Any				,       SCREAMING_SNAKE_CASE__				:       float				,       SCREAMING_SNAKE_CASE__				:       Optional[float] = None					):
          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__				:       torch.LongTensor				,       SCREAMING_SNAKE_CASE__				:       torch.FloatTensor				,       **SCREAMING_SNAKE_CASE__				:       List[str]					):
          return time.time() - self.initial_timestamp > self.max_time





class 	_a   (   SCREAMING_SNAKE_CASE_			):
    @add_start_docstrings(_a					)
    def __call__(			self				:       Any				,       SCREAMING_SNAKE_CASE__				:       torch.LongTensor				,       SCREAMING_SNAKE_CASE__				:       torch.FloatTensor				,       **SCREAMING_SNAKE_CASE__				:       Dict					):
          return any(criteria(_a				,       _a					) for criteria in self					)


    @property
    def   _UpperCamelCase							(			self				:       List[str]					):
          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    (					_a:  str		, _a:  Optional[int]				)->		StoppingCriteriaList:
      '''simple docstring'''


      lowerCamelCase__							     =		stopping_criteria.max_length
      lowerCamelCase__							     =		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








'''simple docstring'''





from typing import TYPE_CHECKING

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


_UpperCamelCase											=		{
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=		['BridgeTowerImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=		[
       'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BridgeTowerForContrastiveLearning',
       'BridgeTowerForImageAndTextRetrieval',
       'BridgeTowerForMaskedLM',
       'BridgeTowerModel',
       'BridgeTowerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_bridgetower import (
       BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BridgeTowerConfig,
       BridgeTowerTextConfig,
       BridgeTowerVisionConfig,
   )
   from .processing_bridgetower import BridgeTowerProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_bridgetower import BridgeTowerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bridgetower import (
          BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
          BridgeTowerForContrastiveLearning,
          BridgeTowerForImageAndTextRetrieval,
          BridgeTowerForMaskedLM,
          BridgeTowerModel,
          BridgeTowerPreTrainedModel,
      )


else:
   import sys

   _UpperCamelCase											=		_LazyModule(__name__, globals()['__file__'], _import_structure)






