




'''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				:				Union[str, Any]       							=		logging.get_logger(__name__)


_snake_case				:				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       A   (		_a   ):







							@add_start_docstrings(lowerCAmelCase_ )
							def __call__(  self	:  Tuple							,      lowerCAmelCase_	:  torch.LongTensor							,      lowerCAmelCase_	:  torch.FloatTensor							,      **lowerCAmelCase_	:  int )      ->							bool:


														"""simple docstring"""


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



class       A   (		_a   ):







							def __init__(  self	:  List[str]							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  Optional[int] = None )      ->							str:


														"""simple docstring"""


														_a     					=    max_length
														_a     					=    max_position_embeddings







							@add_start_docstrings(lowerCAmelCase_ )
							def __call__(  self	:  Optional[int]							,      lowerCAmelCase_	:  torch.LongTensor							,      lowerCAmelCase_	:  torch.FloatTensor							,      **lowerCAmelCase_	:  List[str] )      ->							bool:


														"""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       A   (		_a   ):







							def __init__(  self	:  Any							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  int )      ->							Dict:


														"""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.'''							,      lowerCAmelCase_							,      )
														_a     					=    start_length
														_a     					=    max_new_tokens
														_a     					=    start_length + max_new_tokens







							@add_start_docstrings(lowerCAmelCase_ )
							def __call__(  self	:  List[Any]							,      lowerCAmelCase_	:  torch.LongTensor							,      lowerCAmelCase_	:  torch.FloatTensor							,      **lowerCAmelCase_	:  Optional[int] )      ->							bool:


														"""simple docstring"""


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



class       A   (		_a   ):







							def __init__(  self	:  str							,      lowerCAmelCase_	:  float							,      lowerCAmelCase_	:  Optional[float] = None )      ->							Optional[int]:


														"""simple docstring"""


														_a     					=    max_time
														_a     					=    time.time() if initial_timestamp is None else initial_timestamp







							@add_start_docstrings(lowerCAmelCase_ )
							def __call__(  self	:  Dict							,      lowerCAmelCase_	:  torch.LongTensor							,      lowerCAmelCase_	:  torch.FloatTensor							,      **lowerCAmelCase_	:  List[Any] )      ->							bool:


														"""simple docstring"""


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



class       A   (		_a   ):







							@add_start_docstrings(lowerCAmelCase_ )
							def __call__(  self	:  List[Any]							,      lowerCAmelCase_	:  torch.LongTensor							,      lowerCAmelCase_	:  torch.FloatTensor							,      **lowerCAmelCase_	:  int )      ->							bool:


														"""simple docstring"""


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







							@property
							def        __lowerCAmelCase      (  self	:  Tuple )      ->							Optional[int]:


														"""simple docstring"""


														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 		snake_case_   (UpperCamelCase	:  StoppingCriteriaList			,						UpperCamelCase	:  int				):





							'''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


_snake_case				:				int       							=		{'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				List[str]       							=		['YolosFeatureExtractor']
				_snake_case				:				Optional[int]       							=		['YolosImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				Tuple       							=		[
				    'YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'YolosForObjectDetection',
				    'YolosModel',
				    'YolosPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_yolos import YolosFeatureExtractor
								from .image_processing_yolos import YolosImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_yolos import (
								    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    YolosForObjectDetection,
								    YolosModel,
								    YolosPreTrainedModel,
								)


else:
				import sys

				_snake_case				:				Dict       							=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

