





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 					lowercase__ (			__lowerCAmelCase       ):

			'''simple docstring'''


			@add_start_docstrings(lowerCAmelCase_							)
			def __call__(					self,	__magic_name__,	__magic_name__,	**__magic_name__							)				->   bool:






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






class 					lowercase__ (			__lowerCAmelCase       ):

			'''simple docstring'''


			def __init__(					self,	__magic_name__,	__magic_name__ = None							)				->   List[str]:






							"""simple docstring"""
							UpperCamelCase__							:       Union[str, Any]      =   max_length
							UpperCamelCase__							:       int      =   max_position_embeddings




			@add_start_docstrings(lowerCAmelCase_							)
			def __call__(					self,	__magic_name__,	__magic_name__,	**__magic_name__							)				->   bool:






							"""simple docstring"""
							UpperCamelCase__							:       Union[str, Any]      =   input_ids.shape[-1]
							UpperCamelCase__							:       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 					lowercase__ (			__lowerCAmelCase       ):

			'''simple docstring'''


			def __init__(					self,	__magic_name__,	__magic_name__							)				->   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_,	)
							UpperCamelCase__							:       Optional[Any]      =   start_length
							UpperCamelCase__							:       Any      =   max_new_tokens
							UpperCamelCase__							:       List[Any]      =   start_length + max_new_tokens




			@add_start_docstrings(lowerCAmelCase_							)
			def __call__(					self,	__magic_name__,	__magic_name__,	**__magic_name__							)				->   bool:






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






class 					lowercase__ (			__lowerCAmelCase       ):

			'''simple docstring'''


			def __init__(					self,	__magic_name__,	__magic_name__ = None							)				->   Any:






							"""simple docstring"""
							UpperCamelCase__							:       int      =   max_time
							UpperCamelCase__							:       Dict      =   time.time() if initial_timestamp is None else initial_timestamp




			@add_start_docstrings(lowerCAmelCase_							)
			def __call__(					self,	__magic_name__,	__magic_name__,	**__magic_name__							)				->   bool:






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






class 					lowercase__ (			__lowerCAmelCase       ):

			'''simple docstring'''


			@add_start_docstrings(lowerCAmelCase_							)
			def __call__(					self,	__magic_name__,	__magic_name__,	**__magic_name__							)				->   bool:






							"""simple docstring"""
							return any(criteria(lowerCAmelCase_,	lowerCAmelCase_							) for criteria in self							)




			@property
			def    UpperCamelCase__						(					self							)				->   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 	lowerCAmelCase_					(						__UpperCAmelCase:    StoppingCriteriaList	,     __UpperCAmelCase:    int   )   ->			StoppingCriteriaList:
				UpperCamelCase__							:       Optional[int]      =   stopping_criteria.max_length
				UpperCamelCase__							:       Any      =   deepcopy(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'''	,     snake_case__   )
				elif stopping_max_length is None:
								new_stopping_criteria.append(MaxLengthCriteria(max_length=snake_case__   )   )
				return new_stopping_criteria








from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_  							=				{
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_  							=				[
      'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'InformerForPrediction',
      'InformerModel',
      'InformerPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_informer import (
        INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        InformerForPrediction,
        InformerModel,
        InformerPreTrainedModel,
    )

else:
  import sys

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

