



'''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


_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_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  @add_start_docstrings(_a  )
  def __call__(					self :      Tuple     ,      _a :      torch.LongTensor     ,      _a :      torch.FloatTensor     ,      **_a :      str  )   ->   bool:
    raise NotImplementedError('StoppingCriteria needs to be subclassed'  )






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




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




  @add_start_docstrings(_a  )
  def __call__(					self :      Tuple     ,      _a :      torch.LongTensor     ,      _a :      torch.FloatTensor     ,      **_a :      str  )   ->   bool:
    __lowerCamelCase			:			List[Any]       			=					input_ids.shape[-1]
    __lowerCamelCase			:			Union[str, 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_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  def __init__(					self :      Union[str, Any]     ,      _a :      int     ,      _a :      int  )   ->   List[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.'     ,      _a     ,      )
    __lowerCamelCase			:			int       			=					start_length
    __lowerCamelCase			:			Dict       			=					max_new_tokens
    __lowerCamelCase			:			str       			=					start_length + max_new_tokens




  @add_start_docstrings(_a  )
  def __call__(					self :      Dict     ,      _a :      torch.LongTensor     ,      _a :      torch.FloatTensor     ,      **_a :      Any  )   ->   bool:
    return input_ids.shape[-1] >= self.max_length






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  def __init__(					self :      Any     ,      _a :      float     ,      _a :      Optional[float] = None  )   ->   Dict:
    __lowerCamelCase			:			str       			=					max_time
    __lowerCamelCase			:			Tuple       			=					time.time() if initial_timestamp is None else initial_timestamp




  @add_start_docstrings(_a  )
  def __call__(					self :      int     ,      _a :      torch.LongTensor     ,      _a :      torch.FloatTensor     ,      **_a :      List[str]  )   ->   bool:
    return time.time() - self.initial_timestamp > self.max_time






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  @add_start_docstrings(_a  )
  def __call__(					self :      Any     ,      _a :      torch.LongTensor     ,      _a :      torch.FloatTensor     ,      **_a :      Dict  )   ->   bool:
    return any(criteria(_a     ,      _a  ) for criteria in self  )




  @property
  def 		_lowercase	(					self :      List[str]  )   ->   Optional[int]:
    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 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			) -> StoppingCriteriaList:
  __lowerCamelCase			:			int       			=					stopping_criteria.max_length
  __lowerCamelCase			:			str       			=					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


_UpperCamelCase											=		{'configuration_ibert': ['IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'IBertConfig', 'IBertOnnxConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=		[
       'IBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'IBertForMaskedLM',
       'IBertForMultipleChoice',
       'IBertForQuestionAnswering',
       'IBertForSequenceClassification',
       'IBertForTokenClassification',
       'IBertModel',
       'IBertPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ibert import (
          IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          IBertForMaskedLM,
          IBertForMultipleChoice,
          IBertForQuestionAnswering,
          IBertForSequenceClassification,
          IBertForTokenClassification,
          IBertModel,
          IBertPreTrainedModel,
      )

else:
   import sys

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






