





'''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


lowercase__    :      Any						=    logging.get_logger(__name__)


lowercase__    :      Tuple						=    r'''
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

'''


class  SCREAMING_SNAKE_CASE    (a__							):


     @add_start_docstrings(_UpperCAmelCase)
     def __call__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	**_UpperCAmelCase):

          '''simple docstring'''






          raise NotImplementedError('StoppingCriteria needs to be subclassed')




class  SCREAMING_SNAKE_CASE    (a__							):


     def __init__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase = None):

          '''simple docstring'''






          __A						: int      =       max_length
          __A						: List[str]      =       max_position_embeddings


     @add_start_docstrings(_UpperCAmelCase)
     def __call__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	**_UpperCAmelCase):

          '''simple docstring'''






          __A						: Dict      =       input_ids.shape[-1]
          __A						: str      =       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  SCREAMING_SNAKE_CASE    (a__							):


     def __init__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''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.'						,	_UpperCAmelCase						,	)
          __A						: Optional[Any]      =       start_length
          __A						: List[Any]      =       max_new_tokens
          __A						: List[Any]      =       start_length + max_new_tokens


     @add_start_docstrings(_UpperCAmelCase)
     def __call__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	**_UpperCAmelCase):

          '''simple docstring'''






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




class  SCREAMING_SNAKE_CASE    (a__							):


     def __init__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase = None):

          '''simple docstring'''






          __A						: Any      =       max_time
          __A						: Dict      =       time.time() if initial_timestamp is None else initial_timestamp


     @add_start_docstrings(_UpperCAmelCase)
     def __call__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	**_UpperCAmelCase):

          '''simple docstring'''






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




class  SCREAMING_SNAKE_CASE    (a__							):


     @add_start_docstrings(_UpperCAmelCase)
     def __call__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	**_UpperCAmelCase):

          '''simple docstring'''






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


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for stopping_criterium in self:
               if isinstance(_UpperCAmelCase						,	_UpperCAmelCase):
                    return stopping_criterium.max_length
               elif isinstance(_UpperCAmelCase						,	_UpperCAmelCase):
                    return stopping_criterium.max_length
          return None






def        _lowerCAmelCase			(  __snake_case  : StoppingCriteriaList ,							__snake_case  : int			)     ->     StoppingCriteriaList:
     __A						: Dict      =       stopping_criteria.max_length
     __A						: Optional[int]      =       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






'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__    :      List[str]						=    logging.get_logger(__name__)

lowercase__    :      Dict						=    {
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}


class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       '''roberta'''


     def __init__(						self						,	_UpperCAmelCase=5_0265						,	_UpperCAmelCase=768						,	_UpperCAmelCase=12						,	_UpperCAmelCase=12						,	_UpperCAmelCase=3072						,	_UpperCAmelCase="gelu"						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=512						,	_UpperCAmelCase=2						,	_UpperCAmelCase=0.02						,	_UpperCAmelCase=1e-1_2						,	_UpperCAmelCase=1						,	_UpperCAmelCase=0						,	_UpperCAmelCase=2						,	_UpperCAmelCase="absolute"						,	_UpperCAmelCase=True						,	_UpperCAmelCase=None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          super().__init__(pad_token_id=_UpperCAmelCase						,	bos_token_id=_UpperCAmelCase						,	eos_token_id=_UpperCAmelCase						,	**_UpperCAmelCase)

          __A						: Optional[int]      =       vocab_size
          __A						: int      =       hidden_size
          __A						: Union[str, Any]      =       num_hidden_layers
          __A						: List[str]      =       num_attention_heads
          __A						: Optional[int]      =       hidden_act
          __A						: str      =       intermediate_size
          __A						: Union[str, Any]      =       hidden_dropout_prob
          __A						: Dict      =       attention_probs_dropout_prob
          __A						: int      =       max_position_embeddings
          __A						: str      =       type_vocab_size
          __A						: Any      =       initializer_range
          __A						: int      =       layer_norm_eps
          __A						: Optional[int]      =       position_embedding_type
          __A						: int      =       use_cache
          __A						: Union[str, Any]      =       classifier_dropout




class  SCREAMING_SNAKE_CASE    (a__							):


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          if self.task == "multiple-choice":
               __A						: str      =       {0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
               __A						: List[str]      =       {0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ])