
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


lowerCAmelCase__							=				logging.get_logger(__name__)


lowerCAmelCase__							=				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  snake_case     (							__lowercase   ):

							@add_start_docstrings(__lowerCAmelCase    )
							def __call__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







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







class  snake_case     (							__lowercase   ):

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

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = max_length
									SCREAMING_SNAKE_CASE_						 = max_position_embeddings







							@add_start_docstrings(__lowerCAmelCase    )
							def __call__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = input_ids.shape[-1]
									SCREAMING_SNAKE_CASE_						 = 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  snake_case     (							__lowercase   ):

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""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       ,       )
									SCREAMING_SNAKE_CASE_						 = start_length
									SCREAMING_SNAKE_CASE_						 = max_new_tokens
									SCREAMING_SNAKE_CASE_						 = start_length + max_new_tokens







							@add_start_docstrings(__lowerCAmelCase    )
							def __call__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







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







class  snake_case     (							__lowercase   ):

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

									"""simple docstring"""







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







							@add_start_docstrings(__lowerCAmelCase    )
							def __call__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







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







class  snake_case     (							__lowercase   ):

							@add_start_docstrings(__lowerCAmelCase    )
							def __call__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







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







							@property
							def _lowercase					(self    ):

									"""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 ( __a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = stopping_criteria.max_length
		SCREAMING_SNAKE_CASE_						 = 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 dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput





@dataclass
class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      None
							UpperCAmelCase__						     =      None
							UpperCAmelCase__						     =      None
							UpperCAmelCase__						     =      None







class  snake_case     (							__lowercase   ):

							def __init__(self       ,       SCREAMING_SNAKE_CASE_=1       ,       SCREAMING_SNAKE_CASE_=0       ,       SCREAMING_SNAKE_CASE_=2       ,       SCREAMING_SNAKE_CASE_=5_12       ,       SCREAMING_SNAKE_CASE_="cls"       ,       SCREAMING_SNAKE_CASE_=False       ,       SCREAMING_SNAKE_CASE_=True       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_       ,       bos_token_id=SCREAMING_SNAKE_CASE_       ,       eos_token_id=SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = project_dim
									SCREAMING_SNAKE_CASE_						 = pooler_fn
									SCREAMING_SNAKE_CASE_						 = learn_encoder
									SCREAMING_SNAKE_CASE_						 = use_attention_mask







class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      [R'''pooler''', R'''logit_scale''']
							UpperCAmelCase__						     =      [R'''position_ids''', R'''predictions.decoder.bias''']
							UpperCAmelCase__						     =      '''roberta'''
							UpperCAmelCase__						     =      RobertaSeriesConfig

							def __init__(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									super().__init__(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = XLMRobertaModel(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = nn.Linear(config.hidden_size       ,       config.project_dim    )
									SCREAMING_SNAKE_CASE_						 = getattr(SCREAMING_SNAKE_CASE_       ,       '''has_pre_transformation'''       ,       SCREAMING_SNAKE_CASE_    )
									if self.has_pre_transformation:
											SCREAMING_SNAKE_CASE_						 = nn.Linear(config.hidden_size       ,       config.project_dim    )
											SCREAMING_SNAKE_CASE_						 = nn.LayerNorm(config.hidden_size       ,       eps=config.layer_norm_eps    )
									self.post_init()







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = return_dict if return_dict is not None else self.config.use_return_dict

									SCREAMING_SNAKE_CASE_						 = self.base_model(
									    input_ids=SCREAMING_SNAKE_CASE_       ,       attention_mask=SCREAMING_SNAKE_CASE_       ,       token_type_ids=SCREAMING_SNAKE_CASE_       ,       position_ids=SCREAMING_SNAKE_CASE_       ,       head_mask=SCREAMING_SNAKE_CASE_       ,       inputs_embeds=SCREAMING_SNAKE_CASE_       ,       encoder_hidden_states=SCREAMING_SNAKE_CASE_       ,       encoder_attention_mask=SCREAMING_SNAKE_CASE_       ,       output_attentions=SCREAMING_SNAKE_CASE_       ,       output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,       return_dict=SCREAMING_SNAKE_CASE_       ,       )

									if self.has_pre_transformation:
											SCREAMING_SNAKE_CASE_						 = outputs['''hidden_states'''][-2]
											SCREAMING_SNAKE_CASE_						 = self.pre_LN(SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = self.transformation_pre(SCREAMING_SNAKE_CASE_    )

											return TransformationModelOutput(
											    projection_state=SCREAMING_SNAKE_CASE_       ,       last_hidden_state=outputs.last_hidden_state       ,       hidden_states=outputs.hidden_states       ,       attentions=outputs.attentions       ,       )
									else:
											SCREAMING_SNAKE_CASE_						 = self.transformation(outputs.last_hidden_state    )
											return TransformationModelOutput(
											    projection_state=SCREAMING_SNAKE_CASE_       ,       last_hidden_state=outputs.last_hidden_state       ,       hidden_states=outputs.hidden_states       ,       attentions=outputs.attentions       ,       )