


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 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					None
					__UpperCAmelCase							  =					None
					__UpperCAmelCase							  =					None
					__UpperCAmelCase							  =					None




class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					def __init__(					self			,							snake_case_=1			,							snake_case_=0			,							snake_case_=2			,							snake_case_=5_1_2			,							snake_case_="cls"			,							snake_case_=False			,							snake_case_=True			,							**snake_case_			,							) ->      List[str]:







								'''simple docstring'''

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




class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					[R"pooler", R"logit_scale"]
					__UpperCAmelCase							  =					[R"position_ids", R"predictions.decoder.bias"]
					__UpperCAmelCase							  =					"roberta"
					__UpperCAmelCase							  =					RobertaSeriesConfig






					def __init__(					self			,							snake_case_    ) ->      Optional[Any]:







								'''simple docstring'''

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






					def A   (					self			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							) ->      List[Any]:







								'''simple docstring'''

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

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

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

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






import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase__					)
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					field(default="automatic-speech-recognition"  ,  metadata={"include_in_asdict_even_if_is_default": True}					)
					__UpperCAmelCase							  =					Features({"audio": Audio()}					)
					__UpperCAmelCase							  =					Features({"transcription": Value("string"					)}					)
					__UpperCAmelCase							  =					"audio"
					__UpperCAmelCase							  =					"transcription"






					def A   (					self			,							snake_case_    ) ->      Any:







								'''simple docstring'''

								if self.audio_column not in features:
											raise ValueError(F'Column {self.audio_column} is not present in features.'    )
								if not isinstance(features[self.audio_column]			,							snake_case_    ):
											raise ValueError(F'Column {self.audio_column} is not an Audio type.'    )
								__lowercase			       = copy.deepcopy(self    )
								__lowercase			       = self.input_schema.copy()
								__lowercase			       = features[self.audio_column]
								__lowercase			       = input_schema
								return task_template






					@property
					def A   (					self    ) ->      Dict[str, str]:







								'''simple docstring'''

								return {self.audio_column: "audio", self.transcription_column: "transcription"}


