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

from ..features import Audio, Features, Value
from .base import TaskTemplate




@dataclass(frozen=UpperCamelCase_ )
class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







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






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_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]						,						__SCREAMING_SNAKE_CASE  ):
                 raise ValueError(F'''Column {self.audio_column} is not an Audio type.'''  )
           UpperCamelCase__    :   Any					=				copy.deepcopy(self  )
           UpperCamelCase__    :   Union[str, Any]					=				self.input_schema.copy()
           UpperCamelCase__    :   List[str]					=				features[self.audio_column]
           UpperCamelCase__    :   Any					=				input_schema
           return task_template






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




           """simple docstring"""







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




import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  '''M-CLIP'''






     def __init__(							self						,						__SCREAMING_SNAKE_CASE=1_0_2_4						,						__SCREAMING_SNAKE_CASE=7_6_8						,						**__SCREAMING_SNAKE_CASE  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				transformerDimSize
           UpperCamelCase__    :   Any					=				imageDimSize
           super().__init__(**__SCREAMING_SNAKE_CASE  )




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  MCLIPConfig






     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )  ->   Optional[int]:




           """simple docstring"""







           super().__init__(__SCREAMING_SNAKE_CASE						,						*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, Any]					=				XLMRobertaModel(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Tuple					=				torch.nn.Linear(
               in_features=config.transformerDimensions						,						out_features=config.numDims  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				self.transformer(input_ids=__SCREAMING_SNAKE_CASE						,						attention_mask=__SCREAMING_SNAKE_CASE  )[0]
           UpperCamelCase__    :   Tuple					=				(embs * attention_mask.unsqueeze(2  )).sum(dim=1  ) / attention_mask.sum(dim=1  )[:, None]
           return self.LinearTransformation(__SCREAMING_SNAKE_CASE  ), embs


