




import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class        snake_case__				(_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):
				"""simple docstring"""



				@register_to_config
				def __init__( self							:	str   , __lowerCamelCase							:	int   , __lowerCamelCase							:	int   , __lowerCamelCase							:	int   , __lowerCamelCase							:	float   , __lowerCamelCase							:	int   , __lowerCamelCase							:	int   , __lowerCamelCase							:	int   , __lowerCamelCase							:	int   , __lowerCamelCase							:	str   , __lowerCamelCase							:	bool = False   , )   ->	str:
							super().__init__()

							a       =     nn.Linear(UpperCamelCase_   , UpperCamelCase_   , bias=UpperCamelCase_				)

							a       =     nn.Embedding(UpperCamelCase_   , UpperCamelCase_				)
							a       =     False

							a       =     nn.Dropout(p=UpperCamelCase_				)

							a       =     TaConfig(
							    d_model=UpperCamelCase_   , num_heads=UpperCamelCase_   , d_kv=UpperCamelCase_   , d_ff=UpperCamelCase_   , feed_forward_proj=UpperCamelCase_   , dropout_rate=UpperCamelCase_   , is_decoder=UpperCamelCase_   , is_encoder_decoder=UpperCamelCase_   , )
							a       =     nn.ModuleList()
							for lyr_num in range(UpperCamelCase_				):
										a       =     TaBlock(UpperCamelCase_				)
										self.encoders.append(UpperCamelCase_				)

							a       =     TaLayerNorm(UpperCamelCase_				)
							a       =     nn.Dropout(p=UpperCamelCase_				)





				def 				__UpperCAmelCase				( self							:	str   , __lowerCamelCase							:	List[Any]   , __lowerCamelCase							:	int				)   ->	str:
							a       =     self.input_proj(UpperCamelCase_				)

							# terminal relative positional encodings
							a       =     encoder_inputs.shape[1]
							a       =     torch.arange(UpperCamelCase_   , device=encoder_inputs.device				)

							a       =     encoder_inputs_mask.sum(-1				)
							a       =     torch.roll(input_positions.unsqueeze(0				)   , tuple(seq_lens.tolist()				)   , dims=0				)
							x += self.position_encoding(UpperCamelCase_				)

							a       =     self.dropout_pre(UpperCamelCase_				)

							# inverted the attention mask
							a       =     encoder_inputs.size()
							a       =     self.get_extended_attention_mask(UpperCamelCase_   , UpperCamelCase_				)

							for lyr in self.encoders:
										a       =     lyr(UpperCamelCase_   , UpperCamelCase_				)[0]
							a       =     self.layer_norm(UpperCamelCase_				)

							return self.dropout_post(UpperCamelCase_				), encoder_inputs_mask












from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


UpperCAmelCase__    :							Tuple       		=     [
    'AutomaticSpeechRecognition',
    'AudioClassification',
    'ImageClassification',
    'LanguageModeling',
    'QuestionAnsweringExtractive',
    'Summarization',
    'TaskTemplate',
    'TextClassification',
]

UpperCAmelCase__    :							Tuple       		=     get_logger(__name__)


UpperCAmelCase__    :							List[Any]       		=     {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		Optional[TaskTemplate]:





  """simple docstring"""





  _lowercase:			Union[str, Any]      =     task_template_dict.get('task'				)
  if task_name is None:
    logger.warning(F'Couldn\'t find template for task \'{task_name}\'. Available templates: {list(UpperCamelCase				)}'				)
    return None
  _lowercase:			int      =     NAMEaTEMPLATE.get(UpperCamelCase				)
  return template.from_dict(UpperCamelCase				)
