




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 		lowercase_       (	lowerCAmelCase__						,							lowerCAmelCase__						,							lowerCAmelCase__				):




					@register_to_config
					def __init__(   self:		List[str],     _lowercase:		int,     _lowercase:		int,     _lowercase:		int,     _lowercase:		float,     _lowercase:		int,     _lowercase:		int,     _lowercase:		int,     _lowercase:		int,     _lowercase:		str,     _lowercase:		bool = False,     ):


									'''simple docstring'''


									super().__init__()

									__lowerCAmelCase					=  nn.Linear(_lowercase,     _lowercase,     bias=_lowercase)

									__lowerCAmelCase					=  nn.Embedding(_lowercase,     _lowercase)
									__lowerCAmelCase					=  False

									__lowerCAmelCase					=  nn.Dropout(p=_lowercase)

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

									__lowerCAmelCase					=  TaLayerNorm(_lowercase)
									__lowerCAmelCase					=  nn.Dropout(p=_lowercase)







					def     _lowercase						(   self:		Dict,     _lowercase:		int,     _lowercase:		Any):


									'''simple docstring'''


									__lowerCAmelCase					=  self.input_proj(_lowercase)

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

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

									__lowerCAmelCase					=  self.dropout_pre(_lowercase)

									# inverted the attention mask
									__lowerCAmelCase					=  encoder_inputs.size()
									__lowerCAmelCase					=  self.get_extended_attention_mask(_lowercase,     _lowercase)

									for lyr in self.encoders:
													__lowerCAmelCase					=  lyr(_lowercase,     _lowercase)[0]
									__lowerCAmelCase					=  self.layer_norm(_lowercase)

									return self.dropout_post(_lowercase), 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__	:   int								=     [
    "AutomaticSpeechRecognition",
    "AudioClassification",
    "ImageClassification",
    "LanguageModeling",
    "QuestionAnsweringExtractive",
    "Summarization",
    "TaskTemplate",
    "TextClassification",
]

UpperCamelCase__	:   Any								=     get_logger(__name__)


UpperCamelCase__	:   Union[str, Any]								=     {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		dict							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    task_template_dict.get('task'							)
    if task_name is None:
        logger.warning(f"""Couldn't find template for task '{task_name}'. Available templates: {list(_SCREAMING_SNAKE_CASE							)}"""							)
        return None
    SCREAMING_SNAKE_CASE_						=    NAMEaTEMPLATE.get(_SCREAMING_SNAKE_CASE							)
    return template.from_dict(_SCREAMING_SNAKE_CASE							)


