

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__										=      logging.get_logger(__name__)

A__										=      {
    "google/pegasus-x-base": "https://huggingface.co/google/pegasus-x-base/resolve/main/config.json",
    "google/pegasus-x-large": "https://huggingface.co/google/pegasus-x-large/resolve/main/config.json",
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}







class  UpperCamelCase_      (	_snake_case      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			List[str]					    =   """pegasus_x"""
  SCREAMING_SNAKE_CASE__					:			Optional[Any]					    =   ["""past_key_values"""]
  SCREAMING_SNAKE_CASE__					:			int					    =   {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



  def __init__(						self     :					Optional[Any]  ,	A_     :					Union[str, Any]=9_6103  ,	A_     :					Optional[int]=1_6384  ,	A_     :					Dict=16  ,	A_     :					Optional[int]=4096  ,	A_     :					Union[str, Any]=16  ,	A_     :					Dict=16  ,	A_     :					Optional[int]=4096  ,	A_     :					Union[str, Any]=16  ,	A_     :					Dict=0.0  ,	A_     :					List[str]=0.0  ,	A_     :					int=True  ,	A_     :					Dict=True  ,	A_     :					Tuple="gelu"  ,	A_     :					Union[str, Any]=1024  ,	A_     :					Optional[int]=0.1  ,	A_     :					Dict=0.0  ,	A_     :					Dict=0.0  ,	A_     :					List[str]=0.02  ,	A_     :					Tuple=0  ,	A_     :					List[str]=True  ,	A_     :					List[str]=0  ,	A_     :					List[str]=1  ,	A_     :					str=1  ,	A_     :					int=32  ,	A_     :					List[Any]=512  ,	A_     :					str=True  ,	**A_     :					Tuple  ,	)       ->			str:
      SCREAMING_SNAKE_CASE__         =    vocab_size
      SCREAMING_SNAKE_CASE__         =    max_position_embeddings
      SCREAMING_SNAKE_CASE__         =    d_model
      SCREAMING_SNAKE_CASE__         =    encoder_ffn_dim
      SCREAMING_SNAKE_CASE__         =    encoder_layers
      SCREAMING_SNAKE_CASE__         =    encoder_attention_heads
      SCREAMING_SNAKE_CASE__         =    decoder_ffn_dim
      SCREAMING_SNAKE_CASE__         =    decoder_layers
      SCREAMING_SNAKE_CASE__         =    decoder_attention_heads
      SCREAMING_SNAKE_CASE__         =    dropout
      SCREAMING_SNAKE_CASE__         =    attention_dropout
      SCREAMING_SNAKE_CASE__         =    activation_dropout
      SCREAMING_SNAKE_CASE__         =    activation_function
      SCREAMING_SNAKE_CASE__         =    init_std
      SCREAMING_SNAKE_CASE__         =    encoder_layerdrop
      SCREAMING_SNAKE_CASE__         =    decoder_layerdrop
      SCREAMING_SNAKE_CASE__         =    use_cache
      SCREAMING_SNAKE_CASE__         =    encoder_layers
      SCREAMING_SNAKE_CASE__         =    scale_embedding  # scale factor will be sqrt(d_model) if True

      SCREAMING_SNAKE_CASE__         =    num_global_tokens
      SCREAMING_SNAKE_CASE__         =    block_size
      SCREAMING_SNAKE_CASE__         =    stagger_local_blocks

      super().__init__(
          pad_token_id=A_  ,	eos_token_id=A_  ,	is_encoder_decoder=A_  ,	decoder_start_token_id=A_  ,	forced_eos_token_id=A_  ,	**A_  ,	)



  @property
  def   _UpperCamelCase     (						self     :					List[Any]						)       ->			int:
      return self.encoder_attention_heads







  @property
  def   _UpperCamelCase     (						self     :					int						)       ->			int:
      return self.d_model





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


A__										=      [
    "AutomaticSpeechRecognition",
    "AudioClassification",
    "ImageClassification",
    "LanguageModeling",
    "QuestionAnsweringExtractive",
    "Summarization",
    "TaskTemplate",
    "TextClassification",
]

A__										=      get_logger(__name__)


A__										=      {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}





def 				__a							(       __A						):
    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(__A						)}"						)
        return None
    SCREAMING_SNAKE_CASE__         =    NAMEaTEMPLATE.get(__A						)
    return template.from_dict(__A						)


