



'''simple docstring'''





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

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






@dataclass(frozen=A		)
class __A    (						A		):



	'''simple docstring'''







	__lowerCamelCase					: str    = field(default='text-classification'     ,     metadata={'include_in_asdict_even_if_is_default': True}		)
	__lowerCamelCase					: ClassVar[Features]    = Features({'text': Value('string'		)}		)
	__lowerCamelCase					: ClassVar[Features]    = Features({'labels': ClassLabel}		)
	__lowerCamelCase					: str    = "text"
	__lowerCamelCase					: str    = "labels"



	def       a__						(self				,     A )					->  int:


						"""simple docstring"""



						if self.label_column not in features:
											raise ValueError(f'''Column {self.label_column} is not present in features.''' )
						if not isinstance(features[self.label_column]				,     A ):
											raise ValueError(f'''Column {self.label_column} is not a ClassLabel.''' )
						_a											=						copy.deepcopy(self )
						_a											=						self.label_schema.copy()
						_a											=						features[self.label_column]
						_a											=						label_schema
						return task_template



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


						"""simple docstring"""



						return {
						    self.text_column: "text",
						    self.label_column: "labels",
						}








'''simple docstring'''





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


lowercase_	 =						logging.get_logger(__name__)

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






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: Union[str, Any]    = 'pegasus'
 __lowerCamelCase					: Tuple    = ['past_key_values']
 __lowerCamelCase					: List[Any]    = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



 def __init__(self				,     A=50_265				,     A=1_024				,     A=12				,     A=4_096				,     A=16				,     A=12				,     A=4_096				,     A=16				,     A=0.0				,     A=0.0				,     A=True				,     A=True				,     A="gelu"				,     A=1_024				,     A=0.1				,     A=0.0				,     A=0.0				,     A=0.02				,     A=0				,     A=False				,     A=0				,     A=1				,     A=1				,     **A				,     )					->  str:


      """simple docstring"""



      _a											=						vocab_size
      _a											=						max_position_embeddings
      _a											=						d_model
      _a											=						encoder_ffn_dim
      _a											=						encoder_layers
      _a											=						encoder_attention_heads
      _a											=						decoder_ffn_dim
      _a											=						decoder_layers
      _a											=						decoder_attention_heads
      _a											=						dropout
      _a											=						attention_dropout
      _a											=						activation_dropout
      _a											=						activation_function
      _a											=						init_std
      _a											=						encoder_layerdrop
      _a											=						decoder_layerdrop
      _a											=						use_cache
      _a											=						encoder_layers
      _a											=						scale_embedding  # scale factor will be sqrt(d_model) if True
      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       a__						(self )					->  int:


      """simple docstring"""



      return self.encoder_attention_heads



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      return self.d_model



