






"""simple docstring"""







from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool





class   UpperCamelCase_ (       a_  ):

  '''simple docstring'''





  _A       :					Dict				=	'philschmid/bart-large-cnn-samsum'
  _A       :					Optional[Any]				=	(
      'This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '
      'and returns a summary of the text.'
  )
  _A       :					str				=	'summarizer'
  _A       :					List[Any]				=	AutoTokenizer
  _A       :					Any				=	AutoModelForSeqaSeqLM

  _A       :					str				=	['text']
  _A       :					List[str]				=	['text']



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			str:



         """simple docstring"""





         return self.pre_processor(snake_case__ ,		return_tensors="""pt""" ,		truncation=snake_case__				)



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			Tuple:



         """simple docstring"""





         return self.model.generate(**snake_case__				)[0]



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			List[Any]:



         """simple docstring"""





         return self.pre_processor.decode(snake_case__ ,		skip_special_tokens=snake_case__ ,		clean_up_tokenization_spaces=snake_case__				)








"""simple docstring"""







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


lowerCAmelCase_	:    Any            =      logging.get_logger(__name__)

lowerCAmelCase_	:    Any            =      {
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}





class   UpperCamelCase_ (       a_  ):
  _A       :					Tuple				=	'canine'



  def __init__(      self ,		snake_case__=7_68 ,		snake_case__=12 ,		snake_case__=12 ,		snake_case__=30_72 ,		snake_case__="gelu" ,		snake_case__=0.1 ,		snake_case__=0.1 ,		snake_case__=1_63_84 ,		snake_case__=16 ,		snake_case__=0.02 ,		snake_case__=1e-12 ,		snake_case__=0 ,		snake_case__=0xE_0_0_0 ,		snake_case__=0xE_0_0_1 ,		snake_case__=4 ,		snake_case__=4 ,		snake_case__=8 ,		snake_case__=1_63_84 ,		snake_case__=1_28 ,		**snake_case__ ,		)			->			int:



         """simple docstring"""





         super().__init__(pad_token_id=snake_case__ ,		bos_token_id=snake_case__ ,		eos_token_id=snake_case__ ,		**snake_case__				)

         UpperCAmelCase	    =			max_position_embeddings
         UpperCAmelCase	    =			hidden_size
         UpperCAmelCase	    =			num_hidden_layers
         UpperCAmelCase	    =			num_attention_heads
         UpperCAmelCase	    =			intermediate_size
         UpperCAmelCase	    =			hidden_act
         UpperCAmelCase	    =			hidden_dropout_prob
         UpperCAmelCase	    =			attention_probs_dropout_prob
         UpperCAmelCase	    =			initializer_range
         UpperCAmelCase	    =			type_vocab_size
         UpperCAmelCase	    =			layer_norm_eps

         # Character config:
         UpperCAmelCase	    =			downsampling_rate
         UpperCAmelCase	    =			upsampling_kernel_size
         UpperCAmelCase	    =			num_hash_functions
         UpperCAmelCase	    =			num_hash_buckets
         UpperCAmelCase	    =			local_transformer_stride
