"""simple docstring"""

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

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"philschmid/bart-large-cnn-samsum"
     lowercase		     =		(
         "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."
     )
     lowercase		     =		"summarizer"
     lowercase		     =		AutoTokenizer
     lowercase		     =		AutoModelForSeqaSeqLM

     lowercase		     =		["text"]
     lowercase		     =		["text"]





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      return self.pre_processor(__UpperCAmelCase			,  return_tensors='pt'			,  truncation=__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




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







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




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





"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCamelCase			:       int									=				logging.get_logger(__name__)

UpperCamelCase			:       int									=				{
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"bit"
     lowercase		     =		["preactivation", "bottleneck"]
     lowercase		     =		["SAME", "VALID"]





     def __init__(			self			,  __UpperCAmelCase=3			,  __UpperCAmelCase=64			,  __UpperCAmelCase=[256, 512, 1024, 2048]			,  __UpperCAmelCase=[3, 4, 6, 3]			,  __UpperCAmelCase="preactivation"			,  __UpperCAmelCase="relu"			,  __UpperCAmelCase=None			,  __UpperCAmelCase=32			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=False			,  __UpperCAmelCase=32			,  __UpperCAmelCase=1			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      super().__init__(**__UpperCAmelCase   )
      if layer_type not in self.layer_types:
       raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types   )}'   )
      if global_padding is not None:
       if global_padding.upper() in self.supported_padding:
        __UpperCamelCase       						= global_padding.upper()
       else:
        raise ValueError(F'Padding strategy {global_padding} not supported'   )
      __UpperCamelCase       						= num_channels
      __UpperCamelCase       						= embedding_size
      __UpperCamelCase       						= hidden_sizes
      __UpperCamelCase       						= depths
      __UpperCamelCase       						= layer_type
      __UpperCamelCase       						= hidden_act
      __UpperCamelCase       						= global_padding
      __UpperCamelCase       						= num_groups
      __UpperCamelCase       						= drop_path_rate
      __UpperCamelCase       						= embedding_dynamic_padding
      __UpperCamelCase       						= output_stride
      __UpperCamelCase       						= width_factor

      __UpperCamelCase       						= ['stem'] + [F'stage{idx}' for idx in range(1			,  len(__UpperCAmelCase   ) + 1   )]
      __UpperCamelCase						,			__UpperCamelCase       						= get_aligned_output_features_output_indices(
          out_features=__UpperCAmelCase			,  out_indices=__UpperCAmelCase			,  stage_names=self.stage_names   )



