





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							List[str]     	=	"philschmid/bart-large-cnn-samsum"
   a		:							Optional[int]     	=	(
       "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		:							Optional[Any]     	=	"summarizer"
   a		:							Dict     	=	AutoTokenizer
   a		:							Dict     	=	AutoModelForSeqaSeqLM

   a		:							Union[str, Any]     	=	["text"]
   a		:							List[Any]     	=	["text"]




   def    UpperCamelCase__						(					self,	__magic_name__							)				->   int:






       """simple docstring"""
       return self.pre_processor(__magic_name__,	return_tensors='''pt''',	truncation=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__							)				->   Optional[int]:






       """simple docstring"""
       return self.model.generate(**__magic_name__							)[0]




   def    UpperCamelCase__						(					self,	__magic_name__							)				->   List[Any]:






       """simple docstring"""
       return self.pre_processor.decode(__magic_name__,	skip_special_tokens=__magic_name__,	clean_up_tokenization_spaces=__magic_name__							)








from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_  							=				{
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_  							=				[
      'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'InformerForPrediction',
      'InformerModel',
      'InformerPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_informer import (
        INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        InformerForPrediction,
        InformerModel,
        InformerPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_  							=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

