






# 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_   (      lowercase  ):






      '''simple docstring'''




      __snake_case	    =    '''philschmid/bart-large-cnn-samsum'''
      __snake_case	    =    (
          '''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.'''
      )
      __snake_case	    =    '''summarizer'''
      __snake_case	    =    AutoTokenizer
      __snake_case	    =    AutoModelForSeqaSeqLM

      __snake_case	    =    ['''text''']
      __snake_case	    =    ['''text''']







      def  __lowerCAmelCase   ( self :		List[str]					,   __UpperCAmelCase :		Union[str, Any]    )       ->Dict:




       """simple docstring"""


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







      def  __lowerCAmelCase   ( self :		List[Any]					,   __UpperCAmelCase :		Optional[int]    )       ->Optional[int]:




       """simple docstring"""


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







      def  __lowerCAmelCase   ( self :		Dict					,   __UpperCAmelCase :		str    )       ->List[Any]:




       """simple docstring"""


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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase__							  =						{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						[
         "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "BioGptForCausalLM",
         "BioGptForTokenClassification",
         "BioGptForSequenceClassification",
         "BioGptModel",
         "BioGptPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase__							  =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


