

'''simple docstring'''




# 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_						(		a			):

  '''simple docstring'''





  __lowerCAmelCase       :			Optional[int]     	= "philschmid/bart-large-cnn-samsum"
  __lowerCAmelCase       :			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."
  )
  __lowerCAmelCase       :			Optional[int]     	= "summarizer"
  __lowerCAmelCase       :			Tuple     	= AutoTokenizer
  __lowerCAmelCase       :			Dict     	= AutoModelForSeqaSeqLM

  __lowerCAmelCase       :			List[Any]     	= ["text"]
  __lowerCAmelCase       :			Tuple     	= ["text"]
  def     snake_case_      (  self ,			a_  )				->					Dict:


    """simple docstring"""
    return self.pre_processor(a_ ,			return_tensors='pt' ,			truncation=a_  )
  def     snake_case_      (  self ,			a_  )				->					Union[str, Any]:


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





  def     snake_case_      (  self ,			a_  )				->					Tuple:


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





'''simple docstring'''




from typing import TYPE_CHECKING

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


_a					:			Any							=    {
    'configuration_groupvit': [
        'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'GroupViTConfig',
        'GroupViTOnnxConfig',
        'GroupViTTextConfig',
        'GroupViTVisionConfig',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			Union[str, Any]							=    [
      'GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'GroupViTModel',
      'GroupViTPreTrainedModel',
      'GroupViTTextModel',
      'GroupViTVisionModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			Tuple							=    [
      'TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFGroupViTModel',
      'TFGroupViTPreTrainedModel',
      'TFGroupViTTextModel',
      'TFGroupViTVisionModel',
  ]

if TYPE_CHECKING:
  from .configuration_groupvit import (
      GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      GroupViTConfig,
      GroupViTOnnxConfig,
      GroupViTTextConfig,
      GroupViTVisionConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_groupvit import (
        GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        GroupViTModel,
        GroupViTPreTrainedModel,
        GroupViTTextModel,
        GroupViTVisionModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_groupvit import (
        TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFGroupViTModel,
        TFGroupViTPreTrainedModel,
        TFGroupViTTextModel,
        TFGroupViTVisionModel,
    )

else:
  import sys

  _a					:			Union[str, Any]							=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


