





"""simple docstring"""






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase_			=							logging.get_logger(__name__)



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  ["""pixel_values"""]






       def __init__(   self    ,  a__ = True    ,  a__ = 32    ,  a__=PILImageResampling.BILINEAR    ,  a__ = True    ,  **a__    ,  ):
              _UpperCAmelCase     =     do_resize
              _UpperCAmelCase     =     do_rescale
              _UpperCAmelCase     =     size_divisor
              _UpperCAmelCase     =     resample
              super().__init__(**a__     )






       def 							__A (   self    ,  a__    ,  a__    ,  a__    ,  a__ = None    ,  **a__     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     get_image_size(a__     )
              # Rounds the height and width down to the closest multiple of size_divisor
              _UpperCAmelCase     =     height // size_divisor * size_divisor
              _UpperCAmelCase     =     width // size_divisor * size_divisor
              _UpperCAmelCase     =     resize(a__    ,  (new_h, new_w)    ,  resample=a__    ,  data_format=a__    ,  **a__     )
              return image






       def 							__A (   self    ,  a__    ,  a__    ,  a__ = None    ,  **a__     ):
              return rescale(image=a__    ,  scale=a__    ,  data_format=a__    ,  **a__     )






       def 							__A (   self    ,  a__    ,  a__ = None    ,  a__ = None    ,  a__=None    ,  a__ = None    ,  a__ = None    ,  a__ = ChannelDimension.FIRST    ,  **a__    ,  ):
              _UpperCAmelCase     =     do_resize if do_resize is not None else self.do_resize
              _UpperCAmelCase     =     do_rescale if do_rescale is not None else self.do_rescale
              _UpperCAmelCase     =     size_divisor if size_divisor is not None else self.size_divisor
              _UpperCAmelCase     =     resample if resample is not None else self.resample

              if do_resize and size_divisor is None:
                     raise ValueError('size_divisor is required for resizing'     )

              _UpperCAmelCase     =     make_list_of_images(a__     )

              if not valid_images(a__     ):
                     raise ValueError('Invalid image(s)'     )

              # All transformations expect numpy arrays.
              _UpperCAmelCase     =     [to_numpy_array(a__     ) for img in images]

              if do_resize:
                     _UpperCAmelCase     =     [self.resize(a__    ,  size_divisor=a__    ,  resample=a__     ) for image in images]

              if do_rescale:
                     _UpperCAmelCase     =     [self.rescale(a__    ,  scale=1 / 2_55     ) for image in images]

              _UpperCAmelCase     =     [to_channel_dimension_format(a__    ,  a__     ) for image in images]

              _UpperCAmelCase     =     {'pixel_values': images}
              return BatchFeature(data=a__    ,  tensor_type=a__     )













"""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 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  """philschmid/bart-large-cnn-samsum"""
       lowerCAmelCase__						 =  (
           """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__						 =  """summarizer"""
       lowerCAmelCase__						 =  AutoTokenizer
       lowerCAmelCase__						 =  AutoModelForSeqaSeqLM

       lowerCAmelCase__						 =  ["""text"""]
       lowerCAmelCase__						 =  ["""text"""]






       def 							__A (   self    ,  a__     ):
              return self.pre_processor(a__    ,  return_tensors='pt'    ,  truncation=a__     )






       def 							__A (   self    ,  a__     ):
              return self.model.generate(**a__     )[0]






       def 							__A (   self    ,  a__     ):
              return self.pre_processor.decode(a__    ,  skip_special_tokens=a__    ,  clean_up_tokenization_spaces=a__     )






