'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import MobileNetVaImageProcessor




class  __magic_name__  (		unittest.TestCase    ):





   """simple docstring"""




   def __init__(	self					:  Optional[Any]		,  _lowercase					:  int		,  _lowercase					:  Optional[Any]=7		,  _lowercase					:  Any=3		,  _lowercase					:  List[Any]=18		,  _lowercase					:  Dict=30		,  _lowercase					:  List[str]=400		,  _lowercase					:  Union[str, Any]=True		,  _lowercase					:  int=None		,  _lowercase					:  Any=True		,  _lowercase					:  Any=None		,  ):



    """simple docstring"""



    _UpperCamelCase:     Dict							= size if size is not None else {'''shortest_edge''': 20}
    _UpperCamelCase:     Optional[Any]							= crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
    _UpperCamelCase:     Optional[int]							= parent
    _UpperCamelCase:     Dict							= batch_size
    _UpperCamelCase:     str							= num_channels
    _UpperCamelCase:     Optional[int]							= image_size
    _UpperCamelCase:     int							= min_resolution
    _UpperCamelCase:     int							= max_resolution
    _UpperCamelCase:     Any							= do_resize
    _UpperCamelCase:     List[Any]							= size
    _UpperCamelCase:     str							= do_center_crop
    _UpperCamelCase:     Optional[Any]							= crop_size



   def   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_center_crop": self.do_center_crop,
        "crop_size": self.crop_size,
    }




@require_torch
@require_vision
class  __magic_name__  (		__a      ,	unittest.TestCase    ):





   """simple docstring"""




   lowerCAmelCase      :    Dict  			=   MobileNetVaImageProcessor if is_vision_available() else None



   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= MobileNetVaImageProcessingTester(self							)



   @property
   def   lowerCAmelCase						(	self					:  Any							):



    """simple docstring"""



    return self.image_processor_tester.prepare_image_processor_dict()



   def   lowerCAmelCase						(	self					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     Tuple							= self.image_processing_class(**self.image_processor_dict							)
    self.assertTrue(hasattr(_lowercase		,  '''do_resize'''							)							)
    self.assertTrue(hasattr(_lowercase		,  '''size'''							)							)
    self.assertTrue(hasattr(_lowercase		,  '''do_center_crop'''							)							)
    self.assertTrue(hasattr(_lowercase		,  '''crop_size'''							)							)



   def   lowerCAmelCase						(	self					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     Any							= self.image_processing_class.from_dict(self.image_processor_dict							)
    self.assertEqual(image_processor.size		,  {'''shortest_edge''': 20}							)
    self.assertEqual(image_processor.crop_size		,  {'''height''': 18, '''width''': 18}							)

    _UpperCamelCase:     Optional[int]							= self.image_processing_class.from_dict(self.image_processor_dict		,  size=42		,  crop_size=84							)
    self.assertEqual(image_processor.size		,  {'''shortest_edge''': 42}							)
    self.assertEqual(image_processor.crop_size		,  {'''height''': 84, '''width''': 84}							)



   def   lowerCAmelCase						(	self					:  List[str]							):



    """simple docstring"""



    pass



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     List[Any]							= self.image_processing_class(**self.image_processor_dict							)
    # create random PIL images
    _UpperCamelCase:     Union[str, Any]							= prepare_image_inputs(self.image_processor_tester		,  equal_resolution=_lowercase							)
    for image in image_inputs:
     self.assertIsInstance(_lowercase		,  Image.Image							)

    # Test not batched input
    _UpperCamelCase:     List[Any]							= image_processing(image_inputs[0]		,  return_tensors='''pt'''							).pixel_values
    self.assertEqual(
        encoded_images.shape		,  (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )		,  )

    # Test batched
    _UpperCamelCase:     Union[str, Any]							= image_processing(_lowercase		,  return_tensors='''pt'''							).pixel_values
    self.assertEqual(
        encoded_images.shape		,  (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )		,  )



   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



    _UpperCamelCase:     str							= self.image_processing_class(**self.image_processor_dict							)
    # create random numpy tensors
    _UpperCamelCase:     List[Any]							= prepare_image_inputs(self.image_processor_tester		,  equal_resolution=_lowercase		,  numpify=_lowercase							)
    for image in image_inputs:
     self.assertIsInstance(_lowercase		,  np.ndarray							)

    # Test not batched input
    _UpperCamelCase:     List[Any]							= image_processing(image_inputs[0]		,  return_tensors='''pt'''							).pixel_values
    self.assertEqual(
        encoded_images.shape		,  (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )		,  )

    # Test batched
    _UpperCamelCase:     int							= image_processing(_lowercase		,  return_tensors='''pt'''							).pixel_values
    self.assertEqual(
        encoded_images.shape		,  (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )		,  )



   def   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    _UpperCamelCase:     str							= self.image_processing_class(**self.image_processor_dict							)
    # create random PyTorch tensors
    _UpperCamelCase:     Tuple							= prepare_image_inputs(self.image_processor_tester		,  equal_resolution=_lowercase		,  torchify=_lowercase							)
    for image in image_inputs:
     self.assertIsInstance(_lowercase		,  torch.Tensor							)

    # Test not batched input
    _UpperCamelCase:     Dict							= image_processing(image_inputs[0]		,  return_tensors='''pt'''							).pixel_values
    self.assertEqual(
        encoded_images.shape		,  (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )		,  )

    # Test batched
    _UpperCamelCase:     str							= image_processing(_lowercase		,  return_tensors='''pt'''							).pixel_values
    self.assertEqual(
        encoded_images.shape		,  (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )		,  )
# 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  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    Optional[Any]  			=   '''philschmid/bart-large-cnn-samsum'''
   lowerCAmelCase      :    Any  			=   (
       '''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      :    Any  			=   '''summarizer'''
   lowerCAmelCase      :    Tuple  			=   AutoTokenizer
   lowerCAmelCase      :    Optional[Any]  			=   AutoModelForSeqaSeqLM

   lowerCAmelCase      :    Union[str, Any]  			=   ['''text''']
   lowerCAmelCase      :    Dict  			=   ['''text''']



   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  Union[str, Any]							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  Optional[Any]							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  int		,  _lowercase					:  List[str]							):



    """simple docstring"""



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