





"""simple docstring"""
import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
		from PIL import Image
else:






		class    _UpperCAmelCase  :


							@staticmethod
							def       lowerCamelCase			(   *__UpperCamelCase     :Optional[Any]	,			**__UpperCamelCase     :Any						):
														pass






@is_pipeline_test
@require_torch_or_tf
@require_vision
class    _UpperCAmelCase  (      unittest.TestCase					):
					UpperCamelCase						=				MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
					UpperCamelCase						=				TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


					def       lowerCamelCase			(   self     :str	,			__UpperCamelCase     :int	,			__UpperCamelCase     :List[str]	,			__UpperCamelCase     :Any						):
												A						=    ImageClassificationPipeline(model=lowercase_	,			image_processor=lowercase_	,			top_k=2						)
												A						=    [
												    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						),
												    "http://images.cocodataset.org/val2017/000000039769.jpg",
												]
												return image_classifier, examples


					def       lowerCamelCase			(   self     :Optional[Any]	,			__UpperCamelCase     :int	,			__UpperCamelCase     :Optional[int]						):
												A						=    image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"						)

												self.assertEqual(
												    lowercase_	,			[
												        {"score": ANY(lowercase_						), "label": ANY(lowercase_						)},
												        {"score": ANY(lowercase_						), "label": ANY(lowercase_						)},
												    ]	,			)

												import datasets

												A						=    datasets.load_dataset("hf-internal-testing/fixtures_image_utils"	,			"image"	,			split="test"						)

												# Accepts URL + PIL.Image + lists
												A						=    image_classifier(
												    [
												        Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						),
												        "http://images.cocodataset.org/val2017/000000039769.jpg",
												        # RGBA
												        dataset[0]["file"],
												        # LA
												        dataset[1]["file"],
												        # L
												        dataset[2]["file"],
												    ]						)
												self.assertEqual(
												    lowercase_	,			[
												        [
												            {"score": ANY(lowercase_						), "label": ANY(lowercase_						)},
												            {"score": ANY(lowercase_						), "label": ANY(lowercase_						)},
												        ],
												        [
												            {"score": ANY(lowercase_						), "label": ANY(lowercase_						)},
												            {"score": ANY(lowercase_						), "label": ANY(lowercase_						)},
												        ],
												        [
												            {"score": ANY(lowercase_						), "label": ANY(lowercase_						)},
												            {"score": ANY(lowercase_						), "label": ANY(lowercase_						)},
												        ],
												        [
												            {"score": ANY(lowercase_						), "label": ANY(lowercase_						)},
												            {"score": ANY(lowercase_						), "label": ANY(lowercase_						)},
												        ],
												        [
												            {"score": ANY(lowercase_						), "label": ANY(lowercase_						)},
												            {"score": ANY(lowercase_						), "label": ANY(lowercase_						)},
												        ],
												    ]	,			)


					@require_torch
					def       lowerCamelCase			(   self     :Optional[Any]						):
												A						=    "hf-internal-testing/tiny-random-vit"
												A						=    pipeline("image-classification"	,			model=lowercase_						)

												A						=    image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"						)
												self.assertEqual(
												    nested_simplify(lowercase_	,			decimals=4						)	,			[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}]	,			)

												A						=    image_classifier(
												    [
												        "http://images.cocodataset.org/val2017/000000039769.jpg",
												        "http://images.cocodataset.org/val2017/000000039769.jpg",
												    ]	,			top_k=2	,			)
												self.assertEqual(
												    nested_simplify(lowercase_	,			decimals=4						)	,			[
												        [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
												        [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
												    ]	,			)


					@require_tf
					def       lowerCamelCase			(   self     :str						):
												A						=    "hf-internal-testing/tiny-random-vit"
												A						=    pipeline("image-classification"	,			model=lowercase_	,			framework="tf"						)

												A						=    image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"						)
												self.assertEqual(
												    nested_simplify(lowercase_	,			decimals=4						)	,			[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}]	,			)

												A						=    image_classifier(
												    [
												        "http://images.cocodataset.org/val2017/000000039769.jpg",
												        "http://images.cocodataset.org/val2017/000000039769.jpg",
												    ]	,			top_k=2	,			)
												self.assertEqual(
												    nested_simplify(lowercase_	,			decimals=4						)	,			[
												        [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
												        [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
												    ]	,			)


					def       lowerCamelCase			(   self     :Optional[int]						):
												A						=    PreTrainedTokenizer()

												# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
												A						=    pipeline(
												    "image-classification"	,			model="hf-internal-testing/tiny-random-vit"	,			tokenizer=lowercase_						)

												self.assertIs(image_classifier.tokenizer	,			lowercase_						)


					@slow
					@require_torch
					def       lowerCamelCase			(   self     :Optional[int]						):
												A						=    pipeline("image-classification"	,			model="deepmind/vision-perceiver-conv"						)
												A						=    image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"						)
												self.assertEqual(
												    nested_simplify(lowercase_	,			decimals=4						)	,			[
												        {"score": 0.4_385, "label": "tabby, tabby cat"},
												        {"score": 0.321, "label": "tiger cat"},
												        {"score": 0.0_502, "label": "Egyptian cat"},
												        {"score": 0.0_137, "label": "crib, cot"},
												        {"score": 0.007, "label": "radiator"},
												    ]	,			)

												A						=    pipeline("image-classification"	,			model="deepmind/vision-perceiver-fourier"						)
												A						=    image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"						)
												self.assertEqual(
												    nested_simplify(lowercase_	,			decimals=4						)	,			[
												        {"score": 0.5_658, "label": "tabby, tabby cat"},
												        {"score": 0.1_309, "label": "tiger cat"},
												        {"score": 0.0_722, "label": "Egyptian cat"},
												        {"score": 0.0_707, "label": "remote control, remote"},
												        {"score": 0.0_082, "label": "computer keyboard, keypad"},
												    ]	,			)

												A						=    pipeline("image-classification"	,			model="deepmind/vision-perceiver-learned"						)
												A						=    image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"						)
												self.assertEqual(
												    nested_simplify(lowercase_	,			decimals=4						)	,			[
												        {"score": 0.3_022, "label": "tabby, tabby cat"},
												        {"score": 0.2_362, "label": "Egyptian cat"},
												        {"score": 0.1_856, "label": "tiger cat"},
												        {"score": 0.0_324, "label": "remote control, remote"},
												        {"score": 0.0_096, "label": "quilt, comforter, comfort, puff"},
												    ]	,			)





import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class 		__magic_name__     (				unittest.TestCase				):

   '''simple docstring'''




   @property
   def 					_UpperCAmelCase					(							self			)				->       Tuple:






     """simple docstring"""






     a__    =1
     a__    =3
     a__    =8
     a__    =8

     a__    =torch.rand((batch_size, num_channels, height, width)			)

     return sample



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       int:






     """simple docstring"""






     if isinstance(image[0],       PIL.Image.Image			):
       return np.stack([np.array(lowercase_			) for i in image],       axis=0			)
     elif isinstance(lowercase_,       torch.Tensor			):
       return image.cpu().numpy().transpose(0,       2,       3,       1			)
     return image



   def 					_UpperCAmelCase					(							self			)				->       int:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample
     a__    =self.to_np(lowercase_			)

     for output_type in ["pt", "np", "pil"]:
       a__    =image_processor.postprocess(
           image_processor.preprocess(lowercase_			),       output_type=lowercase_,       )
       a__    =self.to_np(lowercase_			)
       a__    =(input_np * 255).round() if output_type == '''pil''' else input_np
       assert (
           np.abs(in_np - out_np			).max() < 1E-6
       ), F"""decoded output does not match input for output_type {output_type}"""



   def 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)
     a__    =self.dummy_sample.cpu().numpy().transpose(0,       2,       3,       1			)

     for output_type in ["pt", "np", "pil"]:
       a__    =image_processor.postprocess(image_processor.preprocess(lowercase_			),       output_type=lowercase_			)

       a__    =self.to_np(lowercase_			)
       a__    =(input_np * 255).round() if output_type == '''pil''' else input_np
       assert (
           np.abs(in_np - out_np			).max() < 1E-6
       ), F"""decoded output does not match input for output_type {output_type}"""



   def 					_UpperCAmelCase					(							self			)				->       Optional[Any]:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample.cpu().numpy().transpose(0,       2,       3,       1			)
     a__    =image_processor.numpy_to_pil(lowercase_			)

     for output_type in ["pt", "np", "pil"]:
       a__    =image_processor.postprocess(image_processor.preprocess(lowercase_			),       output_type=lowercase_			)
       for i, o in zip(lowercase_,       lowercase_			):
         a__    =np.array(lowercase_			)
         a__    =self.to_np(lowercase_			) if output_type == '''pil''' else (self.to_np(lowercase_			) * 255).round()
         assert (
             np.abs(in_np - out_np			).max() < 1E-6
         ), F"""decoded output does not match input for output_type {output_type}"""



   def 					_UpperCAmelCase					(							self			)				->       Union[str, Any]:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample
     a__    =input_pt_ad.squeeze(0			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )
     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =self.to_np(self.dummy_sample			)
     a__    =input_np_ad.squeeze(0			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )
     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     assert np.abs(out_pt_ad - out_pt_ad			).max() < 1E-6
     assert np.abs(out_np_ad - out_np_ad			).max() < 1E-6



   def 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample
     a__    =list(lowercase_			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =self.to_np(self.dummy_sample			)
     list(lowercase_			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     assert np.abs(out_pt_ad - out_pt_list			).max() < 1E-6
     assert np.abs(out_np_ad - out_np_list			).max() < 1E-6
