


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       _a							:




   @staticmethod
   def 							UpperCAmelCase__(      *_SCREAMING_SNAKE_CASE		:	Optional[Any]	,      **_SCREAMING_SNAKE_CASE		:	Union[str, Any]	)-> Tuple:
     pass





@is_pipeline_test
@require_torch_or_tf
@require_vision
class       _a							(       unittest.TestCase):
 _a						:						Tuple   					=							MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
 _a						:						str   					=							TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING




 def 							UpperCAmelCase__(      self		:	Union[str, Any]	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	Union[str, Any]	,      _SCREAMING_SNAKE_CASE		:	Optional[Any]	)-> Tuple:
   lowerCAmelCase__     :    Dict					  =      ImageClassificationPipeline(model=_SCREAMING_SNAKE_CASE	,      image_processor=_SCREAMING_SNAKE_CASE	,      top_k=2	)
   lowerCAmelCase__     :    Union[str, Any]					  =      [
       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	),
       "http://images.cocodataset.org/val2017/000000039769.jpg",
   ]
   return image_classifier, examples




 def 							UpperCAmelCase__(      self		:	Tuple	,      _SCREAMING_SNAKE_CASE		:	Tuple	,      _SCREAMING_SNAKE_CASE		:	Tuple	)-> Optional[int]:
   lowerCAmelCase__     :    Optional[Any]					  =      image_classifier('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)

   self.assertEqual(
       _SCREAMING_SNAKE_CASE	,      [
           {'''score''': ANY(_SCREAMING_SNAKE_CASE	), '''label''': ANY(_SCREAMING_SNAKE_CASE	)},
           {'''score''': ANY(_SCREAMING_SNAKE_CASE	), '''label''': ANY(_SCREAMING_SNAKE_CASE	)},
       ]	,      )

   import datasets

   lowerCAmelCase__     :    int					  =      datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''	,      '''image'''	,      split='''test'''	)

   # Accepts URL + PIL.Image + lists
   lowerCAmelCase__     :    Tuple					  =      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(
       _SCREAMING_SNAKE_CASE	,      [
           [
               {'''score''': ANY(_SCREAMING_SNAKE_CASE	), '''label''': ANY(_SCREAMING_SNAKE_CASE	)},
               {'''score''': ANY(_SCREAMING_SNAKE_CASE	), '''label''': ANY(_SCREAMING_SNAKE_CASE	)},
           ],
           [
               {'''score''': ANY(_SCREAMING_SNAKE_CASE	), '''label''': ANY(_SCREAMING_SNAKE_CASE	)},
               {'''score''': ANY(_SCREAMING_SNAKE_CASE	), '''label''': ANY(_SCREAMING_SNAKE_CASE	)},
           ],
           [
               {'''score''': ANY(_SCREAMING_SNAKE_CASE	), '''label''': ANY(_SCREAMING_SNAKE_CASE	)},
               {'''score''': ANY(_SCREAMING_SNAKE_CASE	), '''label''': ANY(_SCREAMING_SNAKE_CASE	)},
           ],
           [
               {'''score''': ANY(_SCREAMING_SNAKE_CASE	), '''label''': ANY(_SCREAMING_SNAKE_CASE	)},
               {'''score''': ANY(_SCREAMING_SNAKE_CASE	), '''label''': ANY(_SCREAMING_SNAKE_CASE	)},
           ],
           [
               {'''score''': ANY(_SCREAMING_SNAKE_CASE	), '''label''': ANY(_SCREAMING_SNAKE_CASE	)},
               {'''score''': ANY(_SCREAMING_SNAKE_CASE	), '''label''': ANY(_SCREAMING_SNAKE_CASE	)},
           ],
       ]	,      )




 @require_torch
 def 							UpperCAmelCase__(      self		:	List[str]	)-> Optional[int]:
   lowerCAmelCase__     :    List[Any]					  =      "hf-internal-testing/tiny-random-vit"
   lowerCAmelCase__     :    List[str]					  =      pipeline('''image-classification'''	,      model=_SCREAMING_SNAKE_CASE	)

   lowerCAmelCase__     :    List[str]					  =      image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''	)
   self.assertEqual(
       nested_simplify(_SCREAMING_SNAKE_CASE	,      decimals=4	)	,      [{'''label''': '''LABEL_1''', '''score''': 0.574}, {'''label''': '''LABEL_0''', '''score''': 0.426}]	,      )

   lowerCAmelCase__     :    Optional[Any]					  =      image_classifier(
       [
           '''http://images.cocodataset.org/val2017/000000039769.jpg''',
           '''http://images.cocodataset.org/val2017/000000039769.jpg''',
       ]	,      top_k=2	,      )
   self.assertEqual(
       nested_simplify(_SCREAMING_SNAKE_CASE	,      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 							UpperCAmelCase__(      self		:	int	)-> Dict:
   lowerCAmelCase__     :    str					  =      "hf-internal-testing/tiny-random-vit"
   lowerCAmelCase__     :    int					  =      pipeline('''image-classification'''	,      model=_SCREAMING_SNAKE_CASE	,      framework='''tf'''	)

   lowerCAmelCase__     :    List[Any]					  =      image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''	)
   self.assertEqual(
       nested_simplify(_SCREAMING_SNAKE_CASE	,      decimals=4	)	,      [{'''label''': '''LABEL_1''', '''score''': 0.574}, {'''label''': '''LABEL_0''', '''score''': 0.426}]	,      )

   lowerCAmelCase__     :    int					  =      image_classifier(
       [
           '''http://images.cocodataset.org/val2017/000000039769.jpg''',
           '''http://images.cocodataset.org/val2017/000000039769.jpg''',
       ]	,      top_k=2	,      )
   self.assertEqual(
       nested_simplify(_SCREAMING_SNAKE_CASE	,      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 							UpperCAmelCase__(      self		:	Optional[int]	)-> List[Any]:
   lowerCAmelCase__     :    Any					  =      PreTrainedTokenizer()

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

   self.assertIs(image_classifier.tokenizer	,      _SCREAMING_SNAKE_CASE	)




 @slow
 @require_torch
 def 							UpperCAmelCase__(      self		:	int	)-> Any:
   lowerCAmelCase__     :    Optional[Any]					  =      pipeline('''image-classification'''	,      model='''deepmind/vision-perceiver-conv'''	)
   lowerCAmelCase__     :    Any					  =      image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''	)
   self.assertEqual(
       nested_simplify(_SCREAMING_SNAKE_CASE	,      decimals=4	)	,      [
           {'''score''': 0.4385, '''label''': '''tabby, tabby cat'''},
           {'''score''': 0.321, '''label''': '''tiger cat'''},
           {'''score''': 0.0502, '''label''': '''Egyptian cat'''},
           {'''score''': 0.0137, '''label''': '''crib, cot'''},
           {'''score''': 0.007, '''label''': '''radiator'''},
       ]	,      )

   lowerCAmelCase__     :    Optional[Any]					  =      pipeline('''image-classification'''	,      model='''deepmind/vision-perceiver-fourier'''	)
   lowerCAmelCase__     :    List[str]					  =      image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''	)
   self.assertEqual(
       nested_simplify(_SCREAMING_SNAKE_CASE	,      decimals=4	)	,      [
           {'''score''': 0.5658, '''label''': '''tabby, tabby cat'''},
           {'''score''': 0.1309, '''label''': '''tiger cat'''},
           {'''score''': 0.0722, '''label''': '''Egyptian cat'''},
           {'''score''': 0.0707, '''label''': '''remote control, remote'''},
           {'''score''': 0.0082, '''label''': '''computer keyboard, keypad'''},
       ]	,      )

   lowerCAmelCase__     :    List[str]					  =      pipeline('''image-classification'''	,      model='''deepmind/vision-perceiver-learned'''	)
   lowerCAmelCase__     :    Optional[int]					  =      image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''	)
   self.assertEqual(
       nested_simplify(_SCREAMING_SNAKE_CASE	,      decimals=4	)	,      [
           {'''score''': 0.3022, '''label''': '''tabby, tabby cat'''},
           {'''score''': 0.2362, '''label''': '''Egyptian cat'''},
           {'''score''': 0.1856, '''label''': '''tiger cat'''},
           {'''score''': 0.0324, '''label''': '''remote control, remote'''},
           {'''score''': 0.0096, '''label''': '''quilt, comforter, comfort, puff'''},
       ]	,      )








import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()





class       _a							(       unittest.TestCase):




	@property
	def 							UpperCAmelCase__(      self		:	Dict	)-> int:
			torch.manual_seed(0	)
			lowerCAmelCase__     :    int					  =      UNetaDModel(
			    block_out_channels=(32, 64)	,      layers_per_block=2	,      sample_size=32	,      in_channels=3	,      out_channels=3	,      down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')	,      up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')	,      )
			return model




	def 							UpperCAmelCase__(      self		:	Union[str, Any]	)-> Dict:
			lowerCAmelCase__     :    Union[str, Any]					  =      '''cpu'''
			lowerCAmelCase__     :    List[Any]					  =      self.dummy_uncond_unet
			lowerCAmelCase__     :    Optional[Any]					  =      DDPMScheduler()

			lowerCAmelCase__     :    List[str]					  =      DDPMPipeline(unet=_SCREAMING_SNAKE_CASE	,      scheduler=_SCREAMING_SNAKE_CASE	)
			ddpm.to(_SCREAMING_SNAKE_CASE	)
			ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    Optional[Any]					  =      torch.Generator(device=_SCREAMING_SNAKE_CASE	).manual_seed(0	)
			lowerCAmelCase__     :    int					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      num_inference_steps=2	,      output_type='''numpy'''	).images

			lowerCAmelCase__     :    str					  =      torch.Generator(device=_SCREAMING_SNAKE_CASE	).manual_seed(0	)
			lowerCAmelCase__     :    Optional[int]					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      num_inference_steps=2	,      output_type='''numpy'''	,      return_dict=_SCREAMING_SNAKE_CASE	)[0]

			lowerCAmelCase__     :    Tuple					  =      image[0, -3:, -3:, -1]
			lowerCAmelCase__     :    List[str]					  =      image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCAmelCase__     :    int					  =      np.array(
			    [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.000, 1.199E-03, 2.648E-04, 5.101E-04]	)

			assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
			assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1E-2




	def 							UpperCAmelCase__(      self		:	List[str]	)-> Any:
			lowerCAmelCase__     :    int					  =      self.dummy_uncond_unet
			lowerCAmelCase__     :    List[Any]					  =      DDPMScheduler(prediction_type='''sample'''	)

			lowerCAmelCase__     :    int					  =      DDPMPipeline(unet=_SCREAMING_SNAKE_CASE	,      scheduler=_SCREAMING_SNAKE_CASE	)
			ddpm.to(_SCREAMING_SNAKE_CASE	)
			ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    Optional[int]					  =      torch.manual_seed(0	)
			lowerCAmelCase__     :    List[Any]					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      num_inference_steps=2	,      output_type='''numpy'''	).images

			lowerCAmelCase__     :    Optional[Any]					  =      torch.manual_seed(0	)
			lowerCAmelCase__     :    Union[str, Any]					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      num_inference_steps=2	,      output_type='''numpy'''	)[0]

			lowerCAmelCase__     :    str					  =      image[0, -3:, -3:, -1]
			lowerCAmelCase__     :    Optional[Any]					  =      image_eps[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCAmelCase__     :    Any					  =      1E-2 if torch_device != '''mps''' else 3E-2
			assert np.abs(image_slice.flatten() - image_eps_slice.flatten()	).max() < tolerance





@slow
@require_torch_gpu
class       _a							(       unittest.TestCase):




	def 							UpperCAmelCase__(      self		:	Any	)-> List[Any]:
			lowerCAmelCase__     :    str					  =      '''google/ddpm-cifar10-32'''

			lowerCAmelCase__     :    List[Any]					  =      UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Any					  =      DDPMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    List[str]					  =      DDPMPipeline(unet=_SCREAMING_SNAKE_CASE	,      scheduler=_SCREAMING_SNAKE_CASE	)
			ddpm.to(_SCREAMING_SNAKE_CASE	)
			ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    List[Any]					  =      torch.manual_seed(0	)
			lowerCAmelCase__     :    Optional[int]					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      output_type='''numpy'''	).images

			lowerCAmelCase__     :    List[Any]					  =      image[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCAmelCase__     :    List[str]					  =      np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385]	)
			assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2




