
'''simple docstring'''



import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class   UpperCAmelCase_     (		_SCREAMING_SNAKE_CASE       ,							unittest.TestCase ):






						'''simple docstring'''






						_lowercase		:      str									=   RePaintPipeline
						_lowercase		:      Dict									=   IMAGE_INPAINTING_PARAMS - {'''width''', '''height''', '''guidance_scale'''}
						_lowercase		:      List[str]									=   PipelineTesterMixin.required_optional_params - {
						    '''latents''',
						    '''num_images_per_prompt''',
						    '''callback''',
						    '''callback_steps''',
						}
						_lowercase		:      Optional[Any]									=   IMAGE_INPAINTING_BATCH_PARAMS
						_lowercase		:      List[str]									=   False




						def 					_lowercase	(					self      ):

												"""simple docstring"""



												torch.manual_seed(0      )
												torch.manual_seed(0      )
												_lowerCAmelCase  						= 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""")    ,   )
												_lowerCAmelCase  						= RePaintScheduler()
												_lowerCAmelCase  						= {"unet": unet, "scheduler": scheduler}
												return components




						def 					_lowercase	(					self    ,   _lowercase    ,   _lowercase=0      ):

												"""simple docstring"""



												if str(A_      ).startswith("""mps"""      ):
																		_lowerCAmelCase  						= torch.manual_seed(A_      )
												else:
																		_lowerCAmelCase  						= torch.Generator(device=A_      ).manual_seed(A_      )
												_lowerCAmelCase  						= np.random.RandomState(A_      ).standard_normal((1, 3, 32, 32)      )
												_lowerCAmelCase  						= torch.from_numpy(A_      ).to(device=A_    ,   dtype=torch.floataa      )
												_lowerCAmelCase  						= (image > 0).to(device=A_    ,   dtype=torch.floataa      )
												_lowerCAmelCase  						= {
												    "image": image,
												    "mask_image": mask,
												    "generator": generator,
												    "num_inference_steps": 5,
												    "eta": 0.0,
												    "jump_length": 2,
												    "jump_n_sample": 2,
												    "output_type": "numpy",
												}
												return inputs




						def 					_lowercase	(					self      ):

												"""simple docstring"""



												_lowerCAmelCase  						= "cpu"  # ensure determinism for the device-dependent torch.Generator
												_lowerCAmelCase  						= self.get_dummy_components()
												_lowerCAmelCase  						= RePaintPipeline(**A_      )
												_lowerCAmelCase  						= sd_pipe.to(A_      )
												sd_pipe.set_progress_bar_config(disable=A_      )

												_lowerCAmelCase  						= self.get_dummy_inputs(A_      )
												_lowerCAmelCase  						= sd_pipe(**A_      ).images
												_lowerCAmelCase  						= image[0, -3:, -3:, -1]

												assert image.shape == (1, 32, 32, 3)
												_lowerCAmelCase  						= np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]      )

												assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3




						@skip_mps
						def 					_lowercase	(					self      ):

												"""simple docstring"""



												return super().test_save_load_local()




						@unittest.skip("""non-deterministic pipeline"""      )
						def 					_lowercase	(					self      ):

												"""simple docstring"""



												return super().test_inference_batch_single_identical()




						@skip_mps
						def 					_lowercase	(					self      ):

												"""simple docstring"""



												return super().test_dict_tuple_outputs_equivalent()




						@skip_mps
						def 					_lowercase	(					self      ):

												"""simple docstring"""



												return super().test_save_load_optional_components()




						@skip_mps
						def 					_lowercase	(					self      ):

												"""simple docstring"""



												return super().test_attention_slicing_forward_pass()






@nightly
@require_torch_gpu
class   UpperCAmelCase_     (		unittest.TestCase ):






						'''simple docstring'''






						def 					_lowercase	(					self      ):

												"""simple docstring"""



												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()




						def 					_lowercase	(					self      ):

												"""simple docstring"""



												_lowerCAmelCase  						= load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
												    """repaint/celeba_hq_256.png"""      )
												_lowerCAmelCase  						= load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""      )
												_lowerCAmelCase  						= load_numpy(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
												    """repaint/celeba_hq_256_result.npy"""      )

												_lowerCAmelCase  						= "google/ddpm-ema-celebahq-256"
												_lowerCAmelCase  						= UNetaDModel.from_pretrained(A_      )
												_lowerCAmelCase  						= RePaintScheduler.from_pretrained(A_      )

												_lowerCAmelCase  						= RePaintPipeline(unet=A_    ,   scheduler=A_      ).to(A_      )
												repaint.set_progress_bar_config(disable=A_      )
												repaint.enable_attention_slicing()

												_lowerCAmelCase  						= torch.manual_seed(0      )
												_lowerCAmelCase  						= repaint(
												    A_    ,   A_    ,   num_inference_steps=250    ,   eta=0.0    ,   jump_length=10    ,   jump_n_sample=10    ,   generator=A_    ,   output_type="""np"""    ,   )
												_lowerCAmelCase  						= output.images[0]

												assert image.shape == (256, 256, 3)
												assert np.abs(expected_image - image      ).mean() < 1e-2



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 (				*A_  ,    **A_		):







															'''simple docstring'''


															pass

@is_pipeline_test
@require_torch_or_tf
@require_vision
class  a     (		unittest.TestCase ):
						_lowercase			  =	MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
						_lowercase			  =	TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Any       						=			ImageClassificationPipeline(model=A_  ,    image_processor=A_  ,    top_k=2		)
										_UpperCAmelCase				:						Optional[Any]       						=			[
										    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		),
										    "http://images.cocodataset.org/val2017/000000039769.jpg",
										]
										return image_classifier, examples


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Tuple       						=			image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"		)

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

										import datasets

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

										# Accepts URL + PIL.Image + lists
										_UpperCAmelCase				:						Any       						=			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(
										    A_  ,    [
										        [
										            {"score": ANY(A_		), "label": ANY(A_		)},
										            {"score": ANY(A_		), "label": ANY(A_		)},
										        ],
										        [
										            {"score": ANY(A_		), "label": ANY(A_		)},
										            {"score": ANY(A_		), "label": ANY(A_		)},
										        ],
										        [
										            {"score": ANY(A_		), "label": ANY(A_		)},
										            {"score": ANY(A_		), "label": ANY(A_		)},
										        ],
										        [
										            {"score": ANY(A_		), "label": ANY(A_		)},
										            {"score": ANY(A_		), "label": ANY(A_		)},
										        ],
										        [
										            {"score": ANY(A_		), "label": ANY(A_		)},
										            {"score": ANY(A_		), "label": ANY(A_		)},
										        ],
										    ]  ,    )


						@require_torch
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Any       						=			"hf-internal-testing/tiny-random-vit"
										_UpperCAmelCase				:						List[Any]       						=			pipeline("image-classification"  ,    model=A_		)

										_UpperCAmelCase				:						Optional[int]       						=			image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
										self.assertEqual(
										    nested_simplify(A_  ,    decimals=4		)  ,    [{"label": "LABEL_1", "score": 0.5_74}, {"label": "LABEL_0", "score": 0.4_26}]  ,    )

										_UpperCAmelCase				:						List[str]       						=			image_classifier(
										    [
										        "http://images.cocodataset.org/val2017/000000039769.jpg",
										        "http://images.cocodataset.org/val2017/000000039769.jpg",
										    ]  ,    top_k=2  ,    )
										self.assertEqual(
										    nested_simplify(A_  ,    decimals=4		)  ,    [
										        [{"label": "LABEL_1", "score": 0.5_74}, {"label": "LABEL_0", "score": 0.4_26}],
										        [{"label": "LABEL_1", "score": 0.5_74}, {"label": "LABEL_0", "score": 0.4_26}],
										    ]  ,    )


						@require_tf
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						int       						=			"hf-internal-testing/tiny-random-vit"
										_UpperCAmelCase				:						List[Any]       						=			pipeline("image-classification"  ,    model=A_  ,    framework="tf"		)

										_UpperCAmelCase				:						Union[str, Any]       						=			image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
										self.assertEqual(
										    nested_simplify(A_  ,    decimals=4		)  ,    [{"label": "LABEL_1", "score": 0.5_74}, {"label": "LABEL_0", "score": 0.4_26}]  ,    )

										_UpperCAmelCase				:						Optional[int]       						=			image_classifier(
										    [
										        "http://images.cocodataset.org/val2017/000000039769.jpg",
										        "http://images.cocodataset.org/val2017/000000039769.jpg",
										    ]  ,    top_k=2  ,    )
										self.assertEqual(
										    nested_simplify(A_  ,    decimals=4		)  ,    [
										        [{"label": "LABEL_1", "score": 0.5_74}, {"label": "LABEL_0", "score": 0.4_26}],
										        [{"label": "LABEL_1", "score": 0.5_74}, {"label": "LABEL_0", "score": 0.4_26}],
										    ]  ,    )


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[Any]       						=			PreTrainedTokenizer()

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

										self.assertIs(image_classifier.tokenizer  ,    A_		)





						@slow
						@require_torch
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Dict       						=			pipeline("image-classification"  ,    model="deepmind/vision-perceiver-conv"		)
										_UpperCAmelCase				:						Union[str, Any]       						=			image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
										self.assertEqual(
										    nested_simplify(A_  ,    decimals=4		)  ,    [
										        {"score": 0.43_85, "label": "tabby, tabby cat"},
										        {"score": 0.3_21, "label": "tiger cat"},
										        {"score": 0.05_02, "label": "Egyptian cat"},
										        {"score": 0.01_37, "label": "crib, cot"},
										        {"score": 0.0_07, "label": "radiator"},
										    ]  ,    )

										_UpperCAmelCase				:						str       						=			pipeline("image-classification"  ,    model="deepmind/vision-perceiver-fourier"		)
										_UpperCAmelCase				:						Optional[int]       						=			image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
										self.assertEqual(
										    nested_simplify(A_  ,    decimals=4		)  ,    [
										        {"score": 0.56_58, "label": "tabby, tabby cat"},
										        {"score": 0.13_09, "label": "tiger cat"},
										        {"score": 0.07_22, "label": "Egyptian cat"},
										        {"score": 0.07_07, "label": "remote control, remote"},
										        {"score": 0.00_82, "label": "computer keyboard, keypad"},
										    ]  ,    )

										_UpperCAmelCase				:						List[Any]       						=			pipeline("image-classification"  ,    model="deepmind/vision-perceiver-learned"		)
										_UpperCAmelCase				:						str       						=			image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
										self.assertEqual(
										    nested_simplify(A_  ,    decimals=4		)  ,    [
										        {"score": 0.30_22, "label": "tabby, tabby cat"},
										        {"score": 0.23_62, "label": "Egyptian cat"},
										        {"score": 0.18_56, "label": "tiger cat"},
										        {"score": 0.03_24, "label": "remote control, remote"},
										        {"score": 0.00_96, "label": "quilt, comforter, comfort, puff"},
										    ]  ,    )


