

import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

SCREAMING_SNAKE_CASE_        =      imread(R'digital_image_processing/image_data/lena_small.jpg')
SCREAMING_SNAKE_CASE_        =      cvtColor(img, COLOR_BGR2GRAY)

def 							__SCREAMING_SNAKE_CASE							(				)   ->       Any:
				_UpperCAmelCase       :		int 		=    cn.convert_to_negative(lowerCAmelCase )
				# assert negative_img array for at least one True
				assert negative_img.any()

def 							__SCREAMING_SNAKE_CASE							(				)   ->       Optional[int]:
				with Image.open("digital_image_processing/image_data/lena_small.jpg" ) as img:
								# Work around assertion for response
								assert str(cc.change_contrast(lowerCAmelCase  ,      110 ) ).startswith(
								    "<PIL.Image.Image image mode=RGB size=100x100 at" )

def 							__SCREAMING_SNAKE_CASE							(				)   ->       Optional[Any]:
				_UpperCAmelCase       :		Optional[int] 		=    canny.gen_gaussian_kernel(9  ,      sigma=1.4 )
				# Assert ambiguous array
				assert resp.all()

def 							__SCREAMING_SNAKE_CASE							(				)   ->       int:
				_UpperCAmelCase       :		Optional[int] 		=    imread("digital_image_processing/image_data/lena_small.jpg"  ,      0 )
				# assert ambiguous array for all == True
				assert canny_img.all()
				_UpperCAmelCase       :		Optional[Any] 		=    canny.canny(lowerCAmelCase )
				# assert canny array for at least one True
				assert canny_array.any()

def 							__SCREAMING_SNAKE_CASE							(				)   ->       Dict:
				assert gg.gaussian_filter(lowerCAmelCase  ,      5  ,      sigma=0.9 ).all()

def 							__SCREAMING_SNAKE_CASE							(				)   ->       Tuple:
				# laplace diagonals
				_UpperCAmelCase       :		Any 		=    array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]] )
				_UpperCAmelCase       :		List[Any] 		=    conv.img_convolve(lowerCAmelCase  ,      lowerCAmelCase ).astype(lowerCAmelCase )
				assert res.any()

def 							__SCREAMING_SNAKE_CASE							(				)   ->       Dict:
				assert med.median_filter(lowerCAmelCase  ,      3 ).any()

def 							__SCREAMING_SNAKE_CASE							(				)   ->       Union[str, Any]:
				_UpperCAmelCase		,							_UpperCAmelCase       :		Dict 		=    sob.sobel_filter(lowerCAmelCase )
				assert grad.any() and theta.any()

def 							__SCREAMING_SNAKE_CASE							(				)   ->       Optional[int]:
				_UpperCAmelCase       :		int 		=    sp.make_sepia(lowerCAmelCase  ,      20 )
				assert sepia.all()

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str = "digital_image_processing/image_data/lena_small.jpg" )   ->       Dict:
				_UpperCAmelCase       :		Optional[int] 		=    bs.Burkes(imread(lowerCAmelCase  ,      1 )  ,      120 )
				burkes.process()
				assert burkes.output_img.any()

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str = "digital_image_processing/image_data/lena_small.jpg"  ,      )   ->       Union[str, Any]:
				_UpperCAmelCase       :		Any 		=    rs.NearestNeighbour(imread(lowerCAmelCase  ,      1 )  ,      400  ,      200 )
				nn.process()
				assert nn.output.any()





def 							__SCREAMING_SNAKE_CASE							(				)   ->       int:
				_UpperCAmelCase       :		Dict 		=    "digital_image_processing/image_data/lena.jpg"

				# Reading the image and converting it to grayscale.
				_UpperCAmelCase       :		int 		=    imread(lowerCAmelCase  ,      0 )

				# Test for get_neighbors_pixel function() return not None
				_UpperCAmelCase       :		str 		=    0
				_UpperCAmelCase       :		Optional[int] 		=    0
				_UpperCAmelCase       :		int 		=    image[x_coordinate][y_coordinate]

				_UpperCAmelCase       :		Any 		=    lbp.get_neighbors_pixel(
				    lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )

				assert neighbors_pixels is not None

				# Test for local_binary_pattern function()
				# Create a numpy array as the same height and width of read image
				_UpperCAmelCase       :		Dict 		=    np.zeros((image.shape[0], image.shape[1]) )

				# Iterating through the image and calculating the local binary pattern value
				# for each pixel.
				for i in range(0  ,      image.shape[0] ):
								for j in range(0  ,      image.shape[1] ):
												_UpperCAmelCase       :		Union[str, Any] 		=    lbp.local_binary_value(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )

				assert lbp_image.any()





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


SCREAMING_SNAKE_CASE_        =      False

class 				a  (	unittest.TestCase				):


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return 12


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return 12


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return 32


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										torch.manual_seed(0	)
										_UpperCAmelCase       :		Optional[int] 		=    VQModel(
										    block_out_channels=[32, 64]						,					in_channels=3						,					out_channels=3						,					down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]						,					up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]						,					latent_channels=3						,					num_vq_embeddings=self.num_embed						,					vq_embed_dim=3						,					)
										return model


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)
										return tokenizer


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										torch.manual_seed(0	)
										_UpperCAmelCase       :		int 		=    CLIPTextConfig(
										    bos_token_id=0						,					eos_token_id=2						,					hidden_size=self.text_embedder_hidden_size						,					intermediate_size=37						,					layer_norm_eps=1e-05						,					num_attention_heads=4						,					num_hidden_layers=5						,					pad_token_id=1						,					vocab_size=1000						,					)
										return CLIPTextModel(A_	)


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										torch.manual_seed(0	)

										_UpperCAmelCase       :		Optional[int] 		=    12
										_UpperCAmelCase       :		Union[str, Any] 		=    12

										_UpperCAmelCase       :		str 		=    {
										    "attention_bias": True,
										    "cross_attention_dim": 32,
										    "attention_head_dim": height * width,
										    "num_attention_heads": 1,
										    "num_vector_embeds": self.num_embed,
										    "num_embeds_ada_norm": self.num_embeds_ada_norm,
										    "norm_num_groups": 32,
										    "sample_size": width,
										    "activation_fn": "geglu-approximate",
										}

										_UpperCAmelCase       :		Any 		=    TransformeraDModel(**A_	)
										return model


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    "cpu"

										_UpperCAmelCase       :		List[str] 		=    self.dummy_vqvae
										_UpperCAmelCase       :		Any 		=    self.dummy_text_encoder
										_UpperCAmelCase       :		Optional[int] 		=    self.dummy_tokenizer
										_UpperCAmelCase       :		List[Any] 		=    self.dummy_transformer
										_UpperCAmelCase       :		Union[str, Any] 		=    VQDiffusionScheduler(self.num_embed	)
										_UpperCAmelCase       :		Union[str, Any] 		=    LearnedClassifierFreeSamplingEmbeddings(learnable=A_	)

										_UpperCAmelCase       :		Optional[Any] 		=    VQDiffusionPipeline(
										    vqvae=A_						,					text_encoder=A_						,					tokenizer=A_						,					transformer=A_						,					scheduler=A_						,					learned_classifier_free_sampling_embeddings=A_						,					)
										_UpperCAmelCase       :		Optional[int] 		=    pipe.to(A_	)
										pipe.set_progress_bar_config(disable=A_	)

										_UpperCAmelCase       :		Union[str, Any] 		=    "teddy bear playing in the pool"

										_UpperCAmelCase       :		Union[str, Any] 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		List[Any] 		=    pipe([prompt]						,					generator=A_						,					num_inference_steps=2						,					output_type="np"	)
										_UpperCAmelCase       :		List[Any] 		=    output.images

										_UpperCAmelCase       :		Dict 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		Union[str, Any] 		=    pipe(
										    [prompt]						,					generator=A_						,					output_type="np"						,					return_dict=A_						,					num_inference_steps=2	)[0]

										_UpperCAmelCase       :		Optional[Any] 		=    image[0, -3:, -3:, -1]
										_UpperCAmelCase       :		int 		=    image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 24, 24, 3)

										_UpperCAmelCase       :		List[str] 		=    np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92]	)

										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	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    "cpu"

										_UpperCAmelCase       :		Tuple 		=    self.dummy_vqvae
										_UpperCAmelCase       :		Optional[Any] 		=    self.dummy_text_encoder
										_UpperCAmelCase       :		List[str] 		=    self.dummy_tokenizer
										_UpperCAmelCase       :		List[Any] 		=    self.dummy_transformer
										_UpperCAmelCase       :		Dict 		=    VQDiffusionScheduler(self.num_embed	)
										_UpperCAmelCase       :		Dict 		=    LearnedClassifierFreeSamplingEmbeddings(
										    learnable=A_						,					hidden_size=self.text_embedder_hidden_size						,					length=tokenizer.model_max_length	)

										_UpperCAmelCase       :		Optional[int] 		=    VQDiffusionPipeline(
										    vqvae=A_						,					text_encoder=A_						,					tokenizer=A_						,					transformer=A_						,					scheduler=A_						,					learned_classifier_free_sampling_embeddings=A_						,					)
										_UpperCAmelCase       :		Union[str, Any] 		=    pipe.to(A_	)
										pipe.set_progress_bar_config(disable=A_	)

										_UpperCAmelCase       :		int 		=    "teddy bear playing in the pool"

										_UpperCAmelCase       :		Dict 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		int 		=    pipe([prompt]						,					generator=A_						,					num_inference_steps=2						,					output_type="np"	)
										_UpperCAmelCase       :		Union[str, Any] 		=    output.images

										_UpperCAmelCase       :		Optional[Any] 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		int 		=    pipe(
										    [prompt]						,					generator=A_						,					output_type="np"						,					return_dict=A_						,					num_inference_steps=2	)[0]

										_UpperCAmelCase       :		Any 		=    image[0, -3:, -3:, -1]
										_UpperCAmelCase       :		Optional[int] 		=    image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 24, 24, 3)

										_UpperCAmelCase       :		Union[str, Any] 		=    np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88]	)

										assert np.abs(image_slice.flatten() - expected_slice	).max() < 2.0
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2







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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"	)

										_UpperCAmelCase       :		Dict 		=    VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq"	)
										_UpperCAmelCase       :		Optional[int] 		=    pipeline.to(A_	)
										pipeline.set_progress_bar_config(disable=A_	)

										# requires GPU generator for gumbel softmax
										# don't use GPU generator in tests though
										_UpperCAmelCase       :		Optional[int] 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		Optional[Any] 		=    pipeline(
										    "teddy bear playing in the pool"						,					num_images_per_prompt=1						,					generator=A_						,					output_type="np"						,					)

										_UpperCAmelCase       :		str 		=    output.images[0]

										assert image.shape == (256, 256, 3)
										assert np.abs(expected_image - image	).max() < 2.0


