



"""simple docstring"""
import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor




class 			snake_case_						(						unittest.TestCase				):






							def __init__(   self			,     a_			,     a_=7			,     a_=3			,     a_=1_8			,     a_=3_0			,     a_=4_0_0			,     a_=True			,     a_=None			,     a_=True			,     a_=None			,     a_=True			,     a_=[0.5, 0.5, 0.5]			,     a_=[0.5, 0.5, 0.5]			,     a_=False			,     ):
									a_     :      Union[str, Any] 						=     size if size is not None else {"height": 2_0, "width": 2_0}
									a_     :      Any 						=     crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
									a_     :      Any 						=     parent
									a_     :      Dict 						=     batch_size
									a_     :      int 						=     num_channels
									a_     :      int 						=     image_size
									a_     :      Optional[int] 						=     min_resolution
									a_     :      Optional[int] 						=     max_resolution
									a_     :      List[Any] 						=     do_resize
									a_     :      Any 						=     size
									a_     :      Optional[Any] 						=     do_center_crop
									a_     :      Any 						=     crop_size
									a_     :      List[Any] 						=     do_normalize
									a_     :      Union[str, Any] 						=     image_mean
									a_     :      Optional[Any] 						=     image_std
									a_     :      Dict 						=     do_reduce_labels






							def snake_case_  (   self      ):
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_reduce_labels": self.do_reduce_labels,
									}



def        lowerCAmelCase_						(							)			->      Dict:
		a_     :      List[Any] 						=     load_dataset("hf-internal-testing/fixtures_ade20k", split="test"	)

		a_     :      Optional[int] 						=     Image.open(dataset[0]["file"]	)
		a_     :      Optional[Any] 						=     Image.open(dataset[1]["file"]	)

		return image, map



def        lowerCAmelCase_						(							)			->      str:
		a_     :      Dict 						=     load_dataset("hf-internal-testing/fixtures_ade20k", split="test"	)

		a_     :      List[str] 						=     Image.open(ds[0]["file"]	)
		a_     :      Optional[int] 						=     Image.open(ds[1]["file"]	)
		a_     :      Dict 						=     Image.open(ds[2]["file"]	)
		a_     :      Tuple 						=     Image.open(ds[3]["file"]	)

		return [imagea, imagea], [mapa, mapa]




@require_torch
@require_vision
class 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					BeitImageProcessor if is_vision_available() else None






							def snake_case_  (   self      ):
									a_     :      Any 						=     BeitImageProcessingTester(self      )






							@property
							def snake_case_  (   self      ):
									return self.image_processor_tester.prepare_image_processor_dict()






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     self.image_processing_class(**self.image_processor_dict      )
									self.assertTrue(hasattr(a_			,     "do_resize"      )      )
									self.assertTrue(hasattr(a_			,     "size"      )      )
									self.assertTrue(hasattr(a_			,     "do_center_crop"      )      )
									self.assertTrue(hasattr(a_			,     "center_crop"      )      )
									self.assertTrue(hasattr(a_			,     "do_normalize"      )      )
									self.assertTrue(hasattr(a_			,     "image_mean"      )      )
									self.assertTrue(hasattr(a_			,     "image_std"      )      )






							def snake_case_  (   self      ):
									a_     :      Union[str, Any] 						=     self.image_processing_class.from_dict(self.image_processor_dict      )
									self.assertEqual(image_processor.size			,     {"height": 2_0, "width": 2_0}      )
									self.assertEqual(image_processor.crop_size			,     {"height": 1_8, "width": 1_8}      )
									self.assertEqual(image_processor.do_reduce_labels			,     a_      )

									a_     :      List[str] 						=     self.image_processing_class.from_dict(
									    self.image_processor_dict			,     size=4_2			,     crop_size=8_4			,     reduce_labels=a_      )
									self.assertEqual(image_processor.size			,     {"height": 4_2, "width": 4_2}      )
									self.assertEqual(image_processor.crop_size			,     {"height": 8_4, "width": 8_4}      )
									self.assertEqual(image_processor.do_reduce_labels			,     a_      )






							def snake_case_  (   self      ):
									pass






							def snake_case_  (   self      ):
									# Initialize image_processing
									a_     :      Any 						=     self.image_processing_class(**self.image_processor_dict      )
									# create random PIL images
									a_     :      Dict 						=     prepare_image_inputs(self.image_processor_tester			,     equal_resolution=a_      )
									for image in image_inputs:
											self.assertIsInstance(a_			,     Image.Image      )

									# Test not batched input
									a_     :      int 						=     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
									a_     :      str 						=     image_processing(a_			,     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 snake_case_  (   self      ):
									# Initialize image_processing
									a_     :      List[Any] 						=     self.image_processing_class(**self.image_processor_dict      )
									# create random numpy tensors
									a_     :      int 						=     prepare_image_inputs(self.image_processor_tester			,     equal_resolution=a_			,     numpify=a_      )
									for image in image_inputs:
											self.assertIsInstance(a_			,     np.ndarray      )

									# Test not batched input
									a_     :      Tuple 						=     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
									a_     :      Union[str, Any] 						=     image_processing(a_			,     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 snake_case_  (   self      ):
									# Initialize image_processing
									a_     :      Dict 						=     self.image_processing_class(**self.image_processor_dict      )
									# create random PyTorch tensors
									a_     :      Dict 						=     prepare_image_inputs(self.image_processor_tester			,     equal_resolution=a_			,     torchify=a_      )
									for image in image_inputs:
											self.assertIsInstance(a_			,     torch.Tensor      )

									# Test not batched input
									a_     :      int 						=     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
									a_     :      Tuple 						=     image_processing(a_			,     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 snake_case_  (   self      ):
									# Initialize image_processing
									a_     :      Dict 						=     self.image_processing_class(**self.image_processor_dict      )
									# create random PyTorch tensors
									a_     :      Optional[Any] 						=     prepare_image_inputs(self.image_processor_tester			,     equal_resolution=a_			,     torchify=a_      )
									a_     :      List[Any] 						=     []
									for image in image_inputs:
											self.assertIsInstance(a_			,     torch.Tensor      )
											maps.append(torch.zeros(image.shape[-2:]      ).long()      )

									# Test not batched input
									a_     :      int 						=     image_processing(image_inputs[0]			,     maps[0]			,     return_tensors="pt"      )
									self.assertEqual(
									    encoding["pixel_values"].shape			,     (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )			,     )
									self.assertEqual(
									    encoding["labels"].shape			,     (
									        1,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )			,     )
									self.assertEqual(encoding["labels"].dtype			,     torch.long      )
									self.assertTrue(encoding["labels"].min().item() >= 0      )
									self.assertTrue(encoding["labels"].max().item() <= 2_5_5      )

									# Test batched
									a_     :      Dict 						=     image_processing(a_			,     a_			,     return_tensors="pt"      )
									self.assertEqual(
									    encoding["pixel_values"].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"],
									    )			,     )
									self.assertEqual(
									    encoding["labels"].shape			,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )			,     )
									self.assertEqual(encoding["labels"].dtype			,     torch.long      )
									self.assertTrue(encoding["labels"].min().item() >= 0      )
									self.assertTrue(encoding["labels"].max().item() <= 2_5_5      )

									# Test not batched input (PIL images)
									a_     :      Optional[Any] 						=     prepare_semantic_single_inputs()

									a_     :      Tuple 						=     image_processing(a_			,     a_			,     return_tensors="pt"      )
									self.assertEqual(
									    encoding["pixel_values"].shape			,     (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )			,     )
									self.assertEqual(
									    encoding["labels"].shape			,     (
									        1,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )			,     )
									self.assertEqual(encoding["labels"].dtype			,     torch.long      )
									self.assertTrue(encoding["labels"].min().item() >= 0      )
									self.assertTrue(encoding["labels"].max().item() <= 2_5_5      )

									# Test batched input (PIL images)
									a_     :      Tuple 						=     prepare_semantic_batch_inputs()

									a_     :      Union[str, Any] 						=     image_processing(a_			,     a_			,     return_tensors="pt"      )
									self.assertEqual(
									    encoding["pixel_values"].shape			,     (
									        2,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )			,     )
									self.assertEqual(
									    encoding["labels"].shape			,     (
									        2,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )			,     )
									self.assertEqual(encoding["labels"].dtype			,     torch.long      )
									self.assertTrue(encoding["labels"].min().item() >= 0      )
									self.assertTrue(encoding["labels"].max().item() <= 2_5_5      )






							def snake_case_  (   self      ):
									# Initialize image_processing
									a_     :      int 						=     self.image_processing_class(**self.image_processor_dict      )

									# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
									a_     :      List[Any] 						=     prepare_semantic_single_inputs()
									a_     :      Optional[int] 						=     image_processing(a_			,     a_			,     return_tensors="pt"      )
									self.assertTrue(encoding["labels"].min().item() >= 0      )
									self.assertTrue(encoding["labels"].max().item() <= 1_5_0      )

									a_     :      Optional[int] 						=     True
									a_     :      Optional[Any] 						=     image_processing(a_			,     a_			,     return_tensors="pt"      )
									self.assertTrue(encoding["labels"].min().item() >= 0      )
									self.assertTrue(encoding["labels"].max().item() <= 2_5_5      )




"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					KandinskyImgaImgPipeline
							__lowerCAmelCase    					=					["prompt", "image_embeds", "negative_image_embeds", "image"]
							__lowerCAmelCase    					=					[
							    "prompt",
							    "negative_prompt",
							    "image_embeds",
							    "negative_image_embeds",
							    "image",
							]
							__lowerCAmelCase    					=					[
							    "generator",
							    "height",
							    "width",
							    "strength",
							    "guidance_scale",
							    "negative_prompt",
							    "num_inference_steps",
							    "return_dict",
							    "guidance_scale",
							    "num_images_per_prompt",
							    "output_type",
							    "return_dict",
							]
							__lowerCAmelCase    					=					False






							@property
							def snake_case_  (   self      ):
									return 3_2






							@property
							def snake_case_  (   self      ):
									return 3_2






							@property
							def snake_case_  (   self      ):
									return self.time_input_dim






							@property
							def snake_case_  (   self      ):
									return self.time_input_dim * 4






							@property
							def snake_case_  (   self      ):
									return 1_0_0






							@property
							def snake_case_  (   self      ):
									a_     :      List[str] 						=     XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"      )
									return tokenizer






							@property
							def snake_case_  (   self      ):
									torch.manual_seed(0      )
									a_     :      List[Any] 						=     MCLIPConfig(
									    numDims=self.cross_attention_dim			,     transformerDimensions=self.text_embedder_hidden_size			,     hidden_size=self.text_embedder_hidden_size			,     intermediate_size=3_7			,     num_attention_heads=4			,     num_hidden_layers=5			,     vocab_size=1_0_0_5			,     )

									a_     :      str 						=     MultilingualCLIP(a_      )
									a_     :      Any 						=     text_encoder.eval()

									return text_encoder






							@property
							def snake_case_  (   self      ):
									torch.manual_seed(0      )

									a_     :      Union[str, Any] 						=     {
									    "in_channels": 4,
									    # Out channels is double in channels because predicts mean and variance
									    "out_channels": 8,
									    "addition_embed_type": "text_image",
									    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
									    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
									    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
									    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
									    "layers_per_block": 1,
									    "encoder_hid_dim": self.text_embedder_hidden_size,
									    "encoder_hid_dim_type": "text_image_proj",
									    "cross_attention_dim": self.cross_attention_dim,
									    "attention_head_dim": 4,
									    "resnet_time_scale_shift": "scale_shift",
									    "class_embed_type": None,
									}

									a_     :      Dict 						=     UNetaDConditionModel(**a_      )
									return model






							@property
							def snake_case_  (   self      ):
									return {
									    "block_out_channels": [3_2, 6_4],
									    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
									    "in_channels": 3,
									    "latent_channels": 4,
									    "layers_per_block": 1,
									    "norm_num_groups": 8,
									    "norm_type": "spatial",
									    "num_vq_embeddings": 1_2,
									    "out_channels": 3,
									    "up_block_types": [
									        "AttnUpDecoderBlock2D",
									        "UpDecoderBlock2D",
									    ],
									    "vq_embed_dim": 4,
									}






							@property
							def snake_case_  (   self      ):
									torch.manual_seed(0      )
									a_     :      int 						=     VQModel(**self.dummy_movq_kwargs      )
									return model






							def snake_case_  (   self      ):
									a_     :      Dict 						=     self.dummy_text_encoder
									a_     :      Dict 						=     self.dummy_tokenizer
									a_     :      Optional[int] 						=     self.dummy_unet
									a_     :      Dict 						=     self.dummy_movq

									a_     :      List[str] 						=     {
									    "num_train_timesteps": 1_0_0_0,
									    "beta_schedule": "linear",
									    "beta_start": 0.00_085,
									    "beta_end": 0.012,
									    "clip_sample": False,
									    "set_alpha_to_one": False,
									    "steps_offset": 0,
									    "prediction_type": "epsilon",
									    "thresholding": False,
									}

									a_     :      List[Any] 						=     DDIMScheduler(**a_      )

									a_     :      Union[str, Any] 						=     {
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "unet": unet,
									    "scheduler": scheduler,
									    "movq": movq,
									}

									return components






							def snake_case_  (   self			,     a_			,     a_=0      ):
									a_     :      Optional[int] 						=     floats_tensor((1, self.cross_attention_dim)			,     rng=random.Random(a_      )      ).to(a_      )
									a_     :      Tuple 						=     floats_tensor((1, self.cross_attention_dim)			,     rng=random.Random(seed + 1      )      ).to(a_      )
									# create init_image
									a_     :      Optional[int] 						=     floats_tensor((1, 3, 6_4, 6_4)			,     rng=random.Random(a_      )      ).to(a_      )
									a_     :      Any 						=     image.cpu().permute(0			,     2			,     3			,     1      )[0]
									a_     :      int 						=     Image.fromarray(np.uinta(a_      )      ).convert("RGB"      ).resize((2_5_6, 2_5_6)      )

									if str(a_      ).startswith("mps"      ):
											a_     :      Any 						=     torch.manual_seed(a_      )
									else:
											a_     :      Any 						=     torch.Generator(device=a_      ).manual_seed(a_      )
									a_     :      List[Any] 						=     {
									    "prompt": "horse",
									    "image": init_image,
									    "image_embeds": image_embeds,
									    "negative_image_embeds": negative_image_embeds,
									    "generator": generator,
									    "height": 6_4,
									    "width": 6_4,
									    "num_inference_steps": 1_0,
									    "guidance_scale": 7.0,
									    "strength": 0.2,
									    "output_type": "np",
									}
									return inputs






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     "cpu"

									a_     :      List[Any] 						=     self.get_dummy_components()

									a_     :      Union[str, Any] 						=     self.pipeline_class(**a_      )
									a_     :      Tuple 						=     pipe.to(a_      )

									pipe.set_progress_bar_config(disable=a_      )

									a_     :      Union[str, Any] 						=     pipe(**self.get_dummy_inputs(a_      )      )
									a_     :      Any 						=     output.images

									a_     :      str 						=     pipe(
									    **self.get_dummy_inputs(a_      )			,     return_dict=a_			,     )[0]

									a_     :      List[str] 						=     image[0, -3:, -3:, -1]
									a_     :      Optional[Any] 						=     image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 6_4, 6_4, 3)

									a_     :      Optional[int] 						=     np.array(
									    [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]      )
									assert (
									    np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
									), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
									assert (
									    np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2
									), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""




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






							def snake_case_  (   self      ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






							def snake_case_  (   self      ):
									a_     :      Optional[int] 						=     load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/kandinsky/kandinsky_img2img_frog.npy"      )

									a_     :      Union[str, Any] 						=     load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"      )
									a_     :      Optional[Any] 						=     "A red cartoon frog, 4k"

									a_     :      int 						=     KandinskyPriorPipeline.from_pretrained(
									    "kandinsky-community/kandinsky-2-1-prior"			,     torch_dtype=torch.floataa      )
									pipe_prior.to(a_      )

									a_     :      int 						=     KandinskyImgaImgPipeline.from_pretrained(
									    "kandinsky-community/kandinsky-2-1"			,     torch_dtype=torch.floataa      )
									a_     :      List[Any] 						=     pipeline.to(a_      )

									pipeline.set_progress_bar_config(disable=a_      )

									a_     :      int 						=     torch.Generator(device="cpu"      ).manual_seed(0      )
									a_						,				a_     :      Optional[int] 						=     pipe_prior(
									    a_			,     generator=a_			,     num_inference_steps=5			,     negative_prompt=""			,     ).to_tuple()

									a_     :      List[Any] 						=     pipeline(
									    a_			,     image=a_			,     image_embeds=a_			,     negative_image_embeds=a_			,     generator=a_			,     num_inference_steps=1_0_0			,     height=7_6_8			,     width=7_6_8			,     strength=0.2			,     output_type="np"			,     )

									a_     :      int 						=     output.images[0]

									assert image.shape == (7_6_8, 7_6_8, 3)

									assert_mean_pixel_difference(a_			,     a_      )