

'''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, KandinskyInpaintPipeline, 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 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    KandinskyInpaintPipeline
				lowerCamelCase_		       =    ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
				lowerCamelCase_		       =    [
				    'prompt',
				    'negative_prompt',
				    'image_embeds',
				    'negative_image_embeds',
				    'image',
				    'mask_image',
				]
				lowerCamelCase_		       =    [
				    'generator',
				    'height',
				    'width',
				    'latents',
				    'guidance_scale',
				    'negative_prompt',
				    'num_inference_steps',
				    'return_dict',
				    'guidance_scale',
				    'num_images_per_prompt',
				    'output_type',
				    'return_dict',
				]
				lowerCamelCase_		       =    False







				@property
				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										return 32







				@property
				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										return 32







				@property
				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										return self.time_input_dim







				@property
				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										return self.time_input_dim * 4







				@property
				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										return 100







				@property
				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''   )
										return tokenizer







				@property
				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										torch.manual_seed(0   )
										lowercase :							int    		=MCLIPConfig(
										    numDims=self.cross_attention_dim		,					transformerDimensions=self.text_embedder_hidden_size		,					hidden_size=self.text_embedder_hidden_size		,					intermediate_size=37		,					num_attention_heads=4		,					num_hidden_layers=5		,					vocab_size=1005		,					)

										lowercase :							Union[str, Any]    		=MultilingualCLIP(UpperCAmelCase__   )
										lowercase :							int    		=text_encoder.eval()

										return text_encoder







				@property
				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										torch.manual_seed(0   )

										lowercase :							Optional[Any]    		={
										    '''in_channels''': 9,
										    # 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,
										}

										lowercase :							Optional[int]    		=UNetaDConditionModel(**UpperCAmelCase__   )
										return model







				@property
				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										return {
										    "block_out_channels": [32, 64],
										    "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": 12,
										    "out_channels": 3,
										    "up_block_types": [
										        "AttnUpDecoderBlock2D",
										        "UpDecoderBlock2D",
										    ],
										    "vq_embed_dim": 4,
										}







				@property
				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										torch.manual_seed(0   )
										lowercase :							str    		=VQModel(**self.dummy_movq_kwargs   )
										return model







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							int    		=self.dummy_text_encoder
										lowercase :							int    		=self.dummy_tokenizer
										lowercase :							Optional[int]    		=self.dummy_unet
										lowercase :							Optional[int]    		=self.dummy_movq

										lowercase :							Any    		=DDIMScheduler(
										    num_train_timesteps=1000		,					beta_schedule='''linear'''		,					beta_start=0.0_00_85		,					beta_end=0.0_12		,					clip_sample=UpperCAmelCase__		,					set_alpha_to_one=UpperCAmelCase__		,					steps_offset=1		,					prediction_type='''epsilon'''		,					thresholding=UpperCAmelCase__		,					)

										lowercase :							Dict    		={
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''movq''': movq,
										}

										return components







				def  lowerCamelCase_	(       self     :		str		,					UpperCAmelCase__     :		List[str]		,					UpperCAmelCase__     :		Any=0   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=floats_tensor((1, self.cross_attention_dim)		,					rng=random.Random(UpperCAmelCase__   )   ).to(UpperCAmelCase__   )
										lowercase :							Tuple    		=floats_tensor((1, self.cross_attention_dim)		,					rng=random.Random(seed + 1   )   ).to(UpperCAmelCase__   )
										# create init_image
										lowercase :							Tuple    		=floats_tensor((1, 3, 64, 64)		,					rng=random.Random(UpperCAmelCase__   )   ).to(UpperCAmelCase__   )
										lowercase :							Any    		=image.cpu().permute(0		,					2		,					3		,					1   )[0]
										lowercase :							List[Any]    		=Image.fromarray(np.uinta(UpperCAmelCase__   )   ).convert('''RGB'''   ).resize((256, 256)   )
										# create mask
										lowercase :							List[str]    		=np.ones((64, 64)		,					dtype=np.floataa   )
										lowercase :							List[str]    		=0

										if str(UpperCAmelCase__   ).startswith('''mps'''   ):
																lowercase :							Optional[int]    		=torch.manual_seed(UpperCAmelCase__   )
										else:
																lowercase :							Dict    		=torch.Generator(device=UpperCAmelCase__   ).manual_seed(UpperCAmelCase__   )
										lowercase :							Optional[int]    		={
										    '''prompt''': '''horse''',
										    '''image''': init_image,
										    '''mask_image''': mask,
										    '''image_embeds''': image_embeds,
										    '''negative_image_embeds''': negative_image_embeds,
										    '''generator''': generator,
										    '''height''': 64,
										    '''width''': 64,
										    '''num_inference_steps''': 2,
										    '''guidance_scale''': 4.0,
										    '''output_type''': '''np''',
										}
										return inputs







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							Dict    		='''cpu'''

										lowercase :							Optional[int]    		=self.get_dummy_components()

										lowercase :							Any    		=self.pipeline_class(**UpperCAmelCase__   )
										lowercase :							Any    		=pipe.to(UpperCAmelCase__   )

										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

										lowercase :							Tuple    		=pipe(**self.get_dummy_inputs(UpperCAmelCase__   )   )
										lowercase :							Any    		=output.images

										lowercase :							List[str]    		=pipe(
										    **self.get_dummy_inputs(UpperCAmelCase__   )		,					return_dict=UpperCAmelCase__		,					)[0]

										lowercase :							Dict    		=image[0, -3:, -3:, -1]
										lowercase :							str    		=image_from_tuple[0, -3:, -3:, -1]

										print(F'''image.shape {image.shape}'''   )

										assert image.shape == (1, 64, 64, 3)

										lowercase :							List[Any]    		=np.array(
										    [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]   )

										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()}'''







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										super().test_inference_batch_single_identical(expected_max_diff=3E-3   )





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







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							str    		=load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''   )

										lowercase :							List[str]    		=load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''   )
										lowercase :							Optional[Any]    		=np.ones((768, 768)		,					dtype=np.floataa   )
										lowercase :							Tuple    		=0

										lowercase :							Tuple    		='''a hat'''

										lowercase :							Tuple    		=KandinskyPriorPipeline.from_pretrained(
										    '''kandinsky-community/kandinsky-2-1-prior'''		,					torch_dtype=torch.floataa   )
										pipe_prior.to(UpperCAmelCase__   )

										lowercase :							List[str]    		=KandinskyInpaintPipeline.from_pretrained(
										    '''kandinsky-community/kandinsky-2-1-inpaint'''		,					torch_dtype=torch.floataa   )
										lowercase :							List[str]    		=pipeline.to(UpperCAmelCase__   )
										pipeline.set_progress_bar_config(disable=UpperCAmelCase__   )

										lowercase :							Dict    		=torch.Generator(device='''cpu'''   ).manual_seed(0   )
										lowercase		,	lowercase :							Optional[Any]    		=pipe_prior(
										    UpperCAmelCase__		,					generator=UpperCAmelCase__		,					num_inference_steps=5		,					negative_prompt=''''''		,					).to_tuple()

										lowercase :							List[str]    		=pipeline(
										    UpperCAmelCase__		,					image=UpperCAmelCase__		,					mask_image=UpperCAmelCase__		,					image_embeds=UpperCAmelCase__		,					negative_image_embeds=UpperCAmelCase__		,					generator=UpperCAmelCase__		,					num_inference_steps=100		,					height=768		,					width=768		,					output_type='''np'''		,					)

										lowercase :							List[Any]    		=output.images[0]

										assert image.shape == (768, 768, 3)

										assert_mean_pixel_difference(UpperCAmelCase__		,					UpperCAmelCase__   )









import unittest

import numpy as np

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 ViTImageProcessor


class     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""


     def __init__(	self  ,       __A  ,       __A=13  ,       __A=3  ,       __A=224  ,       __A=30  ,       __A=400  ,       __A=True  ,       __A=None  ,       __A=True  ,       __A=[0.5, 0.5, 0.5]  ,       __A=[0.5, 0.5, 0.5]  ,       ):
        __a					=  size if size is not None else {"""height""": 18, """width""": 18}
        __a					=  parent
        __a					=  batch_size
        __a					=  num_channels
        __a					=  image_size
        __a					=  min_resolution
        __a					=  max_resolution
        __a					=  do_resize
        __a					=  size
        __a					=  do_normalize
        __a					=  image_mean
        __a					=  image_std


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







@require_torch
@require_vision
class     __UpperCAmelCase							(       __A ,					unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      ViTImageProcessor if is_vision_available() else None


     def 	snake_case_  (	self    ):
        __a					=  EfficientFormerImageProcessorTester(self    )


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


     def 	snake_case_  (	self    ):
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        self.assertTrue(hasattr(__A  ,       """image_mean"""    )    )
        self.assertTrue(hasattr(__A  ,       """image_std"""    )    )
        self.assertTrue(hasattr(__A  ,       """do_normalize"""    )    )
        self.assertTrue(hasattr(__A  ,       """do_resize"""    )    )
        self.assertTrue(hasattr(__A  ,       """size"""    )    )


     def 	snake_case_  (	self    ):
        pass


     def 	snake_case_  (	self    ):
        # Initialize image_processor
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        # create random PIL images
        __a					=  prepare_image_inputs(self.image_proc_tester  ,       equal_resolution=__A    )
        for image in image_inputs:
           self.assertIsInstance(__A  ,       Image.Image    )

        # Test not batched input
        __a					=  image_processor(image_inputs[0]  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )

        # Test batched
        __a					=  image_processor(__A  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )


     def 	snake_case_  (	self    ):
        # Initialize image_processor
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        # create random numpy tensors
        __a					=  prepare_image_inputs(self.image_proc_tester  ,       equal_resolution=__A  ,       numpify=__A    )
        for image in image_inputs:
           self.assertIsInstance(__A  ,       np.ndarray    )

        # Test not batched input
        __a					=  image_processor(image_inputs[0]  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )

        # Test batched
        __a					=  image_processor(__A  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )


     def 	snake_case_  (	self    ):
        # Initialize image_processor
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        # create random PyTorch tensors
        __a					=  prepare_image_inputs(self.image_proc_tester  ,       equal_resolution=__A  ,       torchify=__A    )
        for image in image_inputs:
           self.assertIsInstance(__A  ,       torch.Tensor    )

        # Test not batched input
        __a					=  image_processor(image_inputs[0]  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )

        # Test batched
        __a					=  image_processor(__A  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )



