
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


SCREAMING_SNAKE_CASE				:int									=							logging.get_logger(__name__)





class 				UpperCAmelCase (    __lowercase    ):




	'''simple docstring'''





	snake_case_          =	["pixel_values"]






	def __init__(			self   :					Dict				,A   :					List[Any] = True				,A   :					str = None				,A   :					Any = PILImageResampling.BICUBIC				,A   :					Tuple = True				,A   :					List[Any] = 1 / 2_55				,A   :					Optional[Any] = True				,A   :					Optional[Any] = None				,A   :					Optional[Any] = None				,A   :					Tuple = True				,**A   :					List[Any]				,):
					super().__init__(**_a					)
					__A       =					size if size is not None else {'''height''': 3_84, '''width''': 3_84}
					__A       =					get_size_dict(_a				,default_to_square=_a					)

					__A       =					do_resize
					__A       =					size
					__A       =					resample
					__A       =					do_rescale
					__A       =					rescale_factor
					__A       =					do_normalize
					__A       =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
					__A       =					image_std if image_std is not None else OPENAI_CLIP_STD
					__A       =					do_convert_rgb






	def 							UpperCamelCase_    (			self   :					int				,A   :					Union[str, Any]				,A   :					List[str]				,A   :					str = PILImageResampling.BICUBIC				,A   :					Union[str, Any] = None				,**A   :					Union[str, Any]				,):
					__A       =					get_size_dict(_a				,default_to_square=_a					)
					if "height" not in size or "width" not in size:
									raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''					)
					__A       =					(size['''height'''], size['''width'''])
					return resize(_a				,size=_a				,resample=_a				,data_format=_a				,**_a					)






	def 							UpperCamelCase_    (			self   :					Union[str, Any]				,A   :					Union[str, Any]				,A   :					int				,A   :					List[str] = None				,**A   :					Optional[int]				,):
					return rescale(_a				,scale=_a				,data_format=_a				,**_a					)






	def 							UpperCamelCase_    (			self   :					int				,A   :					int				,A   :					Any				,A   :					Optional[Any]				,A   :					Any = None				,**A   :					List[Any]				,):
					return normalize(_a				,mean=_a				,std=_a				,data_format=_a				,**_a					)







	def 							UpperCamelCase_    (			self   :					Optional[Any]				,A   :					List[str]				,A   :					Optional[Any] = None				,A   :					int = None				,A   :					str = None				,A   :					List[str] = None				,A   :					Dict = None				,A   :					Optional[Any] = None				,A   :					List[Any] = None				,A   :					Dict = None				,A   :					str = None				,A   :					Any = None				,A   :					Tuple = ChannelDimension.FIRST				,**A   :					Optional[int]				,):
					__A       =					do_resize if do_resize is not None else self.do_resize
					__A       =					resample if resample is not None else self.resample
					__A       =					do_rescale if do_rescale is not None else self.do_rescale
					__A       =					rescale_factor if rescale_factor is not None else self.rescale_factor
					__A       =					do_normalize if do_normalize is not None else self.do_normalize
					__A       =					image_mean if image_mean is not None else self.image_mean
					__A       =					image_std if image_std is not None else self.image_std
					__A       =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

					__A       =					size if size is not None else self.size
					__A       =					get_size_dict(_a				,default_to_square=_a					)

					__A       =					make_list_of_images(_a					)

					if not valid_images(_a					):
									raise ValueError(
									    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
									    "torch.Tensor, tf.Tensor or jax.ndarray."					)

					if do_resize and size is None or resample is None:
									raise ValueError("Size and resample must be specified if do_resize is True."					)

					if do_rescale and rescale_factor is None:
									raise ValueError("Rescale factor must be specified if do_rescale is True."					)

					if do_normalize and (image_mean is None or image_std is None):
									raise ValueError("Image mean and std must be specified if do_normalize is True."					)

					# PIL RGBA images are converted to RGB
					if do_convert_rgb:
									__A       =					[convert_to_rgb(_a					) for image in images]

					# All transformations expect numpy arrays.
					__A       =					[to_numpy_array(_a					) for image in images]

					if do_resize:
									__A       =					[self.resize(image=_a				,size=_a				,resample=_a					) for image in images]

					if do_rescale:
									__A       =					[self.rescale(image=_a				,scale=_a					) for image in images]

					if do_normalize:
									__A       =					[self.normalize(image=_a				,mean=_a				,std=_a					) for image in images]

					__A       =					[to_channel_dimension_format(_a				,_a					) for image in images]

					__A       =					BatchFeature(data={"pixel_values": images}				,tensor_type=_a					)

					return encoded_outputs



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 							UpperCAmelCase_						(     __lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Union[str, Any]             =       KandinskyInpaintPipeline
							UpperCAmelCase__			:		Optional[int]             =       ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
							UpperCAmelCase__			:		Optional[Any]             =       [
							    "prompt",
							    "negative_prompt",
							    "image_embeds",
							    "negative_image_embeds",
							    "image",
							    "mask_image",
							]
							UpperCAmelCase__			:		Optional[int]             =       [
							    "generator",
							    "height",
							    "width",
							    "latents",
							    "guidance_scale",
							    "negative_prompt",
							    "num_inference_steps",
							    "return_dict",
							    "guidance_scale",
							    "num_images_per_prompt",
							    "output_type",
							    "return_dict",
							]
							UpperCAmelCase__			:		Any             =       False


							@property
							def 							__lowercase							(     self			)       ->       Optional[int]:
												return 3_2


							@property
							def 							__lowercase							(     self			)       ->       int:
												return 3_2


							@property
							def 							__lowercase							(     self			)       ->       List[str]:
												return self.time_input_dim


							@property
							def 							__lowercase							(     self			)       ->       List[str]:
												return self.time_input_dim * 4


							@property
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												return 1_0_0


							@property
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Any   =						XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''			)
												return tokenizer


							@property
							def 							__lowercase							(     self			)       ->       str:
												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      :       Optional[int]   =						MultilingualCLIP(_a			)
												_a      :       Tuple   =						text_encoder.eval()

												return text_encoder


							@property
							def 							__lowercase							(     self			)       ->       str:
												torch.manual_seed(0			)

												_a      :       List[str]   =						{
												    '''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,
												}

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


							@property
							def 							__lowercase							(     self			)       ->       Optional[int]:
												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 							__lowercase							(     self			)       ->       Tuple:
												torch.manual_seed(0			)
												_a      :       List[Any]   =						VQModel(**self.dummy_movq_kwargs			)
												return model


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       List[Any]   =						self.dummy_text_encoder
												_a      :       Optional[Any]   =						self.dummy_tokenizer
												_a      :       Optional[Any]   =						self.dummy_unet
												_a      :       Union[str, Any]   =						self.dummy_movq

												_a      :       Tuple   =						DDIMScheduler(
												    num_train_timesteps=1_0_0_0			,				beta_schedule='''linear'''			,				beta_start=0.0_0085			,				beta_end=0.012			,				clip_sample=_a			,				set_alpha_to_one=_a			,				steps_offset=1			,				prediction_type='''epsilon'''			,				thresholding=_a			,				)

												_a      :       str   =						{
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''unet''': unet,
												    '''scheduler''': scheduler,
												    '''movq''': movq,
												}

												return components


							def 							__lowercase							(     self			,				_a			,				_a=0			)       ->       int:
												_a      :       Union[str, Any]   =						floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(_a			)			).to(_a			)
												_a      :       List[str]   =						floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(seed + 1			)			).to(_a			)
												# create init_image
												_a      :       Tuple   =						floats_tensor((1, 3, 6_4, 6_4)			,				rng=random.Random(_a			)			).to(_a			)
												_a      :       Dict   =						image.cpu().permute(0			,				2			,				3			,				1			)[0]
												_a      :       Optional[int]   =						Image.fromarray(np.uinta(_a			)			).convert('''RGB'''			).resize((2_5_6, 2_5_6)			)
												# create mask
												_a      :       Union[str, Any]   =						np.ones((6_4, 6_4)			,				dtype=np.floataa			)
												_a      :       List[str]   =						0

												if str(_a			).startswith('''mps'''			):
																	_a      :       Tuple   =						torch.manual_seed(_a			)
												else:
																	_a      :       Any   =						torch.Generator(device=_a			).manual_seed(_a			)
												_a      :       Any   =						{
												    '''prompt''': '''horse''',
												    '''image''': init_image,
												    '''mask_image''': mask,
												    '''image_embeds''': image_embeds,
												    '''negative_image_embeds''': negative_image_embeds,
												    '''generator''': generator,
												    '''height''': 6_4,
												    '''width''': 6_4,
												    '''num_inference_steps''': 2,
												    '''guidance_scale''': 4.0,
												    '''output_type''': '''np''',
												}
												return inputs


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Optional[Any]   =						'''cpu'''

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

												_a      :       Tuple   =						self.pipeline_class(**_a			)
												_a      :       int   =						pipe.to(_a			)

												pipe.set_progress_bar_config(disable=_a			)

												_a      :       Any   =						pipe(**self.get_dummy_inputs(_a			)			)
												_a      :       str   =						output.images

												_a      :       Tuple   =						pipe(
												    **self.get_dummy_inputs(_a			)			,				return_dict=_a			,				)[0]

												_a      :       Union[str, Any]   =						image[0, -3:, -3:, -1]
												_a      :       Tuple   =						image_from_tuple[0, -3:, -3:, -1]

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

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

												_a      :       str   =						np.array(
												    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]			)

												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 							__lowercase							(     self			)       ->       Dict:
												super().test_inference_batch_single_identical(expected_max_diff=3e-3			)



@slow
@require_torch_gpu
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







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




							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       Tuple   =						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''			)

												_a      :       str   =						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''			)
												_a      :       Tuple   =						np.ones((7_6_8, 7_6_8)			,				dtype=np.floataa			)
												_a      :       Any   =						0

												_a      :       Optional[Any]   =						'''a hat'''

												_a      :       Optional[Any]   =						KandinskyPriorPipeline.from_pretrained(
												    '''kandinsky-community/kandinsky-2-1-prior'''			,				torch_dtype=torch.floataa			)
												pipe_prior.to(_a			)

												_a      :       Tuple   =						KandinskyInpaintPipeline.from_pretrained(
												    '''kandinsky-community/kandinsky-2-1-inpaint'''			,				torch_dtype=torch.floataa			)
												_a      :       Union[str, Any]   =						pipeline.to(_a			)
												pipeline.set_progress_bar_config(disable=_a			)

												_a      :       Union[str, Any]   =						torch.Generator(device='''cpu'''			).manual_seed(0			)
												_a ,   _a      :       Dict   =						pipe_prior(
												    _a			,				generator=_a			,				num_inference_steps=5			,				negative_prompt=''''''			,				).to_tuple()

												_a      :       Optional[int]   =						pipeline(
												    _a			,				image=_a			,				mask_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			,				output_type='''np'''			,				)

												_a      :       Optional[int]   =						output.images[0]

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

												assert_mean_pixel_difference(_a			,				_a			)



