


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




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return 32




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return 32




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return self.time_input_dim




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return self.time_input_dim * 4




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return 100




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Dict    =  XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'							)
						return tokenizer




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						torch.manual_seed(0							)
						__a      :	Union[str, Any]    =  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						,    )

						__a      :	List[Any]    =  MultilingualCLIP(__a							)
						__a      :	int    =  text_encoder.eval()

						return text_encoder




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						torch.manual_seed(0							)

						__a      :	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,
						}

						__a      :	str    =  UNetaDConditionModel(**__a							)
						return model




	@property
	def 				__UpperCAmelCase							(       self							):

						'''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 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						torch.manual_seed(0							)
						__a      :	List[Any]    =  VQModel(**self.dummy_movq_kwargs							)
						return model




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Tuple    =  self.dummy_text_encoder
						__a      :	List[str]    =  self.dummy_tokenizer
						__a      :	Optional[Any]    =  self.dummy_unet
						__a      :	str    =  self.dummy_movq

						__a      :	str    =  DDIMScheduler(
						    num_train_timesteps=1000						,    beta_schedule='linear'						,    beta_start=0.00085						,    beta_end=0.012						,    clip_sample=__a						,    set_alpha_to_one=__a						,    steps_offset=1						,    prediction_type='epsilon'						,    thresholding=__a						,    )

						__a      :	Optional[int]    =  {
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'unet': unet,
						    'scheduler': scheduler,
						    'movq': movq,
						}

						return components




	def 				__UpperCAmelCase							(       self						,    __a						,    __a=0							):

						'''simple docstring'''





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

						if str(__a							).startswith('mps'							):
											__a      :	str    =  torch.manual_seed(__a							)
						else:
											__a      :	str    =  torch.Generator(device=__a							).manual_seed(__a							)
						__a      :	List[Any]    =  {
						    '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 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[Any]    =  'cpu'

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

						__a      :	str    =  self.pipeline_class(**__a							)
						__a      :	List[str]    =  pipe.to(__a							)

						pipe.set_progress_bar_config(disable=__a							)

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

						__a      :	Optional[int]    =  pipe(
						    **self.get_dummy_inputs(__a							)						,    return_dict=__a						,    )[0]

						__a      :	int    =  image[0, -3:, -3:, -1]
						__a      :	Tuple    =  image_from_tuple[0, -3:, -3:, -1]

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

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

						__a      :	Union[str, Any]    =  np.array(
						    [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786]							)

						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 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						super().test_inference_batch_single_identical(expected_max_diff=3E-3							)





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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[str]    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'							)

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

						__a      :	Dict    =  'a hat'

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

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

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

						__a      :	Tuple    =  pipeline(
						    __a						,    image=__a						,    mask_image=__a						,    image_embeds=__a						,    negative_image_embeds=__a						,    generator=__a						,    num_inference_steps=100						,    height=768						,    width=768						,    output_type='np'						,    )

						__a      :	Dict    =  output.images[0]

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

						assert_mean_pixel_difference(__a						,    __a							)







'''simple docstring'''



# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__lowercase     :						Dict	=     abspath(join(dirname(dirname(dirname(__file__))), 'src'))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Optional[Any]       ):
					from transformers.testing_utils import pytest_addoption_shared

					pytest_addoption_shared(_SCREAMING_SNAKE_CASE       )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       List[str]       ):
					from transformers.testing_utils import pytest_terminal_summary_main

					__a      :	Any    =  terminalreporter.config.getoption('--make-reports'       )
					if make_reports:
										pytest_terminal_summary_main(_SCREAMING_SNAKE_CASE						,				id=_SCREAMING_SNAKE_CASE       )



