


"""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 _lowerCAmelCase       (   snake_case_					,							unittest.TestCase			):
			__UpperCAmelCase				: List[str]													=						KandinskyInpaintPipeline
			__UpperCAmelCase				: Any													=						['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
			__UpperCAmelCase				: Optional[int]													=						[
			    '''prompt''',
			    '''negative_prompt''',
			    '''image_embeds''',
			    '''negative_image_embeds''',
			    '''image''',
			    '''mask_image''',
			]
			__UpperCAmelCase				: Union[str, Any]													=						[
			    '''generator''',
			    '''height''',
			    '''width''',
			    '''latents''',
			    '''guidance_scale''',
			    '''negative_prompt''',
			    '''num_inference_steps''',
			    '''return_dict''',
			    '''guidance_scale''',
			    '''num_images_per_prompt''',
			    '''output_type''',
			    '''return_dict''',
			]
			__UpperCAmelCase				: str													=						False







			@property
			def 				lowerCamelCase	(			self		)			->	Any:

							'''simple docstring'''




							return 32







			@property
			def 				lowerCamelCase	(			self		)			->	str:

							'''simple docstring'''




							return 32







			@property
			def 				lowerCamelCase	(			self		)			->	Any:

							'''simple docstring'''




							return self.time_input_dim







			@property
			def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

							'''simple docstring'''




							return self.time_input_dim * 4







			@property
			def 				lowerCamelCase	(			self		)			->	int:

							'''simple docstring'''




							return 100







			@property
			def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

							'''simple docstring'''




							snake_case					:   Union[str, Any]         =					XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"		)
							return tokenizer







			@property
			def 				lowerCamelCase	(			self		)			->	Tuple:

							'''simple docstring'''




							torch.manual_seed(0		)
							snake_case					:   Optional[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		,			)

							snake_case					:   List[str]         =					MultilingualCLIP(UpperCamelCase__		)
							snake_case					:   Dict         =					text_encoder.eval()

							return text_encoder







			@property
			def 				lowerCamelCase	(			self		)			->	str:

							'''simple docstring'''




							torch.manual_seed(0		)

							snake_case					:   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,
							}

							snake_case					:   Dict         =					UNetaDConditionModel(**UpperCamelCase__		)
							return model







			@property
			def 				lowerCamelCase	(			self		)			->	Dict:

							'''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		)			->	Union[str, Any]:

							'''simple docstring'''




							torch.manual_seed(0		)
							snake_case					:   Union[str, Any]         =					VQModel(**self.dummy_movq_kwargs		)
							return model







			def 				lowerCamelCase	(			self		)			->	str:

							'''simple docstring'''




							snake_case					:   int         =					self.dummy_text_encoder
							snake_case					:   Dict         =					self.dummy_tokenizer
							snake_case					:   List[str]         =					self.dummy_unet
							snake_case					:   Union[str, Any]         =					self.dummy_movq

							snake_case					:   int         =					DDIMScheduler(
							    num_train_timesteps=1000		,			beta_schedule="linear"		,			beta_start=0.00085		,			beta_end=0.012		,			clip_sample=UpperCamelCase__		,			set_alpha_to_one=UpperCamelCase__		,			steps_offset=1		,			prediction_type="epsilon"		,			thresholding=UpperCamelCase__		,			)

							snake_case					:   List[str]         =					{
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "movq": movq,
							}

							return components







			def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__=0		)			->	Any:

							'''simple docstring'''




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

							if str(UpperCamelCase__		).startswith("mps"		):
											snake_case					:   int         =					torch.manual_seed(UpperCamelCase__		)
							else:
											snake_case					:   str         =					torch.Generator(device=UpperCamelCase__		).manual_seed(UpperCamelCase__		)
							snake_case					:   str         =					{
							    "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		)			->	Dict:

							'''simple docstring'''




							snake_case					:   List[str]         =					"cpu"

							snake_case					:   str         =					self.get_dummy_components()

							snake_case					:   str         =					self.pipeline_class(**UpperCamelCase__		)
							snake_case					:   str         =					pipe.to(UpperCamelCase__		)

							pipe.set_progress_bar_config(disable=UpperCamelCase__		)

							snake_case					:   str         =					pipe(**self.get_dummy_inputs(UpperCamelCase__		)		)
							snake_case					:   int         =					output.images

							snake_case					:   List[Any]         =					pipe(
							    **self.get_dummy_inputs(UpperCamelCase__		)		,			return_dict=UpperCamelCase__		,			)[0]

							snake_case					:   str         =					image[0, -3:, -3:, -1]
							snake_case					:   Any         =					image_from_tuple[0, -3:, -3:, -1]

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

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

							snake_case					:   int         =					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 				lowerCamelCase	(			self		)			->	Dict:

							'''simple docstring'''




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







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







			def 				lowerCamelCase	(			self		)			->	Any:

							'''simple docstring'''




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







			def 				lowerCamelCase	(			self		)			->	Dict:

							'''simple docstring'''




							snake_case					:   Optional[Any]         =					load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"		)

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

							snake_case					:   Any         =					"a hat"

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

							snake_case					:   Dict         =					KandinskyInpaintPipeline.from_pretrained(
							    "kandinsky-community/kandinsky-2-1-inpaint"		,			torch_dtype=torch.floataa		)
							snake_case					:   str         =					pipeline.to(UpperCamelCase__		)
							pipeline.set_progress_bar_config(disable=UpperCamelCase__		)

							snake_case					:   int         =					torch.Generator(device="cpu"		).manual_seed(0		)
							snake_case					:   Tuple         =					pipe_prior(
							    UpperCamelCase__		,			generator=UpperCamelCase__		,			num_inference_steps=5		,			negative_prompt=""		,			).to_tuple()

							snake_case					:   Any         =					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"		,			)

							snake_case					:   List[str]         =					output.images[0]

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

							assert_mean_pixel_difference(UpperCamelCase__		,			UpperCamelCase__		)









"""simple docstring"""



# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


__snake_case         =					subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
__snake_case         =					(
    subprocess.check_output(F'''git diff --diff-filter=d --name-only {fork_point_sha}'''.split()).decode("""utf-8""").split()
)

__snake_case         =					"""|""".join(sys.argv[1:])
__snake_case         =					re.compile(RF'''^({joined_dirs}).*?\.py$''')

__snake_case         =					[x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")





