



'''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       (_UpperCAmelCase			,   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   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							return 32







	@property
	def   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






							return 32







	@property
	def   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






							return self.time_input_dim







	@property
	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






							return self.time_input_dim * 4







	@property
	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							return 100







	@property
	def   _a   (    self						)    ->    str:
							'''simple docstring'''






							lowercase        =							XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""						)
							return tokenizer







	@property
	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							torch.manual_seed(0						)
							lowercase        =							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        =							MultilingualCLIP(_lowerCAmelCase						)
							lowercase        =							text_encoder.eval()

							return text_encoder







	@property
	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							torch.manual_seed(0						)

							lowercase        =							{
							    """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        =							UNetaDConditionModel(**_lowerCAmelCase						)
							return model







	@property
	def   _a   (    self						)    ->    Tuple:
							'''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   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							torch.manual_seed(0						)
							lowercase        =							VQModel(**self.dummy_movq_kwargs						)
							return model







	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






							lowercase        =							self.dummy_text_encoder
							lowercase        =							self.dummy_tokenizer
							lowercase        =							self.dummy_unet
							lowercase        =							self.dummy_movq

							lowercase        =							DDIMScheduler(
							    num_train_timesteps=1000 ,				beta_schedule="""linear""" ,				beta_start=0.0_0085 ,				beta_end=0.012 ,				clip_sample=_lowerCAmelCase ,				set_alpha_to_one=_lowerCAmelCase ,				steps_offset=1 ,				prediction_type="""epsilon""" ,				thresholding=_lowerCAmelCase ,				)

							lowercase        =							{
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """movq""": movq,
							}

							return components







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase=0						)    ->    Dict:
							'''simple docstring'''






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

							if str(_lowerCAmelCase						).startswith("""mps"""						):
													lowercase        =							torch.manual_seed(_lowerCAmelCase						)
							else:
													lowercase        =							torch.Generator(device=_lowerCAmelCase						).manual_seed(_lowerCAmelCase						)
							lowercase        =							{
							    """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   _a   (    self						)    ->    Optional[int]:
							'''simple docstring'''






							lowercase        =							"""cpu"""

							lowercase        =							self.get_dummy_components()

							lowercase        =							self.pipeline_class(**_lowerCAmelCase						)
							lowercase        =							pipe.to(_lowerCAmelCase						)

							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

							lowercase        =							pipe(**self.get_dummy_inputs(_lowerCAmelCase						)						)
							lowercase        =							output.images

							lowercase        =							pipe(
							    **self.get_dummy_inputs(_lowerCAmelCase						) ,				return_dict=_lowerCAmelCase ,				)[0]

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

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

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

							lowercase        =							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   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






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




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







	def   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""						)

							lowercase        =							load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""						)
							lowercase        =							np.ones((768, 768) ,				dtype=np.floataa						)
							lowercase        =							0

							lowercase        =							"""a hat"""

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

							lowercase        =							KandinskyInpaintPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-1-inpaint""" ,				torch_dtype=torch.floataa						)
							lowercase        =							pipeline.to(_lowerCAmelCase						)
							pipeline.set_progress_bar_config(disable=_lowerCAmelCase						)

							lowercase        =							torch.Generator(device="""cpu"""						).manual_seed(0						)
							lowercase     ,   lowercase        =							pipe_prior(
							    _lowerCAmelCase ,				generator=_lowerCAmelCase ,				num_inference_steps=5 ,				negative_prompt="""""" ,				).to_tuple()

							lowercase        =							pipeline(
							    _lowerCAmelCase ,				image=_lowerCAmelCase ,				mask_image=_lowerCAmelCase ,				image_embeds=_lowerCAmelCase ,				negative_image_embeds=_lowerCAmelCase ,				generator=_lowerCAmelCase ,				num_inference_steps=100 ,				height=768 ,				width=768 ,				output_type="""np""" ,				)

							lowercase        =							output.images[0]

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

							assert_mean_pixel_difference(_lowerCAmelCase ,				_lowerCAmelCase						)







'''simple docstring'''


import requests







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str ,  lowercase_    :      str						):
						lowercase        =							{"""Content-Type""": """application/json"""}
						lowercase        =							requests.post(lowercase_ ,  json={"""text""": message_body} ,  headers=lowercase_						)
						if response.status_code != 200:
												lowercase        =							(
												    """Request to slack returned an error """
												    F"""{response.status_code}, the response is:\n{response.text}"""
												)
												raise ValueError(lowercase_						)


if __name__ == "__main__":
						# Set the slack url to the one provided by Slack when you create the webhook at
						# https://my.slack.com/services/new/incoming-webhook/
						send_slack_message('''<YOUR MESSAGE BODY>''', '''<SLACK CHANNEL URL>''')


