
'''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			):






						'''simple docstring'''






						_lowercase		:				Any									=       KandinskyInpaintPipeline
						_lowercase		:				Optional[int]									=       ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
						_lowercase		:				str									=       [
						    '''prompt''',
						    '''negative_prompt''',
						    '''image_embeds''',
						    '''negative_image_embeds''',
						    '''image''',
						    '''mask_image''',
						]
						_lowercase		:				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''',
						]
						_lowercase		:				List[str]									=       False




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												return 32




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												return 32




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												return self.time_input_dim




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												return self.time_input_dim * 4




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												return 100




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



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




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												torch.manual_seed(0				)
												_lowerCAmelCase  						=			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=1_005   ,       )

												_lowerCAmelCase  						=			MultilingualCLIP(__snake_case				)
												_lowerCAmelCase  						=			text_encoder.eval()

												return text_encoder




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												torch.manual_seed(0				)

												_lowerCAmelCase  						=			{
												    """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,
												}

												_lowerCAmelCase  						=			UNetaDConditionModel(**__snake_case				)
												return model




						@property
						def  _lowercase     ( 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  _lowercase     ( self				):

												"""simple docstring"""



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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.dummy_text_encoder
												_lowerCAmelCase  						=			self.dummy_tokenizer
												_lowerCAmelCase  						=			self.dummy_unet
												_lowerCAmelCase  						=			self.dummy_movq

												_lowerCAmelCase  						=			DDIMScheduler(
												    num_train_timesteps=1_000   ,       beta_schedule="""linear"""   ,       beta_start=0.0_0085   ,       beta_end=0.012   ,       clip_sample=__snake_case   ,       set_alpha_to_one=__snake_case   ,       steps_offset=1   ,       prediction_type="""epsilon"""   ,       thresholding=__snake_case   ,       )

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

												return components




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase=0				):

												"""simple docstring"""



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

												if str(__snake_case				).startswith("""mps"""				):
																		_lowerCAmelCase  						=			torch.manual_seed(__snake_case				)
												else:
																		_lowerCAmelCase  						=			torch.Generator(device=__snake_case				).manual_seed(__snake_case				)
												_lowerCAmelCase  						=			{
												    """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  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			"""cpu"""

												_lowerCAmelCase  						=			self.get_dummy_components()

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

												pipe.set_progress_bar_config(disable=__snake_case				)

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

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

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

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

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

												_lowerCAmelCase  						=			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				):

												"""simple docstring"""



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






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






						'''simple docstring'''






						def  _lowercase     ( self				):

												"""simple docstring"""



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




						def  _lowercase     ( self				):

												"""simple docstring"""



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

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

												_lowerCAmelCase  						=			"""a hat"""

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

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

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

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

												_lowerCAmelCase  						=			output.images[0]

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

												assert_mean_pixel_difference(__snake_case   ,       __snake_case				)


'''simple docstring'''



from __future__ import annotations

from typing import Any




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						pass






class   UpperCAmelCase_					:






						'''simple docstring'''






						def __init__( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			data
												_lowerCAmelCase  						=			None




						def __iter__( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self
												_lowerCAmelCase  						=			[]
												while node:
																		if node in visited:
																								raise ContainsLoopError
																		visited.append(_lowercase				)
																		yield node.data
																		_lowerCAmelCase  						=			node.next_node




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												try:
																		list(self				)
																		return False
												except ContainsLoopError:
																		return True


if __name__ == "__main__":
						_lowercase    =		Node(1)
						_lowercase    =		Node(2)
						_lowercase    =		Node(3)
						_lowercase    =		Node(4)
						print(root_node.has_loop)  # False
						_lowercase    =		root_node.next_node
						print(root_node.has_loop)  # True

						_lowercase    =		Node(5)
						_lowercase    =		Node(6)
						_lowercase    =		Node(5)
						_lowercase    =		Node(6)
						print(root_node.has_loop)  # False

						_lowercase    =		Node(1)
						print(root_node.has_loop)  # False
