






'''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, KandinskyImgaImgPipeline, 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   __SCREAMING_SNAKE_CASE(	a_    ,  unittest.TestCase	):
							_UpperCAmelCase     =       KandinskyImgaImgPipeline
							_UpperCAmelCase     =       ["prompt", "image_embeds", "negative_image_embeds", "image"]
							_UpperCAmelCase     =       [
							    "prompt",
							    "negative_prompt",
							    "image_embeds",
							    "negative_image_embeds",
							    "image",
							]
							_UpperCAmelCase     =       [
							    "generator",
							    "height",
							    "width",
							    "strength",
							    "guidance_scale",
							    "negative_prompt",
							    "num_inference_steps",
							    "return_dict",
							    "guidance_scale",
							    "num_images_per_prompt",
							    "output_type",
							    "return_dict",
							]
							_UpperCAmelCase     =       False
							@property
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Optional[int]:
											return 32
							@property
							def lowerCAmelCase_ (     self:      Dict						)		->		int:
											return 32
							@property
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Dict:
											return self.time_input_dim
							@property
							def lowerCAmelCase_ (     self:      str						)		->		Dict:
											return self.time_input_dim * 4
							@property
							def lowerCAmelCase_ (     self:      Tuple						)		->		Optional[int]:
											return 1_00
							@property
							def lowerCAmelCase_ (     self:      Tuple						)		->		int:
											snake_case__									=     XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'						)
											return tokenizer
							@property
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Tuple:
											torch.manual_seed(0						)
											snake_case__									=     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=10_05       ,	)

											snake_case__									=     MultilingualCLIP(UpperCamelCase						)
											snake_case__									=     text_encoder.eval()

											return text_encoder
							@property
							def lowerCAmelCase_ (     self:      List[Any]						)		->		Tuple:
											torch.manual_seed(0						)

											snake_case__									=     {
											    'in_channels': 4,
											    # 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__									=     UNetaDConditionModel(**UpperCamelCase						)
											return model
							@property
							def lowerCAmelCase_ (     self:      int						)		->		List[str]:
											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:      Optional[int]						)		->		List[str]:
											torch.manual_seed(0						)
											snake_case__									=     VQModel(**self.dummy_movq_kwargs						)
											return model
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Tuple:
											snake_case__									=     self.dummy_text_encoder
											snake_case__									=     self.dummy_tokenizer
											snake_case__									=     self.dummy_unet
											snake_case__									=     self.dummy_movq

											snake_case__									=     {
											    'num_train_timesteps': 10_00,
											    'beta_schedule': 'linear',
											    'beta_start': 0.00_085,
											    'beta_end': 0.012,
											    'clip_sample': False,
											    'set_alpha_to_one': False,
											    'steps_offset': 0,
											    'prediction_type': 'epsilon',
											    'thresholding': False,
											}

											snake_case__									=     DDIMScheduler(**UpperCamelCase						)

											snake_case__									=     {
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											    'unet': unet,
											    'scheduler': scheduler,
											    'movq': movq,
											}

											return components
							def lowerCAmelCase_ (     self:      Union[str, Any]       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      Union[str, Any]=0						)		->		Optional[Any]:
											snake_case__									=     floats_tensor((1, self.cross_attention_dim)       ,	rng=random.Random(UpperCamelCase						)						).to(UpperCamelCase						)
											snake_case__									=     floats_tensor((1, self.cross_attention_dim)       ,	rng=random.Random(seed + 1						)						).to(UpperCamelCase						)
											# create init_image
											snake_case__									=     floats_tensor((1, 3, 64, 64)       ,	rng=random.Random(UpperCamelCase						)						).to(UpperCamelCase						)
											snake_case__									=     image.cpu().permute(0       ,	2       ,	3       ,	1						)[0]
											snake_case__									=     Image.fromarray(np.uinta(UpperCamelCase						)						).convert('RGB'						).resize((2_56, 2_56)						)

											if str(UpperCamelCase						).startswith('mps'						):
															snake_case__									=     torch.manual_seed(UpperCamelCase						)
											else:
															snake_case__									=     torch.Generator(device=UpperCamelCase						).manual_seed(UpperCamelCase						)
											snake_case__									=     {
											    'prompt': 'horse',
											    'image': init_image,
											    'image_embeds': image_embeds,
											    'negative_image_embeds': negative_image_embeds,
											    'generator': generator,
											    'height': 64,
											    'width': 64,
											    'num_inference_steps': 10,
											    'guidance_scale': 7.0,
											    'strength': 0.2,
											    'output_type': 'np',
											}
											return inputs


							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Optional[int]:
											snake_case__									=     'cpu'

											snake_case__									=     self.get_dummy_components()

											snake_case__									=     self.pipeline_class(**UpperCamelCase						)
											snake_case__									=     pipe.to(UpperCamelCase						)

											pipe.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     pipe(**self.get_dummy_inputs(UpperCamelCase						)						)
											snake_case__									=     output.images

											snake_case__									=     pipe(
											    **self.get_dummy_inputs(UpperCamelCase						)       ,	return_dict=UpperCamelCase       ,	)[0]

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

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

											snake_case__									=     np.array(
											    [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]						)
											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()}'''


@slow
@require_torch_gpu
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def lowerCAmelCase_ (     self:      List[str]						)		->		List[str]:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()


							def lowerCAmelCase_ (     self:      Dict						)		->		int:
											snake_case__									=     load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/kandinsky/kandinsky_img2img_frog.npy'						)

											snake_case__									=     load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'						)
											snake_case__									=     'A red cartoon frog, 4k'

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

											snake_case__									=     KandinskyImgaImgPipeline.from_pretrained(
											    'kandinsky-community/kandinsky-2-1'       ,	torch_dtype=torch.floataa						)
											snake_case__									=     pipeline.to(UpperCamelCase						)

											pipeline.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     torch.Generator(device='cpu'						).manual_seed(0						)
											snake_case__							,						snake_case__									=     pipe_prior(
											    UpperCamelCase       ,	generator=UpperCamelCase       ,	num_inference_steps=5       ,	negative_prompt=''       ,	).to_tuple()

											snake_case__									=     pipeline(
											    UpperCamelCase       ,	image=UpperCamelCase       ,	image_embeds=UpperCamelCase       ,	negative_image_embeds=UpperCamelCase       ,	generator=UpperCamelCase       ,	num_inference_steps=1_00       ,	height=7_68       ,	width=7_68       ,	strength=0.2       ,	output_type='np'       ,	)

											snake_case__									=     output.images[0]

											assert image.shape == (7_68, 7_68, 3)

											assert_mean_pixel_difference(UpperCamelCase       ,	UpperCamelCase						)












from __future__ import annotations

from collections.abc import Iterator
from typing import Any


class   __SCREAMING_SNAKE_CASE:
							def __init__(     self:      int       ,	UpperCamelCase:      Any						)		->		List[Any]:
											snake_case__									=     data
											snake_case__									=     None


class   __SCREAMING_SNAKE_CASE:
							def __init__(     self:      Dict						)		->		Optional[Any]:
											snake_case__									=     None
											snake_case__									=     None
							def __iter__(     self:      Optional[int]						)		->		Iterator[Any]:
											snake_case__									=     self.head
											while self.head:
															yield node.data
															snake_case__									=     node.next
															if node == self.head:
																			break
							def __len__(     self:      int						)		->		int:
											return sum(1 for _ in self						)
							def __repr__(     self:      int						)		->		Optional[int]:
											return "->".join(str(UpperCamelCase						) for item in iter(self						)						)
							def lowerCAmelCase_ (     self:      Union[str, Any]       ,	UpperCamelCase:      Any						)		->		None:
											self.insert_nth(len(self						)       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Tuple       ,	UpperCamelCase:      Any						)		->		None:
											self.insert_nth(0       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      int       ,	UpperCamelCase:      int       ,	UpperCamelCase:      Any						)		->		None:
											if index < 0 or index > len(self						):
															raise IndexError('list index out of range.'						)
											snake_case__									=     Node(UpperCamelCase						)
											if self.head is None:
															snake_case__									=     new_node  # first node points itself
															snake_case__									=     snake_case__									=     new_node
											elif index == 0:  # insert at head
															snake_case__									=     self.head
															snake_case__									=     snake_case__									=     new_node
											else:
															snake_case__									=     self.head
															for _ in range(index - 1						):
																			snake_case__									=     temp.next
															snake_case__									=     temp.next
															snake_case__									=     new_node
															if index == len(self						) - 1:  # insert at tail
																			snake_case__									=     new_node
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Union[str, Any]:
											return self.delete_nth(0						)
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Any:
											return self.delete_nth(len(self						) - 1						)
							def lowerCAmelCase_ (     self:      str       ,	UpperCamelCase:      int = 0						)		->		Any:
											if not 0 <= index < len(self						):
															raise IndexError('list index out of range.'						)
											snake_case__									=     self.head
											if self.head == self.tail:  # just one node
															snake_case__									=     snake_case__									=     None
											elif index == 0:  # delete head node
															snake_case__									=     self.tail.next.next
															snake_case__									=     self.head.next
											else:
															snake_case__									=     self.head
															for _ in range(index - 1						):
																			snake_case__									=     temp.next
															snake_case__									=     temp.next
															snake_case__									=     temp.next.next
															if index == len(self						) - 1:  # delete at tail
																			snake_case__									=     temp
											return delete_node.data


							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		bool:
											return len(self						) == 0


def      a_ (      )			->			None:







				"""simple docstring"""
				snake_case__									=     CircularLinkedList()
				assert len(_A  ) == 0
				assert circular_linked_list.is_empty() is True
				assert str(_A  ) == ""

				try:
								circular_linked_list.delete_front()
								raise AssertionError  # This should not happen
				except IndexError:
								assert True  # This should happen

				try:
								circular_linked_list.delete_tail()
								raise AssertionError  # This should not happen
				except IndexError:
								assert True  # This should happen

				try:
								circular_linked_list.delete_nth(-1  )
								raise AssertionError
				except IndexError:
								assert True

				try:
								circular_linked_list.delete_nth(0  )
								raise AssertionError
				except IndexError:
								assert True

				assert circular_linked_list.is_empty() is True
				for i in range(5  ):
								assert len(_A  ) == i
								circular_linked_list.insert_nth(_A      ,   i + 1  )
				assert str(_A  ) == "->".join(str(_A  ) for i in range(1      ,   6  )  )

				circular_linked_list.insert_tail(6  )
				assert str(_A  ) == "->".join(str(_A  ) for i in range(1      ,   7  )  )
				circular_linked_list.insert_head(0  )
				assert str(_A  ) == "->".join(str(_A  ) for i in range(0      ,   7  )  )

				assert circular_linked_list.delete_front() == 0
				assert circular_linked_list.delete_tail() == 6
				assert str(_A  ) == "->".join(str(_A  ) for i in range(1      ,   6  )  )
				assert circular_linked_list.delete_nth(2  ) == 3

				circular_linked_list.insert_nth(2      ,   3  )
				assert str(_A  ) == "->".join(str(_A  ) for i in range(1      ,   6  )  )

				assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
							import doctest

							doctest.testmod()




