"""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 	UpperCamelCase    (					lowercase   ,			unittest.TestCase		):
				UpperCAmelCase       :   Any					=	KandinskyImgaImgPipeline
				UpperCAmelCase       :   Optional[Any]					=	["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
				UpperCAmelCase       :   str					=	[
				    """prompt""",
				    """negative_prompt""",
				    """image_embeds""",
				    """negative_image_embeds""",
				    """image""",
				]
				UpperCAmelCase       :   Union[str, Any]					=	[
				    """generator""",
				    """height""",
				    """width""",
				    """strength""",
				    """guidance_scale""",
				    """negative_prompt""",
				    """num_inference_steps""",
				    """return_dict""",
				    """guidance_scale""",
				    """num_images_per_prompt""",
				    """output_type""",
				    """return_dict""",
				]
				UpperCAmelCase       :   Union[str, Any]					=	False



				@property
				def       _lowercase  (self       :   Optional[int])					->			List[Any]:
					return 32



				@property
				def       _lowercase  (self       :   List[Any])					->			Optional[int]:
					return 32



				@property
				def       _lowercase  (self       :   Any)					->			Optional[Any]:
					return self.time_input_dim



				@property
				def       _lowercase  (self       :   Dict)					->			int:
					return self.time_input_dim * 4



				@property
				def       _lowercase  (self       :   int)					->			int:
					return 1_00



				@property
				def       _lowercase  (self       :   Optional[Any])					->			int:
					__snake_case    :							Optional[int]   	=				XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
					return tokenizer



				@property
				def       _lowercase  (self       :   Any)					->			str:
					torch.manual_seed(0)
					__snake_case    :							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=10_05			,						)

					__snake_case    :							Dict   	=				MultilingualCLIP(__UpperCamelCase)
					__snake_case    :							int   	=				text_encoder.eval()

					return text_encoder



				@property
				def       _lowercase  (self       :   int)					->			int:
					torch.manual_seed(0)

					__snake_case    :							int   	=				{
					    '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    :							str   	=				UNetaDConditionModel(**__UpperCamelCase)
					return model



				@property
				def       _lowercase  (self       :   Any)					->			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       _lowercase  (self       :   Optional[int])					->			Optional[Any]:
					torch.manual_seed(0)
					__snake_case    :							Tuple   	=				VQModel(**self.dummy_movq_kwargs)
					return model



				def       _lowercase  (self       :   str)					->			int:
					__snake_case    :							str   	=				self.dummy_text_encoder
					__snake_case    :							Optional[int]   	=				self.dummy_tokenizer
					__snake_case    :							int   	=				self.dummy_unet
					__snake_case    :							Tuple   	=				self.dummy_movq

					__snake_case    :							str   	=				{
					    '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    :							List[str]   	=				DDIMScheduler(**__UpperCamelCase)

					__snake_case    :							Tuple   	=				{
					    'text_encoder': text_encoder,
					    'tokenizer': tokenizer,
					    'unet': unet,
					    'scheduler': scheduler,
					    'movq': movq,
					}

					return components



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

					if str(__UpperCamelCase).startswith('mps'):
						__snake_case    :							List[Any]   	=				torch.manual_seed(__UpperCamelCase)
					else:
						__snake_case    :							Dict   	=				torch.Generator(device=__UpperCamelCase).manual_seed(__UpperCamelCase)
					__snake_case    :							str   	=				{
					    '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       _lowercase  (self       :   int)					->			Union[str, Any]:
					__snake_case    :							Any   	=				'cpu'

					__snake_case    :							Optional[int]   	=				self.get_dummy_components()

					__snake_case    :							Optional[Any]   	=				self.pipeline_class(**__UpperCamelCase)
					__snake_case    :							Any   	=				pipe.to(__UpperCamelCase)

					pipe.set_progress_bar_config(disable=__UpperCamelCase)

					__snake_case    :							Optional[int]   	=				pipe(**self.get_dummy_inputs(__UpperCamelCase))
					__snake_case    :							int   	=				output.images

					__snake_case    :							Union[str, Any]   	=				pipe(
					    **self.get_dummy_inputs(__UpperCamelCase)			,						return_dict=__UpperCamelCase			,						)[0]

					__snake_case    :							Union[str, Any]   	=				image[0, -3:, -3:, -1]
					__snake_case    :							List[Any]   	=				image_from_tuple[0, -3:, -3:, -1]

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

					__snake_case    :							Union[str, Any]   	=				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 	UpperCamelCase    (					unittest.TestCase		):



				def       _lowercase  (self       :   Union[str, Any])					->			Optional[int]:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()






				def       _lowercase  (self       :   Tuple)					->			Tuple:
					__snake_case    :							List[Any]   	=				load_numpy(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
					    '/kandinsky/kandinsky_img2img_frog.npy')

					__snake_case    :							Dict   	=				load_image(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
					__snake_case    :							Union[str, Any]   	=				'A red cartoon frog, 4k'

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

					__snake_case    :							Union[str, Any]   	=				KandinskyImgaImgPipeline.from_pretrained(
					    'kandinsky-community/kandinsky-2-1'			,						torch_dtype=torch.floataa)
					__snake_case    :							Tuple   	=				pipeline.to(__UpperCamelCase)

					pipeline.set_progress_bar_config(disable=__UpperCamelCase)

					__snake_case    :							int   	=				torch.Generator(device='cpu').manual_seed(0)
					__snake_case	,       __snake_case    :							Optional[int]   	=				pipe_prior(
					    __UpperCamelCase			,						generator=__UpperCamelCase			,						num_inference_steps=5			,						negative_prompt=''			,						).to_tuple()

					__snake_case    :							Optional[Any]   	=				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    :							Tuple   	=				output.images[0]

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

					assert_mean_pixel_difference(__UpperCamelCase			,						__UpperCamelCase)






"""simple docstring"""







from typing import Any

class 	UpperCamelCase    :



				def __init__(self       :   List[str]			,						_A       :   Any)					->			int:
					__snake_case    :							Any   	=				data
					__snake_case    :							Dict   	=				None






				def __repr__(self       :   Tuple)					->			str:
					return f"Node({self.data})"







class 	UpperCamelCase    :



				def __init__(self       :   Union[str, Any])					->			Union[str, Any]:
					__snake_case    :							Any   	=				None



				def __iter__(self       :   Tuple)					->			Any:
					__snake_case    :							List[str]   	=				self.head
					while node:
						yield node.data
						__snake_case    :							Any   	=				node.next



				def __len__(self       :   str)					->			int:
					return sum(1 for _ in self)



				def __repr__(self       :   int)					->			str:
					return "->".join([str(_A) for item in self])



				def __getitem__(self       :   List[Any]			,						_A       :   int)					->			Any:
					if not 0 <= index < len(self):
						raise ValueError('list index out of range.')
					for i, node in enumerate(self):
						if i == index:
							return node
					return None



				def __setitem__(self       :   int			,						_A       :   int			,						_A       :   Any)					->			None:
					if not 0 <= index < len(self):
						raise ValueError('list index out of range.')
					__snake_case    :							Optional[int]   	=				self.head
					for _ in range(_A):
						__snake_case    :							Any   	=				current.next
					__snake_case    :							Dict   	=				data



				def       _lowercase  (self       :   List[Any]			,						_A       :   Any)					->			None:
					self.insert_nth(len(self)			,						_A)



				def       _lowercase  (self       :   List[str]			,						_A       :   Any)					->			None:
					self.insert_nth(0			,						_A)



				def       _lowercase  (self       :   Optional[Any]			,						_A       :   int			,						_A       :   Any)					->			None:
					if not 0 <= index <= len(self):
						raise IndexError('list index out of range')
					__snake_case    :							str   	=				Node(_A)
					if self.head is None:
						__snake_case    :							str   	=				new_node
					elif index == 0:
						__snake_case    :							Union[str, Any]   	=				self.head  # link new_node to head
						__snake_case    :							int   	=				new_node
					else:
						__snake_case    :							Any   	=				self.head
						for _ in range(index - 1):
							__snake_case    :							Any   	=				temp.next
						__snake_case    :							Dict   	=				temp.next
						__snake_case    :							str   	=				new_node



				def       _lowercase  (self       :   Optional[int])					->			None:  # print every node data
					print(self)



				def       _lowercase  (self       :   Optional[Any])					->			Any:
					return self.delete_nth(0)



				def       _lowercase  (self       :   List[str])					->			Any:  # delete from tail
					return self.delete_nth(len(self) - 1)



				def       _lowercase  (self       :   int			,						_A       :   int = 0)					->			Any:
					if not 0 <= index <= len(self) - 1:  # test if index is valid
						raise IndexError('List index out of range.')
					__snake_case    :							int   	=				self.head  # default first node
					if index == 0:
						__snake_case    :							Any   	=				self.head.next
					else:
						__snake_case    :							List[Any]   	=				self.head
						for _ in range(index - 1):
							__snake_case    :							List[str]   	=				temp.next
						__snake_case    :							Union[str, Any]   	=				temp.next
						__snake_case    :							str   	=				temp.next.next
					return delete_node.data



				def       _lowercase  (self       :   str)					->			bool:
					return self.head is None






				def       _lowercase  (self       :   Tuple)					->			None:
					__snake_case    :							List[Any]   	=				None
					__snake_case    :							Optional[Any]   	=				self.head

					while current:
						# Store the current node's next node.
						__snake_case    :							List[str]   	=				current.next
						# Make the current node's next point backwards
						__snake_case    :							Optional[Any]   	=				prev
						# Make the previous node be the current node
						__snake_case    :							Optional[Any]   	=				current
						# Make the current node the next node (to progress iteration)
						__snake_case    :							Any   	=				next_node
					# Return prev in order to put the head at the end
					__snake_case    :							Optional[Any]   	=				prev







def 						__UpperCAmelCase       (					)							->    None:

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				LinkedList()
	assert linked_list.is_empty() is True
	assert str(UpperCAmelCase_						) == ""

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

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

	for i in range(10						):
		assert len(UpperCAmelCase_						) == i
		linked_list.insert_nth(UpperCAmelCase_    ,							i + 1						)
	assert str(UpperCAmelCase_						) == "->".join(str(UpperCAmelCase_						) for i in range(1    ,							11						)						)

	linked_list.insert_head(0						)
	linked_list.insert_tail(11						)
	assert str(UpperCAmelCase_						) == "->".join(str(UpperCAmelCase_						) for i in range(0    ,							12						)						)

	assert linked_list.delete_head() == 0
	assert linked_list.delete_nth(9						) == 10
	assert linked_list.delete_tail() == 11
	assert len(UpperCAmelCase_						) == 9
	assert str(UpperCAmelCase_						) == "->".join(str(UpperCAmelCase_						) for i in range(1    ,							10						)						)

	assert all(linked_list[i] == i + 1 for i in range(0    ,							9						)						) is True

	for i in range(0    ,							9						):
		__snake_case    :							Tuple   	=				-i
	assert all(linked_list[i] == -i for i in range(0    ,							9						)						) is True

	linked_list.reverse()
	assert str(UpperCAmelCase_						) == "->".join(str(UpperCAmelCase_						) for i in range(-8    ,							1						)						)
def 						__UpperCAmelCase       (					)							->    None:

	'''simple docstring'''





	__snake_case    :							str   	=				[
	    -9,
	    1_00,
	    Node(77_34_51_12						),
	    'dlrow olleH',
	    7,
	    55_55,
	    0,
	    -192.55_555,
	    'Hello, world!',
	    77.9,
	    Node(10						),
	    None,
	    None,
	    12.20,
	]
	__snake_case    :							Optional[Any]   	=				LinkedList()

	for i in test_input:
		linked_list.insert_tail(UpperCAmelCase_						)

	# Check if it's empty or not
	assert linked_list.is_empty() is False
	assert (
	    str(UpperCAmelCase_						) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
	    "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
	)

	# Delete the head
	__snake_case    :							int   	=				linked_list.delete_head()
	assert result == -9
	assert (
	    str(UpperCAmelCase_						) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
	    "Hello, world!->77.9->Node(10)->None->None->12.2"
	)

	# Delete the tail
	__snake_case    :							Any   	=				linked_list.delete_tail()
	assert result == 12.2
	assert (
	    str(UpperCAmelCase_						) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
	    "Hello, world!->77.9->Node(10)->None->None"
	)

	# Delete a node in specific location in linked list
	__snake_case    :							Tuple   	=				linked_list.delete_nth(10						)
	assert result is None
	assert (
	    str(UpperCAmelCase_						) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
	    "Hello, world!->77.9->Node(10)->None"
	)

	# Add a Node instance to its head
	linked_list.insert_head(Node('Hello again, world!'						)						)
	assert (
	    str(UpperCAmelCase_						)
	    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
	    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
	)

	# Add None to its tail
	linked_list.insert_tail(UpperCAmelCase_						)
	assert (
	    str(UpperCAmelCase_						)
	    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
	    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
	)

	# Reverse the linked list
	linked_list.reverse()
	assert (
	    str(UpperCAmelCase_						)
	    == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
	    "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
	)
def 						__UpperCAmelCase       (					)							->    List[Any]:

	'''simple docstring'''





	from doctest import testmod

	testmod()

	__snake_case    :							int   	=				LinkedList()
	linked_list.insert_head(input('Inserting 1st at head '						).strip()						)
	linked_list.insert_head(input('Inserting 2nd at head '						).strip()						)
	print('\nPrint list:'						)
	linked_list.print_list()
	linked_list.insert_tail(input('\nInserting 1st at tail '						).strip()						)
	linked_list.insert_tail(input('Inserting 2nd at tail '						).strip()						)
	print('\nPrint list:'						)
	linked_list.print_list()
	print('\nDelete head'						)
	linked_list.delete_head()
	print('Delete tail'						)
	linked_list.delete_tail()
	print('\nPrint list:'						)
	linked_list.print_list()
	print('\nReverse linked list'						)
	linked_list.reverse()
	print('\nPrint list:'						)
	linked_list.print_list()
	print('\nString representation of linked list:'						)
	print(UpperCAmelCase_						)
	print('\nReading/changing Node data using indexing:'						)
	print(F"Element at Position 1: {linked_list[1]}"						)
	__snake_case    :							Optional[Any]   	=				input('Enter New Value: '						).strip()
	print('New list:'						)
	print(UpperCAmelCase_						)
	print(F"length of linked_list is : {len(UpperCAmelCase_						)}"						)


if __name__ == "__main__":
	main()





