
import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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__					(     _snake_case							,unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Optional[Any]          = KandinskyVaaPipeline
	_UpperCAmelCase	:		Any          = [
	    "image_embeds",
	    "negative_image_embeds",
	]
	_UpperCAmelCase	:		List[Any]          = ["image_embeds", "negative_image_embeds"]
	_UpperCAmelCase	:		List[str]          = [
	    "generator",
	    "height",
	    "width",
	    "latents",
	    "guidance_scale",
	    "num_inference_steps",
	    "return_dict",
	    "guidance_scale",
	    "num_images_per_prompt",
	    "output_type",
	    "return_dict",
	]
	_UpperCAmelCase	:		int          = False



	@property
	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				return 32



	@property
	def 			A							(  self     :   str     ):


				'''simple docstring'''


				return 32



	@property
	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				return self.time_input_dim



	@property
	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				return self.time_input_dim * 4



	@property
	def 			A							(  self     :   int     ):


				'''simple docstring'''


				return 100



	@property
	def 			A							(  self     :   Tuple     ):


				'''simple docstring'''


				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': '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': '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 			A							(  self     :   List[Any]     ):


				'''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     :   List[str]     ):


				'''simple docstring'''


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



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      self.dummy_unet
				_snake_case          =      self.dummy_movq

				_snake_case          =      DDIMScheduler(
				    num_train_timesteps=1_000      ,  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          =      {
				    'unet': unet,
				    'scheduler': scheduler,
				    'movq': movq,
				}
				return components



	def 			A							(  self     :   Optional[int]      ,  lowercase     :   Dict      ,  lowercase     :   Dict=0     ):


				'''simple docstring'''


				_snake_case          =      floats_tensor((1, self.text_embedder_hidden_size)      ,  rng=random.Random(UpperCamelCase__     )     ).to(UpperCamelCase__     )
				_snake_case          =      floats_tensor((1, self.text_embedder_hidden_size)      ,  rng=random.Random(seed + 1     )     ).to(
				    UpperCamelCase__     )
				if str(UpperCamelCase__     ).startswith('mps'     ):
							_snake_case          =      torch.manual_seed(UpperCamelCase__     )
				else:
							_snake_case          =      torch.Generator(device=UpperCamelCase__     ).manual_seed(UpperCamelCase__     )
				_snake_case          =      {
				    'image_embeds': image_embeds,
				    'negative_image_embeds': negative_image_embeds,
				    'generator': generator,
				    'height': 64,
				    'width': 64,
				    'guidance_scale': 4.0,
				    'num_inference_steps': 2,
				    'output_type': 'np',
				}
				return inputs





	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_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(
				    [0.6237976, 1.0, 0.36441332, 1.0, 0.70639634, 0.29877186, 0.85652125, 0.5216843, 0.54454046]     )

				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 ):





	'''simple docstring'''



	def 			A							(  self     :   str     ):


				'''simple docstring'''


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





	def 			A							(  self     :   Tuple     ):


				'''simple docstring'''


				_snake_case          =      load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'     )

				_snake_case          =      KandinskyVaaPriorPipeline.from_pretrained(
				    'kandinsky-community/kandinsky-2-2-prior'      ,  torch_dtype=torch.floataa     )
				pipe_prior.to(UpperCamelCase__     )

				_snake_case          =      KandinskyVaaPipeline.from_pretrained(
				    'kandinsky-community/kandinsky-2-2-decoder'      ,  torch_dtype=torch.floataa     )
				_snake_case          =      pipeline.to(UpperCamelCase__     )
				pipeline.set_progress_bar_config(disable=UpperCamelCase__     )

				_snake_case          =      'red cat, 4k photo'

				_snake_case          =      torch.Generator(device='cuda'     ).manual_seed(0     )
				_snake_case     , _snake_case          =      pipe_prior(
				    UpperCamelCase__      ,  generator=UpperCamelCase__      ,  num_inference_steps=5      ,  negative_prompt=''      ,  ).to_tuple()

				_snake_case          =      torch.Generator(device='cuda'     ).manual_seed(0     )
				_snake_case          =      pipeline(
				    image_embeds=UpperCamelCase__      ,  negative_image_embeds=UpperCamelCase__      ,  generator=UpperCamelCase__      ,  num_inference_steps=100      ,  output_type='np'      ,  )

				_snake_case          =      output.images[0]

				assert image.shape == (512, 512, 3)

				assert_mean_pixel_difference(UpperCamelCase__      ,  UpperCamelCase__     )

from __future__ import annotations

_lowerCamelCase	:    Optional[Any]          =      [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
_lowerCamelCase	:    Optional[int]          =      [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]






def 	a_		(	__lowercase       :     list[float]			)			->  list[float]:
			_snake_case          =      []
			_snake_case          =      len(__lowercase			)

			for i in range(__lowercase			):
						_snake_case          =      -1
						for j in range(i + 1	, __lowercase			):
									if arr[i] < arr[j]:
												_snake_case          =      arr[j]
												break
						result.append(__lowercase			)
			return result






def 	a_		(	__lowercase       :     list[float]			)			->  list[float]:
			_snake_case          =      []
			for i, outer in enumerate(__lowercase			):
						_snake_case          =      -1
						for inner in arr[i + 1 :]:
									if outer < inner:
												_snake_case          =      inner
												break
						result.append(__lowercase			)
			return result






def 	a_		(	__lowercase       :     list[float]			)			->  list[float]:
			_snake_case          =      len(__lowercase			)
			_snake_case          =      []
			_snake_case          =      [-1] * arr_size

			for index in reversed(range(__lowercase			)			):
						if stack:
									while stack[-1] <= arr[index]:
												stack.pop()
												if not stack:
															break
						if stack:
									_snake_case          =      stack[-1]
						stack.append(arr[index]			)
			return result


if __name__ == "__main__":
			from doctest import testmod
			from timeit import timeit

			testmod()
			print(next_greatest_element_slow(arr))
			print(next_greatest_element_fast(arr))
			print(next_greatest_element(arr))

			_lowerCamelCase	:    Union[str, Any]          =      (
			    '''from __main__ import arr, next_greatest_element_slow, '''
			    '''next_greatest_element_fast, next_greatest_element'''
			)
			print(
			    '''next_greatest_element_slow():''',
			    timeit('''next_greatest_element_slow(arr)''', setup=setup),
			)
			print(
			    '''next_greatest_element_fast():''',
			    timeit('''next_greatest_element_fast(arr)''', setup=setup),
			)
			print(
			    '''     next_greatest_element():''',
			    timeit('''next_greatest_element(arr)''', setup=setup),
			)