






"""simple docstring"""



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 					_UpperCAmelCase       ( _A   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				List[str]							    =   KandinskyVaaPipeline
					SCREAMING_SNAKE_CASE_		:				Union[str, Any]							    =   [
					    "image_embeds",
					    "negative_image_embeds",
					]
					SCREAMING_SNAKE_CASE_		:				int							    =   ["image_embeds", "negative_image_embeds"]
					SCREAMING_SNAKE_CASE_		:				Tuple							    =   [
					    "generator",
					    "height",
					    "width",
					    "latents",
					    "guidance_scale",
					    "num_inference_steps",
					    "return_dict",
					    "guidance_scale",
					    "num_images_per_prompt",
					    "output_type",
					    "return_dict",
					]
					SCREAMING_SNAKE_CASE_		:				List[str]							    =   False




					@property
					def   A  (				self     :						int   )  ->  Any:
							return 32




					@property
					def   A  (				self     :						Dict   )  ->  Tuple:
							return 32




					@property
					def   A  (				self     :						Union[str, Any]   )  ->  List[Any]:
							return self.time_input_dim




					@property
					def   A  (				self     :						List[Any]   )  ->  Dict:
							return self.time_input_dim * 4




					@property
					def   A  (				self     :						List[Any]   )  ->  Optional[int]:
							return 1_00




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

							lowercase_ : Optional[Any]							       =   {
							    '''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,
							}

							lowercase_ : Optional[int]							       =   UNetaDConditionModel(**A   )
							return model




					@property
					def   A  (				self     :						str   )  ->  Optional[Any]:
							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     :						Dict   )  ->  Any:
							torch.manual_seed(0   )
							lowercase_ : Optional[Any]							       =   VQModel(**self.dummy_movq_kwargs   )
							return model




					def   A  (				self     :						Dict   )  ->  Dict:
							lowercase_ : Union[str, Any]							       =   self.dummy_unet
							lowercase_ : Optional[Any]							       =   self.dummy_movq

							lowercase_ : Dict							       =   DDIMScheduler(
							    num_train_timesteps=10_00    ,						beta_schedule='''linear'''    ,						beta_start=0.00085    ,						beta_end=0.012    ,						clip_sample=A    ,						set_alpha_to_one=A    ,						steps_offset=1    ,						prediction_type='''epsilon'''    ,						thresholding=A    ,						)

							lowercase_ : List[str]							       =   {
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''movq''': movq,
							}
							return components




					def   A  (				self     :						List[Any]    ,						A     :						List[Any]    ,						A     :						List[Any]=0   )  ->  List[Any]:
							lowercase_ : Any							       =   floats_tensor((1, self.text_embedder_hidden_size)    ,						rng=random.Random(A   )   ).to(A   )
							lowercase_ : Dict							       =   floats_tensor((1, self.text_embedder_hidden_size)    ,						rng=random.Random(seed + 1   )   ).to(
							    A   )
							if str(A   ).startswith('''mps'''   ):
									lowercase_ : Dict							       =   torch.manual_seed(A   )
							else:
									lowercase_ : List[Any]							       =   torch.Generator(device=A   ).manual_seed(A   )
							lowercase_ : List[str]							       =   {
							    '''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     :						Tuple   )  ->  List[Any]:
							lowercase_ : Union[str, Any]							       =   '''cpu'''

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

							lowercase_ : Optional[int]							       =   self.pipeline_class(**A   )
							lowercase_ : List[Any]							       =   pipe.to(A   )

							pipe.set_progress_bar_config(disable=A   )

							lowercase_ : str							       =   pipe(**self.get_dummy_inputs(A   )   )
							lowercase_ : Union[str, Any]							       =   output.images

							lowercase_ : List[str]							       =   pipe(
							    **self.get_dummy_inputs(A   )    ,						return_dict=A    ,						)[0]

							lowercase_ : Tuple							       =   image[0, -3:, -3:, -1]
							lowercase_ : str							       =   image_from_tuple[0, -3:, -3:, -1]

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

							lowercase_ : Union[str, Any]							       =   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 					_UpperCAmelCase       ( unittest.TestCase							):




					def   A  (				self     :						List[Any]   )  ->  Tuple:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






					def   A  (				self     :						Optional[int]   )  ->  List[str]:
							lowercase_ : Dict							       =   load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'''   )

							lowercase_ : List[str]							       =   KandinskyVaaPriorPipeline.from_pretrained(
							    '''kandinsky-community/kandinsky-2-2-prior'''    ,						torch_dtype=torch.floataa   )
							pipe_prior.to(A   )

							lowercase_ : int							       =   KandinskyVaaPipeline.from_pretrained(
							    '''kandinsky-community/kandinsky-2-2-decoder'''    ,						torch_dtype=torch.floataa   )
							lowercase_ : Any							       =   pipeline.to(A   )
							pipeline.set_progress_bar_config(disable=A   )

							lowercase_ : int							       =   '''red cat, 4k photo'''

							lowercase_ : Tuple							       =   torch.Generator(device='''cuda'''   ).manual_seed(0   )
							lowercase_       ,					lowercase_ : Dict							       =   pipe_prior(
							    A    ,						generator=A    ,						num_inference_steps=5    ,						negative_prompt=''''''    ,						).to_tuple()

							lowercase_ : Any							       =   torch.Generator(device='''cuda'''   ).manual_seed(0   )
							lowercase_ : List[Any]							       =   pipeline(
							    image_embeds=A    ,						negative_image_embeds=A    ,						generator=A    ,						num_inference_steps=1_00    ,						output_type='''np'''    ,						)

							lowercase_ : List[str]							       =   output.images[0]

							assert image.shape == (5_12, 5_12, 3)

							assert_mean_pixel_difference(A    ,						A   )











"""simple docstring"""



def 						lowercase    (				__snake_case				: Optional[int]	):
		lowercase_ : int							       =   0
		lowercase_ : Optional[Any]							       =   len(__snake_case	)

		for i in range(n - 1	):
				for j in range(i + 1		,					__snake_case	):
						if arr[i] > arr[j]:
								num_inversions += 1

		return num_inversions



def 						lowercase    (				__snake_case				: str	):
		if len(__snake_case	) <= 1:
				return arr, 0
		lowercase_ : Optional[Any]							       =   len(__snake_case	) // 2
		lowercase_ : List[Any]							       =   arr[0:mid]
		lowercase_ : Union[str, Any]							       =   arr[mid:]

		lowercase_       ,					lowercase_ : Tuple							       =   count_inversions_recursive(__snake_case	)
		lowercase_       ,					lowercase_ : List[Any]							       =   count_inversions_recursive(__snake_case	)
		lowercase_       ,					lowercase_ : List[Any]							       =   _count_cross_inversions(__snake_case		,					__snake_case	)

		lowercase_ : List[Any]							       =   inversion_p + inversions_q + cross_inversions
		return c, num_inversions



def 						lowercase    (				__snake_case				: str		,					__snake_case				: Optional[int]	):
		lowercase_ : Optional[Any]							       =   []
		lowercase_ : Any							       =   0
		while i < len(__snake_case	) and j < len(__snake_case	):
				if p[i] > q[j]:
						# if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
						# These are all inversions. The claim emerges from the
						# property that P is sorted.
						num_inversion += len(__snake_case	) - i
						r.append(q[j]	)
						j += 1
				else:
						r.append(p[i]	)
						i += 1

		if i < len(__snake_case	):
				r.extend(p[i:]	)
		else:
				r.extend(q[j:]	)

		return r, num_inversion



def 						lowercase    (				):
		lowercase_ : Union[str, Any]							       =   [1_0, 2, 1, 5, 5, 2, 1_1]

		# this arr has 8 inversions:
		# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

		lowercase_ : int							       =   count_inversions_bf(__snake_case	)
		lowercase_       ,					lowercase_ : Dict							       =   count_inversions_recursive(__snake_case	)

		assert num_inversions_bf == num_inversions_recursive == 8

		print('''number of inversions = '''		,					__snake_case	)

		# testing an array with zero inversion (a sorted arr_1)

		arr_a.sort()
		lowercase_ : Dict							       =   count_inversions_bf(__snake_case	)
		lowercase_       ,					lowercase_ : Dict							       =   count_inversions_recursive(__snake_case	)

		assert num_inversions_bf == num_inversions_recursive == 0
		print('''number of inversions = '''		,					__snake_case	)

		# an empty list should also have zero inversions
		lowercase_ : List[Any]							       =   []
		lowercase_ : Any							       =   count_inversions_bf(__snake_case	)
		lowercase_       ,					lowercase_ : List[str]							       =   count_inversions_recursive(__snake_case	)

		assert num_inversions_bf == num_inversions_recursive == 0
		print('''number of inversions = '''		,					__snake_case	)


if __name__ == "__main__":
						main()



