
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    (					__snake_case    ,		unittest.TestCase				):

				"""simple docstring"""







				lowerCAmelCase 		= KandinskyVaaPipeline
				lowerCAmelCase 		= [
				    'image_embeds',
				    'negative_image_embeds',
				]
				lowerCAmelCase 		= ['image_embeds', 'negative_image_embeds']
				lowerCAmelCase 		= [
				    'generator',
				    'height',
				    'width',
				    'latents',
				    'guidance_scale',
				    'num_inference_steps',
				    'return_dict',
				    'guidance_scale',
				    'num_images_per_prompt',
				    'output_type',
				    'return_dict',
				]
				lowerCAmelCase 		= False

				@property
				def      _UpperCAmelCase					(						self   )	->				Optional[Any]:
					return 32

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

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

				@property
				def      _UpperCAmelCase					(						self   )	->				Optional[int]:
					return self.time_input_dim * 4

				@property
				def      _UpperCAmelCase					(						self   )	->				int:
					return 100

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

					A								=				{
					    """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,
					}

					A								=				UNetaDConditionModel(**a__   )
					return model

				@property
				def      _UpperCAmelCase					(						self   )	->				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      _UpperCAmelCase					(						self   )	->				str:
					torch.manual_seed(0   )
					A								=				VQModel(**self.dummy_movq_kwargs   )
					return model

				def      _UpperCAmelCase					(						self   )	->				List[str]:
					A								=				self.dummy_unet
					A								=				self.dummy_movq

					A								=				DDIMScheduler(
					    num_train_timesteps=1000				,   beta_schedule="""linear"""				,   beta_start=0.0_00_85				,   beta_end=0.0_12				,   clip_sample=a__				,   set_alpha_to_one=a__				,   steps_offset=1				,   prediction_type="""epsilon"""				,   thresholding=a__				,   )

					A								=				{
					    """unet""": unet,
					    """scheduler""": scheduler,
					    """movq""": movq,
					}
					return components

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

					A								=				self.get_dummy_components()

					A								=				self.pipeline_class(**a__   )
					A								=				pipe.to(a__   )

					pipe.set_progress_bar_config(disable=a__   )

					A								=				pipe(**self.get_dummy_inputs(a__   )   )
					A								=				output.images

					A								=				pipe(
					    **self.get_dummy_inputs(a__   )				,   return_dict=a__				,   )[0]

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

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

					A								=				np.array(
					    [0.6_23_79_76, 1.0, 0.36_44_13_32, 1.0, 0.70_63_96_34, 0.29_87_71_86, 0.85_65_21_25, 0.5_21_68_43, 0.54_45_40_46]   )

					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				):

				"""simple docstring"""







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

				def      _UpperCAmelCase					(						self   )	->				Any:
					A								=				load_numpy(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
					    """/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"""   )

					A								=				KandinskyVaaPriorPipeline.from_pretrained(
					    """kandinsky-community/kandinsky-2-2-prior"""				,   torch_dtype=torch.floataa   )
					pipe_prior.to(a__   )

					A								=				KandinskyVaaPipeline.from_pretrained(
					    """kandinsky-community/kandinsky-2-2-decoder"""				,   torch_dtype=torch.floataa   )
					A								=				pipeline.to(a__   )
					pipeline.set_progress_bar_config(disable=a__   )

					A								=				"""red cat, 4k photo"""

					A								=				torch.Generator(device="""cuda"""   ).manual_seed(0   )
					A					,			A								=				pipe_prior(
					    a__				,   generator=a__				,   num_inference_steps=5				,   negative_prompt=""""""				,   ).to_tuple()

					A								=				torch.Generator(device="""cuda"""   ).manual_seed(0   )
					A								=				pipeline(
					    image_embeds=a__				,   negative_image_embeds=a__				,   generator=a__				,   num_inference_steps=100				,   output_type="""np"""				,   )

					A								=				output.images[0]

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

					assert_mean_pixel_difference(a__				,   a__   )







import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD





def   _lowerCAmelCase				(   UpperCamelCase__:     Any						) ->   Tuple:





	"""simple docstring"""







	def wrapper(*UpperCamelCase__:     Union[str, Any]							,					**UpperCamelCase__:     List[str]						):
		A								=				timeit.default_timer()
		A								=				func(*UpperCamelCase__							,					**UpperCamelCase__						)
		A								=				timeit.default_timer() - starttime
		return delta

	A								=				func.__name__

	return wrapper





def   _lowerCAmelCase				(   UpperCamelCase__:     dict							,					UpperCamelCase__:     List[str]=1_00							,					UpperCamelCase__:     int=None						) ->   Any:





	"""simple docstring"""







	A								=				[]
	A								=				seq_shapes or {}
	for i in range(UpperCamelCase__						):
		A								=				{}
		for col_id, (k, v) in enumerate(features.items()						):
			if isinstance(UpperCamelCase__							,					_ArrayXD						):
				A								=				np.random.rand(*v.shape						).astype(v.dtype						)
			elif isinstance(UpperCamelCase__							,					datasets.Value						):
				if v.dtype == "string":
					A								=				"""The small grey turtle was surprisingly fast when challenged."""
				else:
					A								=				np.random.randint(10							,					size=1						).astype(v.dtype						).item()
			elif isinstance(UpperCamelCase__							,					datasets.Sequence						):
				while isinstance(UpperCamelCase__							,					datasets.Sequence						):
					A								=				v.feature
				A								=				seq_shapes[k]
				A								=				np.random.rand(*UpperCamelCase__						).astype(v.dtype						)
			A								=				data

		dummy_data.append((i, example)						)

	return dummy_data





def   _lowerCAmelCase				(   UpperCamelCase__:     int							,					UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     List[str]=1_00							,					UpperCamelCase__:     str=None						) ->   Optional[int]:





	"""simple docstring"""







	A								=				generate_examples(UpperCamelCase__							,					num_examples=UpperCamelCase__							,					seq_shapes=UpperCamelCase__						)

	with ArrowWriter(features=UpperCamelCase__							,					path=UpperCamelCase__						) as writer:
		for key, record in dummy_data:
			A								=				features.encode_example(UpperCamelCase__						)
			writer.write(UpperCamelCase__						)

		A					,			A								=				writer.finalize()

	if not num_final_examples == num_examples:
		raise ValueError(
		    f'Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'						)

	A								=				datasets.Dataset.from_file(filename=UpperCamelCase__							,					info=datasets.DatasetInfo(features=UpperCamelCase__						)						)

	return dataset



