"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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   lowerCAmelCase_    (			_snake_case     ,    unittest.TestCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    int			  =						KandinskyVaaInpaintPipeline
				_lowerCAmelCase     :    List[Any]			  =						['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
				_lowerCAmelCase     :    Any			  =						[
				    'image_embeds',
				    'negative_image_embeds',
				    'image',
				    'mask_image',
				]
				_lowerCAmelCase     :    List[str]			  =						[
				    'generator',
				    'height',
				    'width',
				    'latents',
				    'guidance_scale',
				    'num_inference_steps',
				    'return_dict',
				    'guidance_scale',
				    'num_images_per_prompt',
				    'output_type',
				    'return_dict',
				]
				_lowerCAmelCase     :    Optional[int]			  =						False

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return 32

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return 32

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return self.time_input_dim

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return self.time_input_dim * 4

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return 1_00

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										torch.manual_seed(0    )

										snake_case								=			{
										    'in_channels': 9,
										    # 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(**lowerCAmelCase    )
										return model

				@property
				def 		snake_case (				self    ):






										"""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 		snake_case (				self    ):






										"""simple docstring"""

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

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.dummy_unet
										snake_case								=			self.dummy_movq

										snake_case								=			DDIMScheduler(
										    num_train_timesteps=10_00		,    beta_schedule='linear'		,    beta_start=0.0_00_85		,    beta_end=0.0_12		,    clip_sample=lowerCAmelCase		,    set_alpha_to_one=lowerCAmelCase		,    steps_offset=1		,    prediction_type='epsilon'		,    thresholding=lowerCAmelCase		,    )

										snake_case								=			{
										    'unet': unet,
										    'scheduler': scheduler,
										    'movq': movq,
										}

										return components

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase=0    ):






										"""simple docstring"""

										snake_case								=			floats_tensor((1, self.text_embedder_hidden_size)		,    rng=random.Random(lowerCAmelCase    )    ).to(lowerCAmelCase    )
										snake_case								=			floats_tensor((1, self.text_embedder_hidden_size)		,    rng=random.Random(seed + 1    )    ).to(
										    lowerCAmelCase    )
										# create init_image
										snake_case								=			floats_tensor((1, 3, 64, 64)		,    rng=random.Random(lowerCAmelCase    )    ).to(lowerCAmelCase    )
										snake_case								=			image.cpu().permute(0		,    2		,    3		,    1    )[0]
										snake_case								=			Image.fromarray(np.uinta(lowerCAmelCase    )    ).convert('RGB'    ).resize((2_56, 2_56)    )
										# create mask
										snake_case								=			np.ones((64, 64)		,    dtype=np.floataa    )
										snake_case								=			0

										if str(lowerCAmelCase    ).startswith('mps'    ):
																snake_case								=			torch.manual_seed(lowerCAmelCase    )
										else:
																snake_case								=			torch.Generator(device=lowerCAmelCase    ).manual_seed(lowerCAmelCase    )
										snake_case								=			{
										    'image': init_image,
										    'mask_image': mask,
										    'image_embeds': image_embeds,
										    'negative_image_embeds': negative_image_embeds,
										    'generator': generator,
										    'height': 64,
										    'width': 64,
										    'num_inference_steps': 2,
										    'guidance_scale': 4.0,
										    'output_type': 'np',
										}
										return inputs

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			'cpu'

										snake_case								=			self.get_dummy_components()

										snake_case								=			self.pipeline_class(**lowerCAmelCase    )
										snake_case								=			pipe.to(lowerCAmelCase    )

										pipe.set_progress_bar_config(disable=lowerCAmelCase    )

										snake_case								=			pipe(**self.get_dummy_inputs(lowerCAmelCase    )    )
										snake_case								=			output.images

										snake_case								=			pipe(
										    **self.get_dummy_inputs(lowerCAmelCase    )		,    return_dict=lowerCAmelCase		,    )[0]

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

										print(F"""image.shape {image.shape}"""    )

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

										snake_case								=			np.array(
										    [0.50_77_59_03, 0.49_52_71_95, 0.48_82_45_43, 0.50_19_22_37, 0.48_64_49_06, 0.49_37_38_14, 0.4_78_05_98, 0.47_23_48_27, 0.48_32_78_48]    )

										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()}"""

				def 		snake_case (				self    ):






										"""simple docstring"""

										super().test_inference_batch_single_identical(expected_max_diff=3E-3    )





@slow
@require_torch_gpu
class   lowerCAmelCase_    (			unittest.TestCase					):



				"""simple docstring"""

				def 		snake_case (				self    ):






										"""simple docstring"""

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

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			load_numpy(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
										    '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'    )

										snake_case								=			load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'    )
										snake_case								=			np.ones((7_68, 7_68)		,    dtype=np.floataa    )
										snake_case								=			0

										snake_case								=			'a hat'

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

										snake_case								=			KandinskyVaaInpaintPipeline.from_pretrained(
										    'kandinsky-community/kandinsky-2-2-decoder-inpaint'		,    torch_dtype=torch.floataa    )
										snake_case								=			pipeline.to(lowerCAmelCase    )
										pipeline.set_progress_bar_config(disable=lowerCAmelCase    )

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

										snake_case								=			pipeline(
										    image=lowerCAmelCase		,    mask_image=lowerCAmelCase		,    image_embeds=lowerCAmelCase		,    negative_image_embeds=lowerCAmelCase		,    generator=lowerCAmelCase		,    num_inference_steps=1_00		,    height=7_68		,    width=7_68		,    output_type='np'		,    )

										snake_case								=			output.images[0]

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

										assert_mean_pixel_difference(lowerCAmelCase		,    lowerCAmelCase    )
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


SCREAMING_SNAKE_CASE__      						=  get_logger(__name__)



class   lowerCAmelCase_    (			enum.Enum					):



				"""simple docstring"""
				_lowerCAmelCase     :    Union[str, Any]			  =						"""all_checks"""
				_lowerCAmelCase     :    Optional[Any]			  =						"""basic_checks"""
				_lowerCAmelCase     :    Optional[int]			  =						"""no_checks"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[dict] ,			_UpperCamelCase       :				dict ,			_UpperCamelCase       :				str=None					)					->				Dict:





						"""simple docstring"""






						if expected_checksums is None:
												logger.info('Unable to verify checksums.'					)
												return
						if len(set(_UpperCamelCase					) - set(_UpperCamelCase					)					) > 0:
												raise ExpectedMoreDownloadedFiles(str(set(_UpperCamelCase					) - set(_UpperCamelCase					)					)					)
						if len(set(_UpperCamelCase					) - set(_UpperCamelCase					)					) > 0:
												raise UnexpectedDownloadedFile(str(set(_UpperCamelCase					) - set(_UpperCamelCase					)					)					)
						snake_case								=			[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
						snake_case								=			' for ' + verification_name if verification_name is not None else ''
						if len(_UpperCamelCase					) > 0:
												raise NonMatchingChecksumError(
												    f"""Checksums didn't match{for_verification_name}:\n"""
												    f"""{bad_urls}\n"""
												    'Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error'					)
						logger.info('All the checksums matched successfully' + for_verification_name					)



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[dict] ,			_UpperCamelCase       :				dict					)					->				Tuple:





						"""simple docstring"""






						if expected_splits is None:
												logger.info('Unable to verify splits sizes.'					)
												return
						if len(set(_UpperCamelCase					) - set(_UpperCamelCase					)					) > 0:
												raise ExpectedMoreSplits(str(set(_UpperCamelCase					) - set(_UpperCamelCase					)					)					)
						if len(set(_UpperCamelCase					) - set(_UpperCamelCase					)					) > 0:
												raise UnexpectedSplits(str(set(_UpperCamelCase					) - set(_UpperCamelCase					)					)					)
						snake_case								=			[
						    {'expected': expected_splits[name], 'recorded': recorded_splits[name]}
						    for name in expected_splits
						    if expected_splits[name].num_examples != recorded_splits[name].num_examples
						]
						if len(_UpperCamelCase					) > 0:
												raise NonMatchingSplitsSizesError(str(_UpperCamelCase					)					)
						logger.info('All the splits matched successfully.'					)





def    lowerCAmelCase__    (			_UpperCamelCase       :				str ,			_UpperCamelCase       :				bool = True					)					->				dict:





						"""simple docstring"""






						if record_checksum:
												snake_case								=			shaaaa()
												with open(_UpperCamelCase ,			'rb'					) as f:
																		for chunk in iter(lambda: f.read(1 << 2_0					) ,			B''					):
																								m.update(_UpperCamelCase					)
																		snake_case								=			m.hexdigest()
						else:
												snake_case								=			None
						return {"num_bytes": os.path.getsize(_UpperCamelCase					), "checksum": checksum}





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[int]					)					->				int:





						"""simple docstring"""






						if dataset_size and config.IN_MEMORY_MAX_SIZE:
												return dataset_size < config.IN_MEMORY_MAX_SIZE
						else:
												return False


