


import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


a					  =     logging.get_logger(__name__)

enable_full_determinism()







class        UpperCamelCase__   (	__magic_name__							,				__magic_name__							,				unittest.TestCase   ):
					__SCREAMING_SNAKE_CASE       :  int						=   UNetaDModel
					__SCREAMING_SNAKE_CASE       :  Tuple						=   'sample'

					@property
					def   UpperCAmelCase__       (     self       :   Union[str, Any]       ):






										'''simple docstring'''






										lowercase_  							=			4
										lowercase_  							=			3
										lowercase_  							=			(32, 32)

										lowercase_  							=			floats_tensor((batch_size, num_channels) + sizes       ).to(UpperCamelCase__       )
										lowercase_  							=			torch.tensor([10]       ).to(UpperCamelCase__       )

										return {"sample": noise, "timestep": time_step}

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






										'''simple docstring'''






										return (3, 32, 32)

					@property
					def   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






										return (3, 32, 32)







					def   UpperCAmelCase__       (     self       :   Optional[int]       ):






										'''simple docstring'''






										lowercase_  							=			{
										    """block_out_channels""": (32, 64),
										    """down_block_types""": ("""DownBlock2D""", """AttnDownBlock2D"""),
										    """up_block_types""": ("""AttnUpBlock2D""", """UpBlock2D"""),
										    """attention_head_dim""": 3,
										    """out_channels""": 3,
										    """in_channels""": 3,
										    """layers_per_block""": 2,
										    """sample_size""": 32,
										}
										lowercase_  							=			self.dummy_input
										return init_dict, inputs_dict







class        UpperCamelCase__   (	__magic_name__							,				__magic_name__							,				unittest.TestCase   ):
					__SCREAMING_SNAKE_CASE       :  List[str]						=   UNetaDModel
					__SCREAMING_SNAKE_CASE       :  Any						=   'sample'

					@property
					def   UpperCAmelCase__       (     self       :   Union[str, Any]       ):






										'''simple docstring'''






										lowercase_  							=			4
										lowercase_  							=			4
										lowercase_  							=			(32, 32)

										lowercase_  							=			floats_tensor((batch_size, num_channels) + sizes       ).to(UpperCamelCase__       )
										lowercase_  							=			torch.tensor([10]       ).to(UpperCamelCase__       )

										return {"sample": noise, "timestep": time_step}

					@property
					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										return (4, 32, 32)

					@property
					def   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






										return (4, 32, 32)

					def   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






										lowercase_  							=			{
										    """sample_size""": 32,
										    """in_channels""": 4,
										    """out_channels""": 4,
										    """layers_per_block""": 2,
										    """block_out_channels""": (32, 64),
										    """attention_head_dim""": 32,
										    """down_block_types""": ("""DownBlock2D""", """DownBlock2D"""),
										    """up_block_types""": ("""UpBlock2D""", """UpBlock2D"""),
										}
										lowercase_  							=			self.dummy_input
										return init_dict, inputs_dict

					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										lowercase_							,					lowercase_  							=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""							,					output_loading_info=UpperCamelCase__       )

										self.assertIsNotNone(UpperCamelCase__       )
										self.assertEqual(len(loading_info["""missing_keys"""]       )							,					0       )

										model.to(UpperCamelCase__       )
										lowercase_  							=			model(**self.dummy_input       ).sample

										assert image is not None, "Make sure output is not None"

					@unittest.skipIf(torch_device != """cuda"""							,					"""This test is supposed to run on GPU"""       )
					def   UpperCAmelCase__       (     self       :   Dict       ):






										'''simple docstring'''






										lowercase_							,					lowercase_  							=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""							,					output_loading_info=UpperCamelCase__       )
										model.to(UpperCamelCase__       )
										lowercase_  							=			model(**self.dummy_input       ).sample

										assert image is not None, "Make sure output is not None"

					@unittest.skipIf(torch_device != """cuda"""							,					"""This test is supposed to run on GPU"""       )
					def   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






										lowercase_							,					lowercase_  							=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""							,					output_loading_info=UpperCamelCase__       )
										model_accelerate.to(UpperCamelCase__       )
										model_accelerate.eval()

										lowercase_  							=			torch.randn(
										    1							,					model_accelerate.config.in_channels							,					model_accelerate.config.sample_size							,					model_accelerate.config.sample_size							,					generator=torch.manual_seed(0       )							,					)
										lowercase_  							=			noise.to(UpperCamelCase__       )
										lowercase_  							=			torch.tensor([10] * noise.shape[0]       ).to(UpperCamelCase__       )

										lowercase_  							=			model_accelerate(UpperCamelCase__							,					UpperCamelCase__       )["""sample"""]

										# two models don't need to stay in the device at the same time
										del model_accelerate
										torch.cuda.empty_cache()
										gc.collect()

										lowercase_							,					lowercase_  							=			UNetaDModel.from_pretrained(
										    """fusing/unet-ldm-dummy-update"""							,					output_loading_info=UpperCamelCase__							,					low_cpu_mem_usage=UpperCamelCase__       )
										model_normal_load.to(UpperCamelCase__       )
										model_normal_load.eval()
										lowercase_  							=			model_normal_load(UpperCamelCase__							,					UpperCamelCase__       )["""sample"""]

										assert torch_all_close(UpperCamelCase__							,					UpperCamelCase__							,					rtol=1e-3       )







					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										lowercase_  							=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""       )
										model.eval()
										model.to(UpperCamelCase__       )

										lowercase_  							=			torch.randn(
										    1							,					model.config.in_channels							,					model.config.sample_size							,					model.config.sample_size							,					generator=torch.manual_seed(0       )							,					)
										lowercase_  							=			noise.to(UpperCamelCase__       )
										lowercase_  							=			torch.tensor([10] * noise.shape[0]       ).to(UpperCamelCase__       )

										with torch.no_grad():
															lowercase_  							=			model(UpperCamelCase__							,					UpperCamelCase__       ).sample

										lowercase_  							=			output[0, -1, -3:, -3:].flatten().cpu()
										# fmt: off
										lowercase_  							=			torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800]       )
										# fmt: on

										self.assertTrue(torch_all_close(UpperCamelCase__							,					UpperCamelCase__							,					rtol=1e-3       )       )







class        UpperCamelCase__   (	__magic_name__							,				__magic_name__							,				unittest.TestCase   ):
					__SCREAMING_SNAKE_CASE       :  List[Any]						=   UNetaDModel
					__SCREAMING_SNAKE_CASE       :  Tuple						=   'sample'

					@property
					def   UpperCAmelCase__       (     self       :   List[Any]							,					UpperCamelCase__       :   List[str]=(32, 32)       ):






										'''simple docstring'''






										lowercase_  							=			4
										lowercase_  							=			3

										lowercase_  							=			floats_tensor((batch_size, num_channels) + sizes       ).to(UpperCamelCase__       )
										lowercase_  							=			torch.tensor(batch_size * [10]       ).to(dtype=torch.intaa							,					device=UpperCamelCase__       )

										return {"sample": noise, "timestep": time_step}

					@property
					def   UpperCAmelCase__       (     self       :   List[Any]       ):






										'''simple docstring'''






										return (3, 32, 32)

					@property
					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										return (3, 32, 32)

					def   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






										lowercase_  							=			{
										    """block_out_channels""": [32, 64, 64, 64],
										    """in_channels""": 3,
										    """layers_per_block""": 1,
										    """out_channels""": 3,
										    """time_embedding_type""": """fourier""",
										    """norm_eps""": 1e-6,
										    """mid_block_scale_factor""": math.sqrt(2.0       ),
										    """norm_num_groups""": None,
										    """down_block_types""": [
										        """SkipDownBlock2D""",
										        """AttnSkipDownBlock2D""",
										        """SkipDownBlock2D""",
										        """SkipDownBlock2D""",
										    ],
										    """up_block_types""": [
										        """SkipUpBlock2D""",
										        """SkipUpBlock2D""",
										        """AttnSkipUpBlock2D""",
										        """SkipUpBlock2D""",
										    ],
										}
										lowercase_  							=			self.dummy_input
										return init_dict, inputs_dict

					@slow
					def   UpperCAmelCase__       (     self       :   Optional[int]       ):






										'''simple docstring'''






										lowercase_							,					lowercase_  							=			UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""							,					output_loading_info=UpperCamelCase__       )
										self.assertIsNotNone(UpperCamelCase__       )
										self.assertEqual(len(loading_info["""missing_keys"""]       )							,					0       )

										model.to(UpperCamelCase__       )
										lowercase_  							=			self.dummy_input
										lowercase_  							=			floats_tensor((4, 3) + (256, 256)       ).to(UpperCamelCase__       )
										lowercase_  							=			noise
										lowercase_  							=			model(**UpperCamelCase__       )

										assert image is not None, "Make sure output is not None"

					@slow
					def   UpperCAmelCase__       (     self       :   List[Any]       ):






										'''simple docstring'''






										lowercase_  							=			UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""       )
										model.to(UpperCamelCase__       )

										lowercase_  							=			4
										lowercase_  							=			3
										lowercase_  							=			(256, 256)

										lowercase_  							=			torch.ones((batch_size, num_channels) + sizes       ).to(UpperCamelCase__       )
										lowercase_  							=			torch.tensor(batch_size * [1e-4]       ).to(UpperCamelCase__       )

										with torch.no_grad():
															lowercase_  							=			model(UpperCamelCase__							,					UpperCamelCase__       ).sample

										lowercase_  							=			output[0, -3:, -3:, -1].flatten().cpu()
										# fmt: off
										lowercase_  							=			torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608]       )
										# fmt: on

										self.assertTrue(torch_all_close(UpperCamelCase__							,					UpperCamelCase__							,					rtol=1e-2       )       )

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										lowercase_  							=			UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""       )
										model.to(UpperCamelCase__       )

										lowercase_  							=			4
										lowercase_  							=			3
										lowercase_  							=			(32, 32)

										lowercase_  							=			torch.ones((batch_size, num_channels) + sizes       ).to(UpperCamelCase__       )
										lowercase_  							=			torch.tensor(batch_size * [1e-4]       ).to(UpperCamelCase__       )

										with torch.no_grad():
															lowercase_  							=			model(UpperCamelCase__							,					UpperCamelCase__       ).sample

										lowercase_  							=			output[0, -3:, -3:, -1].flatten().cpu()
										# fmt: off
										lowercase_  							=			torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256]       )
										# fmt: on

										self.assertTrue(torch_all_close(UpperCamelCase__							,					UpperCamelCase__							,					rtol=1e-2       )       )







					def   UpperCAmelCase__       (     self       :   Dict       ):






										'''simple docstring'''






										pass






from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


a					  =     logging.get_logger(__name__)







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  List[Any]						=   ['pixel_values']

					def __init__(     self       :   List[str]							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   Dict[str, int] = None							,					UpperCamelCase__       :   PILImageResampling = PILImageResampling.BICUBIC							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   Dict[str, int] = None							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   Union[int, float] = 1 / 255							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN							,					UpperCamelCase__       :   Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD							,					**UpperCamelCase__       :   Dict							,					):






										'''simple docstring'''






										super().__init__(**UpperCamelCase__       )
										lowercase_  							=			size if size is not None else {"""shortest_edge""": 224}
										lowercase_  							=			get_size_dict(UpperCamelCase__							,					default_to_square=UpperCamelCase__       )
										lowercase_  							=			crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
										lowercase_  							=			get_size_dict(UpperCamelCase__							,					param_name="""crop_size"""       )

										lowercase_  							=			do_resize
										lowercase_  							=			size
										lowercase_  							=			resample
										lowercase_  							=			do_center_crop
										lowercase_  							=			crop_size
										lowercase_  							=			do_rescale
										lowercase_  							=			rescale_factor
										lowercase_  							=			do_normalize
										lowercase_  							=			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
										lowercase_  							=			image_std if image_std is not None else IMAGENET_DEFAULT_STD

					def   UpperCAmelCase__       (     self       :   int							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   Dict[str, int]							,					UpperCamelCase__       :   PILImageResampling = PILImageResampling.BICUBIC							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None							,					**UpperCamelCase__       :   Optional[int]							,					):






										'''simple docstring'''






										lowercase_  							=			get_size_dict(UpperCamelCase__							,					default_to_square=UpperCamelCase__       )
										# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
										if "shortest_edge" in size:
															lowercase_  							=			int((256 / 224) * size["""shortest_edge"""]       )
															lowercase_  							=			get_resize_output_image_size(UpperCamelCase__							,					size=UpperCamelCase__							,					default_to_square=UpperCamelCase__       )
															lowercase_  							=			{"""height""": output_size[0], """width""": output_size[1]}
										if "height" not in size_dict or "width" not in size_dict:
															raise ValueError(
															    F'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''       )
										return resize(
										    UpperCamelCase__							,					size=(size_dict["""height"""], size_dict["""width"""])							,					resample=UpperCamelCase__							,					data_format=UpperCamelCase__							,					**UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   str							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   Dict[str, int]							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None							,					**UpperCamelCase__       :   str							,					):






										'''simple docstring'''






										lowercase_  							=			get_size_dict(UpperCamelCase__       )
										if "height" not in size or "width" not in size:
															raise ValueError(F'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''       )
										return center_crop(UpperCamelCase__							,					size=(size["""height"""], size["""width"""])							,					data_format=UpperCamelCase__							,					**UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Optional[int]							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   Union[int, float]							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None							,					**UpperCamelCase__       :   str							,					):






										'''simple docstring'''






										return rescale(UpperCamelCase__							,					scale=UpperCamelCase__							,					data_format=UpperCamelCase__							,					**UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   List[str]							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   Union[float, List[float]]							,					UpperCamelCase__       :   Union[float, List[float]]							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None							,					**UpperCamelCase__       :   Optional[int]							,					):






										'''simple docstring'''






										return normalize(UpperCamelCase__							,					mean=UpperCamelCase__							,					std=UpperCamelCase__							,					data_format=UpperCamelCase__							,					**UpperCamelCase__       )







					def   UpperCAmelCase__       (     self       :   Dict							,					UpperCamelCase__       :   ImageInput							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[Dict[str, int]] = None							,					UpperCamelCase__       :   PILImageResampling = None							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[Dict[str, int]] = None							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[float] = None							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[Union[float, Iterable[float]]] = None							,					UpperCamelCase__       :   Optional[Union[float, Iterable[float]]] = None							,					UpperCamelCase__       :   Optional[TensorType] = None							,					UpperCamelCase__       :   ChannelDimension = ChannelDimension.FIRST							,					**UpperCamelCase__       :   Optional[int]							,					):






										'''simple docstring'''






										lowercase_  							=			do_resize if do_resize is not None else self.do_resize
										lowercase_  							=			resample if resample is not None else self.resample
										lowercase_  							=			do_center_crop if do_center_crop is not None else self.do_center_crop
										lowercase_  							=			do_rescale if do_rescale is not None else self.do_rescale
										lowercase_  							=			rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase_  							=			do_normalize if do_normalize is not None else self.do_normalize
										lowercase_  							=			image_mean if image_mean is not None else self.image_mean
										lowercase_  							=			image_std if image_std is not None else self.image_std

										lowercase_  							=			size if size is not None else self.size
										lowercase_  							=			get_size_dict(UpperCamelCase__							,					default_to_square=UpperCamelCase__       )
										lowercase_  							=			crop_size if crop_size is not None else self.crop_size
										lowercase_  							=			get_size_dict(UpperCamelCase__							,					param_name="""crop_size"""       )

										lowercase_  							=			make_list_of_images(UpperCamelCase__       )

										if not valid_images(UpperCamelCase__       ):
															raise ValueError(
															    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
															    """torch.Tensor, tf.Tensor or jax.ndarray."""       )

										if do_resize and size is None:
															raise ValueError("""Size must be specified if do_resize is True."""       )

										if do_center_crop and crop_size is None:
															raise ValueError("""Crop size must be specified if do_center_crop is True."""       )

										if do_rescale and rescale_factor is None:
															raise ValueError("""Rescale factor must be specified if do_rescale is True."""       )

										if do_normalize and (image_mean is None or image_std is None):
															raise ValueError("""Image mean and std must be specified if do_normalize is True."""       )

										# All transformations expect numpy arrays.
										lowercase_  							=			[to_numpy_array(UpperCamelCase__       ) for image in images]

										if do_resize:
															lowercase_  							=			[self.resize(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__       ) for image in images]

										if do_center_crop:
															lowercase_  							=			[self.center_crop(UpperCamelCase__							,					UpperCamelCase__       ) for image in images]

										if do_rescale:
															lowercase_  							=			[self.rescale(UpperCamelCase__							,					UpperCamelCase__       ) for image in images]

										if do_normalize:
															lowercase_  							=			[self.normalize(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__       ) for image in images]

										lowercase_  							=			[to_channel_dimension_format(UpperCamelCase__							,					UpperCamelCase__       ) for image in images]

										lowercase_  							=			{"""pixel_values""": images}
										return BatchFeature(data=UpperCamelCase__							,					tensor_type=UpperCamelCase__       )


