

'''simple docstring'''

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


lowercase__         =      logging.get_logger(__name__)

enable_full_determinism()




class 	A_			(    SCREAMING_SNAKE_CASE__	,       SCREAMING_SNAKE_CASE__	,       unittest.TestCase							):






					'''simple docstring'''



					UpperCAmelCase_						:     str  		=  UNetaDModel
					UpperCAmelCase_						:     List[str]  		=  'sample'


					@property
					def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     List[str]:
						UpperCAmelCase	:     Any						 =			4
						UpperCAmelCase	:     Tuple						 =			3
						UpperCAmelCase	:     Tuple						 =			(32, 32)

						UpperCAmelCase	:     Optional[Any]						 =			floats_tensor((batch_size, num_channels) + sizes	).to(a_	)
						UpperCAmelCase	:     Dict						 =			torch.tensor([10]	).to(a_	)

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


					@property
					def        UpperCAmelCase_	(		self			:					int	)						->     List[Any]:
						return (3, 32, 32)


					@property
					def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     Tuple:
						return (3, 32, 32)




					def        UpperCAmelCase_	(		self			:					Any	)						->     str:
						UpperCAmelCase	:     Dict						 =			{
						    '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,
						}
						UpperCAmelCase	:     Union[str, Any]						 =			self.dummy_input
						return init_dict, inputs_dict




class 	A_			(    SCREAMING_SNAKE_CASE__	,       SCREAMING_SNAKE_CASE__	,       unittest.TestCase							):






					'''simple docstring'''



					UpperCAmelCase_						:     Optional[int]  		=  UNetaDModel
					UpperCAmelCase_						:     List[Any]  		=  'sample'


					@property
					def        UpperCAmelCase_	(		self			:					str	)						->     Dict:
						UpperCAmelCase	:     Union[str, Any]						 =			4
						UpperCAmelCase	:     Optional[int]						 =			4
						UpperCAmelCase	:     Optional[Any]						 =			(32, 32)

						UpperCAmelCase	:     Dict						 =			floats_tensor((batch_size, num_channels) + sizes	).to(a_	)
						UpperCAmelCase	:     List[Any]						 =			torch.tensor([10]	).to(a_	)

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


					@property
					def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     Dict:
						return (4, 32, 32)


					@property
					def        UpperCAmelCase_	(		self			:					int	)						->     Optional[int]:
						return (4, 32, 32)


					def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Tuple:
						UpperCAmelCase	:     List[Any]						 =			{
						    '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'),
						}
						UpperCAmelCase	:     Union[str, Any]						 =			self.dummy_input
						return init_dict, inputs_dict


					def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     List[Any]:
						UpperCAmelCase ,			UpperCAmelCase	:     int						 =			UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'					,      output_loading_info=a_	)

						self.assertIsNotNone(a_	)
						self.assertEqual(len(loading_info['missing_keys']	)					,      0	)

						model.to(a_	)
						UpperCAmelCase	:     List[str]						 =			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	)						->     Union[str, Any]:
						UpperCAmelCase ,			UpperCAmelCase	:     int						 =			UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'					,      output_loading_info=a_	)
						model.to(a_	)
						UpperCAmelCase	:     List[str]						 =			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			:					Union[str, Any]	)						->     Any:
						UpperCAmelCase ,			UpperCAmelCase	:     Dict						 =			UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'					,      output_loading_info=a_	)
						model_accelerate.to(a_	)
						model_accelerate.eval()

						UpperCAmelCase	:     Union[str, Any]						 =			torch.randn(
						    1					,      model_accelerate.config.in_channels					,      model_accelerate.config.sample_size					,      model_accelerate.config.sample_size					,      generator=torch.manual_seed(0	)					,      )
						UpperCAmelCase	:     Tuple						 =			noise.to(a_	)
						UpperCAmelCase	:     Tuple						 =			torch.tensor([10] * noise.shape[0]	).to(a_	)

						UpperCAmelCase	:     List[str]						 =			model_accelerate(a_					,      a_	)['sample']

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

						UpperCAmelCase ,			UpperCAmelCase	:     List[str]						 =			UNetaDModel.from_pretrained(
						    'fusing/unet-ldm-dummy-update'					,      output_loading_info=a_					,      low_cpu_mem_usage=a_	)
						model_normal_load.to(a_	)
						model_normal_load.eval()
						UpperCAmelCase	:     Tuple						 =			model_normal_load(a_					,      a_	)['sample']

						assert torch_all_close(a_					,      a_					,      rtol=1E-3	)




					def        UpperCAmelCase_	(		self			:					Any	)						->     List[str]:
						UpperCAmelCase	:     Optional[Any]						 =			UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'	)
						model.eval()
						model.to(a_	)

						UpperCAmelCase	:     int						 =			torch.randn(
						    1					,      model.config.in_channels					,      model.config.sample_size					,      model.config.sample_size					,      generator=torch.manual_seed(0	)					,      )
						UpperCAmelCase	:     Tuple						 =			noise.to(a_	)
						UpperCAmelCase	:     Dict						 =			torch.tensor([10] * noise.shape[0]	).to(a_	)

						with torch.no_grad():
							UpperCAmelCase	:     Any						 =			model(a_					,      a_	).sample

						UpperCAmelCase	:     List[Any]						 =			output[0, -1, -3:, -3:].flatten().cpu()
						# fmt: off
						UpperCAmelCase	:     Dict						 =			torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800]	)
						# fmt: on

						self.assertTrue(torch_all_close(a_					,      a_					,      rtol=1E-3	)	)




class 	A_			(    SCREAMING_SNAKE_CASE__	,       SCREAMING_SNAKE_CASE__	,       unittest.TestCase							):






					'''simple docstring'''



					UpperCAmelCase_						:     List[Any]  		=  UNetaDModel
					UpperCAmelCase_						:     List[str]  		=  'sample'


					@property
					def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					Dict=(32, 32)	)						->     str:
						UpperCAmelCase	:     List[str]						 =			4
						UpperCAmelCase	:     Optional[Any]						 =			3

						UpperCAmelCase	:     Tuple						 =			floats_tensor((batch_size, num_channels) + sizes	).to(a_	)
						UpperCAmelCase	:     int						 =			torch.tensor(batch_size * [10]	).to(dtype=torch.intaa					,      device=a_	)

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


					@property
					def        UpperCAmelCase_	(		self			:					str	)						->     Tuple:
						return (3, 32, 32)


					@property
					def        UpperCAmelCase_	(		self			:					int	)						->     int:
						return (3, 32, 32)


					def        UpperCAmelCase_	(		self			:					Tuple	)						->     Dict:
						UpperCAmelCase	:     str						 =			{
						    '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',
						    ],
						}
						UpperCAmelCase	:     List[Any]						 =			self.dummy_input
						return init_dict, inputs_dict


					@slow
					def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Optional[int]:
						UpperCAmelCase ,			UpperCAmelCase	:     str						 =			UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'					,      output_loading_info=a_	)
						self.assertIsNotNone(a_	)
						self.assertEqual(len(loading_info['missing_keys']	)					,      0	)

						model.to(a_	)
						UpperCAmelCase	:     Any						 =			self.dummy_input
						UpperCAmelCase	:     Any						 =			floats_tensor((4, 3) + (256, 256)	).to(a_	)
						UpperCAmelCase	:     List[Any]						 =			noise
						UpperCAmelCase	:     Any						 =			model(**a_	)

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


					@slow
					def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Tuple:
						UpperCAmelCase	:     List[Any]						 =			UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'	)
						model.to(a_	)

						UpperCAmelCase	:     Union[str, Any]						 =			4
						UpperCAmelCase	:     List[Any]						 =			3
						UpperCAmelCase	:     Union[str, Any]						 =			(256, 256)

						UpperCAmelCase	:     List[str]						 =			torch.ones((batch_size, num_channels) + sizes	).to(a_	)
						UpperCAmelCase	:     List[Any]						 =			torch.tensor(batch_size * [1E-4]	).to(a_	)

						with torch.no_grad():
							UpperCAmelCase	:     Tuple						 =			model(a_					,      a_	).sample

						UpperCAmelCase	:     Tuple						 =			output[0, -3:, -3:, -1].flatten().cpu()
						# fmt: off
						UpperCAmelCase	:     Tuple						 =			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, 567.7608]	)
						# fmt: on

						self.assertTrue(torch_all_close(a_					,      a_					,      rtol=1E-2	)	)


					def        UpperCAmelCase_	(		self			:					int	)						->     Optional[Any]:
						UpperCAmelCase	:     Any						 =			UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update'	)
						model.to(a_	)

						UpperCAmelCase	:     Tuple						 =			4
						UpperCAmelCase	:     Dict						 =			3
						UpperCAmelCase	:     Union[str, Any]						 =			(32, 32)

						UpperCAmelCase	:     Union[str, Any]						 =			torch.ones((batch_size, num_channels) + sizes	).to(a_	)
						UpperCAmelCase	:     Tuple						 =			torch.tensor(batch_size * [1E-4]	).to(a_	)

						with torch.no_grad():
							UpperCAmelCase	:     Any						 =			model(a_					,      a_	).sample

						UpperCAmelCase	:     int						 =			output[0, -3:, -3:, -1].flatten().cpu()
						# fmt: off
						UpperCAmelCase	:     Any						 =			torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256]	)
						# fmt: on

						self.assertTrue(torch_all_close(a_					,      a_					,      rtol=1E-2	)	)




					def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     Any:
						pass







'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

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


if is_vision_available():
 import PIL


lowercase__         =      logging.get_logger(__name__)




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[Any]						 =			b.T
 UpperCAmelCase	:     Optional[int]						 =			np.sum(np.square(UpperCAmelCase_     )  ,	axis=1     )
 UpperCAmelCase	:     List[Any]						 =			np.sum(np.square(UpperCAmelCase_     )  ,	axis=0     )
 UpperCAmelCase	:     List[str]						 =			np.matmul(UpperCAmelCase_  ,	UpperCAmelCase_     )
 UpperCAmelCase	:     Union[str, Any]						 =			aa[:, None] - 2 * ab + ba[None, :]
 return d




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     int						 =			x.reshape(-1  ,	3     )
 UpperCAmelCase	:     Optional[int]						 =			squared_euclidean_distance(UpperCAmelCase_  ,	UpperCAmelCase_     )
 return np.argmin(UpperCAmelCase_  ,	axis=1     )




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     List[Any]  		=  ["""pixel_values"""]


     def __init__(		self			:					List[Any]					,      lowercase_			:					Optional[Union[List[List[int]], np.ndarray]] = None					,      lowercase_			:					bool = True					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = PILImageResampling.BILINEAR					,      lowercase_			:					bool = True					,      lowercase_			:					bool = True					,      **lowercase_			:					Optional[Any]					,      )						->     None:
      super().__init__(**lowercase_	)
      UpperCAmelCase	:     Any						 =			size if size is not None else {'height': 256, 'width': 256}
      UpperCAmelCase	:     List[Any]						 =			get_size_dict(lowercase_	)
      UpperCAmelCase	:     str						 =			np.array(lowercase_	) if clusters is not None else None
      UpperCAmelCase	:     Any						 =			do_resize
      UpperCAmelCase	:     List[Any]						 =			size
      UpperCAmelCase	:     Any						 =			resample
      UpperCAmelCase	:     Dict						 =			do_normalize
      UpperCAmelCase	:     List[Any]						 =			do_color_quantize


     def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					np.ndarray					,      lowercase_			:					Dict[str, int]					,      lowercase_			:					PILImageResampling = PILImageResampling.BILINEAR					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      **lowercase_			:					Any					,      )						->     np.ndarray:
      UpperCAmelCase	:     Dict						 =			get_size_dict(lowercase_	)
      if "height" not in size or "width" not in size:
       raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}"""	)
      return resize(
          lowercase_					,      size=(size['height'], size['width'])					,      resample=lowercase_					,      data_format=lowercase_					,      **lowercase_	)


     def        UpperCAmelCase_	(		self			:					Optional[int]					,      lowercase_			:					np.ndarray					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      )						->     np.ndarray:
      UpperCAmelCase	:     int						 =			rescale(image=lowercase_					,      scale=1 / 127.5					,      data_format=lowercase_	)
      UpperCAmelCase	:     Dict						 =			image - 1
      return image




     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					ImageInput					,      lowercase_			:					bool = None					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = None					,      lowercase_			:					bool = None					,      lowercase_			:					Optional[bool] = None					,      lowercase_			:					Optional[Union[List[List[int]], np.ndarray]] = None					,      lowercase_			:					Optional[Union[str, TensorType]] = None					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST					,      **lowercase_			:					List[str]					,      )						->     PIL.Image.Image:
      UpperCAmelCase	:     Optional[int]						 =			do_resize if do_resize is not None else self.do_resize
      UpperCAmelCase	:     Optional[Any]						 =			size if size is not None else self.size
      UpperCAmelCase	:     Optional[int]						 =			get_size_dict(lowercase_	)
      UpperCAmelCase	:     Any						 =			resample if resample is not None else self.resample
      UpperCAmelCase	:     Optional[Any]						 =			do_normalize if do_normalize is not None else self.do_normalize
      UpperCAmelCase	:     str						 =			do_color_quantize if do_color_quantize is not None else self.do_color_quantize
      UpperCAmelCase	:     Optional[int]						 =			clusters if clusters is not None else self.clusters
      UpperCAmelCase	:     List[str]						 =			np.array(lowercase_	)

      UpperCAmelCase	:     int						 =			make_list_of_images(lowercase_	)

      if not valid_images(lowercase_	):
       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 or resample is None:
       raise ValueError('Size and resample must be specified if do_resize is True.'	)

      if do_color_quantize and clusters is None:
       raise ValueError('Clusters must be specified if do_color_quantize is True.'	)

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

      if do_resize:
       UpperCAmelCase	:     List[Any]						 =			[self.resize(image=lowercase_					,      size=lowercase_					,      resample=lowercase_	) for image in images]

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

      if do_color_quantize:
       UpperCAmelCase	:     List[str]						 =			[to_channel_dimension_format(lowercase_					,      ChannelDimension.LAST	) for image in images]
       # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
       UpperCAmelCase	:     int						 =			np.array(lowercase_	)
       UpperCAmelCase	:     str						 =			color_quantize(lowercase_					,      lowercase_	).reshape(images.shape[:-1]	)

       # flatten to (batch_size, height*width)
       UpperCAmelCase	:     Optional[int]						 =			images.shape[0]
       UpperCAmelCase	:     Union[str, Any]						 =			images.reshape(lowercase_					,      -1	)

       # We need to convert back to a list of images to keep consistent behaviour across processors.
       UpperCAmelCase	:     int						 =			list(lowercase_	)
      else:
       UpperCAmelCase	:     Dict						 =			[to_channel_dimension_format(lowercase_					,      lowercase_	) for image in images]

      UpperCAmelCase	:     Any						 =			{'input_ids': images}
      return BatchFeature(data=lowercase_					,      tensor_type=lowercase_	)




