




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  A		(  __UpperCAmelCase      ,				__UpperCAmelCase      ,				unittest.TestCase     ):
		__snake_case									=   UNetaDModel
		__snake_case									=   'sample'



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  4
								lowerCAmelCase_      			=  3
								lowerCAmelCase_      			=  (32, 32)

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

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



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return (3, 32, 32)



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return (3, 32, 32)







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  {
								    '''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,
								}
								lowerCAmelCase_      			=  self.dummy_input
								return init_dict, inputs_dict







class  A		(  __UpperCAmelCase      ,				__UpperCAmelCase      ,				unittest.TestCase     ):
		__snake_case									=   UNetaDModel
		__snake_case									=   'sample'



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  4
								lowerCAmelCase_      			=  4
								lowerCAmelCase_      			=  (32, 32)

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

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



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return (4, 32, 32)



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return (4, 32, 32)



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  {
								    '''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'''),
								}
								lowerCAmelCase_      			=  self.dummy_input
								return init_dict, inputs_dict



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_			,					lowerCAmelCase_      			=  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__   )
								lowerCAmelCase_      			=  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        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_			,					lowerCAmelCase_      			=  UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update''',	output_loading_info=UpperCamelCase__   )
								model.to(UpperCamelCase__   )
								lowerCAmelCase_      			=  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        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



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

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

								lowerCAmelCase_      			=  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()

								lowerCAmelCase_			,					lowerCAmelCase_      			=  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()
								lowerCAmelCase_      			=  model_normal_load(UpperCamelCase__,	UpperCamelCase__   )['''sample''']

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







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



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

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

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

								lowerCAmelCase_      			=  output[0, -1, -3:, -3:].flatten().cpu()
								# fmt: off
								lowerCAmelCase_      			=  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  A		(  __UpperCAmelCase      ,				__UpperCAmelCase      ,				unittest.TestCase     ):
		__snake_case									=   UNetaDModel
		__snake_case									=   'sample'



		@property
		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__=(32, 32)   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  4
								lowerCAmelCase_      			=  3

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

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



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return (3, 32, 32)



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return (3, 32, 32)



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  {
								    '''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''',
								    ],
								}
								lowerCAmelCase_      			=  self.dummy_input
								return init_dict, inputs_dict



		@slow
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



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

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



		@slow
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''   )
								model.to(UpperCamelCase__   )

								lowerCAmelCase_      			=  4
								lowerCAmelCase_      			=  3
								lowerCAmelCase_      			=  (256, 256)

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

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

								lowerCAmelCase_      			=  output[0, -3:, -3:, -1].flatten().cpu()
								# fmt: off
								lowerCAmelCase_      			=  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        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



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

								lowerCAmelCase_      			=  4
								lowerCAmelCase_      			=  3
								lowerCAmelCase_      			=  (32, 32)

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

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

								lowerCAmelCase_      			=  output[0, -3:, -3:, -1].flatten().cpu()
								# fmt: off
								lowerCAmelCase_      			=  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        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass










import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


_A     =			'''scheduler_config.json'''


class  A		(  __UpperCAmelCase     ):
		__snake_case									=   1
		__snake_case									=   2
		__snake_case									=   3
		__snake_case									=   4
		__snake_case									=   5
		__snake_case									=   6
		__snake_case									=   7
		__snake_case									=   8
		__snake_case									=   9
		__snake_case									=   10
		__snake_case									=   11
		__snake_case									=   12
		__snake_case									=   13
		__snake_case									=   14







@dataclass
class  A		(  __UpperCAmelCase     ):
		__snake_case									=   42







class  A		:
		__snake_case									=   SCHEDULER_CONFIG_NAME
		__snake_case									=   []
		__snake_case									=   True



		@classmethod
		def        SCREAMING_SNAKE_CASE__			(     cls,	UpperCamelCase__ = None,	UpperCamelCase__ = None,	UpperCamelCase__=False,	**UpperCamelCase__,	):







								"""simple docstring"""



								lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_      			=  cls.load_config(
								    pretrained_model_name_or_path=UpperCamelCase__,	subfolder=UpperCamelCase__,	return_unused_kwargs=UpperCamelCase__,	return_commit_hash=UpperCamelCase__,	**UpperCamelCase__,	)
								return cls.from_config(UpperCamelCase__,	return_unused_kwargs=UpperCamelCase__,	**UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = False,	**UpperCamelCase__   ):







								"""simple docstring"""



								self.save_config(save_directory=UpperCamelCase__,	push_to_hub=UpperCamelCase__,	**UpperCamelCase__   )



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return self._get_compatibles()







		@classmethod
		def        SCREAMING_SNAKE_CASE__			(     cls   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  list(set([cls.__name__] + cls._compatibles   )   )
								lowerCAmelCase_      			=  importlib.import_module(__name__.split('''.'''   )[0]   )
								lowerCAmelCase_      			=  [
								    getattr(UpperCamelCase__,	UpperCamelCase__   ) for c in compatible_classes_str if hasattr(UpperCamelCase__,	UpperCamelCase__   )
								]
								return compatible_classes




