

'''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


UpperCamelCase_    						=       logging.get_logger(__name__)

enable_full_determinism()





class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    UNetaDModel
				lowerCamelCase_		       =    'sample'







				@property
				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		=4
										lowercase :							Tuple    		=3
										lowercase :							List[str]    		=(32, 32)

										lowercase :							Dict    		=floats_tensor((batch_size, num_channels) + sizes   ).to(UpperCAmelCase__   )
										lowercase :							Any    		=torch.tensor([10]   ).to(UpperCAmelCase__   )

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







				@property
				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										return (3, 32, 32)







				@property
				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										return (3, 32, 32)







				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										lowercase :							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,
										}
										lowercase :							int    		=self.dummy_input
										return init_dict, inputs_dict





class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    UNetaDModel
				lowerCamelCase_		       =    'sample'







				@property
				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										lowercase :							List[str]    		=4
										lowercase :							Any    		=4
										lowercase :							Any    		=(32, 32)

										lowercase :							List[Any]    		=floats_tensor((batch_size, num_channels) + sizes   ).to(UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=torch.tensor([10]   ).to(UpperCAmelCase__   )

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







				@property
				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										return (4, 32, 32)







				@property
				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										return (4, 32, 32)







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							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'''),
										}
										lowercase :							str    		=self.dummy_input
										return init_dict, inputs_dict







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										lowercase		,	lowercase :							Dict    		=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 :							Union[str, Any]    		=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  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										lowercase		,	lowercase :							List[Any]    		=UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''		,					output_loading_info=UpperCAmelCase__   )
										model.to(UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=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  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
										lowercase		,	lowercase :							Tuple    		=UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''		,					output_loading_info=UpperCAmelCase__   )
										model_accelerate.to(UpperCAmelCase__   )
										model_accelerate.eval()

										lowercase :							int    		=torch.randn(
										    1		,					model_accelerate.config.in_channels		,					model_accelerate.config.sample_size		,					model_accelerate.config.sample_size		,					generator=torch.manual_seed(0   )		,					)
										lowercase :							Tuple    		=noise.to(UpperCAmelCase__   )
										lowercase :							Optional[Any]    		=torch.tensor([10] * noise.shape[0]   ).to(UpperCAmelCase__   )

										lowercase :							Dict    		=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 :							Optional[Any]    		=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 :							List[str]    		=model_normal_load(UpperCAmelCase__		,					UpperCAmelCase__   )['''sample''']

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







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							List[str]    		=UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''   )
										model.eval()
										model.to(UpperCAmelCase__   )

										lowercase :							Dict    		=torch.randn(
										    1		,					model.config.in_channels		,					model.config.sample_size		,					model.config.sample_size		,					generator=torch.manual_seed(0   )		,					)
										lowercase :							List[Any]    		=noise.to(UpperCAmelCase__   )
										lowercase :							Dict    		=torch.tensor([10] * noise.shape[0]   ).to(UpperCAmelCase__   )

										with torch.no_grad():
																lowercase :							Optional[int]    		=model(UpperCAmelCase__		,					UpperCAmelCase__   ).sample

										lowercase :							List[str]    		=output[0, -1, -3:, -3:].flatten().cpu()
										# fmt: off
										lowercase :							Tuple    		=torch.tensor([-13.32_58, -20.11_00, -15.98_73, -17.66_17, -23.05_96, -17.94_19, -13.36_75, -16.18_89, -12.38_00]   )
										# fmt: on

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





class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    UNetaDModel
				lowerCamelCase_		       =    'sample'







				@property
				def  lowerCamelCase_	(       self     :		Union[str, Any]		,					UpperCAmelCase__     :		Optional[Any]=(32, 32)   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		=4
										lowercase :							Dict    		=3

										lowercase :							Optional[Any]    		=floats_tensor((batch_size, num_channels) + sizes   ).to(UpperCAmelCase__   )
										lowercase :							Dict    		=torch.tensor(batch_size * [10]   ).to(dtype=torch.intaa		,					device=UpperCAmelCase__   )

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







				@property
				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										return (3, 32, 32)







				@property
				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										return (3, 32, 32)







				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase :							Dict    		={
										    '''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 :							Union[str, Any]    		=self.dummy_input
										return init_dict, inputs_dict







				@slow
				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase		,	lowercase :							Dict    		=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 :							int    		=self.dummy_input
										lowercase :							Tuple    		=floats_tensor((4, 3) + (256, 256)   ).to(UpperCAmelCase__   )
										lowercase :							Optional[int]    		=noise
										lowercase :							List[str]    		=model(**UpperCAmelCase__   )

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







				@slow
				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										lowercase :							str    		=UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''   )
										model.to(UpperCAmelCase__   )

										lowercase :							Union[str, Any]    		=4
										lowercase :							Tuple    		=3
										lowercase :							int    		=(256, 256)

										lowercase :							Optional[int]    		=torch.ones((batch_size, num_channels) + sizes   ).to(UpperCAmelCase__   )
										lowercase :							Optional[Any]    		=torch.tensor(batch_size * [1E-4]   ).to(UpperCAmelCase__   )

										with torch.no_grad():
																lowercase :							Dict    		=model(UpperCAmelCase__		,					UpperCAmelCase__   ).sample

										lowercase :							Optional[Any]    		=output[0, -3:, -3:, -1].flatten().cpu()
										# fmt: off
										lowercase :							int    		=torch.tensor([-48_42.86_91, -64_99.66_31, -38_00.19_53, -79_78.26_86, -1_09_80.71_29, -2_00_28.85_35, 81_48.28_22, 23_42.29_05, 5_67.76_08]   )
										# fmt: on

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







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							Dict    		=UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update'''   )
										model.to(UpperCAmelCase__   )

										lowercase :							List[Any]    		=4
										lowercase :							List[str]    		=3
										lowercase :							Optional[Any]    		=(32, 32)

										lowercase :							Dict    		=torch.ones((batch_size, num_channels) + sizes   ).to(UpperCAmelCase__   )
										lowercase :							Optional[Any]    		=torch.tensor(batch_size * [1E-4]   ).to(UpperCAmelCase__   )

										with torch.no_grad():
																lowercase :							int    		=model(UpperCAmelCase__		,					UpperCAmelCase__   ).sample

										lowercase :							List[Any]    		=output[0, -3:, -3:, -1].flatten().cpu()
										# fmt: off
										lowercase :							str    		=torch.tensor([-0.03_25, -0.09_00, -0.08_69, -0.03_32, -0.07_25, -0.02_70, -0.01_01, 0.02_27, 0.02_56]   )
										# fmt: on

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







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										# not required for this model
										pass













lowerCamelCase      :       List[Any]    =						{
    '''meter''': '''m''',
    '''kilometer''': '''km''',
    '''megametre''': '''Mm''',
    '''gigametre''': '''Gm''',
    '''terametre''': '''Tm''',
    '''petametre''': '''Pm''',
    '''exametre''': '''Em''',
    '''zettametre''': '''Zm''',
    '''yottametre''': '''Ym''',
}
# Exponent of the factor(meter)
lowerCamelCase      :       Union[str, Any]    =						{
    '''m''': 0,
    '''km''': 3,
    '''Mm''': 6,
    '''Gm''': 9,
    '''Tm''': 12,
    '''Pm''': 15,
    '''Em''': 18,
    '''Zm''': 21,
    '''Ym''': 24,
}





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       from_type.lower().strip("s"							)
     __lowerCAmelCase             =       to_type.lower().strip("s"							)

     __lowerCAmelCase             =       UNIT_SYMBOL.get(__snake_case       ,			__snake_case							)
     __lowerCAmelCase             =       UNIT_SYMBOL.get(__snake_case       ,			__snake_case							)

     if from_sanitized not in METRIC_CONVERSION:
          __lowerCAmelCase             =       (
              F"""Invalid 'from_type' value: {from_type!r}.\n"""
              F"""Conversion abbreviations are: {", ".join(__snake_case							)}"""
          )
          raise ValueError(__snake_case							)
     if to_sanitized not in METRIC_CONVERSION:
          __lowerCAmelCase             =       (
              F"""Invalid 'to_type' value: {to_type!r}.\n"""
              F"""Conversion abbreviations are: {", ".join(__snake_case							)}"""
          )
          raise ValueError(__snake_case							)
     __lowerCAmelCase             =       METRIC_CONVERSION[from_sanitized]
     __lowerCAmelCase             =       METRIC_CONVERSION[to_sanitized]
     __lowerCAmelCase             =       1

     if from_exponent > to_exponent:
          __lowerCAmelCase             =       from_exponent - to_exponent
     else:
          __lowerCAmelCase             =       -(to_exponent - from_exponent)

     return value * pow(10       ,			__snake_case							)


if __name__ == "__main__":
 from doctest import testmod

 testmod()






