


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 UpperCAmelCase						( _UpperCamelCase ,	_UpperCamelCase ,	unittest.TestCase  ):


				'''simple docstring'''






				lowerCamelCase_    = UNetaDModel
				lowerCamelCase_    = 'sample'
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[int]         =				4
						A_		:    Union[str, Any]         =				3
						A_		:    int         =				(3_2, 3_2)

						A_		:    Tuple         =				floats_tensor((batch_size, num_channels) + sizes ).to(_SCREAMING_SNAKE_CASE )
						A_		:    Union[str, Any]         =				torch.tensor([1_0] ).to(_SCREAMING_SNAKE_CASE )

						return {"sample": noise, "timestep": time_step}
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return (3, 3_2, 3_2)
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return (3, 3_2, 3_2)







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[str]         =				{
						    'block_out_channels': (3_2, 6_4),
						    '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': 3_2,
						}
						A_		:    Optional[int]         =				self.dummy_input
						return init_dict, inputs_dict





class UpperCAmelCase						( _UpperCamelCase ,	_UpperCamelCase ,	unittest.TestCase  ):


				'''simple docstring'''






				lowerCamelCase_    = UNetaDModel
				lowerCamelCase_    = 'sample'
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[str]         =				4
						A_		:    Union[str, Any]         =				4
						A_		:    List[str]         =				(3_2, 3_2)

						A_		:    Union[str, Any]         =				floats_tensor((batch_size, num_channels) + sizes ).to(_SCREAMING_SNAKE_CASE )
						A_		:    List[str]         =				torch.tensor([1_0] ).to(_SCREAMING_SNAKE_CASE )

						return {"sample": noise, "timestep": time_step}
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return (4, 3_2, 3_2)
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return (4, 3_2, 3_2)
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    str         =				{
						    'sample_size': 3_2,
						    'in_channels': 4,
						    'out_channels': 4,
						    'layers_per_block': 2,
						    'block_out_channels': (3_2, 6_4),
						    'attention_head_dim': 3_2,
						    'down_block_types': ('DownBlock2D', 'DownBlock2D'),
						    'up_block_types': ('UpBlock2D', 'UpBlock2D'),
						}
						A_		:    Optional[Any]         =				self.dummy_input
						return init_dict, inputs_dict
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_       ,      A_		:    Tuple         =				UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'		,     output_loading_info=_SCREAMING_SNAKE_CASE )

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

						model.to(_SCREAMING_SNAKE_CASE )
						A_		:    int         =				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 ):



						"""simple docstring"""





						A_       ,      A_		:    Any         =				UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'		,     output_loading_info=_SCREAMING_SNAKE_CASE )
						model.to(_SCREAMING_SNAKE_CASE )
						A_		:    Optional[int]         =				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 ):



						"""simple docstring"""





						A_       ,      A_		:    Dict         =				UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'		,     output_loading_info=_SCREAMING_SNAKE_CASE )
						model_accelerate.to(_SCREAMING_SNAKE_CASE )
						model_accelerate.eval()

						A_		:    List[str]         =				torch.randn(
						    1		,     model_accelerate.config.in_channels		,     model_accelerate.config.sample_size		,     model_accelerate.config.sample_size		,     generator=torch.manual_seed(0 )		,     )
						A_		:    Any         =				noise.to(_SCREAMING_SNAKE_CASE )
						A_		:    Tuple         =				torch.tensor([1_0] * noise.shape[0] ).to(_SCREAMING_SNAKE_CASE )

						A_		:    Optional[int]         =				model_accelerate(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE )['sample']

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

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

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







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[str]         =				UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update' )
						model.eval()
						model.to(_SCREAMING_SNAKE_CASE )

						A_		:    Dict         =				torch.randn(
						    1		,     model.config.in_channels		,     model.config.sample_size		,     model.config.sample_size		,     generator=torch.manual_seed(0 )		,     )
						A_		:    List[Any]         =				noise.to(_SCREAMING_SNAKE_CASE )
						A_		:    Optional[int]         =				torch.tensor([1_0] * noise.shape[0] ).to(_SCREAMING_SNAKE_CASE )

						with torch.no_grad():
								A_		:    List[Any]         =				model(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE ).sample

						A_		:    Any         =				output[0, -1, -3:, -3:].flatten().cpu()
						# fmt: off
						A_		:    str         =				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(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     rtol=1E-3 ) )





class UpperCAmelCase						( _UpperCamelCase ,	_UpperCamelCase ,	unittest.TestCase  ):


				'''simple docstring'''






				lowerCamelCase_    = UNetaDModel
				lowerCamelCase_    = 'sample'
				@property
				def   lowerCAmelCase_       (						self		,     lowercase=(3_2, 3_2) ):



						"""simple docstring"""





						A_		:    Tuple         =				4
						A_		:    Tuple         =				3

						A_		:    Optional[Any]         =				floats_tensor((batch_size, num_channels) + sizes ).to(_SCREAMING_SNAKE_CASE )
						A_		:    Any         =				torch.tensor(batch_size * [1_0] ).to(dtype=torch.intaa		,     device=_SCREAMING_SNAKE_CASE )

						return {"sample": noise, "timestep": time_step}
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return (3, 3_2, 3_2)
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return (3, 3_2, 3_2)
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				{
						    'block_out_channels': [3_2, 6_4, 6_4, 6_4],
						    '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',
						    ],
						}
						A_		:    Dict         =				self.dummy_input
						return init_dict, inputs_dict
				@slow
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_       ,      A_		:    Any         =				UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'		,     output_loading_info=_SCREAMING_SNAKE_CASE )
						self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
						self.assertEqual(len(loading_info['missing_keys'] )		,     0 )

						model.to(_SCREAMING_SNAKE_CASE )
						A_		:    Union[str, Any]         =				self.dummy_input
						A_		:    int         =				floats_tensor((4, 3) + (2_5_6, 2_5_6) ).to(_SCREAMING_SNAKE_CASE )
						A_		:    List[str]         =				noise
						A_		:    Dict         =				model(**_SCREAMING_SNAKE_CASE )

						assert image is not None, "Make sure output is not None"
				@slow
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Any         =				UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256' )
						model.to(_SCREAMING_SNAKE_CASE )

						A_		:    Tuple         =				4
						A_		:    Dict         =				3
						A_		:    Union[str, Any]         =				(2_5_6, 2_5_6)

						A_		:    str         =				torch.ones((batch_size, num_channels) + sizes ).to(_SCREAMING_SNAKE_CASE )
						A_		:    Optional[int]         =				torch.tensor(batch_size * [1E-4] ).to(_SCREAMING_SNAKE_CASE )

						with torch.no_grad():
								A_		:    Tuple         =				model(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE ).sample

						A_		:    str         =				output[0, -3:, -3:, -1].flatten().cpu()
						# fmt: off
						A_		:    List[Any]         =				torch.tensor([-4_8_4_2.8_6_9_1, -6_4_9_9.6_6_3_1, -3_8_0_0.1_9_5_3, -7_9_7_8.2_6_8_6, -1_0_9_8_0.7_1_2_9, -2_0_0_2_8.8_5_3_5, 8_1_4_8.2_8_2_2, 2_3_4_2.2_9_0_5, 567.7608] )
						# fmt: on

						self.assertTrue(torch_all_close(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     rtol=1E-2 ) )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    str         =				UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update' )
						model.to(_SCREAMING_SNAKE_CASE )

						A_		:    Optional[int]         =				4
						A_		:    List[str]         =				3
						A_		:    Optional[Any]         =				(3_2, 3_2)

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

						with torch.no_grad():
								A_		:    List[str]         =				model(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE ).sample

						A_		:    Union[str, Any]         =				output[0, -3:, -3:, -1].flatten().cpu()
						# fmt: off
						A_		:    int         =				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(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     rtol=1E-2 ) )







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						pass







_UpperCAmelCase            =		"""ABCDEFGHIJKLMNOPQRSTUVWXYZ"""




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Tuple         =				input('Enter message: '     )
		A_		:    int         =				input('Enter key [alphanumeric]: '     )
		A_		:    Optional[Any]         =				input('Encrypt/Decrypt [e/d]: '     )

		if mode.lower().startswith('e'     ):
				A_		:    List[Any]         =				'encrypt'
				A_		:    int         =				encrypt_message(__lowercase						,__lowercase     )
		elif mode.lower().startswith('d'     ):
				A_		:    Optional[Any]         =				'decrypt'
				A_		:    Dict         =				decrypt_message(__lowercase						,__lowercase     )

		print(f'''\n{mode.title()}ed message:'''     )
		print(__lowercase     )




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			str     ):
		'''simple docstring'''




		return translate_message(__lowercase						,__lowercase						,'encrypt'     )




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			str     ):
		'''simple docstring'''




		return translate_message(__lowercase						,__lowercase						,'decrypt'     )




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			str						,__lowercase						:			str     ):
		'''simple docstring'''




		A_		:    Tuple         =				[]
		A_		:    str         =				0
		A_		:    Optional[int]         =				key.upper()

		for symbol in message:
				A_		:    Optional[Any]         =				LETTERS.find(symbol.upper()     )
				if num != -1:
						if mode == "encrypt":
								num += LETTERS.find(key[key_index]     )
						elif mode == "decrypt":
								num -= LETTERS.find(key[key_index]     )

						num %= len(__lowercase     )

						if symbol.isupper():
								translated.append(LETTERS[num]     )
						elif symbol.islower():
								translated.append(LETTERS[num].lower()     )

						key_index += 1
						if key_index == len(__lowercase     ):
								A_		:    str         =				0
				else:
						translated.append(__lowercase     )
		return "".join(__lowercase     )


if __name__ == "__main__":
	main()






