

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


_lowerCAmelCase					:			Tuple  				=			logging.get_logger(__name__)

enable_full_determinism()






class 	__magic_name__    (			lowerCAmelCase_  ,    lowerCAmelCase_  ,    unittest.TestCase      ):
			SCREAMING_SNAKE_CASE							   =						UNetaDModel
			SCREAMING_SNAKE_CASE							   =						'sample'





			@property
			def 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						__a        =4
						__a        =3
						__a        =(32, 32)

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

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





			@property
			def 		__magic_name__    (		self					)   -> Union[str, Any]:




						'''simple docstring'''
						return (3, 32, 32)





			@property
			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						return (3, 32, 32)





			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						__a        ={
						    '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,
						}
						__a        =self.dummy_input
						return init_dict, inputs_dict






class 	__magic_name__    (			lowerCAmelCase_  ,    lowerCAmelCase_  ,    unittest.TestCase      ):
			SCREAMING_SNAKE_CASE							   =						UNetaDModel
			SCREAMING_SNAKE_CASE							   =						'sample'





			@property
			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						__a        =4
						__a        =4
						__a        =(32, 32)

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

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





			@property
			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						return (4, 32, 32)





			@property
			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						return (4, 32, 32)





			def 		__magic_name__    (		self					)   -> Union[str, Any]:




						'''simple docstring'''
						__a        ={
						    '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'),
						}
						__a        =self.dummy_input
						return init_dict, inputs_dict





			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						__a      ,    __a        =UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'					,  output_loading_info=__snake_case					)

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

						model.to(__snake_case					)
						__a        =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 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						__a      ,    __a        =UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'					,  output_loading_info=__snake_case					)
						model.to(__snake_case					)
						__a        =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 		__magic_name__    (		self					)   -> List[str]:




						'''simple docstring'''
						# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
						__a      ,    __a        =UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'					,  output_loading_info=__snake_case					)
						model_accelerate.to(__snake_case					)
						model_accelerate.eval()

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

						__a        =model_accelerate(__snake_case					,  __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        =UNetaDModel.from_pretrained(
						    'fusing/unet-ldm-dummy-update'					,  output_loading_info=__snake_case					,  low_cpu_mem_usage=__snake_case					)
						model_normal_load.to(__snake_case					)
						model_normal_load.eval()
						__a        =model_normal_load(__snake_case					,  __snake_case					)['sample']

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





			def 		__magic_name__    (		self					)   -> int:




						'''simple docstring'''
						__a        =UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'					)
						model.eval()
						model.to(__snake_case					)

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

						with torch.no_grad():
									__a        =model(__snake_case					,  __snake_case					).sample

						__a        =output[0, -1, -3:, -3:].flatten().cpu()
						# fmt: off
						__a        =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(__snake_case					,  __snake_case					,  rtol=1e-3					)					)






class 	__magic_name__    (			lowerCAmelCase_  ,    lowerCAmelCase_  ,    unittest.TestCase      ):
			SCREAMING_SNAKE_CASE							   =						UNetaDModel
			SCREAMING_SNAKE_CASE							   =						'sample'





			@property
			def 		__magic_name__    (		self					,  __snake_case=(32, 32)					)   -> Optional[Any]:




						'''simple docstring'''
						__a        =4
						__a        =3

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

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





			@property
			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						return (3, 32, 32)





			@property
			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						return (3, 32, 32)





			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						__a        ={
						    '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',
						    ],
						}
						__a        =self.dummy_input
						return init_dict, inputs_dict





			@slow
			def 		__magic_name__    (		self					)   -> Union[str, Any]:




						'''simple docstring'''
						__a      ,    __a        =UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'					,  output_loading_info=__snake_case					)
						self.assertIsNotNone(__snake_case					)
						self.assertEqual(len(loading_info['missing_keys']					)					,  0					)

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

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





			@slow
			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						__a        =UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'					)
						model.to(__snake_case					)

						__a        =4
						__a        =3
						__a        =(256, 256)

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

						with torch.no_grad():
									__a        =model(__snake_case					,  __snake_case					).sample

						__a        =output[0, -3:, -3:, -1].flatten().cpu()
						# fmt: off
						__a        =torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -1_0980.7129, -2_0028.8535, 8148.2822, 2342.2905, 567.7608]					)
						# fmt: on

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





			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						__a        =UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update'					)
						model.to(__snake_case					)

						__a        =4
						__a        =3
						__a        =(32, 32)

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

						with torch.no_grad():
									__a        =model(__snake_case					,  __snake_case					).sample

						__a        =output[0, -3:, -3:, -1].flatten().cpu()
						# fmt: off
						__a        =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(__snake_case					,  __snake_case					,  rtol=1e-2					)					)





			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						# not required for this model
						pass









from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase					:			Tuple  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			Union[str, Any]  				=			{"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'ctrl'
			SCREAMING_SNAKE_CASE							   =						['past_key_values']
			SCREAMING_SNAKE_CASE							   =						{
			    'max_position_embeddings': 'n_positions',
			    'hidden_size': 'n_embd',
			    'num_attention_heads': 'n_head',
			    'num_hidden_layers': 'n_layer',
			}





			def __init__(		self					,  __snake_case=24_6534					,  __snake_case=256					,  __snake_case=1280					,  __snake_case=8192					,  __snake_case=48					,  __snake_case=16					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=1e-6					,  __snake_case=0.02					,  __snake_case=True					,  **__snake_case					,  )   -> Union[str, Any]:




						'''simple docstring'''
						__a        =vocab_size
						__a        =n_positions
						__a        =n_embd
						__a        =n_layer
						__a        =n_head
						__a        =dff
						__a        =resid_pdrop
						__a        =embd_pdrop
						__a        =layer_norm_epsilon
						__a        =initializer_range

						__a        =use_cache

						super().__init__(**__snake_case					)






