

'''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


lowerCAmelCase__								=		logging.get_logger(__name__)

enable_full_determinism()







class 	lowercase    (				__UpperCAmelCase    ,							__UpperCAmelCase    ,							unittest.TestCase							):


		"""simple docstring"""




		a__					       =  UNetaDModel
		a__					       =  "sample"


		@property
		def 		A__					(				self):
			_UpperCamelCase   :		Dict			=						4
			_UpperCamelCase   :		Tuple			=						3
			_UpperCamelCase   :		Dict			=						(32, 32)

			_UpperCamelCase   :		str			=						floats_tensor((batch_size, num_channels) + sizes).to(_lowerCamelCase)
			_UpperCamelCase   :		Any			=						torch.tensor([10]).to(_lowerCamelCase)

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


		@property
		def 		A__					(				self):
			return (3, 32, 32)


		@property
		def 		A__					(				self):
			return (3, 32, 32)






		def 		A__					(				self):
			_UpperCamelCase   :		List[Any]			=						{
			    '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   :		int			=						self.dummy_input
			return init_dict, inputs_dict







class 	lowercase    (				__UpperCAmelCase    ,							__UpperCAmelCase    ,							unittest.TestCase							):


		"""simple docstring"""




		a__					       =  UNetaDModel
		a__					       =  "sample"


		@property
		def 		A__					(				self):
			_UpperCamelCase   :		str			=						4
			_UpperCamelCase   :		int			=						4
			_UpperCamelCase   :		Union[str, Any]			=						(32, 32)

			_UpperCamelCase   :		Optional[int]			=						floats_tensor((batch_size, num_channels) + sizes).to(_lowerCamelCase)
			_UpperCamelCase   :		Dict			=						torch.tensor([10]).to(_lowerCamelCase)

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


		@property
		def 		A__					(				self):
			return (4, 32, 32)


		@property
		def 		A__					(				self):
			return (4, 32, 32)


		def 		A__					(				self):
			_UpperCamelCase   :		List[str]			=						{
			    '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   :		Any			=						self.dummy_input
			return init_dict, inputs_dict


		def 		A__					(				self):
			_UpperCamelCase	,		_UpperCamelCase   :		Optional[Any]			=						UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'	,       output_loading_info=_lowerCamelCase)

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

			model.to(_lowerCamelCase)
			_UpperCamelCase   :		List[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 		A__					(				self):
			_UpperCamelCase	,		_UpperCamelCase   :		Tuple			=						UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'	,       output_loading_info=_lowerCamelCase)
			model.to(_lowerCamelCase)
			_UpperCamelCase   :		List[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 		A__					(				self):
			# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
			_UpperCamelCase	,		_UpperCamelCase   :		List[Any]			=						UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'	,       output_loading_info=_lowerCamelCase)
			model_accelerate.to(_lowerCamelCase)
			model_accelerate.eval()

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

			_UpperCamelCase   :		Any			=						model_accelerate(_lowerCamelCase	,       _lowerCamelCase)['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   :		Tuple			=						UNetaDModel.from_pretrained(
			    'fusing/unet-ldm-dummy-update'	,       output_loading_info=_lowerCamelCase	,       low_cpu_mem_usage=_lowerCamelCase)
			model_normal_load.to(_lowerCamelCase)
			model_normal_load.eval()
			_UpperCamelCase   :		int			=						model_normal_load(_lowerCamelCase	,       _lowerCamelCase)['sample']

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






		def 		A__					(				self):
			_UpperCamelCase   :		Dict			=						UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update')
			model.eval()
			model.to(_lowerCamelCase)

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

			with torch.no_grad():
				_UpperCamelCase   :		List[Any]			=						model(_lowerCamelCase	,       _lowerCamelCase).sample

			_UpperCamelCase   :		Union[str, Any]			=						output[0, -1, -3:, -3:].flatten().cpu()
			# fmt: off
			_UpperCamelCase   :		Tuple			=						torch.tensor([-1_3.3_2_5_8, -2_0.1_1_0_0, -1_5.9_8_7_3, -1_7.6_6_1_7, -2_3.0_5_9_6, -1_7.9_4_1_9, -1_3.3_6_7_5, -1_6.1_8_8_9, -1_2.3_8_0_0])
			# fmt: on

			self.assertTrue(torch_all_close(_lowerCamelCase	,       _lowerCamelCase	,       rtol=1e-3))







class 	lowercase    (				__UpperCAmelCase    ,							__UpperCAmelCase    ,							unittest.TestCase							):


		"""simple docstring"""




		a__					       =  UNetaDModel
		a__					       =  "sample"


		@property
		def 		A__					(				self	,       __snake_case=(32, 32)):
			_UpperCamelCase   :		Optional[Any]			=						4
			_UpperCamelCase   :		int			=						3

			_UpperCamelCase   :		Dict			=						floats_tensor((batch_size, num_channels) + sizes).to(_lowerCamelCase)
			_UpperCamelCase   :		Union[str, Any]			=						torch.tensor(batch_size * [10]).to(dtype=torch.intaa	,       device=_lowerCamelCase)

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


		@property
		def 		A__					(				self):
			return (3, 32, 32)


		@property
		def 		A__					(				self):
			return (3, 32, 32)


		def 		A__					(				self):
			_UpperCamelCase   :		Optional[int]			=						{
			    '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   :		Tuple			=						self.dummy_input
			return init_dict, inputs_dict


		@slow
		def 		A__					(				self):
			_UpperCamelCase	,		_UpperCamelCase   :		Tuple			=						UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'	,       output_loading_info=_lowerCamelCase)
			self.assertIsNotNone(_lowerCamelCase)
			self.assertEqual(len(loading_info['missing_keys'])	,       0)

			model.to(_lowerCamelCase)
			_UpperCamelCase   :		Dict			=						self.dummy_input
			_UpperCamelCase   :		Dict			=						floats_tensor((4, 3) + (2_56, 2_56)).to(_lowerCamelCase)
			_UpperCamelCase   :		str			=						noise
			_UpperCamelCase   :		Union[str, Any]			=						model(**_lowerCamelCase)

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


		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		int			=						UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256')
			model.to(_lowerCamelCase)

			_UpperCamelCase   :		List[Any]			=						4
			_UpperCamelCase   :		int			=						3
			_UpperCamelCase   :		Union[str, Any]			=						(2_56, 2_56)

			_UpperCamelCase   :		Union[str, Any]			=						torch.ones((batch_size, num_channels) + sizes).to(_lowerCamelCase)
			_UpperCamelCase   :		Optional[Any]			=						torch.tensor(batch_size * [1e-4]).to(_lowerCamelCase)

			with torch.no_grad():
				_UpperCamelCase   :		Optional[int]			=						model(_lowerCamelCase	,       _lowerCamelCase).sample

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


		def 		A__					(				self):
			_UpperCamelCase   :		Optional[int]			=						UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update')
			model.to(_lowerCamelCase)

			_UpperCamelCase   :		Optional[int]			=						4
			_UpperCamelCase   :		Tuple			=						3
			_UpperCamelCase   :		List[Any]			=						(32, 32)

			_UpperCamelCase   :		Dict			=						torch.ones((batch_size, num_channels) + sizes).to(_lowerCamelCase)
			_UpperCamelCase   :		str			=						torch.tensor(batch_size * [1e-4]).to(_lowerCamelCase)

			with torch.no_grad():
				_UpperCamelCase   :		Union[str, Any]			=						model(_lowerCamelCase	,       _lowerCamelCase).sample

			_UpperCamelCase   :		str			=						output[0, -3:, -3:, -1].flatten().cpu()
			# fmt: off
			_UpperCamelCase   :		int			=						torch.tensor([-0.0_3_2_5, -0.0_9_0_0, -0.0_8_6_9, -0.0_3_3_2, -0.0_7_2_5, -0.0_2_7_0, -0.0_1_0_1, 0.0_2_2_7, 0.0_2_5_6])
			# fmt: on

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






		def 		A__					(				self):
			# not required for this model
			pass







import logging

from transformers.configuration_utils import PretrainedConfig


lowerCAmelCase__								=		logging.getLogger(__name__)







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "masked_bert"


		def __init__(				self	,       __snake_case=3_05_22	,       __snake_case=7_68	,       __snake_case=12	,       __snake_case=12	,       __snake_case=30_72	,       __snake_case="gelu"	,       __snake_case=0.1	,       __snake_case=0.1	,       __snake_case=5_12	,       __snake_case=2	,       __snake_case=0.0_2	,       __snake_case=1e-12	,       __snake_case=0	,       __snake_case="topK"	,       __snake_case="constant"	,       __snake_case=0.0	,       **__snake_case	,       ):
			super().__init__(pad_token_id=__snake_case	,       **__snake_case)

			_UpperCamelCase   :		List[Any]			=						vocab_size
			_UpperCamelCase   :		Union[str, Any]			=						hidden_size
			_UpperCamelCase   :		Optional[int]			=						num_hidden_layers
			_UpperCamelCase   :		Any			=						num_attention_heads
			_UpperCamelCase   :		int			=						hidden_act
			_UpperCamelCase   :		str			=						intermediate_size
			_UpperCamelCase   :		str			=						hidden_dropout_prob
			_UpperCamelCase   :		Any			=						attention_probs_dropout_prob
			_UpperCamelCase   :		Tuple			=						max_position_embeddings
			_UpperCamelCase   :		Dict			=						type_vocab_size
			_UpperCamelCase   :		str			=						initializer_range
			_UpperCamelCase   :		List[Any]			=						layer_norm_eps
			_UpperCamelCase   :		Tuple			=						pruning_method
			_UpperCamelCase   :		Tuple			=						mask_init
			_UpperCamelCase   :		Dict			=						mask_scale




