
'''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


__lowercase							:   Optional[int]					=     logging.get_logger(__name__)

enable_full_determinism()

class 	__lowercase				(  UpperCAmelCase__      ,     UpperCAmelCase__      ,     unittest.TestCase    ):
			lowerCamelCase			:		Tuple	      =			UNetaDModel
			lowerCamelCase			:		Optional[int]	      =			"sample"

			@property
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[Any]     =		4
								lowerCamelCase_					:      Any     =		3
								lowerCamelCase_					:      Tuple     =		(3_2, 3_2)

								lowerCamelCase_					:      Dict     =		floats_tensor((batch_size, num_channels) + sizes      ).to(lowerCamelCase__      )
								lowerCamelCase_					:      Union[str, Any]     =		torch.tensor([1_0]      ).to(lowerCamelCase__      )

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

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

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






			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      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,
								}
								lowerCamelCase_					:      List[str]     =		self.dummy_input
								return init_dict, inputs_dict

class 	__lowercase				(  UpperCAmelCase__      ,     UpperCAmelCase__      ,     unittest.TestCase    ):
			lowerCamelCase			:		Dict	      =			UNetaDModel
			lowerCamelCase			:		Any	      =			"sample"

			@property
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Tuple     =		4
								lowerCamelCase_					:      Optional[int]     =		4
								lowerCamelCase_					:      Any     =		(3_2, 3_2)

								lowerCamelCase_					:      Dict     =		floats_tensor((batch_size, num_channels) + sizes      ).to(lowerCamelCase__      )
								lowerCamelCase_					:      Optional[Any]     =		torch.tensor([1_0]      ).to(lowerCamelCase__      )

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

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

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

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[int]     =		{
								    "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"),
								}
								lowerCamelCase_					:      List[Any]     =		self.dummy_input
								return init_dict, inputs_dict

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[str]     =		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__      )
								lowerCamelCase_					:      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        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Dict     =		UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''			,   output_loading_info=lowerCamelCase__      )
								model.to(lowerCamelCase__      )
								lowerCamelCase_					:      str     =		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        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[int]     =		UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''			,   output_loading_info=lowerCamelCase__      )
								model_accelerate.to(lowerCamelCase__      )
								model_accelerate.eval()

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

								lowerCamelCase_					:      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()

								lowerCamelCase_					:      int     =		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()
								lowerCamelCase_					:      List[str]     =		model_normal_load(lowerCamelCase__			,   lowerCamelCase__      )["sample"]

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






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

								lowerCamelCase_					:      List[str]     =		torch.randn(
								    1			,   model.config.in_channels			,   model.config.sample_size			,   model.config.sample_size			,   generator=torch.manual_seed(0      )			,   )
								lowerCamelCase_					:      List[Any]     =		noise.to(lowerCamelCase__      )
								lowerCamelCase_					:      Union[str, Any]     =		torch.tensor([1_0] * noise.shape[0]      ).to(lowerCamelCase__      )

								with torch.no_grad():
													lowerCamelCase_					:      Any     =		model(lowerCamelCase__			,   lowerCamelCase__      ).sample

								lowerCamelCase_					:      List[str]     =		output[0, -1, -3:, -3:].flatten().cpu()
								# fmt: off
								lowerCamelCase_					:      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    ):
			lowerCamelCase			:		Union[str, Any]	      =			UNetaDModel
			lowerCamelCase			:		Optional[int]	      =			"sample"

			@property
			def        UpperCAmelCase__ (self			,   A=(3_2, 3_2)      ):
								lowerCamelCase_					:      Dict     =		4
								lowerCamelCase_					:      Union[str, Any]     =		3

								lowerCamelCase_					:      List[Any]     =		floats_tensor((batch_size, num_channels) + sizes      ).to(lowerCamelCase__      )
								lowerCamelCase_					:      List[str]     =		torch.tensor(batch_size * [1_0]      ).to(dtype=torch.intaa			,   device=lowerCamelCase__      )

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

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

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

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      int     =		{
								    "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",
								    ],
								}
								lowerCamelCase_					:      Optional[int]     =		self.dummy_input
								return init_dict, inputs_dict

			@slow
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Any     =		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__      )
								lowerCamelCase_					:      Optional[Any]     =		self.dummy_input
								lowerCamelCase_					:      Union[str, Any]     =		floats_tensor((4, 3) + (2_5_6, 2_5_6)      ).to(lowerCamelCase__      )
								lowerCamelCase_					:      int     =		noise
								lowerCamelCase_					:      Dict     =		model(**lowerCamelCase__      )

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

			@slow
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[Any]     =		UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''      )
								model.to(lowerCamelCase__      )

								lowerCamelCase_					:      List[str]     =		4
								lowerCamelCase_					:      int     =		3
								lowerCamelCase_					:      Optional[int]     =		(2_5_6, 2_5_6)

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

								with torch.no_grad():
													lowerCamelCase_					:      Tuple     =		model(lowerCamelCase__			,   lowerCamelCase__      ).sample

								lowerCamelCase_					:      List[Any]     =		output[0, -3:, -3:, -1].flatten().cpu()
								# fmt: off
								lowerCamelCase_					:      Union[str, 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, 5_6_7.7_6_0_8]      )
								# fmt: on

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

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[Any]     =		UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update'''      )
								model.to(lowerCamelCase__      )

								lowerCamelCase_					:      Tuple     =		4
								lowerCamelCase_					:      List[Any]     =		3
								lowerCamelCase_					:      Any     =		(3_2, 3_2)

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

								with torch.no_grad():
													lowerCamelCase_					:      Any     =		model(lowerCamelCase__			,   lowerCamelCase__      ).sample

								lowerCamelCase_					:      Optional[Any]     =		output[0, -3:, -3:, -1].flatten().cpu()
								# fmt: off
								lowerCamelCase_					:      Dict     =		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(lowerCamelCase__			,   lowerCamelCase__			,   rtol=1E-2      )      )






			def        UpperCAmelCase__ (self      ):
								pass





'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__lowercase							:   List[Any]					=     {}


try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:   List[Any]					=     ['''MLukeTokenizer''']

if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mluke import MLukeTokenizer


else:
					import sys

					__lowercase							:   List[str]					=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


