



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_						=       logging.get_logger(__name__)

enable_full_determinism()
class 			_snake_case     (   lowercase__      ,		lowercase__      ,		unittest.TestCase):
				UpperCamelCase__			:   str       						=UNetaDModel
				UpperCamelCase__			:   Any       						="""sample"""




				@property
				def 							A__ (  self   :  str  ):
							lowercase__   					=			4
							lowercase__   					=			3
							lowercase__   					=			(32, 32)

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

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




				@property
				def 							A__ (  self   :  List[str]  ):
							return (3, 32, 32)




				@property
				def 							A__ (  self   :  List[Any]  ):
							return (3, 32, 32)




				def 							A__ (  self   :  Any  ):
							lowercase__   					=			{
							    "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__   					=			self.dummy_input
							return init_dict, inputs_dict
class 			_snake_case     (   lowercase__      ,		lowercase__      ,		unittest.TestCase):
				UpperCamelCase__			:   Tuple       						=UNetaDModel
				UpperCamelCase__			:   Any       						="""sample"""




				@property
				def 							A__ (  self   :  List[str]  ):
							lowercase__   					=			4
							lowercase__   					=			4
							lowercase__   					=			(32, 32)

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

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




				@property
				def 							A__ (  self   :  List[Any]  ):
							return (4, 32, 32)




				@property
				def 							A__ (  self   :  Optional[Any]  ):
							return (4, 32, 32)




				def 							A__ (  self   :  int  ):
							lowercase__   					=			{
							    "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__   					=			self.dummy_input
							return init_dict, inputs_dict




				def 							A__ (  self   :  List[str]  ):
							lowercase__					,    lowercase__   					=			UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update",   output_loading_info=__lowercase  )

							self.assertIsNotNone(__lowercase  )
							self.assertEqual(len(loading_info["missing_keys"]  ),   0  )

							model.to(__lowercase  )
							lowercase__   					=			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   :  Dict  ):
							lowercase__					,    lowercase__   					=			UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update",   output_loading_info=__lowercase  )
							model.to(__lowercase  )
							lowercase__   					=			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   :  List[str]  ):
							# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
							lowercase__					,    lowercase__   					=			UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update",   output_loading_info=__lowercase  )
							model_accelerate.to(__lowercase  )
							model_accelerate.eval()

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

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

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




				def 							A__ (  self   :  List[str]  ):
							lowercase__   					=			UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"  )
							model.eval()
							model.to(__lowercase  )

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

							with torch.no_grad():
										lowercase__   					=			model(__lowercase,   __lowercase  ).sample

							lowercase__   					=			output[0, -1, -3:, -3:].flatten().cpu()
							# fmt: off
							lowercase__   					=			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(__lowercase,   __lowercase,   rtol=1e-3  )  )
class 			_snake_case     (   lowercase__      ,		lowercase__      ,		unittest.TestCase):
				UpperCamelCase__			:   Union[str, Any]       						=UNetaDModel
				UpperCamelCase__			:   Optional[Any]       						="""sample"""




				@property
				def 							A__ (  self   :  List[Any],   __lowercase   :  Union[str, Any]=(32, 32)  ):
							lowercase__   					=			4
							lowercase__   					=			3

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

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




				@property
				def 							A__ (  self   :  List[Any]  ):
							return (3, 32, 32)




				@property
				def 							A__ (  self   :  List[Any]  ):
							return (3, 32, 32)




				def 							A__ (  self   :  Dict  ):
							lowercase__   					=			{
							    "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__   					=			self.dummy_input
							return init_dict, inputs_dict




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

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

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




				@slow
				def 							A__ (  self   :  List[str]  ):
							lowercase__   					=			UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"  )
							model.to(__lowercase  )

							lowercase__   					=			4
							lowercase__   					=			3
							lowercase__   					=			(256, 256)

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

							with torch.no_grad():
										lowercase__   					=			model(__lowercase,   __lowercase  ).sample

							lowercase__   					=			output[0, -3:, -3:, -1].flatten().cpu()
							# fmt: off
							lowercase__   					=			torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -10980.7129, -20028.8535, 8148.2822, 2342.2905, 567.7608]  )
							# fmt: on

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




				def 							A__ (  self   :  List[str]  ):
							lowercase__   					=			UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update"  )
							model.to(__lowercase  )

							lowercase__   					=			4
							lowercase__   					=			3
							lowercase__   					=			(32, 32)

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

							with torch.no_grad():
										lowercase__   					=			model(__lowercase,   __lowercase  ).sample

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




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









import os
from collections.abc import Iterator



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_ = "."					):
			for dir_path, dir_names, filenames in os.walk(SCREAMING_SNAKE_CASE_					):
						lowercase__   					=			[d for d in dir_names if d != "scripts" and d[0] not in "._"]
						for filename in filenames:
									if filename == "__init__.py":
												continue
									if os.path.splitext(SCREAMING_SNAKE_CASE_					)[1] in (".py", ".ipynb"):
												yield os.path.join(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					).lstrip("./"					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			return f'''{i * "  "}*''' if i else "\n##"



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			old_path.split(os.sep					)
			for i, new_part in enumerate(new_path.split(os.sep					)					):
						if (i + 1 > len(SCREAMING_SNAKE_CASE_					) or old_parts[i] != new_part) and new_part:
									print(f'''{md_prefix(SCREAMING_SNAKE_CASE_					)} {new_part.replace("_"  ,					" "					).title()}'''					)
			return new_path



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_ = "."					):
			lowercase__   					=			""
			for filepath in sorted(good_file_paths(SCREAMING_SNAKE_CASE_					)					):
						lowercase__					,    lowercase__   					=			os.path.split(SCREAMING_SNAKE_CASE_					)
						if filepath != old_path:
									lowercase__   					=			print_path(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
						lowercase__   					=			(filepath.count(os.sep					) + 1) if filepath else 0
						lowercase__   					=			f'''{filepath}/{filename}'''.replace(" "  ,					"%20"					)
						lowercase__   					=			os.path.splitext(filename.replace("_"  ,					" "					).title()					)[0]
						print(f'''{md_prefix(SCREAMING_SNAKE_CASE_					)} [{filename}]({url})'''					)


if __name__ == "__main__":
							print_directory_md(""".""")




