





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__  :     int   			=logging.get_logger(__name__)

enable_full_determinism()

class  __A       (  _a      ,   _a      ,   unittest.TestCase      ):
				__A								=			UNetaDModel
				__A								=			"""sample"""


				@property
				def _snake_case    (						self							):
								lowerCamelCase			=4
								lowerCamelCase			=3
								lowerCamelCase			=(32, 32)

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

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


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


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


				def _snake_case    (						self							):
								lowerCamelCase			={
								    "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,
								}
								lowerCamelCase			=self.dummy_input
								return init_dict, inputs_dict




class  __A       (  _a      ,   _a      ,   unittest.TestCase      ):
				__A								=			UNetaDModel
				__A								=			"""sample"""


				@property
				def _snake_case    (						self							):
								lowerCamelCase			=4
								lowerCamelCase			=4
								lowerCamelCase			=(32, 32)

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

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


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


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


				def _snake_case    (						self							):
								lowerCamelCase			={
								    "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"),
								}
								lowerCamelCase			=self.dummy_input
								return init_dict, inputs_dict


				def _snake_case    (						self							):
								lowerCamelCase			=UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""				,	output_loading_info=_a							)

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

								model.to(_a							)
								lowerCamelCase			=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 _snake_case    (						self							):
								lowerCamelCase			=UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""				,	output_loading_info=_a							)
								model.to(_a							)
								lowerCamelCase			=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 _snake_case    (						self							):
								# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
								lowerCamelCase			=UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""				,	output_loading_info=_a							)
								model_accelerate.to(_a							)
								model_accelerate.eval()

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

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

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


				def _snake_case    (						self							):
								lowerCamelCase			=UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""							)
								model.eval()
								model.to(_a							)

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

								with torch.no_grad():
												lowerCamelCase			=model(_a				,	_a							).sample

								lowerCamelCase			=output[0, -1, -3:, -3:].flatten().cpu()
								# fmt: off
								lowerCamelCase			=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(_a				,	_a				,	rtol=1E-3							)							)




class  __A       (  _a      ,   _a      ,   unittest.TestCase      ):
				__A								=			UNetaDModel
				__A								=			"""sample"""


				@property
				def _snake_case    (						self				,	UpperCAmelCase_=(32, 32)							):
								lowerCamelCase			=4
								lowerCamelCase			=3

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

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


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


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


				def _snake_case    (						self							):
								lowerCamelCase			={
								    "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",
								    ],
								}
								lowerCamelCase			=self.dummy_input
								return init_dict, inputs_dict


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

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

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


				@slow
				def _snake_case    (						self							):
								lowerCamelCase			=UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""							)
								model.to(_a							)

								lowerCamelCase			=4
								lowerCamelCase			=3
								lowerCamelCase			=(256, 256)

								lowerCamelCase			=torch.ones((batch_size, num_channels) + sizes							).to(_a							)
								lowerCamelCase			=torch.tensor(batch_size * [1E-4]							).to(_a							)

								with torch.no_grad():
												lowerCamelCase			=model(_a				,	_a							).sample

								lowerCamelCase			=output[0, -3:, -3:, -1].flatten().cpu()
								# fmt: off
								lowerCamelCase			=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(_a				,	_a				,	rtol=1E-2							)							)


				def _snake_case    (						self							):
								lowerCamelCase			=UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""							)
								model.to(_a							)

								lowerCamelCase			=4
								lowerCamelCase			=3
								lowerCamelCase			=(32, 32)

								lowerCamelCase			=torch.ones((batch_size, num_channels) + sizes							).to(_a							)
								lowerCamelCase			=torch.tensor(batch_size * [1E-4]							).to(_a							)

								with torch.no_grad():
												lowerCamelCase			=model(_a				,	_a							).sample

								lowerCamelCase			=output[0, -3:, -3:, -1].flatten().cpu()
								# fmt: off
								lowerCamelCase			=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(_a				,	_a				,	rtol=1E-2							)							)


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












# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


UpperCAmelCase__  :     Union[str, Any]   			=subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
UpperCAmelCase__  :     List[str]   			=(
    subprocess.check_output(F"git diff --diff-filter=d --name-only {fork_point_sha}".split()).decode('''utf-8''').split()
)

UpperCAmelCase__  :     Tuple   			='''|'''.join(sys.argv[1:])
UpperCAmelCase__  :     List[str]   			=re.compile(rF"^({joined_dirs}).*?\.py$")

UpperCAmelCase__  :     Any   			=[x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')





