




import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin






class        __lowerCAmelCase					(			UpperCAmelCase__   ,						UpperCAmelCase__   ,						unittest.TestCase					):
			snake_case_ :					Any						=	UNetaDModel
			snake_case_ :					Any						=	"sample"







			@property
			def UpperCamelCase     (			self       :     Tuple ):




									"""simple docstring"""


									_UpperCAmelCase						=	4
									_UpperCAmelCase						=	14
									_UpperCAmelCase						=	16

									_UpperCAmelCase						=	floats_tensor((batch_size, num_features, seq_len) ).to(snake_case__ )
									_UpperCAmelCase						=	torch.tensor([10] * batch_size ).to(snake_case__ )

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







			@property
			def UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


									return (4, 14, 16)







			@property
			def UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


									return (4, 14, 16)







			def UpperCamelCase     (			self       :     List[str] ):




									"""simple docstring"""


									pass







			def UpperCamelCase     (			self       :     List[str] ):




									"""simple docstring"""


									pass







			@unittest.skipIf(torch_device == "mps"       ,       "mish op not supported in MPS" )
			def UpperCamelCase     (			self       :     int ):




									"""simple docstring"""


									super().test_determinism()







			@unittest.skipIf(torch_device == "mps"       ,       "mish op not supported in MPS" )
			def UpperCamelCase     (			self       :     Union[str, Any] ):




									"""simple docstring"""


									super().test_outputs_equivalence()







			@unittest.skipIf(torch_device == "mps"       ,       "mish op not supported in MPS" )
			def UpperCamelCase     (			self       :     int ):




									"""simple docstring"""


									super().test_from_save_pretrained()







			@unittest.skipIf(torch_device == "mps"       ,       "mish op not supported in MPS" )
			def UpperCamelCase     (			self       :     Optional[Any] ):




									"""simple docstring"""


									super().test_from_save_pretrained_variant()







			@unittest.skipIf(torch_device == "mps"       ,       "mish op not supported in MPS" )
			def UpperCamelCase     (			self       :     str ):




									"""simple docstring"""


									super().test_model_from_pretrained()







			@unittest.skipIf(torch_device == "mps"       ,       "mish op not supported in MPS" )
			def UpperCamelCase     (			self       :     List[str] ):




									"""simple docstring"""


									super().test_output()







			def UpperCamelCase     (			self       :     Any ):




									"""simple docstring"""


									_UpperCAmelCase						=	{
									    "block_out_channels": (32, 64, 128, 256),
									    "in_channels": 14,
									    "out_channels": 14,
									    "time_embedding_type": "positional",
									    "use_timestep_embedding": True,
									    "flip_sin_to_cos": False,
									    "freq_shift": 1.0,
									    "out_block_type": "OutConv1DBlock",
									    "mid_block_type": "MidResTemporalBlock1D",
									    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
									    "up_block_types": ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D"),
									    "act_fn": "mish",
									}
									_UpperCAmelCase						=	self.dummy_input
									return init_dict, inputs_dict







			@unittest.skipIf(torch_device == "mps"       ,       "mish op not supported in MPS" )
			def UpperCamelCase     (			self       :     Dict ):




									"""simple docstring"""


									_UpperCAmelCase				,					_UpperCAmelCase						=	UNetaDModel.from_pretrained(
									    "bglick13/hopper-medium-v2-value-function-hor32"       ,       output_loading_info=snake_case__       ,       subfolder="unet" )
									self.assertIsNotNone(snake_case__ )
									self.assertEqual(len(loading_info["missing_keys"] )       ,       0 )

									model.to(snake_case__ )
									_UpperCAmelCase						=	model(**self.dummy_input )

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







			@unittest.skipIf(torch_device == "mps"       ,       "mish op not supported in MPS" )
			def UpperCamelCase     (			self       :     Any ):




									"""simple docstring"""


									_UpperCAmelCase						=	UNetaDModel.from_pretrained("bglick13/hopper-medium-v2-value-function-hor32"       ,       subfolder="unet" )
									torch.manual_seed(0 )
									if torch.cuda.is_available():
															torch.cuda.manual_seed_all(0 )

									_UpperCAmelCase						=	model.config.in_channels
									_UpperCAmelCase						=	16
									_UpperCAmelCase						=	torch.randn((1, seq_len, num_features) ).permute(
									    0       ,       2       ,       1 )  # match original, we can update values and remove
									_UpperCAmelCase						=	torch.full((num_features,)       ,       0 )

									with torch.no_grad():
															_UpperCAmelCase						=	model(snake_case__       ,       snake_case__ ).sample.permute(0       ,       2       ,       1 )

									_UpperCAmelCase						=	output[0, -3:, -3:].flatten()
									# fmt: off
									_UpperCAmelCase						=	torch.tensor([-2.137_172, 1.1_426_016, 0.3_688_687, -0.766_922, 0.7_303_146, 0.11_038_864, -0.4_760_633, 0.13_270_172, 0.02_591_348] )
									# fmt: on
									self.assertTrue(torch.allclose(snake_case__       ,       snake_case__       ,       rtol=1e-3 ) )







			def UpperCamelCase     (			self       :     Optional[Any] ):




									"""simple docstring"""


									pass







			@slow
			def UpperCamelCase     (			self       :     str ):




									"""simple docstring"""


									_UpperCAmelCase						=	"harmonai/maestro-150k"
									_UpperCAmelCase						=	UNetaDModel.from_pretrained(snake_case__       ,       subfolder="unet" )
									model.to(snake_case__ )

									_UpperCAmelCase						=	65_536
									_UpperCAmelCase						=	torch.sin(torch.arange(snake_case__ )[None, None, :].repeat(1       ,       2       ,       1 ) ).to(snake_case__ )
									_UpperCAmelCase						=	torch.tensor([1] ).to(snake_case__ )

									with torch.no_grad():
															_UpperCAmelCase						=	model(snake_case__       ,       snake_case__ ).sample

									_UpperCAmelCase						=	output.abs().sum()
									_UpperCAmelCase						=	output.abs().max()

									assert (output_sum - 224.0_896).abs() < 0.5
									assert (output_max - 0.0_607).abs() < 4e-4






class        __lowerCAmelCase					(			UpperCAmelCase__   ,						UpperCAmelCase__   ,						unittest.TestCase					):
			snake_case_ :					Optional[Any]						=	UNetaDModel
			snake_case_ :					int						=	"sample"







			@property
			def UpperCamelCase     (			self       :     List[Any] ):




									"""simple docstring"""


									_UpperCAmelCase						=	4
									_UpperCAmelCase						=	14
									_UpperCAmelCase						=	16

									_UpperCAmelCase						=	floats_tensor((batch_size, num_features, seq_len) ).to(snake_case__ )
									_UpperCAmelCase						=	torch.tensor([10] * batch_size ).to(snake_case__ )

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







			@property
			def UpperCamelCase     (			self       :     Dict ):




									"""simple docstring"""


									return (4, 14, 16)







			@property
			def UpperCamelCase     (			self       :     Any ):




									"""simple docstring"""


									return (4, 14, 1)







			@unittest.skipIf(torch_device == "mps"       ,       "mish op not supported in MPS" )
			def UpperCamelCase     (			self       :     Any ):




									"""simple docstring"""


									super().test_determinism()







			@unittest.skipIf(torch_device == "mps"       ,       "mish op not supported in MPS" )
			def UpperCamelCase     (			self       :     Union[str, Any] ):




									"""simple docstring"""


									super().test_outputs_equivalence()







			@unittest.skipIf(torch_device == "mps"       ,       "mish op not supported in MPS" )
			def UpperCamelCase     (			self       :     Optional[Any] ):




									"""simple docstring"""


									super().test_from_save_pretrained()







			@unittest.skipIf(torch_device == "mps"       ,       "mish op not supported in MPS" )
			def UpperCamelCase     (			self       :     Tuple ):




									"""simple docstring"""


									super().test_from_save_pretrained_variant()







			@unittest.skipIf(torch_device == "mps"       ,       "mish op not supported in MPS" )
			def UpperCamelCase     (			self       :     int ):




									"""simple docstring"""


									super().test_model_from_pretrained()







			@unittest.skipIf(torch_device == "mps"       ,       "mish op not supported in MPS" )
			def UpperCamelCase     (			self       :     Dict ):




									"""simple docstring"""


									_UpperCAmelCase				,					_UpperCAmelCase						=	self.prepare_init_args_and_inputs_for_common()
									_UpperCAmelCase						=	self.model_class(**snake_case__ )
									model.to(snake_case__ )
									model.eval()

									with torch.no_grad():
															_UpperCAmelCase						=	model(**snake_case__ )

															if isinstance(snake_case__       ,       snake_case__ ):
																					_UpperCAmelCase						=	output.sample

									self.assertIsNotNone(snake_case__ )
									_UpperCAmelCase						=	torch.Size((inputs_dict["sample"].shape[0], 1) )
									self.assertEqual(output.shape       ,       snake_case__       ,       "Input and output shapes do not match" )







			def UpperCamelCase     (			self       :     Union[str, Any] ):




									"""simple docstring"""


									pass







			def UpperCamelCase     (			self       :     Union[str, Any] ):




									"""simple docstring"""


									pass







			def UpperCamelCase     (			self       :     List[str] ):




									"""simple docstring"""


									_UpperCAmelCase						=	{
									    "in_channels": 14,
									    "out_channels": 14,
									    "down_block_types": ["DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"],
									    "up_block_types": [],
									    "out_block_type": "ValueFunction",
									    "mid_block_type": "ValueFunctionMidBlock1D",
									    "block_out_channels": [32, 64, 128, 256],
									    "layers_per_block": 1,
									    "downsample_each_block": True,
									    "use_timestep_embedding": True,
									    "freq_shift": 1.0,
									    "flip_sin_to_cos": False,
									    "time_embedding_type": "positional",
									    "act_fn": "mish",
									}
									_UpperCAmelCase						=	self.dummy_input
									return init_dict, inputs_dict







			@unittest.skipIf(torch_device == "mps"       ,       "mish op not supported in MPS" )
			def UpperCamelCase     (			self       :     str ):




									"""simple docstring"""


									_UpperCAmelCase				,					_UpperCAmelCase						=	UNetaDModel.from_pretrained(
									    "bglick13/hopper-medium-v2-value-function-hor32"       ,       output_loading_info=snake_case__       ,       subfolder="value_function" )
									self.assertIsNotNone(snake_case__ )
									self.assertEqual(len(vf_loading_info["missing_keys"] )       ,       0 )

									value_function.to(snake_case__ )
									_UpperCAmelCase						=	value_function(**self.dummy_input )

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







			@unittest.skipIf(torch_device == "mps"       ,       "mish op not supported in MPS" )
			def UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


									_UpperCAmelCase				,					_UpperCAmelCase						=	UNetaDModel.from_pretrained(
									    "bglick13/hopper-medium-v2-value-function-hor32"       ,       output_loading_info=snake_case__       ,       subfolder="value_function" )
									torch.manual_seed(0 )
									if torch.cuda.is_available():
															torch.cuda.manual_seed_all(0 )

									_UpperCAmelCase						=	value_function.config.in_channels
									_UpperCAmelCase						=	14
									_UpperCAmelCase						=	torch.randn((1, seq_len, num_features) ).permute(
									    0       ,       2       ,       1 )  # match original, we can update values and remove
									_UpperCAmelCase						=	torch.full((num_features,)       ,       0 )

									with torch.no_grad():
															_UpperCAmelCase						=	value_function(snake_case__       ,       snake_case__ ).sample

									# fmt: off
									_UpperCAmelCase						=	torch.tensor([165.25] * seq_len )
									# fmt: on
									self.assertTrue(torch.allclose(snake_case__       ,       snake_case__       ,       rtol=1e-3 ) )







			def UpperCamelCase     (			self       :     int ):




									"""simple docstring"""


									pass










import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator


def 				__SCREAMING_SNAKE_CASE    (     snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase						=	fname.split(os.path.sep		)[-1]
						return re.search(R"^(.*)_\d+\.jpg$"      ,       snake_case_		).groups()[0]






class        __lowerCAmelCase					(			UpperCAmelCase__					):







			def __init__(			self       :     Union[str, Any]       ,       snake_case__       :     Tuple       ,       snake_case__       :     Dict=None       ,       snake_case__       :     Union[str, Any]=None ):




									"""simple docstring"""


									_UpperCAmelCase						=	file_names
									_UpperCAmelCase						=	image_transform
									_UpperCAmelCase						=	label_to_id







			def __len__(			self       :     Tuple ):




									"""simple docstring"""


									return len(self.file_names )







			def __getitem__(			self       :     List[str]       ,       snake_case__       :     Tuple ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.file_names[idx]
									_UpperCAmelCase						=	PIL.Image.open(snake_case__ )
									_UpperCAmelCase						=	raw_image.convert("RGB" )
									if self.image_transform is not None:
															_UpperCAmelCase						=	self.image_transform(snake_case__ )
									_UpperCAmelCase						=	extract_label(snake_case__ )
									if self.label_to_id is not None:
															_UpperCAmelCase						=	self.label_to_id[label]
									return {"image": image, "label": label}




def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase						=	Accelerator(cpu=args.cpu      ,       mixed_precision=args.mixed_precision		)

						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						_UpperCAmelCase						=	config["lr"]
						_UpperCAmelCase						=	int(config["num_epochs"]		)
						_UpperCAmelCase						=	int(config["seed"]		)
						_UpperCAmelCase						=	int(config["batch_size"]		)
						_UpperCAmelCase						=	config["image_size"]
						if not isinstance(snake_case_      ,       (list, tuple)		):
												_UpperCAmelCase						=	(image_size, image_size)

						# Grab all the image filenames
						_UpperCAmelCase						=	[os.path.join(args.data_dir      ,       snake_case_		) for fname in os.listdir(args.data_dir		) if fname.endswith(".jpg"		)]

						# Build the label correspondences
						_UpperCAmelCase						=	[extract_label(snake_case_		) for fname in file_names]
						_UpperCAmelCase						=	list(set(snake_case_		)		)
						id_to_label.sort()
						_UpperCAmelCase						=	{lbl: i for i, lbl in enumerate(snake_case_		)}

						# Set the seed before splitting the data.
						np.random.seed(snake_case_		)
						torch.manual_seed(snake_case_		)
						torch.cuda.manual_seed_all(snake_case_		)

						# Split our filenames between train and validation
						_UpperCAmelCase						=	np.random.permutation(len(snake_case_		)		)
						_UpperCAmelCase						=	int(0.8 * len(snake_case_		)		)
						_UpperCAmelCase						=	random_perm[:cut]
						_UpperCAmelCase						=	random_perm[cut:]

						# For training we use a simple RandomResizedCrop
						_UpperCAmelCase						=	Compose([RandomResizedCrop(snake_case_      ,       scale=(0.5, 1.0)		), ToTensor()]		)
						_UpperCAmelCase						=	PetsDataset(
						    [file_names[i] for i in train_split]      ,       image_transform=snake_case_      ,       label_to_id=snake_case_		)

						# For evaluation, we use a deterministic Resize
						_UpperCAmelCase						=	Compose([Resize(snake_case_		), ToTensor()]		)
						_UpperCAmelCase						=	PetsDataset([file_names[i] for i in eval_split]      ,       image_transform=snake_case_      ,       label_to_id=snake_case_		)

						# Instantiate dataloaders.
						_UpperCAmelCase						=	DataLoader(snake_case_      ,       shuffle=snake_case_      ,       batch_size=snake_case_      ,       num_workers=4		)
						_UpperCAmelCase						=	DataLoader(snake_case_      ,       shuffle=snake_case_      ,       batch_size=snake_case_      ,       num_workers=4		)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						_UpperCAmelCase						=	create_model("resnet50d"      ,       pretrained=snake_case_      ,       num_classes=len(snake_case_		)		)

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						_UpperCAmelCase						=	model.to(accelerator.device		)

						# Freezing the base model
						for param in model.parameters():
												_UpperCAmelCase						=	False
						for param in model.get_classifier().parameters():
												_UpperCAmelCase						=	True

						# We normalize the batches of images to be a bit faster.
						_UpperCAmelCase						=	torch.tensor(model.default_cfg["mean"]		)[None, :, None, None].to(accelerator.device		)
						_UpperCAmelCase						=	torch.tensor(model.default_cfg["std"]		)[None, :, None, None].to(accelerator.device		)

						# Instantiate optimizer
						_UpperCAmelCase						=	torch.optim.Adam(params=model.parameters()      ,       lr=lr / 25		)

						# Instantiate learning rate scheduler
						_UpperCAmelCase						=	OneCycleLR(optimizer=snake_case_      ,       max_lr=snake_case_      ,       epochs=snake_case_      ,       steps_per_epoch=len(snake_case_		)		)

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						_UpperCAmelCase				,					_UpperCAmelCase				,					_UpperCAmelCase				,					_UpperCAmelCase				,					_UpperCAmelCase						=	accelerator.prepare(
						    snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_		)

						# Now we train the model
						for epoch in range(snake_case_		):
												model.train()
												for step, batch in enumerate(snake_case_		):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		_UpperCAmelCase						=	{k: v.to(accelerator.device		) for k, v in batch.items()}
																		_UpperCAmelCase						=	(batch["image"] - mean) / std
																		_UpperCAmelCase						=	model(snake_case_		)
																		_UpperCAmelCase						=	torch.nn.functional.cross_entropy(snake_case_      ,       batch["label"]		)
																		accelerator.backward(snake_case_		)
																		optimizer.step()
																		lr_scheduler.step()
																		optimizer.zero_grad()

												model.eval()
												_UpperCAmelCase						=	0
												_UpperCAmelCase						=	0
												for _, batch in enumerate(snake_case_		):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		_UpperCAmelCase						=	{k: v.to(accelerator.device		) for k, v in batch.items()}
																		_UpperCAmelCase						=	(batch["image"] - mean) / std
																		with torch.no_grad():
																								_UpperCAmelCase						=	model(snake_case_		)
																		_UpperCAmelCase						=	outputs.argmax(dim=-1		)
																		_UpperCAmelCase				,					_UpperCAmelCase						=	accelerator.gather_for_metrics((predictions, batch["label"])		)
																		_UpperCAmelCase						=	predictions == references
																		num_elems += accurate_preds.shape[0]
																		accurate += accurate_preds.long().sum()

												_UpperCAmelCase						=	accurate.item() / num_elems
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"""epoch {epoch}: {100 * eval_metric:.2f}"""		)


def 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


						_UpperCAmelCase						=	argparse.ArgumentParser(description="Simple example of training script."		)
						parser.add_argument("--data_dir"      ,       required=snake_case_      ,       help="The data folder on disk."		)
						parser.add_argument(
						    "--mixed_precision"      ,       type=snake_case_      ,       default=snake_case_      ,       choices=["no", "fp16", "bf16", "fp8"]      ,       help="Whether to use mixed precision. Choose"
						    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
						    "and an Nvidia Ampere GPU."      ,       )
						parser.add_argument(
						    "--checkpointing_steps"      ,       type=snake_case_      ,       default=snake_case_      ,       help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."      ,       )
						parser.add_argument("--cpu"      ,       action="store_true"      ,       help="If passed, will train on the CPU."		)
						_UpperCAmelCase						=	parser.parse_args()
						_UpperCAmelCase						=	{"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
						training_function(snake_case_      ,       snake_case_		)


if __name__ == "__main__":
				main()




