

"""simple docstring"""


import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


__A		  =     {
    "<": operator.lt,
    "<=": operator.le,
    "==": operator.eq,
    "!=": operator.ne,
    ">=": operator.ge,
    ">": operator.gt,
}






def        a__       (       __SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE					)      ->      int:
		if got_ver is None or want_ver is None:
				raise ValueError(
				    F"Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"
				    F" reinstalling {pkg}."					)
		if not ops[op](version.parse(_SCREAMING_SNAKE_CASE					)   ,					version.parse(_SCREAMING_SNAKE_CASE					)					):
				raise ImportError(
				    F"{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}"					)






def        a__       (       __SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE = None					)      ->      int:
		__lowerCAmelCase:			Optional[int]        =     F"\n{hint}" if hint is not None else """"""

		# non-versioned check
		if re.match(R"^[\w_\-\d]+$"   ,					_SCREAMING_SNAKE_CASE					):
				__lowerCAmelCase:			List[str]        =     requirement, None, None
		else:
				__lowerCAmelCase:			Tuple        =     re.findall(R"^([^!=<>\s]+)([\s!=<>]{1,2}.+)"   ,					_SCREAMING_SNAKE_CASE					)
				if not match:
						raise ValueError(
						    "requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but"
						    F" got {requirement}"					)
				__lowerCAmelCase:			str        =     match[0]
				__lowerCAmelCase:			Tuple        =     want_full.split(","					)  # there could be multiple requirements
				__lowerCAmelCase:			Union[str, Any]        =     {}
				for w in want_range:
						__lowerCAmelCase:			Optional[int]        =     re.findall(R"^([\s!=<>]{1,2})(.+)"   ,					_SCREAMING_SNAKE_CASE					)
						if not match:
								raise ValueError(
								    "requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,"
								    F" but got {requirement}"					)
						__lowerCAmelCase:			str        =     match[0]
						__lowerCAmelCase:			List[str]        =     want_ver
						if op not in ops:
								raise ValueError(F"{requirement}: need one of {list(ops.keys()					)}, but got {op}"					)

    # special case
		if pkg == "python":
				__lowerCAmelCase:			Any        =     """.""".join([str(_SCREAMING_SNAKE_CASE					) for x in sys.version_info[:3]]					)
				for op, want_ver in wanted.items():
						_compare_versions(_SCREAMING_SNAKE_CASE   ,					_SCREAMING_SNAKE_CASE   ,					_SCREAMING_SNAKE_CASE   ,					_SCREAMING_SNAKE_CASE   ,					_SCREAMING_SNAKE_CASE   ,					_SCREAMING_SNAKE_CASE					)
				return

		# check if any version is installed
		try:
				__lowerCAmelCase:			List[str]        =     importlib.metadata.version(_SCREAMING_SNAKE_CASE					)
		except importlib.metadata.PackageNotFoundError:
				raise importlib.metadata.PackageNotFoundError(
				    F"The \'{requirement}\' distribution was not found and is required by this application. {hint}"					)

		# check that the right version is installed if version number or a range was provided
		if want_ver is not None:
				for op, want_ver in wanted.items():
						_compare_versions(_SCREAMING_SNAKE_CASE   ,					_SCREAMING_SNAKE_CASE   ,					_SCREAMING_SNAKE_CASE   ,					_SCREAMING_SNAKE_CASE   ,					_SCREAMING_SNAKE_CASE   ,					_SCREAMING_SNAKE_CASE					)






def        a__       (       __SCREAMING_SNAKE_CASE					)      ->      Dict:
		__lowerCAmelCase:			Optional[int]        =     """Try: pip install transformers -U or pip install -e '.[dev]' if you're working with git main"""
		return require_version(_SCREAMING_SNAKE_CASE   ,					_SCREAMING_SNAKE_CASE					)



import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


SCREAMING_SNAKE_CASE		:     List[str]         =	False

class        UpperCamelCase						(				unittest.TestCase				):





						def        A_							(self			,     __UpperCamelCase=32						)				->				Optional[int]:
												set_seed(0						)
												UpperCamelCase_		:							Optional[Any]  =		UNetaDModel(sample_size=__UpperCamelCase			,     in_channels=3			,     out_channels=3						)
												UpperCamelCase_		:							Union[str, Any]  =		torch.optim.SGD(model.parameters()			,     lr=0.0_001						)
												return model, optimizer





						@slow
						def        A_							(self						)				->				str:
												UpperCamelCase_		:							str  =		"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
												UpperCamelCase_		:							Tuple  =		DDPMScheduler(
												    num_train_timesteps=1_000			,     beta_start=0.0_001			,     beta_end=0.02			,     beta_schedule="""linear"""			,     clip_sample=__UpperCamelCase			,     )
												UpperCamelCase_		:							List[Any]  =		DDIMScheduler(
												    num_train_timesteps=1_000			,     beta_start=0.0_001			,     beta_end=0.02			,     beta_schedule="""linear"""			,     clip_sample=__UpperCamelCase			,     )

												assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

												# shared batches for DDPM and DDIM
												set_seed(0						)
												UpperCamelCase_		:							Any  =		[torch.randn((4, 3, 32, 32)						).clip(-1			,     1						).to(__UpperCamelCase						) for _ in range(4						)]
												UpperCamelCase_		:							str  =		[torch.randn((4, 3, 32, 32)						).to(__UpperCamelCase						) for _ in range(4						)]
												UpperCamelCase_		:							Union[str, Any]  =		[torch.randint(0			,     1_000			,     (4,)						).long().to(__UpperCamelCase						) for _ in range(4						)]

												# train with a DDPM scheduler
												UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		self.get_model_optimizer(resolution=32						)
												model.train().to(__UpperCamelCase						)
												for i in range(4						):
																		optimizer.zero_grad()
																		UpperCamelCase_		:							Optional[Any]  =		ddpm_scheduler.add_noise(clean_images[i]			,     noise[i]			,     timesteps[i]						)
																		UpperCamelCase_		:							List[Any]  =		model(__UpperCamelCase			,     timesteps[i]						).sample
																		UpperCamelCase_		:							Dict  =		torch.nn.functional.mse_loss(__UpperCamelCase			,     noise[i]						)
																		loss.backward()
																		optimizer.step()
												del model, optimizer

												# recreate the model and optimizer, and retry with DDIM
												UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		self.get_model_optimizer(resolution=32						)
												model.train().to(__UpperCamelCase						)
												for i in range(4						):
																		optimizer.zero_grad()
																		UpperCamelCase_		:							str  =		ddim_scheduler.add_noise(clean_images[i]			,     noise[i]			,     timesteps[i]						)
																		UpperCamelCase_		:							Optional[int]  =		model(__UpperCamelCase			,     timesteps[i]						).sample
																		UpperCamelCase_		:							Union[str, Any]  =		torch.nn.functional.mse_loss(__UpperCamelCase			,     noise[i]						)
																		loss.backward()
																		optimizer.step()
												del model, optimizer

												self.assertTrue(torch.allclose(__UpperCamelCase			,     __UpperCamelCase			,     atol=1E-5						)						)
												self.assertTrue(torch.allclose(__UpperCamelCase			,     __UpperCamelCase			,     atol=1E-5						)						)






