


import os
import re
import shutil
import sys
import tempfile
import unittest

import black


lowerCAmelCase	=							os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
lowerCAmelCase	=							'    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'


class 	_a							(      unittest.TestCase      ):

							def 				lowerCamelCase_			(							self:     Union[str, Any]							)      ->       Any:





									"""simple docstring"""







									lowercase__						=				tempfile.mkdtemp()
									os.makedirs(os.path.join(self.diffusers_dir   ,						'''schedulers/'''							)							)
									lowercase__						=				self.diffusers_dir
									shutil.copy(
									    os.path.join(UpperCamelCase_   ,						'''src/diffusers/schedulers/scheduling_ddpm.py'''							)   ,						os.path.join(self.diffusers_dir   ,						'''schedulers/scheduling_ddpm.py'''							)   ,						)

							def 				lowerCamelCase_			(							self:     Any							)      ->       Dict:





									"""simple docstring"""







									lowercase__						=				'''src/diffusers'''
									shutil.rmtree(self.diffusers_dir							)

							def 				lowerCamelCase_			(							self:     List[str]   ,						UpperCamelCase_:     Any   ,						UpperCamelCase_:     Any   ,						UpperCamelCase_:     Dict   ,						UpperCamelCase_:     List[Any]=None							)      ->       str:





									"""simple docstring"""







									lowercase__						=				comment + f'\nclass {class_name}(nn.Module):\n' + class_code
									if overwrite_result is not None:
											lowercase__						=				comment + f'\nclass {class_name}(nn.Module):\n' + overwrite_result
									lowercase__						=				black.Mode(target_versions={black.TargetVersion.PYaa}   ,						line_length=119							)
									lowercase__						=				black.format_str(UpperCamelCase_   ,						mode=UpperCamelCase_							)
									lowercase__						=				os.path.join(self.diffusers_dir   ,						'''new_code.py'''							)
									with open(UpperCamelCase_   ,						'''w'''   ,						newline='''\n'''							) as f:
											f.write(UpperCamelCase_							)
									if overwrite_result is None:
											self.assertTrue(len(check_copies.is_copy_consistent(UpperCamelCase_							)							) == 0							)
									else:
											check_copies.is_copy_consistent(f.name   ,						overwrite=UpperCamelCase_							)
											with open(UpperCamelCase_   ,						'''r'''							) as f:
													self.assertTrue(f.read()   ,						UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       int:





									"""simple docstring"""







									lowercase__						=				check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput'''							)
									self.assertEqual(UpperCamelCase_   ,						UpperCamelCase_							)


							def 				lowerCamelCase_			(							self:     Any							)      ->       Dict:





									"""simple docstring"""







									self.check_copy_consistency(
									    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'''   ,						'''DDPMSchedulerOutput'''   ,						REFERENCE_CODE + '''\n'''   ,						)

									# With no empty line at the end
									self.check_copy_consistency(
									    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'''   ,						'''DDPMSchedulerOutput'''   ,						UpperCamelCase_   ,						)

									# Copy consistency with rename
									self.check_copy_consistency(
									    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'''   ,						'''TestSchedulerOutput'''   ,						re.sub('''DDPM'''   ,						'''Test'''   ,						UpperCamelCase_							)   ,						)

									# Copy consistency with a really long name
									lowercase__						=				'''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
									self.check_copy_consistency(
									    f'# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}'   ,						f'{long_class_name}SchedulerOutput'   ,						re.sub('''Bert'''   ,						UpperCamelCase_   ,						UpperCamelCase_							)   ,						)

									# Copy consistency with overwrite
									self.check_copy_consistency(
									    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'''   ,						'''TestSchedulerOutput'''   ,						UpperCamelCase_   ,						overwrite_result=re.sub('''DDPM'''   ,						'''Test'''   ,						UpperCamelCase_							)   ,						)






import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
lowerCAmelCase	=							logging.getLogger(__name__)
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				git.Repo(search_parent_directories=SCREAMING_SNAKE_CASE					)
		lowercase__						=				{
		    '''repo_id''': str(SCREAMING_SNAKE_CASE					),
		    '''repo_sha''': str(repo.head.object.hexsha					),
		    '''repo_branch''': str(repo.active_branch					),
		}

		with open(os.path.join(SCREAMING_SNAKE_CASE						,    '''git_log.json'''					)						,    '''w'''					) as f:
				json.dump(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    indent=4					)
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		if params.n_gpu <= 0:
				lowercase__						=				0
				lowercase__						=				-1
				lowercase__						=				True
				lowercase__						=				False
				return

		assert torch.cuda.is_available()

		logger.info('''Initializing GPUs'''					)
		if params.n_gpu > 1:
				assert params.local_rank != -1

				lowercase__						=				int(os.environ['''WORLD_SIZE''']					)
				lowercase__						=				int(os.environ['''N_GPU_NODE''']					)
				lowercase__						=				int(os.environ['''RANK''']					)

				# number of nodes / node ID
				lowercase__						=				params.world_size // params.n_gpu_per_node
				lowercase__						=				params.global_rank // params.n_gpu_per_node
				lowercase__						=				True

				assert params.n_nodes == int(os.environ['''N_NODES''']					)
				assert params.node_id == int(os.environ['''NODE_RANK''']					)

		# local job (single GPU)
		else:
				assert params.local_rank == -1

				lowercase__						=				1
				lowercase__						=				0
				lowercase__						=				0
				lowercase__						=				0
				lowercase__						=				1
				lowercase__						=				1
				lowercase__						=				False

		# sanity checks
		assert params.n_nodes >= 1
		assert 0 <= params.node_id < params.n_nodes
		assert 0 <= params.local_rank <= params.global_rank < params.world_size
		assert params.world_size == params.n_nodes * params.n_gpu_per_node

		# define whether this is the master process / if we are in multi-node distributed mode
		lowercase__						=				params.node_id == 0 and params.local_rank == 0
		lowercase__						=				params.n_nodes > 1

		# summary
		lowercase__						=				f'--- Global rank: {params.global_rank} - '
		logger.info(PREFIX + '''Number of nodes: %i''' % params.n_nodes					)
		logger.info(PREFIX + '''Node ID        : %i''' % params.node_id					)
		logger.info(PREFIX + '''Local rank     : %i''' % params.local_rank					)
		logger.info(PREFIX + '''World size     : %i''' % params.world_size					)
		logger.info(PREFIX + '''GPUs per node  : %i''' % params.n_gpu_per_node					)
		logger.info(PREFIX + '''Master         : %s''' % str(params.is_master					)					)
		logger.info(PREFIX + '''Multi-node     : %s''' % str(params.multi_node					)					)
		logger.info(PREFIX + '''Multi-GPU      : %s''' % str(params.multi_gpu					)					)
		logger.info(PREFIX + '''Hostname       : %s''' % socket.gethostname()					)

		# set GPU device
		torch.cuda.set_device(params.local_rank					)

		# initialize multi-GPU
		if params.multi_gpu:
				logger.info('''Initializing PyTorch distributed'''					)
				torch.distributed.init_process_group(
				    init_method='''env://'''						,    backend='''nccl'''						,    )
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		np.random.seed(args.seed					)
		torch.manual_seed(args.seed					)
		if args.n_gpu > 0:
				torch.cuda.manual_seed_all(args.seed					)


