






"""simple docstring"""
import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				List[str]    =				(UnCLIPScheduler,)





	def    lowerCamelCase(self     ,     **lowerCAmelCase_							):
		A_		:						Any	    =   {
		    """num_train_timesteps""": 1000,
		    """variance_type""": """fixed_small_log""",
		    """clip_sample""": True,
		    """clip_sample_range""": 1.0,
		    """prediction_type""": """epsilon""",
		}

		config.update(**lowerCAmelCase_							)
		return config





	def    lowerCamelCase(self							):
		for timesteps in [1, 5, 100, 1000]:
			self.check_over_configs(num_train_timesteps=lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		for variance in ["fixed_small_log", "learned_range"]:
			self.check_over_configs(variance_type=lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		for clip_sample in [True, False]:
			self.check_over_configs(clip_sample=lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		for clip_sample_range in [1, 5, 10, 20]:
			self.check_over_configs(clip_sample_range=lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		for prediction_type in ["epsilon", "sample"]:
			self.check_over_configs(prediction_type=lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		for time_step in [0, 500, 999]:
			for prev_timestep in [None, 5, 100, 250, 500, 750]:
				if prev_timestep is not None and prev_timestep >= time_step:
					continue

				self.check_over_forward(time_step=lowerCAmelCase_     ,     prev_timestep=lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		A_		:						Tuple	    =   self.scheduler_classes[0]
		A_		:						str	    =   self.get_scheduler_config(variance_type="""fixed_small_log"""							)
		A_		:						List[str]	    =   scheduler_class(**lowerCAmelCase_							)

		assert torch.sum(torch.abs(scheduler._get_variance(0							) - 1.0_0_0_0e-1_0							)							) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(487							) - 0.0549625							)							) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(999							) - 0.9994987							)							) < 1e-5





	def    lowerCamelCase(self							):
		A_		:						Union[str, Any]	    =   self.scheduler_classes[0]
		A_		:						Union[str, Any]	    =   self.get_scheduler_config(variance_type="""learned_range"""							)
		A_		:						Any	    =   scheduler_class(**lowerCAmelCase_							)

		A_		:						str	    =   0.5

		assert scheduler._get_variance(1     ,     predicted_variance=lowerCAmelCase_							) - -10.1712790 < 1e-5
		assert scheduler._get_variance(487     ,     predicted_variance=lowerCAmelCase_							) - -5.7998052 < 1e-5
		assert scheduler._get_variance(999     ,     predicted_variance=lowerCAmelCase_							) - -0.0010011 < 1e-5





	def    lowerCamelCase(self							):
		A_		:						str	    =   self.scheduler_classes[0]
		A_		:						Union[str, Any]	    =   self.get_scheduler_config()
		A_		:						Tuple	    =   scheduler_class(**lowerCAmelCase_							)

		A_		:						int	    =   scheduler.timesteps

		A_		:						str	    =   self.dummy_model()
		A_		:						Optional[Any]	    =   self.dummy_sample_deter
		A_		:						List[str]	    =   torch.manual_seed(0							)

		for i, t in enumerate(lowerCAmelCase_							):
			# 1. predict noise residual
			A_		:						Optional[Any]	    =   model(lowerCAmelCase_     ,     lowerCAmelCase_							)

			# 2. predict previous mean of sample x_t-1
			A_		:						str	    =   scheduler.step(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     generator=lowerCAmelCase_							).prev_sample

			A_		:						Any	    =   pred_prev_sample

		A_		:						Optional[Any]	    =   torch.sum(torch.abs(lowerCAmelCase_							)							)
		A_		:						List[Any]	    =   torch.mean(torch.abs(lowerCAmelCase_							)							)

		assert abs(result_sum.item() - 252.2682495							) < 1e-2
		assert abs(result_mean.item() - 0.3284743							) < 1e-3





	def    lowerCamelCase(self							):
		A_		:						List[str]	    =   self.scheduler_classes[0]
		A_		:						Union[str, Any]	    =   self.get_scheduler_config()
		A_		:						str	    =   scheduler_class(**lowerCAmelCase_							)

		scheduler.set_timesteps(25							)

		A_		:						List[str]	    =   scheduler.timesteps

		A_		:						Tuple	    =   self.dummy_model()
		A_		:						Optional[int]	    =   self.dummy_sample_deter
		A_		:						Optional[Any]	    =   torch.manual_seed(0							)

		for i, t in enumerate(lowerCAmelCase_							):
			# 1. predict noise residual
			A_		:						str	    =   model(lowerCAmelCase_     ,     lowerCAmelCase_							)

			if i + 1 == timesteps.shape[0]:
				A_		:						Optional[Any]	    =   None
			else:
				A_		:						Optional[Any]	    =   timesteps[i + 1]

			# 2. predict previous mean of sample x_t-1
			A_		:						Optional[Any]	    =   scheduler.step(
			    lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     prev_timestep=lowerCAmelCase_     ,     generator=lowerCAmelCase_							).prev_sample

			A_		:						Optional[Any]	    =   pred_prev_sample

		A_		:						Tuple	    =   torch.sum(torch.abs(lowerCAmelCase_							)							)
		A_		:						Any	    =   torch.mean(torch.abs(lowerCAmelCase_							)							)

		assert abs(result_sum.item() - 258.2044983							) < 1e-2
		assert abs(result_mean.item() - 0.3362038							) < 1e-3





	def    lowerCamelCase(self							):
		pass





	def    lowerCamelCase(self							):
		pass








"""simple docstring"""
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 			__UpperCamelCase	(		snake_case__					):
	A_		:						List[str]	    =   git.Repo(search_parent_directories=snake_case__					)
	A_		:						List[str]	    =   {
	    """repo_id""": str(snake_case__					),
	    """repo_sha""": str(repo.head.object.hexsha					),
	    """repo_branch""": str(repo.active_branch					),
	}

	with open(os.path.join(snake_case__   ,   """git_log.json"""					)   ,   """w"""					) as f:
		json.dump(snake_case__   ,   snake_case__   ,   indent=4					)






def 			__UpperCamelCase	(		snake_case__					):
	if params.n_gpu <= 0:
		A_		:						Dict	    =   0
		A_		:						str	    =   -1
		A_		:						int	    =   True
		A_		:						Union[str, Any]	    =   False
		return

	assert torch.cuda.is_available()

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

		A_		:						str	    =   int(os.environ["""WORLD_SIZE"""]					)
		A_		:						int	    =   int(os.environ["""N_GPU_NODE"""]					)
		A_		:						int	    =   int(os.environ["""RANK"""]					)

		# number of nodes / node ID
		A_		:						Optional[int]	    =   params.world_size // params.n_gpu_per_node
		A_		:						Optional[int]	    =   params.global_rank // params.n_gpu_per_node
		A_		:						Tuple	    =   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

		A_		:						Dict	    =   1
		A_		:						Tuple	    =   0
		A_		:						Dict	    =   0
		A_		:						Optional[int]	    =   0
		A_		:						List[str]	    =   1
		A_		:						List[Any]	    =   1
		A_		:						List[Any]	    =   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
	A_		:						Optional[int]	    =   params.node_id == 0 and params.local_rank == 0
	A_		:						Optional[Any]	    =   params.n_nodes > 1

	# summary
	A_		:						str	    =   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 			__UpperCamelCase	(		snake_case__					):
	np.random.seed(args.seed					)
	torch.manual_seed(args.seed					)
	if args.n_gpu > 0:
		torch.cuda.manual_seed_all(args.seed					)

