






"""simple docstring"""
import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch



@require_torch
class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	@property
	def    lowerCamelCase(self							):
		return self.get_dummy_input()





	@property
	def    lowerCamelCase(self							):
		if self.block_type == "down":
			return (4, 32, 16, 16)
		elif self.block_type == "mid":
			return (4, 32, 32, 32)
		elif self.block_type == "up":
			return (4, 32, 64, 64)

		raise ValueError(f"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."""							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_=False     ,     ):
		A_		:						Optional[Any]	    =   4
		A_		:						List[Any]	    =   32
		A_		:						Any	    =   (32, 32)

		A_		:						Union[str, Any]	    =   torch.manual_seed(0							)
		A_		:						Any	    =   torch.device(_A							)
		A_		:						Optional[Any]	    =   (batch_size, num_channels) + sizes
		A_		:						str	    =   randn_tensor(_A     ,     generator=_A     ,     device=_A							)
		A_		:						Dict	    =   {"""hidden_states""": hidden_states}

		if include_temb:
			A_		:						str	    =   128
			A_		:						Optional[Any]	    =   randn_tensor((batch_size, temb_channels)     ,     generator=_A     ,     device=_A							)

		if include_res_hidden_states_tuple:
			A_		:						List[Any]	    =   torch.manual_seed(1							)
			A_		:						int	    =   (randn_tensor(_A     ,     generator=_A     ,     device=_A							),)

		if include_encoder_hidden_states:
			A_		:						int	    =   floats_tensor((batch_size, 32, 32)							).to(_A							)

		if include_skip_sample:
			A_		:						Tuple	    =   randn_tensor(((batch_size, 3) + sizes)     ,     generator=_A     ,     device=_A							)

		return dummy_input





	def    lowerCamelCase(self							):
		A_		:						Union[str, Any]	    =   {
		    """in_channels""": 32,
		    """out_channels""": 32,
		    """temb_channels""": 128,
		}
		if self.block_type == "up":
			A_		:						List[Any]	    =   32

		if self.block_type == "mid":
			init_dict.pop("""out_channels"""							)

		A_		:						Tuple	    =   self.dummy_input
		return init_dict, inputs_dict





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_    ,     A_		:						Union[str, Any]	    =   self.prepare_init_args_and_inputs_for_common()
		A_		:						Tuple	    =   self.block_class(**_A							)
		unet_block.to(_A							)
		unet_block.eval()

		with torch.no_grad():
			A_		:						List[str]	    =   unet_block(**_A							)

		if isinstance(_A     ,     _A							):
			A_		:						Tuple	    =   output[0]

		self.assertEqual(output.shape     ,     self.output_shape							)

		A_		:						Dict	    =   output[0, -1, -3:, -3:]
		A_		:						List[Any]	    =   torch.tensor(_A							).to(_A							)
		assert torch_all_close(output_slice.flatten()     ,     _A     ,     atol=5e-3							)





	@unittest.skipIf(torch_device == """mps"""     ,     """Training is not supported in mps"""							)
	def    lowerCamelCase(self							):
		A_    ,     A_		:						Union[str, Any]	    =   self.prepare_init_args_and_inputs_for_common()
		A_		:						int	    =   self.block_class(**_A							)
		model.to(_A							)
		model.train()
		A_		:						str	    =   model(**_A							)

		if isinstance(_A     ,     _A							):
			A_		:						Optional[int]	    =   output[0]

		A_		:						Union[str, Any]	    =   torch.device(_A							)
		A_		:						Union[str, Any]	    =   randn_tensor(output.shape     ,     device=_A							)
		A_		:						Union[str, Any]	    =   torch.nn.functional.mse_loss(_A     ,     _A							)
		loss.backward()








"""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					)

