
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,
)
lowercase__		:Union[str, Any]   =    logging.getLogger(__name__)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						git.Repo(search_parent_directories=lowerCAmelCase__		)
					lowercase							=						{
					    '''repo_id''': str(lowerCAmelCase__		),
					    '''repo_sha''': str(repo.head.object.hexsha		),
					    '''repo_branch''': str(repo.active_branch		),
					}

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



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''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        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					np.random.seed(args.seed		)
					torch.manual_seed(args.seed		)
					if args.n_gpu > 0:
										torch.cuda.manual_seed_all(args.seed		)






import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       List[Any]       					=IFInpaintingPipeline
							lowercase_       :       Optional[int]       					=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
							lowercase_       :       Any       					=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							lowercase_       :       str       					=PipelineTesterMixin.required_optional_params - {'''latents'''}




							def  A__      (				self):
												return self._get_dummy_components()




							def  A__      (				self   ,A__   ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)

												lowercase							=						floats_tensor((1, 3, 3_2, 3_2)   ,rng=random.Random(A__)).to(A__)
												lowercase							=						floats_tensor((1, 3, 3_2, 3_2)   ,rng=random.Random(A__)).to(A__)

												lowercase							=						{
												    '''prompt''': '''A painting of a squirrel eating a burger''',
												    '''image''': image,
												    '''mask_image''': mask_image,
												    '''generator''': generator,
												    '''num_inference_steps''': 2,
												    '''output_type''': '''numpy''',
												}

												return inputs




							@unittest.skipIf(
							    torch_device != '''cuda''' or not is_xformers_available()   ,reason='''XFormers attention is only available with CUDA and `xformers` installed'''   ,)
							def  A__      (				self):
												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)




							def  A__      (				self):
												self._test_save_load_optional_components()




							@unittest.skipIf(torch_device != '''cuda'''   ,reason='''float16 requires CUDA''')
							def  A__      (				self):
												# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
												super().test_save_load_floataa(expected_max_diff=1E-1)




							def  A__      (				self):
												self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)




							def  A__      (				self):
												self._test_save_load_local()





							def  A__      (				self):
												self._test_inference_batch_single_identical(
												    expected_max_diff=1E-2   ,)




