
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    :	List[Any]						=						logging.getLogger(__name__)


def 			UpperCAmelCase_     (  __lowerCAmelCase		)       ->  Any:
    __lowercase						:	Dict					=				git.Repo(search_parent_directories=UpperCamelCase__		)
    __lowercase						:	Optional[Any]					=				{
        "repo_id": str(UpperCamelCase__		),
        "repo_sha": str(repo.head.object.hexsha		),
        "repo_branch": str(repo.active_branch		),
    }

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


def 			UpperCAmelCase_     (  __lowerCAmelCase		)       ->  List[Any]:
    if params.n_gpu <= 0:
        __lowercase						:	Union[str, Any]					=				0
        __lowercase						:	Dict					=				-1
        __lowercase						:	Dict					=				True
        __lowercase						:	str					=				False
        return

    assert torch.cuda.is_available()

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

        __lowercase						:	Any					=				int(os.environ['''WORLD_SIZE''']		)
        __lowercase						:	List[Any]					=				int(os.environ['''N_GPU_NODE''']		)
        __lowercase						:	Any					=				int(os.environ['''RANK''']		)

        # number of nodes / node ID
        __lowercase						:	int					=				params.world_size // params.n_gpu_per_node
        __lowercase						:	str					=				params.global_rank // params.n_gpu_per_node
        __lowercase						:	Any					=				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						:	Dict					=				1
        __lowercase						:	List[str]					=				0
        __lowercase						:	int					=				0
        __lowercase						:	List[Any]					=				0
        __lowercase						:	str					=				1
        __lowercase						:	Union[str, Any]					=				1
        __lowercase						:	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
    __lowercase						:	str					=				params.node_id == 0 and params.local_rank == 0
    __lowercase						:	Any					=				params.n_nodes > 1

    # summary
    __lowercase						:	Optional[Any]					=				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		)       ->  List[Any]:
    np.random.seed(args.seed		)
    torch.manual_seed(args.seed		)
    if args.n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed		)






import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()







class      lowerCAmelCase__       (				a     ,    a     ,    unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= VQModel
     __SCREAMING_SNAKE_CASE  			= "sample"


     @property
     def  _lowerCamelCase (						self ,						__lowerCamelCase=(3_2, 3_2))			->				Optional[Any]:
            _A      :       Optional[int]				    = 4
            _A      :       Tuple				    = 3

            _A      :       List[Any]				    = floats_tensor((batch_size, num_channels) + sizes).to(__lowerCamelCase)

            return {"sample": image}


     @property
     def  _lowerCamelCase (						self)			->				int:
            return (3, 3_2, 3_2)


     @property
     def  _lowerCamelCase (						self)			->				List[Any]:
            return (3, 3_2, 3_2)


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       List[Any]				    = {
                "block_out_channels": [3_2, 6_4],
                "in_channels": 3,
                "out_channels": 3,
                "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
                "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
                "latent_channels": 3,
            }
            _A      :       int				    = self.dummy_input
            return init_dict, inputs_dict


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


     def  _lowerCamelCase (						self)			->				Any:
            pass


     def  _lowerCamelCase (						self)			->				Any:
            _A    ,			_A      :       List[Any]				    = VQModel.from_pretrained("fusing/vqgan-dummy" ,						output_loading_info=__lowerCamelCase)
            self.assertIsNotNone(__lowerCamelCase)
            self.assertEqual(len(loading_info["missing_keys"]) ,						0)

            model.to(__lowerCamelCase)
            _A      :       str				    = model(**self.dummy_input)

            assert image is not None, "Make sure output is not None"


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       Optional[Any]				    = VQModel.from_pretrained("fusing/vqgan-dummy")
            model.to(__lowerCamelCase).eval()

            torch.manual_seed(0)
            if torch.cuda.is_available():
                   torch.cuda.manual_seed_all(0)

            _A      :       Tuple				    = torch.randn(1 ,						model.config.in_channels ,						model.config.sample_size ,						model.config.sample_size)
            _A      :       Optional[int]				    = image.to(__lowerCamelCase)
            with torch.no_grad():
                   _A      :       List[str]				    = model(__lowerCamelCase).sample

            _A      :       int				    = output[0, -1, -3:, -3:].flatten().cpu()
            # fmt: off
            _A      :       Optional[Any]				    = torch.tensor([-0.0_1_5_3, -0.4_0_4_4, -0.1_8_8_0, -0.5_1_6_1, -0.2_4_1_8, -0.4_0_7_2, -0.1_6_1_2, -0.0_6_3_3, -0.0_1_4_3])
            # fmt: on
            self.assertTrue(torch.allclose(__lowerCamelCase ,						__lowerCamelCase ,						atol=1e-3))
