


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,
)
a			:			Any												=					logging.getLogger(__name__)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = git.Repo(search_parent_directories=_UpperCamelCase	)
			__lowercase			       = {
			    '''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 		lowercase_	(  _UpperCamelCase	):


			'''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 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







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





'''simple docstring'''





from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


UpperCamelCase_    						=       logging.get_logger(__name__)





class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):
				lowerCamelCase_		       =    ['pixel_values']







				def __init__(       self     :		List[Any]		,					UpperCAmelCase__     :		bool = True		,					UpperCAmelCase__     :		Union[int, float] = 1 / 255		,					UpperCAmelCase__     :		bool = True		,					UpperCAmelCase__     :		int = 8		,					**UpperCAmelCase__     :		str		,					):
										'''simple docstring'''
										super().__init__(**UpperCAmelCase__   )

										lowercase :							Union[str, Any]    		=do_rescale
										lowercase :							List[Any]    		=rescale_factor
										lowercase :							Tuple    		=do_pad
										lowercase :							List[str]    		=pad_size







				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		np.ndarray		,					UpperCAmelCase__     :		float		,					UpperCAmelCase__     :		Optional[Union[str, ChannelDimension]] = None		,					**UpperCAmelCase__     :		Optional[int]   ):
										'''simple docstring'''
										return rescale(UpperCAmelCase__		,					scale=UpperCAmelCase__		,					data_format=UpperCAmelCase__		,					**UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		np.ndarray		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		Optional[Union[str, ChannelDimension]] = None   ):
										'''simple docstring'''
										lowercase		,	lowercase :							Union[str, Any]    		=get_image_size(UpperCAmelCase__   )
										lowercase :							Tuple    		=(old_height // size + 1) * size - old_height
										lowercase :							Tuple    		=(old_width // size + 1) * size - old_width

										return pad(UpperCAmelCase__		,					((0, pad_height), (0, pad_width))		,					mode='''symmetric'''		,					data_format=UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		ImageInput		,					UpperCAmelCase__     :		Optional[bool] = None		,					UpperCAmelCase__     :		Optional[float] = None		,					UpperCAmelCase__     :		Optional[bool] = None		,					UpperCAmelCase__     :		Optional[int] = None		,					UpperCAmelCase__     :		Optional[Union[str, TensorType]] = None		,					UpperCAmelCase__     :		Union[str, ChannelDimension] = ChannelDimension.FIRST		,					**UpperCAmelCase__     :		Optional[Any]		,					):
										'''simple docstring'''
										lowercase :							int    		=do_rescale if do_rescale is not None else self.do_rescale
										lowercase :							Tuple    		=rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase :							int    		=do_pad if do_pad is not None else self.do_pad
										lowercase :							List[Any]    		=pad_size if pad_size is not None else self.pad_size

										lowercase :							Any    		=make_list_of_images(UpperCAmelCase__   )

										if not valid_images(UpperCAmelCase__   ):
																raise ValueError(
																    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																    '''torch.Tensor, tf.Tensor or jax.ndarray.'''   )

										if do_rescale and rescale_factor is None:
																raise ValueError('''Rescale factor must be specified if do_rescale is True.'''   )

										# All transformations expect numpy arrays.
										lowercase :							Dict    		=[to_numpy_array(UpperCAmelCase__   ) for image in images]

										if do_rescale:
																lowercase :							Tuple    		=[self.rescale(image=UpperCAmelCase__		,					scale=UpperCAmelCase__   ) for image in images]

										if do_pad:
																lowercase :							Union[str, Any]    		=[self.pad(UpperCAmelCase__		,					size=UpperCAmelCase__   ) for image in images]

										lowercase :							Dict    		=[to_channel_dimension_format(UpperCAmelCase__		,					UpperCAmelCase__   ) for image in images]

										lowercase :							Any    		={'''pixel_values''': images}
										return BatchFeature(data=UpperCAmelCase__		,					tensor_type=UpperCAmelCase__   )





