






'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


lowercase_      :				Union[str, Any]   					=    logging.get_logger(__name__)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Tuple						):
						if isinstance(lowercase_ ,  (list, tuple)						) and isinstance(videos[0] ,  (list, tuple)						) and is_valid_image(videos[0][0]						):
												return videos

						elif isinstance(lowercase_ ,  (list, tuple)						) and is_valid_image(videos[0]						):
												return [videos]

						elif is_valid_image(lowercase_						):
												return [[videos]]

						raise ValueError(F"""Could not make batched video from {videos}"""						)




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					['''pixel_values''']







	def __init__(    self ,				_lowerCAmelCase = True ,				_lowerCAmelCase = None ,				_lowerCAmelCase = PILImageResampling.BILINEAR ,				_lowerCAmelCase = True ,				_lowerCAmelCase = None ,				_lowerCAmelCase = True ,				_lowerCAmelCase = 1 / 255 ,				_lowerCAmelCase = True ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				**_lowerCAmelCase ,				)    ->    None:
							'''simple docstring'''






							super().__init__(**_lowerCAmelCase						)
							lowercase        =							size if size is not None else {"""shortest_edge""": 224}
							lowercase        =							get_size_dict(_lowerCAmelCase ,				default_to_square=_lowerCAmelCase						)
							lowercase        =							crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
							lowercase        =							get_size_dict(_lowerCAmelCase ,				param_name="""crop_size"""						)

							lowercase        =							do_resize
							lowercase        =							size
							lowercase        =							do_center_crop
							lowercase        =							crop_size
							lowercase        =							resample
							lowercase        =							do_rescale
							lowercase        =							rescale_factor
							lowercase        =							do_normalize
							lowercase        =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowercase        =							image_std if image_std is not None else IMAGENET_STANDARD_STD







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase = PILImageResampling.BILINEAR ,				_lowerCAmelCase = None ,				**_lowerCAmelCase ,				)    ->    np.ndarray:
							'''simple docstring'''






							lowercase        =							get_size_dict(_lowerCAmelCase ,				default_to_square=_lowerCAmelCase						)
							if "shortest_edge" in size:
													lowercase        =							get_resize_output_image_size(_lowerCAmelCase ,				size["""shortest_edge"""] ,				default_to_square=_lowerCAmelCase						)
							elif "height" in size and "width" in size:
													lowercase        =							(size["""height"""], size["""width"""])
							else:
													raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""						)
							return resize(_lowerCAmelCase ,				size=_lowerCAmelCase ,				resample=_lowerCAmelCase ,				data_format=_lowerCAmelCase ,				**_lowerCAmelCase						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase = None ,				**_lowerCAmelCase ,				)    ->    np.ndarray:
							'''simple docstring'''






							lowercase        =							get_size_dict(_lowerCAmelCase						)
							if "height" not in size or "width" not in size:
													raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""						)
							return center_crop(_lowerCAmelCase ,				size=(size["""height"""], size["""width"""]) ,				data_format=_lowerCAmelCase ,				**_lowerCAmelCase						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase = None ,				**_lowerCAmelCase ,				)    ->    List[str]:
							'''simple docstring'''






							return rescale(_lowerCAmelCase ,				scale=_lowerCAmelCase ,				data_format=_lowerCAmelCase ,				**_lowerCAmelCase						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase = None ,				**_lowerCAmelCase ,				)    ->    np.ndarray:
							'''simple docstring'''






							return normalize(_lowerCAmelCase ,				mean=_lowerCAmelCase ,				std=_lowerCAmelCase ,				data_format=_lowerCAmelCase ,				**_lowerCAmelCase						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = ChannelDimension.FIRST ,				)    ->    np.ndarray:
							'''simple docstring'''






							if do_resize and size is None or resample is None:
													raise ValueError("""Size and resample must be specified if do_resize is True."""						)

							if do_center_crop and crop_size is None:
													raise ValueError("""Crop size must be specified if do_center_crop is True."""						)

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

							if do_normalize and (image_mean is None or image_std is None):
													raise ValueError("""Image mean and std must be specified if do_normalize is True."""						)

							# All transformations expect numpy arrays.
							lowercase        =							to_numpy_array(_lowerCAmelCase						)

							if do_resize:
													lowercase        =							self.resize(image=_lowerCAmelCase ,				size=_lowerCAmelCase ,				resample=_lowerCAmelCase						)

							if do_center_crop:
													lowercase        =							self.center_crop(_lowerCAmelCase ,				size=_lowerCAmelCase						)

							if do_rescale:
													lowercase        =							self.rescale(image=_lowerCAmelCase ,				scale=_lowerCAmelCase						)

							if do_normalize:
													lowercase        =							self.normalize(image=_lowerCAmelCase ,				mean=_lowerCAmelCase ,				std=_lowerCAmelCase						)

							lowercase        =							to_channel_dimension_format(_lowerCAmelCase ,				_lowerCAmelCase						)
							return image







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = ChannelDimension.FIRST ,				**_lowerCAmelCase ,				)    ->    PIL.Image.Image:
							'''simple docstring'''






							lowercase        =							do_resize if do_resize is not None else self.do_resize
							lowercase        =							resample if resample is not None else self.resample
							lowercase        =							do_center_crop if do_center_crop is not None else self.do_center_crop
							lowercase        =							do_rescale if do_rescale is not None else self.do_rescale
							lowercase        =							rescale_factor if rescale_factor is not None else self.rescale_factor
							lowercase        =							do_normalize if do_normalize is not None else self.do_normalize
							lowercase        =							image_mean if image_mean is not None else self.image_mean
							lowercase        =							image_std if image_std is not None else self.image_std

							lowercase        =							size if size is not None else self.size
							lowercase        =							get_size_dict(_lowerCAmelCase ,				default_to_square=_lowerCAmelCase						)
							lowercase        =							crop_size if crop_size is not None else self.crop_size
							lowercase        =							get_size_dict(_lowerCAmelCase ,				param_name="""crop_size"""						)

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

							lowercase        =							make_batched(_lowerCAmelCase						)

							lowercase        =							[
							    [
							        self._preprocess_image(
							            image=_lowerCAmelCase ,				do_resize=_lowerCAmelCase ,				size=_lowerCAmelCase ,				resample=_lowerCAmelCase ,				do_center_crop=_lowerCAmelCase ,				crop_size=_lowerCAmelCase ,				do_rescale=_lowerCAmelCase ,				rescale_factor=_lowerCAmelCase ,				do_normalize=_lowerCAmelCase ,				image_mean=_lowerCAmelCase ,				image_std=_lowerCAmelCase ,				data_format=_lowerCAmelCase ,				)
							        for img in video
							    ]
							    for video in videos
							]

							lowercase        =							{"""pixel_values""": videos}
							return BatchFeature(data=_lowerCAmelCase ,				tensor_type=_lowerCAmelCase						)







'''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,
)
lowercase_      :				Tuple   					=    logging.getLogger(__name__)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str						):
						lowercase        =							git.Repo(search_parent_directories=lowercase_						)
						lowercase        =							{
						    """repo_id""": str(lowercase_						),
						    """repo_sha""": str(repo.head.object.hexsha						),
						    """repo_branch""": str(repo.active_branch						),
						}

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







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str						):
						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      SCREAMING_SNAKE_CASE				(				lowercase_    :      Optional[Any]						):
						np.random.seed(args.seed						)
						torch.manual_seed(args.seed						)
						if args.n_gpu > 0:
												torch.cuda.manual_seed_all(args.seed						)


