

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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


a_    :						int    		=      logging.get_logger(__name__)




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         =["pixel_values"]

    def __init__(			self :      Union[str, Any]							,    snake_case__ :      bool = True							,    snake_case__ :      Dict[str, int] = None							,    snake_case__ :      PILImageResampling = PILImageResampling.BICUBIC							,    snake_case__ :      bool = True							,    snake_case__ :      Union[int, float] = 1 / 2_5_5							,    snake_case__ :      bool = True							,    snake_case__ :      Optional[Union[float, List[float]]] = None							,    snake_case__ :      Optional[Union[float, List[float]]] = None							,    snake_case__ :      bool = True							,    **snake_case__ :      Union[str, Any]							,    ):

     """simple docstring"""






     super().__init__(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			size if size is not None else {'height': 3_8_4, 'width': 3_8_4}
     SCREAMING_SNAKE_CASE								=			get_size_dict(snake_case__							,    default_to_square=snake_case__    )

     SCREAMING_SNAKE_CASE								=			do_resize
     SCREAMING_SNAKE_CASE								=			size
     SCREAMING_SNAKE_CASE								=			resample
     SCREAMING_SNAKE_CASE								=			do_rescale
     SCREAMING_SNAKE_CASE								=			rescale_factor
     SCREAMING_SNAKE_CASE								=			do_normalize
     SCREAMING_SNAKE_CASE								=			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     SCREAMING_SNAKE_CASE								=			image_std if image_std is not None else OPENAI_CLIP_STD
     SCREAMING_SNAKE_CASE								=			do_convert_rgb

    def UpperCamelCase			(			self :      Dict							,    snake_case__ :      np.ndarray							,    snake_case__ :      Dict[str, int]							,    snake_case__ :      PILImageResampling = PILImageResampling.BICUBIC							,    snake_case__ :      Optional[Union[str, ChannelDimension]] = None							,    **snake_case__ :      Dict							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			get_size_dict(snake_case__							,    default_to_square=snake_case__    )
     if "height" not in size or "width" not in size:
      raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""    )
     SCREAMING_SNAKE_CASE								=			(size['height'], size['width'])
     return resize(snake_case__							,    size=snake_case__							,    resample=snake_case__							,    data_format=snake_case__							,    **snake_case__    )

    def UpperCamelCase			(			self :      str							,    snake_case__ :      np.ndarray							,    snake_case__ :      Union[int, float]							,    snake_case__ :      Optional[Union[str, ChannelDimension]] = None							,    **snake_case__ :      Union[str, Any]							,    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      Tuple							,    snake_case__ :      np.ndarray							,    snake_case__ :      Union[float, List[float]]							,    snake_case__ :      Union[float, List[float]]							,    snake_case__ :      Optional[Union[str, ChannelDimension]] = None							,    **snake_case__ :      int							,    ):

     """simple docstring"""






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




    def UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      ImageInput							,    snake_case__ :      Optional[bool] = None							,    snake_case__ :      Optional[Dict[str, int]] = None							,    snake_case__ :      PILImageResampling = None							,    snake_case__ :      Optional[bool] = None							,    snake_case__ :      Optional[float] = None							,    snake_case__ :      Optional[bool] = None							,    snake_case__ :      Optional[Union[float, List[float]]] = None							,    snake_case__ :      Optional[Union[float, List[float]]] = None							,    snake_case__ :      Optional[Union[str, TensorType]] = None							,    snake_case__ :      bool = None							,    snake_case__ :      ChannelDimension = ChannelDimension.FIRST							,    **snake_case__ :      Any							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE								=			resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE								=			do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE								=			rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE								=			do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE								=			image_mean if image_mean is not None else self.image_mean
     SCREAMING_SNAKE_CASE								=			image_std if image_std is not None else self.image_std
     SCREAMING_SNAKE_CASE								=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     SCREAMING_SNAKE_CASE								=			size if size is not None else self.size
     SCREAMING_SNAKE_CASE								=			get_size_dict(snake_case__							,    default_to_square=snake_case__    )

     SCREAMING_SNAKE_CASE								=			make_list_of_images(snake_case__    )

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

     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_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.'    )

     # PIL RGBA images are converted to RGB
     if do_convert_rgb:
      SCREAMING_SNAKE_CASE								=			[convert_to_rgb(snake_case__    ) for image in images]

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE								=			[to_numpy_array(snake_case__    ) for image in images]

     if do_resize:
      SCREAMING_SNAKE_CASE								=			[self.resize(image=snake_case__							,    size=snake_case__							,    resample=snake_case__    ) for image in images]

     if do_rescale:
      SCREAMING_SNAKE_CASE								=			[self.rescale(image=snake_case__							,    scale=snake_case__    ) for image in images]

     if do_normalize:
      SCREAMING_SNAKE_CASE								=			[self.normalize(image=snake_case__							,    mean=snake_case__							,    std=snake_case__    ) for image in images]

     SCREAMING_SNAKE_CASE								=			[to_channel_dimension_format(snake_case__							,    snake_case__    ) for image in images]

     SCREAMING_SNAKE_CASE								=			BatchFeature(data={'pixel_values': images}							,    tensor_type=snake_case__    )

     return encoded_outputs




#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist


def        __lowerCAmelCase    (       *_UpperCamelCase		:  Optional[Any]	)		->							Optional[int]:
 '''simple docstring'''


 with open(_UpperCamelCase		,							'r'	) as fh:
  fcntl.flock(_UpperCamelCase		,							fcntl.LOCK_EX	)
  try:
   print(*_UpperCamelCase	)
  finally:
   fcntl.flock(_UpperCamelCase		,							fcntl.LOCK_UN	)


a_    :						int    		=      int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
a_    :						str    		=      torch.device("cuda", local_rank)
a_    :						Optional[int]    		=      socket.gethostname()

a_    :						Union[str, Any]    		=      F"""[{hostname}-{local_rank}]"""

try:
      # test distributed
      dist.init_process_group("nccl")
      dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
      dist.barrier()

      # test cuda is available and can allocate memory
      torch.cuda.is_available()
      torch.ones(1).cuda(local_rank)

      # global rank
      a_    :						Dict    		=      dist.get_rank()
      a_    :						Any    		=      dist.get_world_size()

      printflock(F"""{gpu} is OK (global rank: {rank}/{world_size})""")

      dist.barrier()
      if rank == 0:
            printflock(F"""pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}""")

except Exception:
      printflock(F"""{gpu} is broken""")
      raise

