import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import LevitImageProcessor





class      lowerCAmelCase						(					unittest.TestCase			):



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__=7				,      lowerCAmelCase__=3				,      lowerCAmelCase__=18				,      lowerCAmelCase__=30				,      lowerCAmelCase__=400				,      lowerCAmelCase__=True				,      lowerCAmelCase__=None				,      lowerCAmelCase__=True				,      lowerCAmelCase__=None				,      lowerCAmelCase__=True				,      lowerCAmelCase__=[0.5, 0.5, 0.5]				,      lowerCAmelCase__=[0.5, 0.5, 0.5]				,      ):
            _A=	size if size is not None else {'shortest_edge': 18}
            _A=	crop_size if crop_size is not None else {'height': 18, 'width': 18}
            _A=	parent
            _A=	batch_size
            _A=	num_channels
            _A=	image_size
            _A=	min_resolution
            _A=	max_resolution
            _A=	do_resize
            _A=	size
            _A=	do_center_crop
            _A=	crop_size
            _A=	do_normalize
            _A=	image_mean
            _A=	image_std





     def 			a__  (					self       ):
            return {
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_normalize": self.do_normalize,
                "do_resize": self.do_resize,
                "do_center_crop": self.do_center_crop,
                "size": self.size,
                "crop_size": self.crop_size,
            }





@require_torch
@require_vision
class      lowerCAmelCase						(					_a			,		unittest.TestCase			):
     _SCREAMING_SNAKE_CASE     :     str		=LevitImageProcessor if is_vision_available() else None



     def 			a__  (					self       ):
            _A=	LevitImageProcessingTester(self       )



     @property
     def 			a__  (					self       ):
            return self.image_processor_tester.prepare_image_processor_dict()



     def 			a__  (					self       ):
            _A=	self.image_processing_class(**self.image_processor_dict       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'image_mean'       )       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'image_std'       )       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'do_normalize'       )       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'do_resize'       )       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'do_center_crop'       )       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'size'       )       )



     def 			a__  (					self       ):
            _A=	self.image_processing_class.from_dict(self.image_processor_dict       )
            self.assertEqual(image_processor.size				,      {'shortest_edge': 18}       )
            self.assertEqual(image_processor.crop_size				,      {'height': 18, 'width': 18}       )

            _A=	self.image_processing_class.from_dict(self.image_processor_dict				,      size=42				,      crop_size=84       )
            self.assertEqual(image_processor.size				,      {'shortest_edge': 42}       )
            self.assertEqual(image_processor.crop_size				,      {'height': 84, 'width': 84}       )



     def 			a__  (					self       ):
            pass



     def 			a__  (					self       ):
            # Initialize image_processing
            _A=	self.image_processing_class(**self.image_processor_dict       )
            # create random PIL images
            _A=	prepare_image_inputs(self.image_processor_tester				,      equal_resolution=lowerCAmelCase__       )
            for image in image_inputs:
                   self.assertIsInstance(lowerCAmelCase__				,      Image.Image       )

            # Test not batched input
            _A=	image_processing(image_inputs[0]				,      return_tensors='pt'       ).pixel_values
            self.assertEqual(
                encoded_images.shape				,      (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                )				,      )

            # Test batched
            _A=	image_processing(lowerCAmelCase__				,      return_tensors='pt'       ).pixel_values
            self.assertEqual(
                encoded_images.shape				,      (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                )				,      )



     def 			a__  (					self       ):
            # Initialize image_processing
            _A=	self.image_processing_class(**self.image_processor_dict       )
            # create random numpy tensors
            _A=	prepare_image_inputs(self.image_processor_tester				,      equal_resolution=lowerCAmelCase__				,      numpify=lowerCAmelCase__       )
            for image in image_inputs:
                   self.assertIsInstance(lowerCAmelCase__				,      np.ndarray       )

            # Test not batched input
            _A=	image_processing(image_inputs[0]				,      return_tensors='pt'       ).pixel_values
            self.assertEqual(
                encoded_images.shape				,      (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                )				,      )

            # Test batched
            _A=	image_processing(lowerCAmelCase__				,      return_tensors='pt'       ).pixel_values
            self.assertEqual(
                encoded_images.shape				,      (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                )				,      )





     def 			a__  (					self       ):
            # Initialize image_processing
            _A=	self.image_processing_class(**self.image_processor_dict       )
            # create random PyTorch tensors
            _A=	prepare_image_inputs(self.image_processor_tester				,      equal_resolution=lowerCAmelCase__				,      torchify=lowerCAmelCase__       )
            for image in image_inputs:
                   self.assertIsInstance(lowerCAmelCase__				,      torch.Tensor       )

            # Test not batched input
            _A=	image_processing(image_inputs[0]				,      return_tensors='pt'       ).pixel_values
            self.assertEqual(
                encoded_images.shape				,      (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                )				,      )

            # Test batched
            _A=	image_processing(lowerCAmelCase__				,      return_tensors='pt'       ).pixel_values
            self.assertEqual(
                encoded_images.shape				,      (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                )				,      )







#
# 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 							UpperCamelCase				(    *lowerCAmelCase_   )		->						Dict:




       '''simple docstring'''






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


UpperCAmelCase_								=							int(os.environ['''LOCAL_RANK'''])
torch.cuda.set_device(local_rank)
UpperCAmelCase_								=							torch.device('''cuda''', local_rank)
UpperCAmelCase_								=							socket.gethostname()

UpperCAmelCase_								=							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
      UpperCAmelCase_								=							dist.get_rank()
      UpperCAmelCase_								=							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