"""simple docstring"""



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


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

__lowercase		:		Tuple														=							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
       __lowercase		:		Union[str, Any]														=							dist.get_rank()
       __lowercase		:		List[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
"""simple docstring"""



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import CLIPSegProcessor, ViTImageProcessor



@require_vision
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def  lowercase					(		self							:     Optional[int]    )    ->       List[str]:
         __snake_case				=							tempfile.mkdtemp()

         # fmt: off
         __snake_case				=							['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
         # fmt: on
         __snake_case				=							dict(zip(A_	,      range(len(A_    )    )    )    )
         __snake_case				=							['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
         __snake_case				=							{'''unk_token''': '''<unk>'''}

         __snake_case				=							os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES['''vocab_file''']    )
         __snake_case				=							os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES['''merges_file''']    )
         with open(self.vocab_file	,      '''w'''	,      encoding='''utf-8'''    ) as fp:
              fp.write(json.dumps(A_    ) + '''\n'''    )
         with open(self.merges_file	,      '''w'''	,      encoding='''utf-8'''    ) as fp:
              fp.write('''\n'''.join(A_    )    )

         __snake_case				=							{
             '''do_resize''': True,
             '''size''': 20,
             '''do_center_crop''': True,
             '''crop_size''': 18,
             '''do_normalize''': True,
             '''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
             '''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
         }
         __snake_case				=							os.path.join(self.tmpdirname	,      A_    )
         with open(self.image_processor_file	,      '''w'''	,      encoding='''utf-8'''    ) as fp:
              json.dump(A_	,      A_    )
    def  lowercase					(		self							:     Optional[Any]	,      **A_							:     Dict    )    ->       Any:
         return CLIPTokenizer.from_pretrained(self.tmpdirname	,      **A_    )
    def  lowercase					(		self							:     Optional[int]	,      **A_							:     str    )    ->       str:
         return CLIPTokenizerFast.from_pretrained(self.tmpdirname	,      **A_    )
    def  lowercase					(		self							:     Any	,      **A_							:     Tuple    )    ->       Tuple:
         return ViTImageProcessor.from_pretrained(self.tmpdirname	,      **A_    )
    def  lowercase					(		self							:     Optional[int]    )    ->       Optional[Any]:
         shutil.rmtree(self.tmpdirname    )
    def  lowercase					(		self							:     int    )    ->       Optional[Any]:
         __snake_case				=							[np.random.randint(255	,      size=(3, 30, 400)	,      dtype=np.uinta    )]

         __snake_case				=							[Image.fromarray(np.moveaxis(A_	,      0	,      -1    )    ) for x in image_inputs]

         return image_inputs
    def  lowercase					(		self							:     Optional[Any]    )    ->       Optional[Any]:
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_rust_tokenizer()
         __snake_case				=							self.get_image_processor()

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )
         processor_slow.save_pretrained(self.tmpdirname    )
         __snake_case				=							CLIPSegProcessor.from_pretrained(self.tmpdirname	,      use_fast=A_    )

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )
         processor_fast.save_pretrained(self.tmpdirname    )
         __snake_case				=							CLIPSegProcessor.from_pretrained(self.tmpdirname    )

         self.assertEqual(processor_slow.tokenizer.get_vocab()	,      tokenizer_slow.get_vocab()    )
         self.assertEqual(processor_fast.tokenizer.get_vocab()	,      tokenizer_fast.get_vocab()    )
         self.assertEqual(tokenizer_slow.get_vocab()	,      tokenizer_fast.get_vocab()    )
         self.assertIsInstance(processor_slow.tokenizer	,      A_    )
         self.assertIsInstance(processor_fast.tokenizer	,      A_    )

         self.assertEqual(processor_slow.image_processor.to_json_string()	,      image_processor.to_json_string()    )
         self.assertEqual(processor_fast.image_processor.to_json_string()	,      image_processor.to_json_string()    )
         self.assertIsInstance(processor_slow.image_processor	,      A_    )
         self.assertIsInstance(processor_fast.image_processor	,      A_    )
    def  lowercase					(		self							:     Union[str, Any]    )    ->       Any:
         __snake_case				=							CLIPSegProcessor(tokenizer=self.get_tokenizer()	,      image_processor=self.get_image_processor()    )
         processor.save_pretrained(self.tmpdirname    )

         __snake_case				=							self.get_tokenizer(bos_token='''(BOS)'''	,      eos_token='''(EOS)'''    )
         __snake_case				=							self.get_image_processor(do_normalize=A_	,      padding_value=1.0    )

         __snake_case				=							CLIPSegProcessor.from_pretrained(
             self.tmpdirname	,      bos_token='''(BOS)'''	,      eos_token='''(EOS)'''	,      do_normalize=A_	,      padding_value=1.0    )

         self.assertEqual(processor.tokenizer.get_vocab()	,      tokenizer_add_kwargs.get_vocab()    )
         self.assertIsInstance(processor.tokenizer	,      A_    )

         self.assertEqual(processor.image_processor.to_json_string()	,      image_processor_add_kwargs.to_json_string()    )
         self.assertIsInstance(processor.image_processor	,      A_    )
    def  lowercase					(		self							:     Any    )    ->       str:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							image_processor(A_	,      return_tensors='''np'''    )
         __snake_case				=							processor(images=A_	,      return_tensors='''np'''    )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()	,      input_processor[key].sum()	,      delta=1E-2    )
    def  lowercase					(		self							:     List[str]    )    ->       List[Any]:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							'''lower newer'''

         __snake_case				=							processor(text=A_    )

         __snake_case				=							tokenizer(A_    )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]	,      encoded_processor[key]    )
    def  lowercase					(		self							:     List[Any]    )    ->       str:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							'''lower newer'''
         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							processor(text=A_	,      images=A_    )

         self.assertListEqual(list(inputs.keys()    )	,      ['''input_ids''', '''attention_mask''', '''pixel_values''']    )

         # test if it raises when no input is passed
         with pytest.raises(A_    ):
              processor()
    def  lowercase					(		self							:     Union[str, Any]    )    ->       Any:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							self.prepare_image_inputs()
         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							processor(images=A_	,      visual_prompt=A_    )

         self.assertListEqual(list(inputs.keys()    )	,      ['''pixel_values''', '''conditional_pixel_values''']    )

         # test if it raises when no input is passed
         with pytest.raises(A_    ):
              processor()







    def  lowercase					(		self							:     Optional[int]    )    ->       Dict:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         __snake_case				=							processor.batch_decode(A_    )
         __snake_case				=							tokenizer.batch_decode(A_    )

         self.assertListEqual(A_	,      A_    )