

'''simple docstring'''





import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

a_    :						Optional[Any]    		=      logging.getLogger()


def        __lowerCAmelCase    (       )		->							Any:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			argparse.ArgumentParser()
 parser.add_argument('-f'	)
 SCREAMING_SNAKE_CASE								=			parser.parse_args()
 return args.f


def        __lowerCAmelCase    (       _UpperCamelCase		:  List[str]	)		->							Union[str, Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			{}
 SCREAMING_SNAKE_CASE								=			os.path.join(_lowerCAmelCase		,							'all_results.json'	)
 if os.path.exists(_lowerCAmelCase	):
  with open(_lowerCAmelCase		,							'r'	) as f:
   SCREAMING_SNAKE_CASE								=			json.load(_lowerCAmelCase	)
 else:
  raise ValueError(f"""can't find {path}"""	)
 return results


def        __lowerCAmelCase    (       )		->							int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			torch.cuda.is_available() and torch_device == 'cuda'
 return is_using_cuda and is_apex_available()


a_    :						Tuple    		=      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class      UpperCamelCase (     __UpperCAmelCase   ):

    @classmethod
    def UpperCamelCase			(			cls :      str    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			tempfile.mkdtemp()
     SCREAMING_SNAKE_CASE								=			os.path.join(cls.tmpdir							,    'default_config.yml'    )
     write_basic_config(save_location=cls.configPath    )
     SCREAMING_SNAKE_CASE								=			['accelerate', 'launch', '--config_file', cls.configPath]

    @classmethod
    def UpperCamelCase			(			cls :      str    ):

     """simple docstring"""






     shutil.rmtree(cls.tmpdir    )

    @mock.patch.dict(os.environ							,    {'WANDB_MODE': 'offline'}    )
    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE								=			F"""\n            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --seed=42\n            --checkpointing_steps epoch\n            --with_tracking\n        """.split()

     if is_cuda_and_apex_available():
      testargs.append('--fp16'    )

     run_command(self._launch_args + testargs    )
     SCREAMING_SNAKE_CASE								=			get_results(_lowerCamelCase    )
     self.assertGreaterEqual(result['eval_accuracy']							,    0.75    )
     self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase							,    'epoch_0'    )    )    )
     self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase							,    'glue_no_trainer'    )    )    )

    @mock.patch.dict(os.environ							,    {'WANDB_MODE': 'offline'}    )
    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE								=			F"""\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        """.split()

     if torch.cuda.device_count() > 1:
      # Skipping because there are not enough batches to train the model + would need a drop_last to work.
      return

     run_command(self._launch_args + testargs    )
     SCREAMING_SNAKE_CASE								=			get_results(_lowerCamelCase    )
     self.assertLess(result['perplexity']							,    1_0_0    )
     self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase							,    'epoch_0'    )    )    )
     self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase							,    'clm_no_trainer'    )    )    )

    @mock.patch.dict(os.environ							,    {'WANDB_MODE': 'offline'}    )
    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE								=			F"""\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        """.split()

     run_command(self._launch_args + testargs    )
     SCREAMING_SNAKE_CASE								=			get_results(_lowerCamelCase    )
     self.assertLess(result['perplexity']							,    4_2    )
     self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase							,    'epoch_0'    )    )    )
     self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase							,    'mlm_no_trainer'    )    )    )

    @mock.patch.dict(os.environ							,    {'WANDB_MODE': 'offline'}    )
    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			7 if get_gpu_count() > 1 else 2

     SCREAMING_SNAKE_CASE								=			self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE								=			F"""\n            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n            --checkpointing_steps epoch\n            --with_tracking\n        """.split()

     run_command(self._launch_args + testargs    )
     SCREAMING_SNAKE_CASE								=			get_results(_lowerCamelCase    )
     self.assertGreaterEqual(result['eval_accuracy']							,    0.75    )
     self.assertLess(result['train_loss']							,    0.5    )
     self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase							,    'epoch_0'    )    )    )
     self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase							,    'ner_no_trainer'    )    )    )

    @unittest.skip(reason='Fix me @muellerzr'    )
    @mock.patch.dict(os.environ							,    {'WANDB_MODE': 'offline'}    )
    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE								=			F"""\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        """.split()

     run_command(self._launch_args + testargs    )
     SCREAMING_SNAKE_CASE								=			get_results(_lowerCamelCase    )
     # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
     self.assertGreaterEqual(result['eval_f1']							,    2_8    )
     self.assertGreaterEqual(result['eval_exact']							,    2_8    )
     self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase							,    'epoch_0'    )    )    )
     self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase							,    'qa_no_trainer'    )    )    )

    @mock.patch.dict(os.environ							,    {'WANDB_MODE': 'offline'}    )
    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE								=			F"""\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        """.split()

     run_command(self._launch_args + testargs    )
     SCREAMING_SNAKE_CASE								=			get_results(_lowerCamelCase    )
     self.assertGreaterEqual(result['eval_accuracy']							,    0.8    )
     self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase							,    'swag_no_trainer'    )    )    )

    @slow
    @mock.patch.dict(os.environ							,    {'WANDB_MODE': 'offline'}    )
    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE								=			F"""\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        """.split()

     run_command(self._launch_args + testargs    )
     SCREAMING_SNAKE_CASE								=			get_results(_lowerCamelCase    )
     self.assertGreaterEqual(result['eval_rouge1']							,    1_0    )
     self.assertGreaterEqual(result['eval_rouge2']							,    2    )
     self.assertGreaterEqual(result['eval_rougeL']							,    7    )
     self.assertGreaterEqual(result['eval_rougeLsum']							,    7    )
     self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase							,    'epoch_0'    )    )    )
     self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase							,    'summarization_no_trainer'    )    )    )

    @slow
    @mock.patch.dict(os.environ							,    {'WANDB_MODE': 'offline'}    )
    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE								=			F"""\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        """.split()

     run_command(self._launch_args + testargs    )
     SCREAMING_SNAKE_CASE								=			get_results(_lowerCamelCase    )
     self.assertGreaterEqual(result['eval_bleu']							,    3_0    )
     self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase							,    'epoch_0'    )    )    )
     self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase							,    'translation_no_trainer'    )    )    )

    @slow
    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			logging.StreamHandler(sys.stdout    )
     logger.addHandler(_lowerCamelCase    )

     SCREAMING_SNAKE_CASE								=			self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE								=			F"""\n            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n            --dataset_name huggingface/semantic-segmentation-test-sample\n            --output_dir {tmp_dir}\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n        """.split()

     run_command(self._launch_args + testargs    )
     SCREAMING_SNAKE_CASE								=			get_results(_lowerCamelCase    )
     self.assertGreaterEqual(result['eval_overall_accuracy']							,    0.10    )




    @mock.patch.dict(os.environ							,    {'WANDB_MODE': 'offline'}    )
    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE								=			F"""\n            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n            --model_name_or_path google/vit-base-patch16-224-in21k\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --max_train_steps 2\n            --train_val_split 0.1\n            --seed 42\n            --output_dir {tmp_dir}\n            --with_tracking\n            --checkpointing_steps 1\n        """.split()

     if is_cuda_and_apex_available():
      testargs.append('--fp16'    )

     run_command(self._launch_args + testargs    )
     SCREAMING_SNAKE_CASE								=			get_results(_lowerCamelCase    )
     # The base model scores a 25%
     self.assertGreaterEqual(result['eval_accuracy']							,    0.6    )
     self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase							,    'step_1'    )    )    )
     self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase							,    'image_classification_no_trainer'    )    )    )




#
# 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

