


import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


lowerCAmelCase__  :Any             =							os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f'''{bindir}/../../examples/pytorch/translation'''):
  from run_translation import main  # noqa


set_seed(4_2)
lowerCAmelCase__  :List[str]             =							'''sshleifer/student_marian_en_ro_6_1'''
lowerCAmelCase__  :Dict             =							'''sshleifer/tiny-mbart'''

@require_torch
class 		__a				(    A__      ):
  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE=False      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=True      ,   )			->	Tuple:




         """simple docstring"""





         _UpperCAmelCase					=      self.run_trainer(
             eval_steps=1      ,   max_len=12      ,   model_name=__snake_case      ,   num_train_epochs=1      ,   distributed=__snake_case      ,   extra_args_str=__snake_case      ,   predict_with_generate=__snake_case      ,   do_train=__snake_case      ,   do_eval=__snake_case      ,   do_predict=__snake_case      ,   )
         _UpperCAmelCase					=      TrainerState.load_from_json(os.path.join(__snake_case      ,   'trainer_state.json'						)						).log_history

         if not do_eval:
                return

         _UpperCAmelCase					=      [log for log in logs if 'eval_loss' in log.keys()]

         _UpperCAmelCase					=      eval_metrics[0]
         if predict_with_generate:
                assert "eval_bleu" in first_step_stats

                _UpperCAmelCase					=      eval_metrics[-1]
                assert isinstance(last_step_stats['eval_bleu']      ,   __snake_case						)
                assert not math.isnan(float(last_step_stats['eval_loss']						)						), "eval_loss must not be `nan`"
  @require_torch_non_multi_gpu
  def      UpperCAmelCase__    (      self						)			->	int:




         """simple docstring"""





         self.run_seqaseq_quick()
  @require_torch_multi_gpu
  def      UpperCAmelCase__    (      self						)			->	Dict:




         """simple docstring"""





         self.run_seqaseq_quick(distributed=__snake_case						)
  @require_torch_multi_gpu
  def      UpperCAmelCase__    (      self						)			->	List[Any]:




         """simple docstring"""





         self.run_seqaseq_quick(distributed=__snake_case						)
  @unittest.skip('Requires an update of the env running those tests'						)
  @require_torch_multi_gpu
  @require_fairscale
  def      UpperCAmelCase__    (      self						)			->	Dict:




         """simple docstring"""





         self.run_seqaseq_quick(distributed=__snake_case      ,   extra_args_str='--sharded_ddp simple'						)
  @unittest.skip('Requires an update of the env running those tests'						)
  @require_torch_multi_gpu
  @require_fairscale
  def      UpperCAmelCase__    (      self						)			->	Dict:




         """simple docstring"""





         self.run_seqaseq_quick(distributed=__snake_case      ,   extra_args_str='--sharded_ddp simple --fp16'						)
  @unittest.skip('Requires an update of the env running those tests'						)
  @require_torch_multi_gpu
  @require_fairscale
  def      UpperCAmelCase__    (      self						)			->	str:




         """simple docstring"""





         self.run_seqaseq_quick(distributed=__snake_case      ,   extra_args_str='--sharded_ddp zero_dp_2'      ,   predict_with_generate=__snake_case						)
  @unittest.skip('Requires an update of the env running those tests'						)
  @require_torch_multi_gpu
  @require_fairscale
  def      UpperCAmelCase__    (      self						)			->	List[Any]:




         """simple docstring"""





         self.run_seqaseq_quick(
             distributed=__snake_case      ,   extra_args_str='--sharded_ddp zero_dp_2 --fp16'      ,   predict_with_generate=__snake_case						)
  @require_apex
  @require_torch_gpu
  def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




         """simple docstring"""





         self.run_seqaseq_quick(distributed=__snake_case      ,   extra_args_str='--fp16 --fp16_backend=apex'						)
         # test 2nd time - was getting eval_loss': nan'
         # to reproduce the problem set distributed=False
         self.run_seqaseq_quick(distributed=__snake_case      ,   extra_args_str='--fp16 --fp16_backend=apex'						)
  @parameterized.expand(['base', 'low', 'high', 'mixed']						)
  @require_torch_multi_gpu
  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[Any]:




         """simple docstring"""





         _UpperCAmelCase					=      {
             # test with the default log_level - should be info and thus log info once
             'base': {'extra_args_str': '', 'n_matches': 1},
             # test with low log_level and log_level_replica - should be noisy on all processes
             # now the info string should appear twice on 2 processes
             'low': {'extra_args_str': '--log_level debug --log_level_replica debug', 'n_matches': 2},
             # test with high log_level and low log_level_replica
             # now the info string should appear once only on the replica
             'high': {'extra_args_str': '--log_level error --log_level_replica debug', 'n_matches': 1},
             # test with high log_level and log_level_replica - should be quiet on all processes
             'mixed': {'extra_args_str': '--log_level error --log_level_replica error', 'n_matches': 0},
         }

         _UpperCAmelCase					=      experiments[experiment_id]
         _UpperCAmelCase					=      {'distributed': True, 'predict_with_generate': False, 'do_eval': False, 'do_predict': False}
         _UpperCAmelCase					=      'Running training'
         with CaptureStderr() as cl:
                self.run_seqaseq_quick(**__snake_case      ,   extra_args_str=data['extra_args_str']						)
         _UpperCAmelCase					=      len(re.findall(__snake_case      ,   cl.err						)						)
         self.assertEqual(__snake_case      ,   data['n_matches']						)
  @slow
  def      UpperCAmelCase__    (      self						)			->	List[Any]:




         """simple docstring"""





         _UpperCAmelCase					=      self.run_trainer(
             eval_steps=2      ,   max_len=128      ,   model_name=__snake_case      ,   learning_rate=3e-4      ,   num_train_epochs=10      ,   distributed=__snake_case      ,   )

         # Check metrics
         _UpperCAmelCase					=      TrainerState.load_from_json(os.path.join(__snake_case      ,   'trainer_state.json'						)						).log_history
         _UpperCAmelCase					=      [log for log in logs if 'eval_loss' in log.keys()]
         _UpperCAmelCase					=      eval_metrics[0]
         _UpperCAmelCase					=      eval_metrics[-1]

         assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
         assert isinstance(last_step_stats['eval_bleu']      ,   __snake_case						)

         # test if do_predict saves generations and metrics
         _UpperCAmelCase					=      os.listdir(__snake_case						)
         _UpperCAmelCase					=      {os.path.basename(__snake_case						) for p in contents}
         assert "generated_predictions.txt" in contents
         assert "predict_results.json" in contents
  @slow
  @require_bitsandbytes
  def      UpperCAmelCase__    (      self						)			->	Any:




         """simple docstring"""





         from transformers.training_args import OptimizerNames

         def train_and_return_metrics(_SCREAMING_SNAKE_CASE						) -> Tuple[int, float]:
                _UpperCAmelCase					=      '--skip_memory_metrics 0'

                _UpperCAmelCase					=      self.run_trainer(
                    max_len=128      ,   model_name=__snake_case      ,   learning_rate=3e-4      ,   num_train_epochs=1      ,   optim=__snake_case      ,   distributed=__snake_case      ,   extra_args_str=__snake_case      ,   do_eval=__snake_case      ,   do_predict=__snake_case      ,   n_gpus_to_use=1      ,   )

                # Check metrics
                _UpperCAmelCase					=      TrainerState.load_from_json(Path(__snake_case      ,   'trainer_state.json'						)						).log_history
                _UpperCAmelCase					=      int(logs[0]['train_mem_gpu_peaked_delta'] / 2**20						)
                _UpperCAmelCase					=      int(logs[0]['train_mem_gpu_alloc_delta'] / 2**20						)

                _UpperCAmelCase					=      logs[0]['train_loss']
                return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

         _UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value						)
         _UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      train_and_return_metrics(OptimizerNames.ADAMW_BNB.value						)

         _UpperCAmelCase					=      gpu_alloc_mem_orig - gpu_alloc_mem_bnb

         _UpperCAmelCase					=      gpu_peak_mem_orig + gpu_alloc_mem_orig
         _UpperCAmelCase					=      gpu_peak_mem_bnb + gpu_alloc_mem_bnb
         _UpperCAmelCase					=      gpu_total_mem_orig - gpu_total_mem_bnb

         # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
         # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
         # in 2 bytes and the diff in optim memory usage is derived as so:
         #
         # - normal 25*8=~200MB (8 bytes per param)
         # - bnb    25*2= ~50MB (2 bytes per param)
         #
         # Thus we should expect ~150MB total memory saved.
         #
         # Peak memory should be the same - the total should be different by about that same margin
         #
         # After leaving a small margin to accommodate for differences between gpus let's check
         # that we have at least 120MB in savings
         _UpperCAmelCase					=      120

         # uncomment the following if this test starts failing - requires py38 for a new print feature
         # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
         # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
         # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
         # print(f"{gpu_alloc_mem_diff=}MB")
         # print(f"{gpu_peak_mem_diff=}MB")
         # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
         # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

         self.assertGreater(
             __snake_case      ,   __snake_case      ,   'should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'
             f''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
             f''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'''      ,   )

         self.assertGreater(
             __snake_case      ,   __snake_case      ,   'should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'
             f''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
             f''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'''      ,   )

         self.assertEqual(
             __snake_case      ,   __snake_case      ,   f'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''						)






  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = 3e-3      ,   _SCREAMING_SNAKE_CASE = "adafactor"      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = 0      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   )			->	Dict:




         """simple docstring"""





         _UpperCAmelCase					=      self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
         _UpperCAmelCase					=      self.get_auto_remove_tmp_dir()
         _UpperCAmelCase					=      f'''
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(__snake_case						)}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(__snake_case						)}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

         _UpperCAmelCase					=      f'''
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(__snake_case						)}
        '''.split()

         _UpperCAmelCase					=      '\n            --do_predict\n        '.split()

         _UpperCAmelCase					=      []
         if do_train:
                args += args_train

         if do_eval:
                args += args_eval

         if do_predict:
                args += args_predict

         if predict_with_generate:
                args += "--predict_with_generate".split()

         if do_train:
                if optim == "adafactor":
                       args += "--adafactor".split()
                else:
                       args += f'''--optim {optim}'''.split()

         if extra_args_str is not None:
                args += extra_args_str.split()

         if distributed:
                if n_gpus_to_use is None:
                       _UpperCAmelCase					=      get_gpu_count()
                _UpperCAmelCase					=      get_torch_dist_unique_port()
                _UpperCAmelCase					=      f'''
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            '''.split()
                _UpperCAmelCase					=      [sys.executable] + distributed_args + args
                # keep for quick debug
                # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
                execute_subprocess_async(__snake_case      ,   env=self.get_env()						)
         else:
                _UpperCAmelCase					=      ['run_translation.py'] + args
                with patch.object(__snake_case      ,   'argv'      ,   __snake_case						):
                       main()

         return output_dir




# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowerCAmelCase__  :Dict             =							'''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''

def 	lowerCAmelCase__			(      a__:		Optional[Any]=None	)  ->		List[Any]:


							'''simple docstring'''





							if subparsers is not None:
														_UpperCAmelCase					=      subparsers.add_parser('tpu-config'      ,  description=_description	)
							else:
														_UpperCAmelCase					=      argparse.ArgumentParser('Accelerate tpu-config command'      ,  description=_description	)
							# Core arguments
							_UpperCAmelCase					=      parser.add_argument_group(
							    'Config Arguments'      ,  'Arguments that can be configured through `accelerate config`.'	)
							config_args.add_argument(
							    '--config_file'      ,  type=a__      ,  default=a__      ,  help='Path to the config file to use for accelerate.'      ,  )
							config_args.add_argument(
							    '--tpu_name'      ,  default=a__      ,  help='The name of the TPU to use. If not specified, will use the TPU specified in the config file.'      ,  )
							config_args.add_argument(
							    '--tpu_zone'      ,  default=a__      ,  help='The zone of the TPU to use. If not specified, will use the zone specified in the config file.'      ,  )
							_UpperCAmelCase					=      parser.add_argument_group('TPU Arguments'      ,  'Arguments for options ran inside the TPU.'	)
							pod_args.add_argument(
							    '--use_alpha'      ,  action='store_true'      ,  help='Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.'      ,  )
							pod_args.add_argument(
							    '--command_file'      ,  default=a__      ,  help='The path to the file containing the commands to run on the pod on startup.'      ,  )
							pod_args.add_argument(
							    '--command'      ,  action='append'      ,  nargs='+'      ,  help='A command to run on the pod. Can be passed multiple times.'      ,  )
							pod_args.add_argument(
							    '--install_accelerate'      ,  action='store_true'      ,  help='Whether to install accelerate on the pod. Defaults to False.'      ,  )
							pod_args.add_argument(
							    '--accelerate_version'      ,  default='latest'      ,  help='The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.'      ,  )
							pod_args.add_argument(
							    '--debug'      ,  action='store_true'      ,  help='If set, will print the command that would be run instead of running it.'	)

							if subparsers is not None:
														parser.set_defaults(func=a__	)
							return parser

def 	lowerCAmelCase__			(      a__:		str	)  ->		Any:


							'''simple docstring'''





							_UpperCAmelCase					=      None

							# Get the default from the config file if it exists.
							if args.config_file is not None or os.path.isfile(a__	):
														_UpperCAmelCase					=      load_config_from_file(args.config_file	)
														if not args.command_file and defaults.command_file is not None and not args.command:
																					_UpperCAmelCase					=      defaults.command_file
														if not args.command and defaults.commands is not None:
																					_UpperCAmelCase					=      defaults.commands
														if not args.tpu_name:
																					_UpperCAmelCase					=      defaults.tpu_name
														if not args.tpu_zone:
																					_UpperCAmelCase					=      defaults.tpu_zone
							if args.accelerate_version == "dev":
														_UpperCAmelCase					=      'git+https://github.com/huggingface/accelerate.git'
							elif args.accelerate_version == "latest":
														_UpperCAmelCase					=      'accelerate -U'
							elif isinstance(parse(args.accelerate_version	)      ,  a__	):
														_UpperCAmelCase					=      F'''accelerate=={args.accelerate_version}'''

							if not args.command_file and not args.command:
														raise ValueError('You must specify either a command file or a command to run on the pod.'	)

							if args.command_file:
														with open(args.command_file      ,  'r'	) as f:
																					_UpperCAmelCase					=      [f.read().splitlines()]

    # To turn list of lists into list of strings
							if isinstance(args.command[0]      ,  a__	):
														_UpperCAmelCase					=      [line for cmd in args.command for line in cmd]
							# Default to the shared folder and install accelerate
							_UpperCAmelCase					=      ['cd /usr/share']
							if args.install_accelerate:
														new_cmd += [F'''pip install {args.accelerate_version}''']
							new_cmd += args.command
							_UpperCAmelCase					=      '; '.join(a__	)

							# Then send it to gcloud
							# Eventually try to use google-api-core to do this instead of subprocess
							_UpperCAmelCase					=      ['gcloud']
							if args.use_alpha:
														cmd += ["alpha"]
							cmd += [
							    "compute",
							    "tpus",
							    "tpu-vm",
							    "ssh",
							    args.tpu_name,
							    "--zone",
							    args.tpu_zone,
							    "--command",
							    args.command,
							    "--worker",
							    "all",
							]
							if args.debug:
														print(F'''Running {" ".join(a__	)}'''	)
														return
							subprocess.run(a__	)
							print('Successfully setup pod.'	)







def 	lowerCAmelCase__			(      )  ->		Optional[Any]:


							'''simple docstring'''





							_UpperCAmelCase					=      tpu_command_parser()
							_UpperCAmelCase					=      parser.parse_args()

							tpu_command_launcher(a__	)
