






'''simple docstring'''

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


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


set_seed(4_2)
SCREAMING_SNAKE_CASE__											=     'sshleifer/student_marian_en_ro_6_1'
SCREAMING_SNAKE_CASE__											=     'sshleifer/tiny-mbart'






@require_torch
class        a_		(    lowerCamelCase       ):

  def 						A__		(   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    ,	)						->   Optional[Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.run_trainer(
            eval_steps=1    ,	max_len=12    ,	model_name=_SCREAMING_SNAKE_CASE    ,	num_train_epochs=1    ,	distributed=_SCREAMING_SNAKE_CASE    ,	extra_args_str=_SCREAMING_SNAKE_CASE    ,	predict_with_generate=_SCREAMING_SNAKE_CASE    ,	do_train=_SCREAMING_SNAKE_CASE    ,	do_eval=_SCREAMING_SNAKE_CASE    ,	do_predict=_SCREAMING_SNAKE_CASE    ,	)
        UpperCamelCase       						=      TrainerState.load_from_json(os.path.join(_SCREAMING_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"""]    ,	_SCREAMING_SNAKE_CASE   )
              assert not math.isnan(float(last_step_stats["""eval_loss"""]   )   ), "eval_loss must not be `nan`"

  @require_torch_non_multi_gpu
  def 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        self.run_seqaseq_quick()

  @require_torch_multi_gpu
  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        self.run_seqaseq_quick(distributed=_SCREAMING_SNAKE_CASE   )

  @require_torch_multi_gpu
  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        self.run_seqaseq_quick(distributed=_SCREAMING_SNAKE_CASE   )

  @unittest.skip("""Requires an update of the env running those tests"""   )
  @require_torch_multi_gpu
  @require_fairscale
  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        self.run_seqaseq_quick(distributed=_SCREAMING_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 						A__		(   self   )						->   Any:



        """simple docstring"""






        self.run_seqaseq_quick(distributed=_SCREAMING_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 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        self.run_seqaseq_quick(distributed=_SCREAMING_SNAKE_CASE    ,	extra_args_str="""--sharded_ddp zero_dp_2"""    ,	predict_with_generate=_SCREAMING_SNAKE_CASE   )

  @unittest.skip("""Requires an update of the env running those tests"""   )
  @require_torch_multi_gpu
  @require_fairscale
  def 						A__		(   self   )						->   List[str]:



        """simple docstring"""






        self.run_seqaseq_quick(
            distributed=_SCREAMING_SNAKE_CASE    ,	extra_args_str="""--sharded_ddp zero_dp_2 --fp16"""    ,	predict_with_generate=_SCREAMING_SNAKE_CASE   )

  @require_apex
  @require_torch_gpu
  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        self.run_seqaseq_quick(distributed=_SCREAMING_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=_SCREAMING_SNAKE_CASE    ,	extra_args_str="""--fp16 --fp16_backend=apex"""   )

  @parameterized.expand(["""base""", """low""", """high""", """mixed"""]   )
  @require_torch_multi_gpu
  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Dict:



        """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(**_SCREAMING_SNAKE_CASE    ,	extra_args_str=data["""extra_args_str"""]   )
        UpperCamelCase       						=      len(re.findall(_SCREAMING_SNAKE_CASE    ,	cl.err   )   )
        self.assertEqual(_SCREAMING_SNAKE_CASE    ,	data["""n_matches"""]   )

  @slow
  def 						A__		(   self   )						->   int:



        """simple docstring"""






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

        # Check metrics
        UpperCamelCase       						=      TrainerState.load_from_json(os.path.join(_SCREAMING_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"""]    ,	_SCREAMING_SNAKE_CASE   )

        # test if do_predict saves generations and metrics
        UpperCamelCase       						=      os.listdir(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      {os.path.basename(_SCREAMING_SNAKE_CASE   ) for p in contents}
        assert "generated_predictions.txt" in contents
        assert "predict_results.json" in contents

  @slow
  @require_bitsandbytes
  def 						A__		(   self   )						->   List[str]:



        """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=_SCREAMING_SNAKE_CASE    ,	learning_rate=3e-4    ,	num_train_epochs=1    ,	optim=_SCREAMING_SNAKE_CASE    ,	distributed=_SCREAMING_SNAKE_CASE    ,	extra_args_str=_SCREAMING_SNAKE_CASE    ,	do_eval=_SCREAMING_SNAKE_CASE    ,	do_predict=_SCREAMING_SNAKE_CASE    ,	n_gpus_to_use=1    ,	)

              # Check metrics
              UpperCamelCase       						=      TrainerState.load_from_json(Path(_SCREAMING_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(
            _SCREAMING_SNAKE_CASE    ,	_SCREAMING_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(
            _SCREAMING_SNAKE_CASE    ,	_SCREAMING_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(
            _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	F"loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"   )


  def 						A__		(   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"\n            --model_name_or_path {model_name}\n            --train_file {data_dir}/train.json\n            --validation_file {data_dir}/val.json\n            --test_file {data_dir}/test.json\n            --output_dir {output_dir}\n            --overwrite_output_dir\n            --max_train_samples 8\n            --max_source_length {max_len}\n            --max_target_length {max_len}\n            --do_train\n            --num_train_epochs {str(_SCREAMING_SNAKE_CASE   )}\n            --per_device_train_batch_size 4\n            --learning_rate {learning_rate}\n            --warmup_steps 8\n            --logging_steps 0\n            --logging_strategy no\n            --save_steps {str(_SCREAMING_SNAKE_CASE   )}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        ".split()

        UpperCamelCase       						=      F"\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(_SCREAMING_SNAKE_CASE   )}\n        ".split()

        UpperCamelCase       						=      """
            --do_predict
        """.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"\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            ".split()
              UpperCamelCase       						=      [sys.executable] + distributed_args + args
              # keep for quick debug
              # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
              execute_subprocess_async(_SCREAMING_SNAKE_CASE    ,	env=self.get_env()   )
        else:
              UpperCamelCase       						=      ["""run_translation.py"""] + args
              with patch.object(_SCREAMING_SNAKE_CASE    ,	"""argv"""    ,	_SCREAMING_SNAKE_CASE   ):
                    main()

        return output_dir








'''simple docstring'''

# 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 platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available







def 					lowercase__				(   __UpperCamelCase=None					)->				Union[str, Any]:
      if subparsers is not None:
            UpperCamelCase       						=      subparsers.add_parser("""env"""					)
      else:
            UpperCamelCase       						=      argparse.ArgumentParser("""Accelerate env command"""					)

      parser.add_argument(
          """--config_file"""	,		default=__UpperCamelCase	,		help="""The config file to use for the default values in the launching script."""					)

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







def 					lowercase__				(   __UpperCamelCase					)->				List[str]:
      UpperCamelCase       						=      torch.__version__
      UpperCamelCase       						=      torch.cuda.is_available()
      UpperCamelCase       						=      is_xpu_available()
      UpperCamelCase       						=      is_npu_available()

      UpperCamelCase       						=      """Not found"""
      # Get the default from the config file.
      if args.config_file is not None or os.path.isfile(__UpperCamelCase					):
            UpperCamelCase       						=      load_config_from_file(args.config_file					).to_dict()

      UpperCamelCase       						=      {
          """`Accelerate` version""": version,
          """Platform""": platform.platform(),
          """Python version""": platform.python_version(),
          """Numpy version""": np.__version__,
          """PyTorch version (GPU?)""": F"{pt_version} ({pt_cuda_available})",
          """PyTorch XPU available""": str(__UpperCamelCase					),
          """PyTorch NPU available""": str(__UpperCamelCase					),
          """System RAM""": F"{psutil.virtual_memory().total / 1024 ** 3:.2f} GB",
      }
      if pt_cuda_available:
            UpperCamelCase       						=      torch.cuda.get_device_name()

      print("""\nCopy-and-paste the text below in your GitHub issue\n"""					)
      print("""\n""".join([F"- {prop}: {val}" for prop, val in info.items()]					)					)

      print("""- `Accelerate` default config:""" if args.config_file is None else """- `Accelerate` config passed:"""					)
      UpperCamelCase       						=      (
          """\n""".join([F"\t- {prop}: {val}" for prop, val in accelerate_config.items()]					)
          if isinstance(__UpperCamelCase	,		__UpperCamelCase					)
          else F"\t{accelerate_config}"
      )
      print(__UpperCamelCase					)

      UpperCamelCase       						=      accelerate_config

      return info







def 					lowercase__				(   )->				int:
      UpperCamelCase       						=      env_command_parser()
      UpperCamelCase       						=      parser.parse_args()
      env_command(__UpperCamelCase					)
      return 0


if __name__ == "__main__":
     raise SystemExit(main())
