import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


__snake_case						=			namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)





def      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase  )->     List[Any]:






      '''simple docstring'''





      return (abs(source - target  ) / target) < 0.01





@pytest.mark.integration
def      lowerCAmelCase_       ( __lowerCAmelCase  )->     int:






      '''simple docstring'''





      UpperCAmelCase			:       Any			     =_TestCommandArgs(dataset=A__						,    all_configs=A__						,    save_infos=A__  )
      UpperCAmelCase			:       str			     =TestCommand(*A__  )
      test_command.run()
      UpperCAmelCase			:       List[Any]			     =os.path.join(A__						,    '''README.md'''  )
      assert os.path.exists(A__  )
      UpperCAmelCase			:       Tuple			     =DatasetInfosDict.from_directory(A__  )
      UpperCAmelCase			:       int			     =DatasetInfosDict(
          {
              '''default''': DatasetInfo(
                  features=Features(
                      {
                          '''tokens''': Sequence(Value('''string'''  )  ),
                          '''ner_tags''': Sequence(
                              ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']  )  ),
                          '''langs''': Sequence(Value('''string'''  )  ),
                          '''spans''': Sequence(Value('''string'''  )  ),
                      }  )						,    splits=[
                      {
                          '''name''': '''train''',
                          '''num_bytes''': 2_35_15_63,
                          '''num_examples''': 1_00_00,
                      },
                      {
                          '''name''': '''validation''',
                          '''num_bytes''': 23_84_18,
                          '''num_examples''': 10_00,
                      },
                  ]						,    download_size=3_94_06_80						,    dataset_size=2_58_99_81						,    )
          }  )
      assert dataset_infos.keys() == expected_dataset_infos.keys()
      for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
            UpperCAmelCase     ,  UpperCAmelCase			:       int			     =getattr(dataset_infos['''default''']						,    A__  ), getattr(expected_dataset_infos['''default''']						,    A__  )
            if key == "num_bytes":
                  assert is_apercent_close(A__						,    A__  )
            elif key == "splits":
                  assert list(A__  ) == list(A__  )
                  for split in result:
                        assert result[split].name == expected[split].name
                        assert result[split].num_examples == expected[split].num_examples
                        assert is_apercent_close(result[split].num_bytes						,    expected[split].num_bytes  )
            else:
                  result == expected



# Copyright 2021 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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


lowercase_									=   'Create a default config file for Accelerate with only a few flags set.'
def      a      (			A__	:   Optional[Any]="no"      ,   A__	:   str = default_json_config_file      ,   A__	:   bool = False     )    ->      Optional[int]:





      """simple docstring"""



      _lowercase	       =Path(A__     )
      path.parent.mkdir(parents=A__      ,   exist_ok=A__     )
      if path.exists():
            print(
                F'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'''     )
            return False
      _lowercase	       =mixed_precision.lower()
      if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
            raise ValueError(
                F'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'''     )
      _lowercase	       ={
          'compute_environment': 'LOCAL_MACHINE',
          'mixed_precision': mixed_precision,
      }
      if torch.cuda.is_available():
            _lowercase	       =torch.cuda.device_count()
            _lowercase	       =num_gpus
            _lowercase	       =False
            if num_gpus > 1:
                  _lowercase	       ='MULTI_GPU'
            else:
                  _lowercase	       ='NO'
      elif is_xpu_available() and use_xpu:
            _lowercase	       =torch.xpu.device_count()
            _lowercase	       =num_xpus
            _lowercase	       =False
            if num_xpus > 1:
                  _lowercase	       ='MULTI_XPU'
            else:
                  _lowercase	       ='NO'
      elif is_npu_available():
            _lowercase	       =torch.npu.device_count()
            _lowercase	       =num_npus
            _lowercase	       =False
            if num_npus > 1:
                  _lowercase	       ='MULTI_NPU'
            else:
                  _lowercase	       ='NO'
      else:
            _lowercase	       =0
            _lowercase	       =True
            _lowercase	       =1
            _lowercase	       ='NO'
      _lowercase	       =ClusterConfig(**A__     )
      config.to_json_file(A__     )
      return path
def      a      (			A__	:   Dict      ,   A__	:   Optional[Any]     )    ->      List[Any]:





      """simple docstring"""



      _lowercase	       =parser.add_parser('default'      ,   parents=A__      ,   help=A__      ,   formatter_class=A__     )
      parser.add_argument(
          '--config_file'      ,   default=A__      ,   help=(
              'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
              'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
              'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
              'with \'huggingface\'.'
          )      ,   dest='save_location'      ,   )

      parser.add_argument(
          '--mixed_precision'      ,   choices=['no', 'fp16', 'bf16']      ,   type=A__      ,   help='Whether or not to use mixed precision training. '
          'Choose between FP16 and BF16 (bfloat16) training. '
          'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'      ,   default='no'      ,   )
      parser.set_defaults(func=A__     )
      return parser







def      a      (			A__	:   List[str]     )    ->      Any:





      """simple docstring"""



      _lowercase	       =write_basic_config(args.mixed_precision      ,   args.save_location     )
      if config_file:
            print(F'''accelerate configuration saved at {config_file}'''     )


