





import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[int]				)->		List[Any]:
  def wrapper(*lowerCAmelCase:	Tuple    ,							**lowerCAmelCase:	int				):
    _snake_case   :				Any														=	timeit.default_timer()
    _snake_case   :				Dict														=	func(*lowerCAmelCase    ,							**lowerCAmelCase				)
    _snake_case   :				Optional[int]														=	timeit.default_timer() - starttime
    return delta

  _snake_case   :				List[str]														=	func.__name__

  return wrapper


def     lowerCamelCase_	(      lowerCAmelCase:	dict    ,							lowerCAmelCase:	Dict=1_00    ,							lowerCAmelCase:	Optional[Any]=None				)->		List[Any]:
  _snake_case   :				Optional[int]														=	[]
  _snake_case   :				List[str]														=	seq_shapes or {}
  for i in range(lowerCAmelCase				):
    _snake_case   :				Optional[Any]														=	{}
    for col_id, (k, v) in enumerate(features.items()				):
      if isinstance(lowerCAmelCase    ,							_ArrayXD				):
        _snake_case   :				Union[str, Any]														=	np.random.rand(*v.shape				).astype(v.dtype				)
      elif isinstance(lowerCAmelCase    ,							datasets.Value				):
        if v.dtype == "string":
          _snake_case   :				Optional[Any]														=	'The small grey turtle was surprisingly fast when challenged.'
        else:
          _snake_case   :				List[Any]														=	np.random.randint(10    ,							size=1				).astype(v.dtype				).item()
      elif isinstance(lowerCAmelCase    ,							datasets.Sequence				):
        while isinstance(lowerCAmelCase    ,							datasets.Sequence				):
          _snake_case   :				Optional[Any]														=	v.feature
        _snake_case   :				Optional[int]														=	seq_shapes[k]
        _snake_case   :				Tuple														=	np.random.rand(*lowerCAmelCase				).astype(v.dtype				)
      _snake_case   :				Optional[int]														=	data

    dummy_data.append((i, example)				)

  return dummy_data







def     lowerCamelCase_	(      lowerCAmelCase:	Any    ,							lowerCAmelCase:	List[str]    ,							lowerCAmelCase:	Dict=1_00    ,							lowerCAmelCase:	Optional[Any]=None				)->		Dict:
  _snake_case   :				List[Any]														=	generate_examples(lowerCAmelCase    ,							num_examples=lowerCAmelCase    ,							seq_shapes=lowerCAmelCase				)

  with ArrowWriter(features=lowerCAmelCase    ,							path=lowerCAmelCase				) as writer:
    for key, record in dummy_data:
      _snake_case   :				Union[str, Any]														=	features.encode_example(lowerCAmelCase				)
      writer.write(lowerCAmelCase				)

    _snake_case							,					_snake_case   :				List[Any]														=	writer.finalize()

  if not num_final_examples == num_examples:
    raise ValueError(
        F"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""				)

  _snake_case   :				Optional[Any]														=	datasets.Dataset.from_file(filename=lowerCAmelCase    ,							info=datasets.DatasetInfo(features=lowerCAmelCase				)				)

  return dataset











# 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.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


lowerCAmelCase_						   =    """Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"""


def     lowerCamelCase_	(      )->		Tuple:
  _snake_case   :				int														=	_ask_options(
      'In which compute environment are you running?'    ,							['This machine', 'AWS (Amazon SageMaker)']    ,							_convert_compute_environment    ,							)
  if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
    _snake_case   :				int														=	get_sagemaker_input()
  else:
    _snake_case   :				Any														=	get_cluster_input()
  return config


def     lowerCamelCase_	(      lowerCAmelCase:	str=None				)->		Any:
  if subparsers is not None:
    _snake_case   :				List[Any]														=	subparsers.add_parser('config'    ,							description=lowerCAmelCase				)
  else:
    _snake_case   :				Dict														=	argparse.ArgumentParser('Accelerate config command'    ,							description=lowerCAmelCase				)

  parser.add_argument(
      '--config_file'    ,							default=lowerCAmelCase    ,							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\'.'
      )    ,							)

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


def     lowerCamelCase_	(      lowerCAmelCase:	Any				)->		Any:
  _snake_case   :				Dict														=	get_user_input()
  if args.config_file is not None:
    _snake_case   :				List[str]														=	args.config_file
  else:
    if not os.path.isdir(lowerCAmelCase				):
      os.makedirs(lowerCAmelCase				)
    _snake_case   :				Union[str, Any]														=	default_yaml_config_file

  if config_file.endswith('.json'				):
    config.to_json_file(lowerCAmelCase				)
  else:
    config.to_yaml_file(lowerCAmelCase				)
  print(F"""accelerate configuration saved at {config_file}"""				)


def     lowerCamelCase_	(      )->		Dict:
  _snake_case   :				List[str]														=	config_command_parser()
  _snake_case   :				str														=	parser.parse_args()
  config_command(lowerCAmelCase				)


if __name__ == "__main__":
      main()




