


"""simple docstring"""







# 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


a__				:							Tuple						=     '''Create a default config file for Accelerate with only a few flags set.'''




def    UpperCAmelCase__       (lowerCAmelCase_="no"  ,						lowerCAmelCase_ = default_json_config_file  ,						lowerCAmelCase_ = False   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				Path(lowerCAmelCase_   )
					path.parent.mkdir(parents=lowerCAmelCase_  ,						exist_ok=lowerCAmelCase_   )
					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
					__SCREAMING_SNAKE_CASE						   =				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}"""   )
					__SCREAMING_SNAKE_CASE						   =				{
					    "compute_environment": "LOCAL_MACHINE",
					    "mixed_precision": mixed_precision,
					}
					if torch.cuda.is_available():
										__SCREAMING_SNAKE_CASE						   =				torch.cuda.device_count()
										__SCREAMING_SNAKE_CASE						   =				num_gpus
										__SCREAMING_SNAKE_CASE						   =				False
										if num_gpus > 1:
															__SCREAMING_SNAKE_CASE						   =				"MULTI_GPU"
										else:
															__SCREAMING_SNAKE_CASE						   =				"NO"
					elif is_xpu_available() and use_xpu:
										__SCREAMING_SNAKE_CASE						   =				torch.xpu.device_count()
										__SCREAMING_SNAKE_CASE						   =				num_xpus
										__SCREAMING_SNAKE_CASE						   =				False
										if num_xpus > 1:
															__SCREAMING_SNAKE_CASE						   =				"MULTI_XPU"
										else:
															__SCREAMING_SNAKE_CASE						   =				"NO"
					elif is_npu_available():
										__SCREAMING_SNAKE_CASE						   =				torch.npu.device_count()
										__SCREAMING_SNAKE_CASE						   =				num_npus
										__SCREAMING_SNAKE_CASE						   =				False
										if num_npus > 1:
															__SCREAMING_SNAKE_CASE						   =				"MULTI_NPU"
										else:
															__SCREAMING_SNAKE_CASE						   =				"NO"
					else:
										__SCREAMING_SNAKE_CASE						   =				0
										__SCREAMING_SNAKE_CASE						   =				True
										__SCREAMING_SNAKE_CASE						   =				1
										__SCREAMING_SNAKE_CASE						   =				"NO"
					__SCREAMING_SNAKE_CASE						   =				ClusterConfig(**lowerCAmelCase_   )
					config.to_json_file(lowerCAmelCase_   )
					return path




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				parser.add_parser("default"  ,						parents=lowerCAmelCase_  ,						help=lowerCAmelCase_  ,						formatter_class=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'."
					    )  ,						dest="save_location"  ,						)

					parser.add_argument(
					    "--mixed_precision"  ,						choices=["no", "fp16", "bf16"]  ,						type=lowerCAmelCase_  ,						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=lowerCAmelCase_   )
					return parser




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				write_basic_config(args.mixed_precision  ,						args.save_location   )
					if config_file:
										print(f"""accelerate configuration saved at {config_file}"""   )

import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size'''		,  [None, 400 * 2**20, 600 * 2**20]						)
@pytest.mark.parametrize('''input_in_memory_max_size'''		,  ['''default''', 0, 100 * 2**20, 900 * 2**20]						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						if input_in_memory_max_size != "default":
												monkeypatch.setattr(datasets.config		,  '''IN_MEMORY_MAX_SIZE'''		,  _lowercase						)
						SCREAMING_SNAKE_CASE							:    Tuple							=							datasets.config.IN_MEMORY_MAX_SIZE
						if input_in_memory_max_size == "default":
												assert in_memory_max_size == 0
						else:
												assert in_memory_max_size == input_in_memory_max_size
						if dataset_size and in_memory_max_size:
												SCREAMING_SNAKE_CASE							:    str							=							dataset_size < in_memory_max_size
						else:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							False
						SCREAMING_SNAKE_CASE							:    Any							=							is_small_dataset(_lowercase						)
						assert result == expected





