


import pytest

from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		monkeypatch.setattr('''datasets.utils.deprecation_utils._emitted_deprecation_warnings'''						,    set()					)
@pytest.fixture
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		class 	_a							:

			def __init__(							self:     Any   ,						UpperCamelCase_:     Optional[int]							)      ->       Optional[int]:





					"""simple docstring"""







					lowercase__						=				metric_id


		class 	_a							:
			_lowercase     :      Tuple  				=			[MetricMock(_a      ) for metric_id in ["""accuracy""", """mse""", """precision""", """codeparrot/apps_metric"""]]

			def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       int:





					"""simple docstring"""







					return self._metrics
		monkeypatch.setattr('''datasets.inspect.huggingface_hub'''						,    HfhMock()					)
@pytest.mark.parametrize(
    '''func, args'''						,    [(load_metric, ('''metrics/mse''',)), (list_metrics, ()), (inspect_metric, ('''metrics/mse''', '''tmp_path'''))]					)
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		if "tmp_path" in args:
				lowercase__						=				tuple(arg if arg != '''tmp_path''' else tmp_path for arg in args					)
		with pytest.warns(a__						,    match='''https://huggingface.co/docs/evaluate'''					):
				func(*a__					)






# 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


lowerCAmelCase	=							'Create a default config file for Accelerate with only a few flags set.'
def       _a			(				SCREAMING_SNAKE_CASE="no"						,    SCREAMING_SNAKE_CASE = default_json_config_file						,    SCREAMING_SNAKE_CASE = False					):


		"""simple docstring"""




		lowercase__						=				Path(SCREAMING_SNAKE_CASE					)
		path.parent.mkdir(parents=SCREAMING_SNAKE_CASE						,    exist_ok=SCREAMING_SNAKE_CASE					)
		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(**SCREAMING_SNAKE_CASE					)
		config.to_json_file(SCREAMING_SNAKE_CASE					)
		return path
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				parser.add_parser('''default'''						,    parents=SCREAMING_SNAKE_CASE						,    help=SCREAMING_SNAKE_CASE						,    formatter_class=SCREAMING_SNAKE_CASE					)
		parser.add_argument(
		    '''--config_file'''						,    default=SCREAMING_SNAKE_CASE						,    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=SCREAMING_SNAKE_CASE						,    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=SCREAMING_SNAKE_CASE					)
		return parser
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




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


