



# 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


__snake_case     :List[Any]			       ='Create a default config file for Accelerate with only a few flags set.'






def  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[Any]="no"			,	lowerCAmelCase__	:	str = default_json_config_file			,	lowerCAmelCase__	:	bool = False				)       ->							str:

							'''simple docstring'''






							A      						=    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
							A      						=    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}'''				)
							A      						=    {
							    'compute_environment': 'LOCAL_MACHINE',
							    'mixed_precision': mixed_precision,
							}
							if torch.cuda.is_available():
														A      						=    torch.cuda.device_count()
														A      						=    num_gpus
														A      						=    False
														if num_gpus > 1:
																					A      						=    'MULTI_GPU'
														else:
																					A      						=    'NO'
							elif is_xpu_available() and use_xpu:
														A      						=    torch.xpu.device_count()
														A      						=    num_xpus
														A      						=    False
														if num_xpus > 1:
																					A      						=    'MULTI_XPU'
														else:
																					A      						=    'NO'
							elif is_npu_available():
														A      						=    torch.npu.device_count()
														A      						=    num_npus
														A      						=    False
														if num_npus > 1:
																					A      						=    'MULTI_NPU'
														else:
																					A      						=    'NO'
							else:
														A      						=    0
														A      						=    True
														A      						=    1
														A      						=    'NO'
							A      						=    ClusterConfig(**lowerCAmelCase__				)
							config.to_json_file(lowerCAmelCase__				)
							return path






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

							'''simple docstring'''






							A      						=    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  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[int]				)       ->							Tuple:

							'''simple docstring'''






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




import argparse
from collections import defaultdict






def  lowerCamelCase_	(      lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	Tuple			,	lowerCAmelCase__	:	Optional[int]			,	lowerCAmelCase__	:	List[str]				)       ->							Union[str, Any]:

							'''simple docstring'''






							A      						=    F'''{file}_{class_name}_{test_name}'''
							done_test[_id] += 1

							with open(lowerCAmelCase__			,	'r'				) as f:
														A      						=    f.readlines()

							A      						=    F'''class {class_name}('''
							A      						=    F'''{4 * " "}def {test_name}('''
							A      						=    F'''{8 * " "}{correct_line.split()[0]}'''
							A      						=    F'''{16 * " "}{correct_line.split()[0]}'''
							A      						=    False
							A      						=    False
							A      						=    False
							A      						=    False
							A      						=    0
							A      						=    0

							A      						=    []
							for line in lines:
														if line.startswith(lowerCAmelCase__				):
																					A      						=    True
														elif in_class and line.startswith(lowerCAmelCase__				):
																					A      						=    True
														elif in_class and in_func and (line.startswith(lowerCAmelCase__				) or line.startswith(lowerCAmelCase__				)):
																					A      						=    len(line.split(correct_line.split()[0]				)[0]				)
																					count += 1

																					if count == done_test[_id]:
																												A      						=    True

														if in_class and in_func and in_line:
																					if ")" not in line:
																												continue
																					else:
																												A      						=    True

														if in_class and in_func and in_line and insert_line:
																					new_lines.append(F'''{spaces * " "}{correct_line}'''				)
																					A      						=    A      						=    A      						=    A      						=    False
														else:
																					new_lines.append(lowerCAmelCase__				)

							with open(lowerCAmelCase__			,	'w'				) as f:
														for line in new_lines:
																					f.write(lowerCAmelCase__				)






def  lowerCamelCase_	(      lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	Tuple=None				)       ->							Union[str, Any]:

							'''simple docstring'''






							if fail is not None:
														with open(lowerCAmelCase__			,	'r'				) as f:
																					A      						=    {l.strip() for l in f.readlines()}
							else:
														A      						=    None

							with open(lowerCAmelCase__			,	'r'				) as f:
														A      						=    f.readlines()

							A      						=    defaultdict(lowerCAmelCase__				)
							for line in correct_lines:
														A						,   A						,   A						,   A      						=    line.split(';'				)
														if test_failures is None or "::".join([file, class_name, test_name]				) in test_failures:
																					overwrite_file(lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__				)


if __name__ == "__main__":
				__snake_case     :List[Any]			       =argparse.ArgumentParser()
				parser.add_argument('--correct_filename', help='filename of tests with expected result')
				parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
				__snake_case     :Optional[int]			       =parser.parse_args()

				main(args.correct_filename, args.fail_filename)