


# 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       :      Any	=	"Create a default config file for Accelerate with only a few flags set."





def   _lowerCAmelCase				(   UpperCamelCase__:     Any="no"							,					UpperCamelCase__:     str = default_json_config_file							,					UpperCamelCase__:     List[str] = False						) ->   Union[str, Any]:





	"""simple docstring"""







	A								=				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
	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(**__A						)
	config.to_json_file(__A						)
	return path





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[Any]							,					UpperCamelCase__:     List[str]						) ->   str:





	"""simple docstring"""







	A								=				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   _lowerCAmelCase				(   UpperCamelCase__:     Dict						) ->   Optional[Any]:





	"""simple docstring"""







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







from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_lowercase       :      Any	=	datasets.load_iris()

_lowercase       :      Tuple	=	np.array(data["data"])
_lowercase       :      int	=	np.array(data["target"])
_lowercase       :      int	=	data["target_names"]

_lowercase	,							_lowercase	,							_lowercase	,							_lowercase       :      Any	=	train_test_split(X, y)





def   _lowerCAmelCase				(   UpperCamelCase__:     int							,					UpperCamelCase__:     Any						) ->   List[Any]:





	"""simple docstring"""







	return np.linalg.norm(np.array(UpperCamelCase__						) - np.array(UpperCamelCase__						)						)





def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     int							,					UpperCamelCase__:     List[Any]							,					UpperCamelCase__:     int=5						) ->   Tuple:





	"""simple docstring"""







	A								=				zip(UpperCamelCase__							,					UpperCamelCase__						)
	# List of distances of all points from the point to be classified
	A								=				[]
	for data_point in data:
		A								=				euclidean_distance(data_point[0]							,					UpperCamelCase__						)
		distances.append((distance, data_point[1])						)
	# Choosing 'k' points with the least distances.
	A								=				[i[1] for i in sorted(UpperCamelCase__						)[:k]]
	# Most commonly occurring class among them
	# is the class into which the point is classified
	A								=				Counter(UpperCamelCase__						).most_common(1						)[0][0]
	return classes[result]


if __name__ == "__main__":
				print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))



