






from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

UpperCAmelCase_ :						Any      =      TypeVar('''T''')

class     _SCREAMING_SNAKE_CASE       (			Generic[T]      ):
	snake_case__							:     deque[T]  # Cache store of keys
	snake_case__							:     set[T]  # References of the keys in cache
	snake_case__							:     int     	=  1_0  # Maximum capacity of cache





	def __init__(		self				:					Optional[int] ,  __lowerCamelCase				:					int	):
		UpperCamelCase     :int          =	deque()
		UpperCamelCase     :Any          =	set()
		if not n:
			UpperCamelCase     :Union[str, Any]          =	sys.maxsize
		elif n < 0:
			raise ValueError("""n should be an integer greater than 0."""	)
		else:
			UpperCamelCase     :Tuple          =	n





	def 						_A (		self				:					List[Any] ,  __lowerCamelCase				:					T	):
		if x not in self.key_reference:
			if len(self.dq_store	) == LRUCache._MAX_CAPACITY:
				UpperCamelCase     :Any          =	self.dq_store.pop()
				self.key_reference.remove(__lowerCamelCase	)
		else:
			self.dq_store.remove(__lowerCamelCase	)

		self.dq_store.appendleft(__lowerCamelCase	)
		self.key_reference.add(__lowerCamelCase	)





	def 						_A (		self				:					int	):
		for k in self.dq_store:
			print(__lowerCamelCase	)





	def __repr__(		self				:					Optional[int]	):
		return F"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store	)}"""


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	UpperCAmelCase_ :						LRUCache[str | int]      =      LRUCache(4)
	lru_cache.refer('''A''')
	lru_cache.refer(2)
	lru_cache.refer(3)
	lru_cache.refer('''A''')
	lru_cache.refer(4)
	lru_cache.refer(5)
	lru_cache.display()

	print(lru_cache)
	assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"








# 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


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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[int]="no"	,			__magic_name__					:  str = default_json_config_file	,			__magic_name__					:  bool = False					)		->			str:






	"""simple docstring"""



	UpperCamelCase     :Any          =	Path(__magic_name__					)
	path.parent.mkdir(parents=__magic_name__	,			exist_ok=__magic_name__					)
	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
	UpperCamelCase     :Dict          =	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}"""					)
	UpperCamelCase     :Optional[Any]          =	{
	    """compute_environment""": """LOCAL_MACHINE""",
	    """mixed_precision""": mixed_precision,
	}
	if torch.cuda.is_available():
		UpperCamelCase     :Union[str, Any]          =	torch.cuda.device_count()
		UpperCamelCase     :List[Any]          =	num_gpus
		UpperCamelCase     :Dict          =	False
		if num_gpus > 1:
			UpperCamelCase     :Any          =	"""MULTI_GPU"""
		else:
			UpperCamelCase     :Any          =	"""NO"""
	elif is_xpu_available() and use_xpu:
		UpperCamelCase     :Optional[Any]          =	torch.xpu.device_count()
		UpperCamelCase     :Optional[int]          =	num_xpus
		UpperCamelCase     :int          =	False
		if num_xpus > 1:
			UpperCamelCase     :Union[str, Any]          =	"""MULTI_XPU"""
		else:
			UpperCamelCase     :Union[str, Any]          =	"""NO"""
	elif is_npu_available():
		UpperCamelCase     :List[Any]          =	torch.npu.device_count()
		UpperCamelCase     :Optional[Any]          =	num_npus
		UpperCamelCase     :Tuple          =	False
		if num_npus > 1:
			UpperCamelCase     :Optional[Any]          =	"""MULTI_NPU"""
		else:
			UpperCamelCase     :List[Any]          =	"""NO"""
	else:
		UpperCamelCase     :Any          =	0
		UpperCamelCase     :Optional[Any]          =	True
		UpperCamelCase     :Optional[Any]          =	1
		UpperCamelCase     :List[str]          =	"""NO"""
	UpperCamelCase     :int          =	ClusterConfig(**__magic_name__					)
	config.to_json_file(__magic_name__					)
	return path

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Dict	,			__magic_name__					:  Tuple					)		->			List[str]:






	"""simple docstring"""



	UpperCamelCase     :Dict          =	parser.add_parser("""default"""	,			parents=__magic_name__	,			help=__magic_name__	,			formatter_class=__magic_name__					)
	parser.add_argument(
	    """--config_file"""	,			default=__magic_name__	,			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=__magic_name__	,			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=__magic_name__					)
	return parser

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[Any]					)		->			List[str]:






	"""simple docstring"""



	UpperCamelCase     :Optional[Any]          =	write_basic_config(args.mixed_precision	,			args.save_location					)
	if config_file:
		print(f"""accelerate configuration saved at {config_file}"""					)
