


# 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     lowerCamelCase_	(      lowerCAmelCase:	Dict="no"    ,							lowerCAmelCase:	str = default_json_config_file    ,							lowerCAmelCase:	bool = False				)->		Union[str, Any]:
  _snake_case   :				str														=	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
  _snake_case   :				Any														=	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}"""				)
  _snake_case   :				Optional[int]														=	{
      'compute_environment': 'LOCAL_MACHINE',
      'mixed_precision': mixed_precision,
  }
  if torch.cuda.is_available():
    _snake_case   :				int														=	torch.cuda.device_count()
    _snake_case   :				Dict														=	num_gpus
    _snake_case   :				str														=	False
    if num_gpus > 1:
      _snake_case   :				Optional[int]														=	'MULTI_GPU'
    else:
      _snake_case   :				Dict														=	'NO'
  elif is_xpu_available() and use_xpu:
    _snake_case   :				Optional[int]														=	torch.xpu.device_count()
    _snake_case   :				Dict														=	num_xpus
    _snake_case   :				Optional[int]														=	False
    if num_xpus > 1:
      _snake_case   :				Dict														=	'MULTI_XPU'
    else:
      _snake_case   :				Optional[Any]														=	'NO'
  elif is_npu_available():
    _snake_case   :				List[Any]														=	torch.npu.device_count()
    _snake_case   :				int														=	num_npus
    _snake_case   :				int														=	False
    if num_npus > 1:
      _snake_case   :				Optional[int]														=	'MULTI_NPU'
    else:
      _snake_case   :				Tuple														=	'NO'
  else:
    _snake_case   :				Optional[Any]														=	0
    _snake_case   :				Tuple														=	True
    _snake_case   :				List[Any]														=	1
    _snake_case   :				Tuple														=	'NO'
  _snake_case   :				List[str]														=	ClusterConfig(**lowerCAmelCase				)
  config.to_json_file(lowerCAmelCase				)
  return path


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[int]    ,							lowerCAmelCase:	Dict				)->		List[str]:
  _snake_case   :				Tuple														=	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[Any]				)->		Dict:
  _snake_case   :				Any														=	write_basic_config(args.mixed_precision    ,							args.save_location				)
  if config_file:
    print(F"""accelerate configuration saved at {config_file}"""				)











import math
import random
from typing import Any

from .hill_climbing import SearchProblem


def     lowerCamelCase_	(      lowerCAmelCase:	Tuple    ,							lowerCAmelCase:	bool = True    ,							lowerCAmelCase:	float = math.inf    ,							lowerCAmelCase:	float = -math.inf    ,							lowerCAmelCase:	float = math.inf    ,							lowerCAmelCase:	float = -math.inf    ,							lowerCAmelCase:	bool = False    ,							lowerCAmelCase:	float = 1_00    ,							lowerCAmelCase:	float = 0.0_1    ,							lowerCAmelCase:	float = 1    ,							)->		Any:
  _snake_case   :				int														=	False
  _snake_case   :				Any														=	search_prob
  _snake_case   :				Tuple														=	start_temperate
  _snake_case   :				Any														=	[]
  _snake_case   :				List[str]														=	0
  _snake_case   :				Optional[Any]														=	None

  while not search_end:
    _snake_case   :				List[Any]														=	current_state.score()
    if best_state is None or current_score > best_state.score():
      _snake_case   :				Dict														=	current_state
    scores.append(lowerCAmelCase				)
    iterations += 1
    _snake_case   :				Optional[int]														=	None
    _snake_case   :				Union[str, Any]														=	current_state.get_neighbors()
    while (
        next_state is None and neighbors
    ):  # till we do not find a neighbor that we can move to
      _snake_case   :				Dict														=	random.randint(0    ,							len(lowerCAmelCase				) - 1				)  # picking a random neighbor
      _snake_case   :				int														=	neighbors.pop(lowerCAmelCase				)
      _snake_case   :				Union[str, Any]														=	picked_neighbor.score() - current_score

      if (
          picked_neighbor.x > max_x
          or picked_neighbor.x < min_x
          or picked_neighbor.y > max_y
          or picked_neighbor.y < min_y
      ):
        continue  # neighbor outside our bounds

      if not find_max:
        _snake_case   :				Union[str, Any]														=	change * -1  # in case we are finding minimum
      if change > 0:  # improves the solution
        _snake_case   :				Union[str, Any]														=	picked_neighbor
      else:
        _snake_case   :				Optional[Any]														=	(math.e) ** (
            change / current_temp
        )  # probability generation function
        if random.random() < probability:  # random number within probability
          _snake_case   :				int														=	picked_neighbor
    _snake_case   :				List[Any]														=	current_temp - (current_temp * rate_of_decrease)

    if current_temp < threshold_temp or next_state is None:
      # temperature below threshold, or could not find a suitable neighbor
      _snake_case   :				List[str]														=	True
    else:
      _snake_case   :				Union[str, Any]														=	next_state

  if visualization:
    from matplotlib import pyplot as plt

    plt.plot(range(lowerCAmelCase				)    ,							lowerCAmelCase				)
    plt.xlabel('Iterations'				)
    plt.ylabel('Function values'				)
    plt.show()
  return best_state


if __name__ == "__main__":


      def     lowerCamelCase_	(      lowerCAmelCase:	Any    ,							lowerCAmelCase:	List[Any]				)->		List[Any]:
        return (x**2) + (y**2)

      # starting the problem with initial coordinates (12, 47)
      lowerCAmelCase_						   =    SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
      lowerCAmelCase_						   =    simulated_annealing(
          prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
      )
      print(
          """The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
          F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
      )

      # starting the problem with initial coordinates (12, 47)
      lowerCAmelCase_						   =    SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
      lowerCAmelCase_						   =    simulated_annealing(
          prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
      )
      print(
          """The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
          F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
      )


      def     lowerCamelCase_	(      lowerCAmelCase:	Any    ,							lowerCAmelCase:	Dict				)->		Dict:
        return (3 * x**2) - (6 * y)

      lowerCAmelCase_						   =    SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
      lowerCAmelCase_						   =    simulated_annealing(prob, find_max=False, visualization=True)
      print(
          """The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
          F"""{local_min.score()}"""
      )

      lowerCAmelCase_						   =    SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
      lowerCAmelCase_						   =    simulated_annealing(prob, find_max=True, visualization=True)







      print(
          """The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
          F"""{local_min.score()}"""
      )




