

import math
import random
from typing import Any

from .hill_climbing import SearchProblem







def       lowercase					(	__A :					Any ,    __A :					bool = True ,    __A :					float = math.inf ,    __A :					float = -math.inf ,    __A :					float = math.inf ,    __A :					float = -math.inf ,    __A :					bool = False ,    __A :					float = 100 ,    __A :					float = 0.01 ,    __A :					float = 1 ,    )				->			Any:





    '''simple docstring'''




    snake_case							:		Tuple        =		False
    snake_case							:		List[Any]        =		search_prob
    snake_case							:		Union[str, Any]        =		start_temperate
    snake_case							:		Optional[Any]        =		[]
    snake_case							:		Optional[int]        =		0
    snake_case							:		Any        =		None

    while not search_end:
        snake_case							:		Tuple        =		current_state.score()
        if best_state is None or current_score > best_state.score():
            snake_case							:		str        =		current_state
        scores.append(__A		)
        iterations += 1
        snake_case							:		Optional[Any]        =		None
        snake_case							:		List[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							:		str        =		random.randint(0 ,    len(__A		) - 1		)  # picking a random neighbor
            snake_case							:		Optional[Any]        =		neighbors.pop(__A		)
            snake_case							:		Tuple        =		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							:		List[str]        =		change * -1  # in case we are finding minimum
            if change > 0:  # improves the solution
                snake_case							:		Dict        =		picked_neighbor
            else:
                snake_case							:		Tuple        =		(math.e) ** (
                    change / current_temp
                )  # probability generation function
                if random.random() < probability:  # random number within probability
                    snake_case							:		Union[str, Any]        =		picked_neighbor
        snake_case							:		Optional[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							:		Tuple        =		True
        else:
            snake_case							:		Tuple        =		next_state

    if visualization:
        from matplotlib import pyplot as plt

        plt.plot(range(__A		) ,    __A		)
        plt.xlabel("""Iterations"""		)
        plt.ylabel("""Function values"""		)
        plt.show()
    return best_state


if __name__ == "__main__":







     def       lowercase					(	__A :					int ,    __A :					int		)				->			Union[str, Any]:





         '''simple docstring'''




         return (x**2) + (y**2)

     # starting the problem with initial coordinates (12, 47)
     __lowercase     : Union[str, Any]       =						SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
     __lowercase     : List[Any]       =						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)
     __lowercase     : List[Any]       =						SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
     __lowercase     : str       =						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       lowercase					(	__A :					str ,    __A :					Any		)				->			Any:





         '''simple docstring'''




         return (3 * x**2) - (6 * y)

     __lowercase     : Any       =						SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
     __lowercase     : Optional[int]       =						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()}'''
     )

     __lowercase     : Any       =						SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
     __lowercase     : List[str]       =						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()}'''
     )










# Copyright 2023 The HuggingFace Inc. 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.
import re

from ..utils import cached_file


# docstyle-ignore
UpperCamelCase__           =			"\nHuman: <<task>>\n\nAssistant: "


UpperCamelCase__           =			"huggingface-tools/default-prompts"
UpperCamelCase__           =			{"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}





def      _UpperCamelCase  (a__ :List[Any] ,      a__ :Optional[Any] ,      a__ :List[str]="run"							):






							"""simple docstring"""





							if prompt_or_repo_id is None:
														UpperCamelCase__					  =      DEFAULT_PROMPTS_REPO

							# prompt is considered a repo ID when it does not contain any kind of space
							if re.search("""\\s""" ,      a__							) is not None:
														return prompt_or_repo_id

							UpperCamelCase__					  =      cached_file(
							    a__ ,      PROMPT_FILES[mode] ,      repo_type="""dataset""" ,      user_agent={"""agent""": agent_name}							)
							with open(a__ ,      """r""" ,      encoding="""utf-8"""							) as f:
														return f.read()




