





from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)








import math
import random
from typing import Any

from .hill_climbing import SearchProblem


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = True							,       SCREAMING_SNAKE_CASE_ = math.inf							,       SCREAMING_SNAKE_CASE_ = -math.inf							,       SCREAMING_SNAKE_CASE_ = math.inf							,       SCREAMING_SNAKE_CASE_ = -math.inf							,       SCREAMING_SNAKE_CASE_ = False							,       SCREAMING_SNAKE_CASE_ = 1_0_0							,       SCREAMING_SNAKE_CASE_ = 0.0_1							,       SCREAMING_SNAKE_CASE_ = 1							,       )   ->     Any:
		lowerCamelCase   : Union[str, Any]								=False
		lowerCamelCase   : int								=search_prob
		lowerCamelCase   : int								=start_temperate
		lowerCamelCase   : Any								=[]
		lowerCamelCase   : Any								=0
		lowerCamelCase   : Union[str, Any]								=None

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

		if visualization:
				from matplotlib import pyplot as plt

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


if __name__ == "__main__":


				def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     List[str]:
						return (x**2) + (y**2)

				# starting the problem with initial coordinates (12, 47)
				snake_case_							=			SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
				snake_case_							=			simulated_annealing(
				    prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, 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)
				snake_case_							=			SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
				snake_case_							=			simulated_annealing(
				    prob, find_max=True, max_x=1_0_0, min_x=5, max_y=5_0, 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 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     Tuple:
						return (3 * x**2) - (6 * y)

				snake_case_							=			SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
				snake_case_							=			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()}"""
				)

				snake_case_							=			SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
				snake_case_							=			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()}"""
				)

