





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case_							=			logging.get_logger(__name__)

snake_case_							=			{
    'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
    'umberto-commoncrawl-cased-v1': (
        'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
    ),
    'umberto-wikipedia-uncased-v1': (
        'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
    ),
}
class        snake_case_		(			SCREAMING_SNAKE_CASE_):
		lowerCamelCase    :Optional[Any]						=     "camembert"







		def __init__(		self			,     __lowercase=3_0_5_2_2			,     __lowercase=7_6_8			,     __lowercase=1_2			,     __lowercase=1_2			,     __lowercase=3_0_7_2			,     __lowercase="gelu"			,     __lowercase=0.1			,     __lowercase=0.1			,     __lowercase=5_1_2			,     __lowercase=2			,     __lowercase=0.0_2			,     __lowercase=1e-12			,     __lowercase=1			,     __lowercase=0			,     __lowercase=2			,     __lowercase="absolute"			,     __lowercase=True			,     __lowercase=None			,     **__lowercase			,     )				->   Union[str, Any]:
				super().__init__(pad_token_id=UpperCamelCase__			,     bos_token_id=UpperCamelCase__			,     eos_token_id=UpperCamelCase__			,     **UpperCamelCase__   )

				lowerCamelCase   : Dict								=vocab_size
				lowerCamelCase   : Dict								=hidden_size
				lowerCamelCase   : Dict								=num_hidden_layers
				lowerCamelCase   : str								=num_attention_heads
				lowerCamelCase   : List[Any]								=hidden_act
				lowerCamelCase   : Dict								=intermediate_size
				lowerCamelCase   : str								=hidden_dropout_prob
				lowerCamelCase   : Dict								=attention_probs_dropout_prob
				lowerCamelCase   : Union[str, Any]								=max_position_embeddings
				lowerCamelCase   : int								=type_vocab_size
				lowerCamelCase   : List[str]								=initializer_range
				lowerCamelCase   : Optional[Any]								=layer_norm_eps
				lowerCamelCase   : Union[str, Any]								=position_embedding_type
				lowerCamelCase   : Optional[Any]								=use_cache
				lowerCamelCase   : str								=classifier_dropout




class        snake_case_		(			SCREAMING_SNAKE_CASE_):







		@property
		def 			__lowercase   (		self   )				->   List[str]:
				if self.task == "multiple-choice":
						lowerCamelCase   : Optional[int]								={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
				else:
						lowerCamelCase   : Tuple								={0: '''batch''', 1: '''sequence'''}
				return OrderedDict(
				    [
				        ('''input_ids''', dynamic_axis),
				        ('''attention_mask''', dynamic_axis),
				    ]   )








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()}"""
				)

