



'''simple docstring'''




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation


a     :    Union[str, Any]      						=					logging.get_logger(__name__)

a     :    Optional[int]      						=					{'''tokenizer_file''': '''tokenizer.json'''}

a     :    Optional[int]      						=					{
    '''tokenizer_file''': {
        '''bigscience/tokenizer''': '''https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json''',
        '''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json''',
        '''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json''',
        '''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json''',
        '''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json''',
        '''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json''',
        '''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json''',
    },
}

class     UpperCamelCase_				(							__UpperCAmelCase    ):
	lowercase    =				VOCAB_FILES_NAMES
	lowercase    =				PRETRAINED_VOCAB_FILES_MAP
	lowercase    =				['''input_ids''', '''attention_mask''']
	lowercase    =				None





	def __init__(						self	,	A=None	,	A=None	,	A=None	,	A="<unk>"	,	A="<s>"	,	A="</s>"	,	A="<pad>"	,	A=False	,	A=False	,	**A	,	)    ->    Optional[Any]:
								super().__init__(
								    __SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE	,	tokenizer_file=__SCREAMING_SNAKE_CASE	,	unk_token=__SCREAMING_SNAKE_CASE	,	bos_token=__SCREAMING_SNAKE_CASE	,	eos_token=__SCREAMING_SNAKE_CASE	,	pad_token=__SCREAMING_SNAKE_CASE	,	add_prefix_space=__SCREAMING_SNAKE_CASE	,	clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE	,	**__SCREAMING_SNAKE_CASE	,	)
								UpperCAmelCase  :	Any							  =	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
								if pre_tok_state.get("""add_prefix_space"""	,	__SCREAMING_SNAKE_CASE					) != add_prefix_space:
															UpperCAmelCase  :	Tuple							  =	getattr(__SCREAMING_SNAKE_CASE	,	pre_tok_state.pop("""type"""					)					)
															UpperCAmelCase  :	Any							  =	add_prefix_space
															UpperCAmelCase  :	Tuple							  =	pre_tok_class(**__SCREAMING_SNAKE_CASE					)

								UpperCAmelCase  :	Optional[int]							  =	add_prefix_space





	def 				_lowercase(						self	,	*A	,	**A					)    ->    str:
								UpperCAmelCase  :	Optional[Any]							  =	kwargs.get("""is_split_into_words"""	,	__SCREAMING_SNAKE_CASE					)
								if not (self.add_prefix_space or not is_split_into_words):
															raise Exception(
															    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
															    """ pretokenized inputs."""					)

								return super()._batch_encode_plus(*__SCREAMING_SNAKE_CASE	,	**__SCREAMING_SNAKE_CASE					)





	def 				_lowercase(						self	,	*A	,	**A					)    ->    int:
								UpperCAmelCase  :	Dict							  =	kwargs.get("""is_split_into_words"""	,	__SCREAMING_SNAKE_CASE					)

								if not (self.add_prefix_space or not is_split_into_words):
															raise Exception(
															    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
															    """ pretokenized inputs."""					)

								return super()._encode_plus(*__SCREAMING_SNAKE_CASE	,	**__SCREAMING_SNAKE_CASE					)





	def 				_lowercase(						self	,	A	,	A = None					)    ->    int:
								UpperCAmelCase  :	Union[str, Any]							  =	self._tokenizer.model.save(__SCREAMING_SNAKE_CASE	,	name=__SCREAMING_SNAKE_CASE					)
								return tuple(__SCREAMING_SNAKE_CASE					)






	def 				_lowercase(						self	,	A					)    ->    str:
								UpperCAmelCase  :	str							  =	[]
								for is_user, text in conversation.iter_texts():
															input_ids.extend(self.encode(__SCREAMING_SNAKE_CASE	,	add_special_tokens=__SCREAMING_SNAKE_CASE					) + [self.eos_token_id]					)

								if len(__SCREAMING_SNAKE_CASE					) > self.model_max_length:
															UpperCAmelCase  :	Tuple							  =	input_ids[-self.model_max_length :]
								return input_ids






'''simple docstring'''




import math
import random
from typing import Any

from .hill_climbing import SearchProblem



def    __lowerCamelCase						(      _lowercase			,       _lowercase = True			,       _lowercase = math.inf			,       _lowercase = -math.inf			,       _lowercase = math.inf			,       _lowercase = -math.inf			,       _lowercase = False			,       _lowercase = 1_0_0			,       _lowercase = 0.01			,       _lowercase = 1			,       )   ->    Any:
							UpperCAmelCase  :	Optional[int]							  =	False
							UpperCAmelCase  :	Any							  =	search_prob
							UpperCAmelCase  :	Any							  =	start_temperate
							UpperCAmelCase  :	Optional[Any]							  =	[]
							UpperCAmelCase  :	Optional[Any]							  =	0
							UpperCAmelCase  :	Optional[Any]							  =	None

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

							if visualization:
														from matplotlib import pyplot as plt

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


if __name__ == "__main__":



							def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    str:
														return (x**2) + (y**2)

							# starting the problem with initial coordinates (12, 47)
							a     :    Dict      						=					SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
							a     :    Dict      						=					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)
							a     :    List[str]      						=					SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
							a     :    Dict      						=					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    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    Optional[Any]:
														return (3 * x**2) - (6 * y)

							a     :    Tuple      						=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							a     :    Any      						=					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()}'''
							)

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

