



'''simple docstring'''




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


a     :    Tuple      						=					logging.get_logger(__name__)

a     :    Tuple      						=					"""▁"""

a     :    Optional[Any]      						=					{"""vocab_file""": """sentencepiece.bpe.model"""}

a     :    List[str]      						=					{
    """vocab_file""": {
        """facebook/mbart-large-en-ro""": (
            """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"""
        ),
        """facebook/mbart-large-cc25""": (
            """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

a     :    Dict      						=					{
    """facebook/mbart-large-en-ro""": 1_0_2_4,
    """facebook/mbart-large-cc25""": 1_0_2_4,
}

# fmt: off
a     :    int      						=					["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN"""]

class     UpperCamelCase_				(							__snake_case    ):
	lowercase    =				VOCAB_FILES_NAMES
	lowercase    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase    =				PRETRAINED_VOCAB_FILES_MAP
	lowercase    =				['input_ids', 'attention_mask']

	lowercase    =				[]
	lowercase    =				[]





	def __init__(						self	,	A	,	A="<s>"	,	A="</s>"	,	A="</s>"	,	A="<s>"	,	A="<unk>"	,	A="<pad>"	,	A="<mask>"	,	A=None	,	A=None	,	A=None	,	A = None	,	A=None	,	**A	,	)    ->    Tuple:
								UpperCAmelCase  :	int							  =	AddedToken(A_	,	lstrip=A_	,	rstrip=A_					) if isinstance(A_	,	A_					) else mask_token

								UpperCAmelCase  :	Optional[int]							  =	{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=A_	,	eos_token=A_	,	unk_token=A_	,	sep_token=A_	,	cls_token=A_	,	pad_token=A_	,	mask_token=A_	,	tokenizer_file=A_	,	src_lang=A_	,	tgt_lang=A_	,	additional_special_tokens=A_	,	sp_model_kwargs=self.sp_model_kwargs	,	**A_	,	)

								UpperCAmelCase  :	Optional[int]							  =	spm.SentencePieceProcessor(**self.sp_model_kwargs					)
								self.sp_model.Load(str(A_					)					)
								UpperCAmelCase  :	Optional[Any]							  =	vocab_file

								# Original fairseq vocab and spm vocab must be "aligned":
								# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
								# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
								# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
								# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

								# Mimic fairseq token-to-id alignment for the first 4 token
								UpperCAmelCase  :	Optional[int]							  =	{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

								# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
								UpperCAmelCase  :	Any							  =	1

								UpperCAmelCase  :	int							  =	len(self.sp_model					)
								UpperCAmelCase  :	str							  =	{
								    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(A_					)
								}
								UpperCAmelCase  :	List[str]							  =	{v: k for k, v in self.lang_code_to_id.items()}
								UpperCAmelCase  :	Union[str, Any]							  =	len(self.sp_model					) + len(self.lang_code_to_id					) + self.fairseq_offset

								self.fairseq_tokens_to_ids.update(self.lang_code_to_id					)
								UpperCAmelCase  :	Union[str, Any]							  =	{v: k for k, v in self.fairseq_tokens_to_ids.items()}
								UpperCAmelCase  :	Any							  =	list(self.lang_code_to_id.keys()					)

								if additional_special_tokens is not None:
															# Only add those special tokens if they are not already there.
															self._additional_special_tokens.extend(
															    [t for t in additional_special_tokens if t not in self._additional_special_tokens]					)

								UpperCAmelCase  :	int							  =	src_lang if src_lang is not None else "en_XX"
								UpperCAmelCase  :	str							  =	self.lang_code_to_id[self._src_lang]
								UpperCAmelCase  :	Optional[Any]							  =	tgt_lang
								self.set_src_lang_special_tokens(self._src_lang					)





	def __getstate__(						self					)    ->    int:
								UpperCAmelCase  :	Dict							  =	self.__dict__.copy()
								UpperCAmelCase  :	Tuple							  =	None
								UpperCAmelCase  :	str							  =	self.sp_model.serialized_model_proto()
								return state





	def __setstate__(						self	,	A					)    ->    List[Any]:
								UpperCAmelCase  :	int							  =	d

								# for backward compatibility
								if not hasattr(self	,	"""sp_model_kwargs"""					):
															UpperCAmelCase  :	Optional[int]							  =	{}

								UpperCAmelCase  :	Tuple							  =	spm.SentencePieceProcessor(**self.sp_model_kwargs					)
								self.sp_model.LoadFromSerializedProto(self.sp_model_proto					)





	@property
	def 				_lowercase(						self					)    ->    str:
								return len(self.sp_model					) + len(self.lang_code_to_id					) + self.fairseq_offset + 1  # Plus 1 for the mask token





	@property
	def 				_lowercase(						self					)    ->    Dict:
								return self._src_lang





	@src_lang.setter
	def 				_lowercase(						self	,	A					)    ->    Optional[Any]:
								UpperCAmelCase  :	Dict							  =	new_src_lang
								self.set_src_lang_special_tokens(self._src_lang					)





	def 				_lowercase(						self	,	A	,	A = None	,	A = False					)    ->    Any:




								if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=A_	,	token_ids_a=A_	,	already_has_special_tokens=A_					)

								UpperCAmelCase  :	int							  =	[1] * len(self.prefix_tokens					)
								UpperCAmelCase  :	List[Any]							  =	[1] * len(self.suffix_tokens					)
								if token_ids_a is None:
															return prefix_ones + ([0] * len(A_					)) + suffix_ones
								return prefix_ones + ([0] * len(A_					)) + ([0] * len(A_					)) + suffix_ones





	def 				_lowercase(						self	,	A	,	A = None					)    ->    Optional[Any]:




								if token_ids_a is None:
															return self.prefix_tokens + token_ids_a + self.suffix_tokens
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens





	def 				_lowercase(						self	,	A	,	A = None					)    ->    List[Any]:
								UpperCAmelCase  :	Dict							  =	[self.sep_token_id]
								UpperCAmelCase  :	Union[str, Any]							  =	[self.cls_token_id]

								if token_ids_a is None:
															return len(cls + token_ids_a + sep					) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]





	def 				_lowercase(						self	,	A	,	A	,	A	,	A	,	**A					)    ->    Union[str, Any]:




								if src_lang is None or tgt_lang is None:
															raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""					)
								UpperCAmelCase  :	Tuple							  =	src_lang
								UpperCAmelCase  :	Tuple							  =	self(A_	,	add_special_tokens=A_	,	return_tensors=A_	,	**A_					)
								UpperCAmelCase  :	Optional[int]							  =	self.convert_tokens_to_ids(A_					)
								UpperCAmelCase  :	Dict							  =	tgt_lang_id
								return inputs





	def 				_lowercase(						self					)    ->    Union[str, Any]:
								UpperCAmelCase  :	Tuple							  =	{self.convert_ids_to_tokens(A_					): i for i in range(self.vocab_size					)}
								vocab.update(self.added_tokens_encoder					)
								return vocab





	def 				_lowercase(						self	,	A					)    ->    int:
								return self.sp_model.encode(A_	,	out_type=A_					)





	def 				_lowercase(						self	,	A					)    ->    int:




								if token in self.fairseq_tokens_to_ids:
															return self.fairseq_tokens_to_ids[token]
								UpperCAmelCase  :	Optional[Any]							  =	self.sp_model.PieceToId(A_					)

								# Need to return unknown token if the SP model returned 0
								return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





	def 				_lowercase(						self	,	A					)    ->    int:




								if index in self.fairseq_ids_to_tokens:
															return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(index - self.fairseq_offset					)





	def 				_lowercase(						self	,	A					)    ->    Union[str, Any]:
								UpperCAmelCase  :	Dict							  =	"".join(A_					).replace(A_	,	""" """					).strip()
								return out_string





	def 				_lowercase(						self	,	A	,	A = None					)    ->    str:




								if not os.path.isdir(A_					):
															logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
															return
								UpperCAmelCase  :	List[str]							  =	os.path.join(
								    A_	,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

								if os.path.abspath(self.vocab_file					) != os.path.abspath(A_					) and os.path.isfile(self.vocab_file					):
															copyfile(self.vocab_file	,	A_					)
								elif not os.path.isfile(self.vocab_file					):
															with open(A_	,	"""wb"""					) as fi:
																						UpperCAmelCase  :	str							  =	self.sp_model.serialized_model_proto()
																						fi.write(A_					)

								return (out_vocab_file,)





	def 				_lowercase(						self	,	A	,	A = "en_XX"	,	A = None	,	A = "ro_RO"	,	**A	,	)    ->    Union[str, Any]:
								UpperCAmelCase  :	str							  =	src_lang
								UpperCAmelCase  :	Dict							  =	tgt_lang
								return super().prepare_seqaseq_batch(A_	,	A_	,	**A_					)





	def 				_lowercase(						self					)    ->    List[str]:
								return self.set_src_lang_special_tokens(self.src_lang					)





	def 				_lowercase(						self					)    ->    Any:
								return self.set_tgt_lang_special_tokens(self.tgt_lang					)





	def 				_lowercase(						self	,	A					)    ->    Optional[Any]:
								UpperCAmelCase  :	List[str]							  =	self.lang_code_to_id[src_lang]
								UpperCAmelCase  :	List[str]							  =	[]
								UpperCAmelCase  :	Optional[Any]							  =	[self.eos_token_id, self.cur_lang_code]






	def 				_lowercase(						self	,	A					)    ->    Optional[int]:
								UpperCAmelCase  :	List[Any]							  =	self.lang_code_to_id[lang]
								UpperCAmelCase  :	Any							  =	[]
								UpperCAmelCase  :	Dict							  =	[self.eos_token_id, self.cur_lang_code]






'''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()}'''
							)

