




"""simple docstring"""





import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A   =       logging.get_logger(__name__)

A   =       """▁"""

A   =       {
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
}

A   =       {
    """vocab_file""": {
        """facebook/s2t-small-librispeech-asr""": (
            """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"""
        ),
    },
    """spm_file""": {
        """facebook/s2t-small-librispeech-asr""": (
            """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"""
        )
    },
}

A   =       {
    """facebook/s2t-small-librispeech-asr""": 1_024,
}

A   =       ["""pt""", """fr""", """ru""", """nl""", """ro""", """it""", """es""", """de"""]

A   =       {"""mustc""": MUSTC_LANGS}



class      a__				(	__magic_name__   ):
			lowercase_    =	VOCAB_FILES_NAMES
			lowercase_    =	PRETRAINED_VOCAB_FILES_MAP
			lowercase_    =	MAX_MODEL_INPUT_SIZES
			lowercase_    =	["input_ids", "attention_mask"]

			lowercase_    =	[]


			def __init__(      self      :    int      ,  UpperCamelCase_      :    Union[str, Any]      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    int="<s>"      ,  UpperCamelCase_      :    Any="</s>"      ,  UpperCamelCase_      :    Any="<pad>"      ,  UpperCamelCase_      :    Tuple="<unk>"      ,  UpperCamelCase_      :    Union[str, Any]=False      ,  UpperCamelCase_      :    List[Any]=False      ,  UpperCamelCase_      :    int=None      ,  UpperCamelCase_      :    List[str]=None      ,  UpperCamelCase_      :    Optional[Dict[str, Any]] = None      ,  **UpperCamelCase_      :    Optional[Any]      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					{} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    bos_token=UpperCamelCase_      ,  eos_token=UpperCamelCase_      ,  unk_token=UpperCamelCase_      ,  pad_token=UpperCamelCase_      ,  do_upper_case=UpperCamelCase_      ,  do_lower_case=UpperCamelCase_      ,  tgt_lang=UpperCamelCase_      ,  lang_codes=UpperCamelCase_      ,  sp_model_kwargs=self.sp_model_kwargs      ,  **UpperCamelCase_      ,  )
						__UpperCAmelCase	: List[str]		=					do_upper_case
						__UpperCAmelCase	: Union[str, Any]		=					do_lower_case

						__UpperCAmelCase	: List[str]		=					load_json(UpperCamelCase_)
						__UpperCAmelCase	: str		=					{v: k for k, v in self.encoder.items()}
						__UpperCAmelCase	: int		=					spm_file
						__UpperCAmelCase	: Dict		=					load_spm(UpperCamelCase_      ,  self.sp_model_kwargs)

						if lang_codes is not None:
									__UpperCAmelCase	: List[Any]		=					lang_codes
									__UpperCAmelCase	: Dict		=					LANGUAGES[lang_codes]
									__UpperCAmelCase	: str		=					[F"<lang:{lang}>" for lang in self.langs]
									__UpperCAmelCase	: List[str]		=					{lang: self.sp_model.PieceToId(F"<lang:{lang}>") for lang in self.langs}

									__UpperCAmelCase	: Tuple		=					self.lang_tokens
									__UpperCAmelCase	: Optional[int]		=					tgt_lang if tgt_lang is not None else self.langs[0]

									self.set_tgt_lang_special_tokens(self._tgt_lang)
						else:
									__UpperCAmelCase	: List[str]		=					{}


			@property
			def       a_     (      self      :    Union[str, Any]):


						"""simple docstring"""




						return len(self.encoder)


			@property
			def       a_     (      self      :    Union[str, Any]):


						"""simple docstring"""




						return self._tgt_lang


			@tgt_lang.setter
			def       a_     (      self      :    Optional[int]      ,  UpperCamelCase_      :    int):


						"""simple docstring"""




						__UpperCAmelCase	: List[Any]		=					new_tgt_lang
						self.set_tgt_lang_special_tokens(UpperCamelCase_)


			def       a_     (      self      :    str      ,  UpperCamelCase_      :    str):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					self.lang_code_to_id[tgt_lang]
						__UpperCAmelCase	: List[str]		=					[lang_code_id]


			def       a_     (      self      :    Union[str, Any]      ,  UpperCamelCase_      :    str):


						"""simple docstring"""




						return self.sp_model.encode(UpperCamelCase_      ,  out_type=UpperCamelCase_)


			def       a_     (      self      :    Union[str, Any]      ,  UpperCamelCase_      :    Any):


						"""simple docstring"""




						return self.encoder.get(UpperCamelCase_      ,  self.encoder[self.unk_token])


			def       a_     (      self      :    str      ,  UpperCamelCase_      :    int):


						"""simple docstring"""




						return self.decoder.get(UpperCamelCase_      ,  self.unk_token)


			def       a_     (      self      :    List[Any]      ,  UpperCamelCase_      :    List[str]):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[int]		=					[]
						__UpperCAmelCase	: Any		=					""
						for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
												__UpperCAmelCase	: Optional[int]		=					self.sp_model.decode(UpperCamelCase_)
												out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
												__UpperCAmelCase	: Any		=					[]
									else:
												current_sub_tokens.append(UpperCamelCase_)
						__UpperCAmelCase	: List[str]		=					self.sp_model.decode(UpperCamelCase_)
						out_string += decoded.upper() if self.do_upper_case else decoded
						return out_string.strip()


			def       a_     (      self      :    List[Any]      ,  UpperCamelCase_      :    List[str]      ,  UpperCamelCase_      :    List[Any]=None):


						"""simple docstring"""




						if token_ids_a is None:
									return self.prefix_tokens + token_ids_a + [self.eos_token_id]
						# 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.eos_token_id]


			def       a_     (      self      :    Union[str, Any]      ,  UpperCamelCase_      :    List[int]      ,  UpperCamelCase_      :    Optional[List[int]] = None      ,  UpperCamelCase_      :    bool = False):


						"""simple docstring"""




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

						__UpperCAmelCase	: Optional[int]		=					[1] * len(self.prefix_tokens)
						__UpperCAmelCase	: Dict		=					[1]
						if token_ids_a is None:
									return prefix_ones + ([0] * len(UpperCamelCase_)) + suffix_ones
						return prefix_ones + ([0] * len(UpperCamelCase_)) + ([0] * len(UpperCamelCase_)) + suffix_ones


			def       a_     (      self      :    List[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: int		=					self.encoder.copy()
						vocab.update(self.added_tokens_encoder)
						return vocab


			def __getstate__(      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					self.__dict__.copy()
						__UpperCAmelCase	: int		=					None
						return state


			def __setstate__(      self      :    List[str]      ,  UpperCamelCase_      :    Dict):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					d

						# for backward compatibility
						if not hasattr(self      ,  "sp_model_kwargs"):
									__UpperCAmelCase	: Tuple		=					{}

						__UpperCAmelCase	: List[str]		=					load_spm(self.spm_file      ,  self.sp_model_kwargs)


			def       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    str      ,  UpperCamelCase_      :    Optional[str] = None):


						"""simple docstring"""




						__UpperCAmelCase	: List[Any]		=					Path(UpperCamelCase_)
						assert save_dir.is_dir(), F"{save_directory} should be a directory"
						__UpperCAmelCase	: Optional[int]		=					save_dir / (
						    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
						)
						__UpperCAmelCase	: List[str]		=					save_dir / (
						    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
						)

						save_json(self.encoder      ,  UpperCamelCase_)

						if os.path.abspath(self.spm_file) != os.path.abspath(UpperCamelCase_) and os.path.isfile(self.spm_file):
									copyfile(self.spm_file      ,  UpperCamelCase_)
						elif not os.path.isfile(self.spm_file):
									with open(UpperCamelCase_      ,  "wb") as fi:
												__UpperCAmelCase	: Any		=					self.sp_model.serialized_model_proto()
												fi.write(UpperCamelCase_)

						return (str(UpperCamelCase_), str(UpperCamelCase_))






def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			sentencepiece.SentencePieceProcessor:


			"""simple docstring"""
			__UpperCAmelCase	: Optional[Any]		=					sentencepiece.SentencePieceProcessor(**UpperCamelCase							)
			spm.Load(str(UpperCamelCase							)							)
			return spm

def 					_UpperCamelCase    (			UpperCamelCase							)			->			Union[Dict, List]:


			"""simple docstring"""
			with open(UpperCamelCase				,       "r"							) as f:
						return json.load(UpperCamelCase							)





def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			None:


			"""simple docstring"""
			with open(UpperCamelCase				,       "w"							) as f:
						json.dump(UpperCamelCase				,       UpperCamelCase				,       indent=2							)








"""simple docstring"""





def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Tuple:


			"""simple docstring"""
			global f  # a global dp table for knapsack
			if f[i][j] < 0:
						if j < wt[i - 1]:
									__UpperCAmelCase	: Optional[int]		=					mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)
						else:
									__UpperCAmelCase	: Any		=					max(
									    mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)				,       mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       j - wt[i - 1]							) + val[i - 1]				,       )
						__UpperCAmelCase	: str		=					val
			return f[i][j]

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			List[Any]:


			"""simple docstring"""
			__UpperCAmelCase	: List[Any]		=					[[0] * (w + 1) for _ in range(n + 1							)]

			for i in range(1				,       n + 1							):
						for w_ in range(1				,       w + 1							):
									if wt[i - 1] <= w_:
												__UpperCAmelCase	: Tuple		=					max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]				,       dp[i - 1][w_]							)
									else:
												__UpperCAmelCase	: int		=					dp[i - 1][w_]

			return dp[n][w_], dp

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			int:


			"""simple docstring"""
			if not (isinstance(UpperCamelCase				,       (list, tuple)							) and isinstance(UpperCamelCase				,       (list, tuple)							)):
						raise ValueError(
						    "Both the weights and values vectors must be either lists or tuples"							)

			__UpperCAmelCase	: List[Any]		=					len(UpperCamelCase							)
			if num_items != len(UpperCamelCase							):
						__UpperCAmelCase	: int		=					(
						    "The number of weights must be the same as the number of values.\n"
						    f"But got {num_items} weights and {len(UpperCamelCase							)} values"
						)
						raise ValueError(UpperCamelCase							)
			for i in range(UpperCamelCase							):
						if not isinstance(wt[i]				,       UpperCamelCase							):
									__UpperCAmelCase	: Tuple		=					(
									    "All weights must be integers but got weight of "
									    f"type {type(wt[i]							)} at index {i}"
									)
									raise TypeError(UpperCamelCase							)

			__UpperCAmelCase	,     __UpperCAmelCase	: Tuple		=					knapsack(UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)
			__UpperCAmelCase	: set		=					set()
			_construct_solution(UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)

			return optimal_val, example_optional_set

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Dict:


			"""simple docstring"""
			# for the current item i at a maximum weight j to be part of an optimal subset,
			# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
			# where i - 1 means considering only the previous items at the given maximum weight
			if i > 0 and j > 0:
						if dp[i - 1][j] == dp[i][j]:
									_construct_solution(UpperCamelCase				,       UpperCamelCase				,       i - 1				,       UpperCamelCase				,       UpperCamelCase							)
						else:
									optimal_set.add(UpperCamelCase							)
									_construct_solution(UpperCamelCase				,       UpperCamelCase				,       i - 1				,       j - wt[i - 1]				,       UpperCamelCase							)


if __name__ == "__main__":
					A   =       [3, 2, 4, 4]
					A   =       [4, 3, 2, 3]
					A   =       4
					A   =       6
					A   =       [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
					A ,						A   =       knapsack(w, wt, val, n)
					print(optimal_solution)
					print(mf_knapsack(n, wt, val, w))  # switched the n and w

					# testing the dynamic programming problem with example
					# the optimal subset for the above example are items 3 and 4
					A ,						A   =       knapsack_with_example_solution(w, wt, val)
					assert optimal_solution == 8
					assert optimal_subset == {3, 4}
					print("""optimal_value = """, optimal_solution)
					print("""An optimal subset corresponding to the optimal value""", optimal_subset)


