
'''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 BatchEncoding, PreTrainedTokenizer
from ...utils import logging


lowercase__						       =      logging.get_logger(__name__)

lowercase__						       =      "▁"

lowercase__						       =      {
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowercase__						       =      {
    "vocab_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
    },
    "spm_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_config_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
    },
}

lowercase__						       =      {
    "facebook/m2m100_418M": 1_0_2_4,
}

# fmt: off
lowercase__						       =      {
    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
    "wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						VOCAB_FILES_NAMES
		lowerCamelCase     							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase     							=						PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase     							=						["""input_ids""", """attention_mask"""]

		lowerCamelCase     							=						[]
		lowerCamelCase     							=						[]



		def __init__(      self				:			int  ,	UpperCamelCase__				:			Optional[Any]  ,	UpperCamelCase__				:			Optional[int]  ,	UpperCamelCase__				:			Optional[Any]=None  ,	UpperCamelCase__				:			List[str]=None  ,	UpperCamelCase__				:			Tuple="<s>"  ,	UpperCamelCase__				:			str="</s>"  ,	UpperCamelCase__				:			Tuple="</s>"  ,	UpperCamelCase__				:			str="<pad>"  ,	UpperCamelCase__				:			List[str]="<unk>"  ,	UpperCamelCase__				:			str="m2m100"  ,	UpperCamelCase__				:			Optional[Dict[str, Any]] = None  ,	UpperCamelCase__				:			Tuple=8  ,	**UpperCamelCase__				:			Union[str, Any]  ,	)						->			None:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						{} if sp_model_kwargs is None else sp_model_kwargs

				snake_case  :       List[Any]     			=						language_codes
				snake_case  :       Optional[int]     			=						FAIRSEQ_LANGUAGE_CODES[language_codes]
				snake_case  :       Any     			=						{lang_code: f'__{lang_code}__' for lang_code in fairseq_language_code}

				snake_case  :       Optional[Any]     			=						kwargs.get('''additional_special_tokens'''  ,	[]      )
				kwargs["additional_special_tokens"] += [
				    self.get_lang_token(UpperCamelCase__      )
				    for lang_code in fairseq_language_code
				    if self.get_lang_token(UpperCamelCase__      ) not in kwargs["additional_special_tokens"]
				]

				super().__init__(
				    src_lang=UpperCamelCase__  ,	tgt_lang=UpperCamelCase__  ,	bos_token=UpperCamelCase__  ,	eos_token=UpperCamelCase__  ,	sep_token=UpperCamelCase__  ,	unk_token=UpperCamelCase__  ,	pad_token=UpperCamelCase__  ,	language_codes=UpperCamelCase__  ,	sp_model_kwargs=self.sp_model_kwargs  ,	num_madeup_words=UpperCamelCase__  ,	**UpperCamelCase__  ,	)

				snake_case  :       List[str]     			=						vocab_file
				snake_case  :       List[str]     			=						load_json(UpperCamelCase__      )
				snake_case  :       Tuple     			=						{v: k for k, v in self.encoder.items()}
				snake_case  :       Tuple     			=						spm_file
				snake_case  :       Tuple     			=						load_spm(UpperCamelCase__  ,	self.sp_model_kwargs      )

				snake_case  :       List[str]     			=						len(self.encoder      )

				snake_case  :       str     			=						{
				    self.get_lang_token(UpperCamelCase__      ): self.encoder_size + i for i, lang_code in enumerate(UpperCamelCase__      )
				}
				snake_case  :       Dict     			=						{lang_code: self.encoder_size + i for i, lang_code in enumerate(UpperCamelCase__      )}
				snake_case  :       str     			=						{v: k for k, v in self.lang_token_to_id.items()}

				snake_case  :       Optional[int]     			=						src_lang if src_lang is not None else '''en'''
				snake_case  :       Dict     			=						tgt_lang
				snake_case  :       List[Any]     			=						self.get_lang_id(self._src_lang      )
				self.set_src_lang_special_tokens(self._src_lang      )

				snake_case  :       Tuple     			=						num_madeup_words



		@property
		def       lowerCAmelCase				(      self				:			Any      )						->			int:






				"""simple docstring"""







				return len(self.encoder      ) + len(self.lang_token_to_id      )



		@property
		def       lowerCAmelCase				(      self				:			str      )						->			str:






				"""simple docstring"""







				return self._src_lang



		@src_lang.setter
		def       lowerCAmelCase				(      self				:			Any  ,	UpperCamelCase__				:			str      )						->			None:






				"""simple docstring"""







				snake_case  :       Dict     			=						new_src_lang
				self.set_src_lang_special_tokens(self._src_lang      )



		def       lowerCAmelCase				(      self				:			Optional[int]  ,	UpperCamelCase__				:			str      )						->			List[str]:






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			str  ,	UpperCamelCase__				:			Tuple      )						->			Optional[int]:






				"""simple docstring"""







				if token in self.lang_token_to_id:
						return self.lang_token_to_id[token]
				return self.encoder.get(UpperCamelCase__  ,	self.encoder[self.unk_token]      )



		def       lowerCAmelCase				(      self				:			List[Any]  ,	UpperCamelCase__				:			int      )						->			str:






				"""simple docstring"""







				if index in self.id_to_lang_token:
						return self.id_to_lang_token[index]
				return self.decoder.get(UpperCamelCase__  ,	self.unk_token      )



		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			List[str]      )						->			int:






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			Tuple  ,	UpperCamelCase__				:			List[int]  ,	UpperCamelCase__				:			Optional[List[int]] = None  ,	UpperCamelCase__				:			bool = False      )						->			List[int]:






				"""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__      )

				snake_case  :       Optional[Any]     			=						[1] * len(self.prefix_tokens      )
				snake_case  :       Dict     			=						[1] * len(self.suffix_tokens      )
				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       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			List[int]  ,	UpperCamelCase__				:			Optional[List[int]] = None      )						->			List[int]:






				"""simple docstring"""







				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       lowerCAmelCase				(      self				:			int      )						->			Dict:






				"""simple docstring"""







				snake_case  :       str     			=						{self.convert_ids_to_tokens(UpperCamelCase__      ): i for i in range(self.vocab_size      )}
				vocab.update(self.added_tokens_encoder      )
				return vocab



		def __getstate__(      self				:			Any      )						->			Dict:






				"""simple docstring"""







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



		def __setstate__(      self				:			Optional[int]  ,	UpperCamelCase__				:			Dict      )						->			None:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						d

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

				snake_case  :       Union[str, Any]     			=						load_spm(self.spm_file  ,	self.sp_model_kwargs      )



		def       lowerCAmelCase				(      self				:			Union[str, Any]  ,	UpperCamelCase__				:			str  ,	UpperCamelCase__				:			Optional[str] = None      )						->			Tuple[str]:






				"""simple docstring"""







				snake_case  :       str     			=						Path(UpperCamelCase__      )
				if not save_dir.is_dir():
						raise OSError(f'{save_directory} should be a directory'      )
				snake_case  :       Union[str, Any]     			=						save_dir / (
				    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
				)
				snake_case  :       Dict     			=						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:
								snake_case  :       int     			=						self.sp_model.serialized_model_proto()
								fi.write(UpperCamelCase__      )

				return (str(UpperCamelCase__      ), str(UpperCamelCase__      ))



		def       lowerCAmelCase				(      self				:			Optional[int]  ,	UpperCamelCase__				:			List[str]  ,	UpperCamelCase__				:			str = "en"  ,	UpperCamelCase__				:			Optional[List[str]] = None  ,	UpperCamelCase__				:			str = "ro"  ,	**UpperCamelCase__				:			Any  ,	)						->			BatchEncoding:






				"""simple docstring"""







				snake_case  :       int     			=						src_lang
				snake_case  :       Dict     			=						tgt_lang
				self.set_src_lang_special_tokens(self.src_lang      )
				return super().prepare_seqaseq_batch(UpperCamelCase__  ,	UpperCamelCase__  ,	**UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Dict  ,	UpperCamelCase__				:			List[Any]  ,	UpperCamelCase__				:			Optional[str]  ,	UpperCamelCase__				:			Optional[str]  ,	**UpperCamelCase__				:			Dict      )						->			Dict:






				"""simple docstring"""







				if src_lang is None or tgt_lang is None:
						raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''      )
				snake_case  :       int     			=						src_lang
				snake_case  :       Optional[Any]     			=						self(UpperCamelCase__  ,	add_special_tokens=UpperCamelCase__  ,	**UpperCamelCase__      )
				snake_case  :       List[Any]     			=						self.get_lang_id(UpperCamelCase__      )
				snake_case  :       Dict     			=						tgt_lang_id
				return inputs



		def       lowerCAmelCase				(      self				:			List[str]      )						->			str:






				"""simple docstring"""







				self.set_src_lang_special_tokens(self.src_lang      )



		def       lowerCAmelCase				(      self				:			Dict      )						->			Optional[Any]:






				"""simple docstring"""







				self.set_tgt_lang_special_tokens(self.tgt_lang      )



		def       lowerCAmelCase				(      self				:			int  ,	UpperCamelCase__				:			str      )						->			None:






				"""simple docstring"""







				snake_case  :       List[str]     			=						self.get_lang_token(UpperCamelCase__      )
				snake_case  :       Any     			=						self.lang_token_to_id[lang_token]
				snake_case  :       Tuple     			=						[self.cur_lang_id]
				snake_case  :       Optional[int]     			=						[self.eos_token_id]



		def       lowerCAmelCase				(      self				:			Dict  ,	UpperCamelCase__				:			str      )						->			None:






				"""simple docstring"""







				snake_case  :       List[str]     			=						self.get_lang_token(UpperCamelCase__      )
				snake_case  :       Any     			=						self.lang_token_to_id[lang_token]
				snake_case  :       List[Any]     			=						[self.cur_lang_id]
				snake_case  :       str     			=						[self.eos_token_id]



		def       lowerCAmelCase				(      self				:			str  ,	UpperCamelCase__				:			str      )						->			str:






				"""simple docstring"""







				return self.lang_code_to_token[lang]



		def       lowerCAmelCase				(      self				:			List[Any]  ,	UpperCamelCase__				:			str      )						->			int:






				"""simple docstring"""







				snake_case  :       str     			=						self.get_lang_token(UpperCamelCase__      )
				return self.lang_token_to_id[lang_token]







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


		'''simple docstring'''


		snake_case  :       Optional[int]     			=						sentencepiece.SentencePieceProcessor(**SCREAMING_SNAKE_CASE__			)
		spm.Load(str(SCREAMING_SNAKE_CASE__			)			)
		return spm




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


		'''simple docstring'''


		with open(SCREAMING_SNAKE_CASE__ ,       '''r'''			) as f:
				return json.load(SCREAMING_SNAKE_CASE__			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							None:


		'''simple docstring'''


		with open(SCREAMING_SNAKE_CASE__ ,       '''w'''			) as f:
				json.dump(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       indent=2			)




'''simple docstring'''
from __future__ import annotations

from math import ceil, floor, sqrt




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ = 200_0000			)  ->							int:


		'''simple docstring'''


		snake_case  :       list[int]     			=						[0]
		snake_case  :       int

		for idx in range(1 ,       ceil(sqrt(target * 2			) * 1.1			)			):
				triangle_numbers.append(triangle_numbers[-1] + idx			)

		# we want this to be as close as possible to target
		snake_case  :       int     			=						0
		# the area corresponding to the grid that gives the product closest to target
		snake_case  :       int     			=						0
		# an estimate of b, using the quadratic formula
		snake_case  :       float
		# the largest integer less than b_estimate
		snake_case  :       int
		# the largest integer less than b_estimate
		snake_case  :       int
		# the triangle number corresponding to b_floor
		snake_case  :       int
		# the triangle number corresponding to b_ceil
		snake_case  :       int

		for idx_a, triangle_a in enumerate(triangle_numbers[1:] ,       1			):
				snake_case  :       int     			=						(-1 + sqrt(1 + 8 * target / triangle_a			)) / 2
				snake_case  :       Optional[int]     			=						floor(SCREAMING_SNAKE_CASE__			)
				snake_case  :       int     			=						ceil(SCREAMING_SNAKE_CASE__			)
				snake_case  :       Optional[Any]     			=						triangle_numbers[b_floor]
				snake_case  :       Union[str, Any]     			=						triangle_numbers[b_ceil]

				if abs(target - triangle_b_first_guess * triangle_a			) < abs(
				    target - best_product			):
						snake_case  :       str     			=						triangle_b_first_guess * triangle_a
						snake_case  :       int     			=						idx_a * b_floor

				if abs(target - triangle_b_second_guess * triangle_a			) < abs(
				    target - best_product			):
						snake_case  :       List[Any]     			=						triangle_b_second_guess * triangle_a
						snake_case  :       Any     			=						idx_a * b_ceil

		return area


if __name__ == "__main__":
						print(f"{solution() = }")


