





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


__A				:    Any       =   logging.get_logger(__name__)

__A				:    Union[str, Any]       =   "▁"

__A				:    Optional[int]       =   {
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

__A				:    Dict       =   {
    "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",
    },
}

__A				:    Union[str, Any]       =   {
    "facebook/m2m100_418M": 1_024,
}

# fmt: off
__A				:    Union[str, Any]       =   {
    "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 	A_       (a_			):
	UpperCAmelCase__					=			VOCAB_FILES_NAMES
	UpperCAmelCase__					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	UpperCAmelCase__					=			PRETRAINED_VOCAB_FILES_MAP
	UpperCAmelCase__					=			['''input_ids''', '''attention_mask''']

	UpperCAmelCase__					=			[]
	UpperCAmelCase__					=			[]
	def __init__(    self							,					_A							,					_A							,					_A=None							,					_A=None							,					_A="<s>"							,					_A="</s>"							,					_A="</s>"							,					_A="<pad>"							,					_A="<unk>"							,					_A="m2m100"							,					_A = None							,					_A=8							,					**_A							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				{} if sp_model_kwargs is None else sp_model_kwargs

				UpperCAmelCase   		=				language_codes
				UpperCAmelCase   		=				FAIRSEQ_LANGUAGE_CODES[language_codes]
				UpperCAmelCase   		=				{lang_code: F"""__{lang_code}__""" for lang_code in fairseq_language_code}

				UpperCAmelCase   		=				kwargs.get('''additional_special_tokens'''							,					[]		)
				kwargs["additional_special_tokens"] += [
				    self.get_lang_token(_A		)
				    for lang_code in fairseq_language_code
				    if self.get_lang_token(_A		) not in kwargs["additional_special_tokens"]
				]

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

				UpperCAmelCase   		=				vocab_file
				UpperCAmelCase   		=				load_json(_A		)
				UpperCAmelCase   		=				{v: k for k, v in self.encoder.items()}
				UpperCAmelCase   		=				spm_file
				UpperCAmelCase   		=				load_spm(_A							,					self.sp_model_kwargs		)

				UpperCAmelCase   		=				len(self.encoder		)

				UpperCAmelCase   		=				{
				    self.get_lang_token(_A		): self.encoder_size + i for i, lang_code in enumerate(_A		)
				}
				UpperCAmelCase   		=				{lang_code: self.encoder_size + i for i, lang_code in enumerate(_A		)}
				UpperCAmelCase   		=				{v: k for k, v in self.lang_token_to_id.items()}

				UpperCAmelCase   		=				src_lang if src_lang is not None else '''en'''
				UpperCAmelCase   		=				tgt_lang
				UpperCAmelCase   		=				self.get_lang_id(self._src_lang		)
				self.set_src_lang_special_tokens(self._src_lang		)

				UpperCAmelCase   		=				num_madeup_words
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return len(self.encoder		) + len(self.lang_token_to_id		)
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return self._src_lang
	@src_lang.setter
	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				new_src_lang
				self.set_src_lang_special_tokens(self._src_lang		)
	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				return self.sp_model.encode(_A							,					out_type=_A		)
	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







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






				'''simple docstring'''







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






				'''simple docstring'''







				UpperCAmelCase   		=				[]
				UpperCAmelCase   		=				''''''
				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(_A		) + token
										UpperCAmelCase   		=				[]
							else:
										current_sub_tokens.append(_A		)
				out_string += self.sp_model.decode(_A		)
				return out_string.strip()
	def      _lowercase				(    self							,					_A							,					_A = None							,					_A = False		):






				'''simple docstring'''







				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   		=				[1] * len(self.prefix_tokens		)
				UpperCAmelCase   		=				[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		):






				'''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      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				{self.convert_ids_to_tokens(_A		): i for i in range(self.vocab_size		)}
				vocab.update(self.added_tokens_encoder		)
				return vocab
	def __getstate__(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.__dict__.copy()
				UpperCAmelCase   		=				None
				return state
	def __setstate__(    self							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				d

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

				UpperCAmelCase   		=				load_spm(self.spm_file							,					self.sp_model_kwargs		)
	def      _lowercase				(    self							,					_A							,					_A = None		):






				'''simple docstring'''







				UpperCAmelCase   		=				Path(_A		)
				if not save_dir.is_dir():
							raise OSError(F"""{save_directory} should be a directory"""		)
				UpperCAmelCase   		=				save_dir / (
				    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
				)
				UpperCAmelCase   		=				save_dir / (
				    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
				)

				save_json(self.encoder							,					_A		)

				if os.path.abspath(self.spm_file		) != os.path.abspath(_A		) and os.path.isfile(self.spm_file		):
							copyfile(self.spm_file							,					_A		)
				elif not os.path.isfile(self.spm_file		):
							with open(_A							,					'''wb'''		) as fi:
										UpperCAmelCase   		=				self.sp_model.serialized_model_proto()
										fi.write(_A		)

				return (str(_A		), str(_A		))
	def      _lowercase				(    self							,					_A							,					_A = "en"							,					_A = None							,					_A = "ro"							,					**_A							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				src_lang
				UpperCAmelCase   		=				tgt_lang
				self.set_src_lang_special_tokens(self.src_lang		)
				return super().prepare_seqaseq_batch(_A							,					_A							,					**_A		)
	def      _lowercase				(    self							,					_A							,					_A							,					_A							,					**_A		):






				'''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'''		)
				UpperCAmelCase   		=				src_lang
				UpperCAmelCase   		=				self(_A							,					add_special_tokens=_A							,					**_A		)
				UpperCAmelCase   		=				self.get_lang_id(_A		)
				UpperCAmelCase   		=				tgt_lang_id
				return inputs
	def      _lowercase				(    self		):






				'''simple docstring'''







				self.set_src_lang_special_tokens(self.src_lang		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				self.set_tgt_lang_special_tokens(self.tgt_lang		)
	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_lang_token(_A		)
				UpperCAmelCase   		=				self.lang_token_to_id[lang_token]
				UpperCAmelCase   		=				[self.cur_lang_id]
				UpperCAmelCase   		=				[self.eos_token_id]
	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_lang_token(_A		)
				UpperCAmelCase   		=				self.lang_token_to_id[lang_token]
				UpperCAmelCase   		=				[self.cur_lang_id]
				UpperCAmelCase   		=				[self.eos_token_id]
	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				return self.lang_code_to_token[lang]







	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_lang_token(_A		)
				return self.lang_token_to_id[lang_token]





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				sentencepiece.SentencePieceProcessor:







			'''simple docstring'''

			UpperCAmelCase   		=				sentencepiece.SentencePieceProcessor(**UpperCamelCase__ )
			spm.Load(str(UpperCamelCase__ ) )
			return spm





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				Union[Dict, List]:







			'''simple docstring'''

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






def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				None:







			'''simple docstring'''

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










from __future__ import annotations

from collections import namedtuple





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				tuple:







			'''simple docstring'''

			UpperCAmelCase   		=				namedtuple('''result'''			,			'''name value''' )
			if (voltage, current, power).count(0 ) != 1:
						raise ValueError('''Only one argument must be 0''' )
			elif power < 0:
						raise ValueError(
						    '''Power cannot be negative in any electrical/electronics system''' )
			elif voltage == 0:
						return result('''voltage'''			,			power / current )
			elif current == 0:
						return result('''current'''			,			power / voltage )
			elif power == 0:
						return result('''power'''			,			float(round(abs(voltage * current )			,			2 ) ) )
			else:
						raise ValueError('''Exactly one argument must be 0''' )


if __name__ == "__main__":
				import doctest

				doctest.testmod()



