


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


UpperCAmelCase   :    List[Any]     =		logging.get_logger(__name__)

UpperCAmelCase   :    Dict     =		"▁"

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

UpperCAmelCase   :    List[str]     =		{
    "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",
    },
}

UpperCAmelCase   :    List[str]     =		{
    "facebook/m2m100_418M": 10_24,
}

# fmt: off
UpperCAmelCase   :    Optional[int]     =		{
    "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        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					List[str]			       =		VOCAB_FILES_NAMES
		UpperCamelCase     :					Optional[int]			       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCamelCase     :					Tuple			       =		PRETRAINED_VOCAB_FILES_MAP
		UpperCamelCase     :					Optional[int]			       =		["input_ids", "attention_mask"]

		UpperCamelCase     :					List[int]			       =		[]
		UpperCamelCase     :					List[int]			       =		[]







		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		,      )  -> None:







					'''simple docstring'''







					lowerCamelCase    		=  {} if sp_model_kwargs is None else sp_model_kwargs

					lowerCamelCase    		=  language_codes
					lowerCamelCase    		=  FAIRSEQ_LANGUAGE_CODES[language_codes]
					lowerCamelCase    		=  {lang_code: F'__{lang_code}__' for lang_code in fairseq_language_code}

					lowerCamelCase    		=  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		,      )

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

					lowerCamelCase    		=  len(self.encoder	)

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

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

					lowerCamelCase    		=  num_madeup_words







		@property
		def 					__A   ( self	)  -> int:







					'''simple docstring'''







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







		@property
		def 					__A   ( self	)  -> str:







					'''simple docstring'''







					return self._src_lang







		@src_lang.setter
		def 					__A   ( self		,      A	)  -> None:







					'''simple docstring'''







					lowerCamelCase    		=  new_src_lang
					self.set_src_lang_special_tokens(self._src_lang	)







		def 					__A   ( self		,      A	)  -> List[str]:







					'''simple docstring'''







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







		def 					__A   ( self		,      A	)  -> Any:







					'''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 					__A   ( self		,      A	)  -> str:







					'''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 					__A   ( self		,      A	)  -> int:







					'''simple docstring'''







					lowerCamelCase    		=  []
					lowerCamelCase    		=  """"""
					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
											lowerCamelCase    		=  []
								else:
											current_sub_tokens.append(A	)
					out_string += self.sp_model.decode(A	)
					return out_string.strip()







		def 					__A   ( self		,      A		,      A = None		,      A = False	)  -> List[int]:







					'''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	)

					lowerCamelCase    		=  [1] * len(self.prefix_tokens	)
					lowerCamelCase    		=  [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 					__A   ( self		,      A		,      A = 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 					__A   ( self	)  -> Dict:







					'''simple docstring'''







					lowerCamelCase    		=  {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	)  -> Dict:







					'''simple docstring'''







					lowerCamelCase    		=  self.__dict__.copy()
					lowerCamelCase    		=  None
					return state







		def __setstate__( self		,      A	)  -> None:







					'''simple docstring'''







					lowerCamelCase    		=  d

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

					lowerCamelCase    		=  load_spm(self.spm_file		,      self.sp_model_kwargs	)







		def 					__A   ( self		,      A		,      A = None	)  -> Tuple[str]:







					'''simple docstring'''







					lowerCamelCase    		=  Path(A	)
					if not save_dir.is_dir():
								raise OSError(F'{save_directory} should be a directory'	)
					lowerCamelCase    		=  save_dir / (
					    (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
					)
					lowerCamelCase    		=  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:
											lowerCamelCase    		=  self.sp_model.serialized_model_proto()
											fi.write(A	)

					return (str(A	), str(A	))







		def 					__A   ( self		,      A		,      A = "en"		,      A = None		,      A = "ro"		,      **A		,      )  -> BatchEncoding:







					'''simple docstring'''







					lowerCamelCase    		=  src_lang
					lowerCamelCase    		=  tgt_lang
					self.set_src_lang_special_tokens(self.src_lang	)
					return super().prepare_seqaseq_batch(A		,      A		,      **A	)







		def 					__A   ( self		,      A		,      A		,      A		,      **A	)  -> Tuple:







					'''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"""	)
					lowerCamelCase    		=  src_lang
					lowerCamelCase    		=  self(A		,      add_special_tokens=A		,      **A	)
					lowerCamelCase    		=  self.get_lang_id(A	)
					lowerCamelCase    		=  tgt_lang_id
					return inputs







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







					'''simple docstring'''







					self.set_src_lang_special_tokens(self.src_lang	)







		def 					__A   ( self	)  -> Optional[Any]:







					'''simple docstring'''







					self.set_tgt_lang_special_tokens(self.tgt_lang	)







		def 					__A   ( self		,      A	)  -> None:







					'''simple docstring'''







					lowerCamelCase    		=  self.get_lang_token(A	)
					lowerCamelCase    		=  self.lang_token_to_id[lang_token]
					lowerCamelCase    		=  [self.cur_lang_id]
					lowerCamelCase    		=  [self.eos_token_id]







		def 					__A   ( self		,      A	)  -> None:







					'''simple docstring'''







					lowerCamelCase    		=  self.get_lang_token(A	)
					lowerCamelCase    		=  self.lang_token_to_id[lang_token]
					lowerCamelCase    		=  [self.cur_lang_id]
					lowerCamelCase    		=  [self.eos_token_id]







		def 					__A   ( self		,      A	)  -> str:







					'''simple docstring'''







					return self.lang_code_to_token[lang]







		def 					__A   ( self		,      A	)  -> int:







					'''simple docstring'''







					lowerCamelCase    		=  self.get_lang_token(A	)
					return self.lang_token_to_id[lang_token]






def 	__lowerCamelCase						(							lowerCamelCase__		:						str  ,							lowerCamelCase__		:						Dict[str, Any]      ):





			'''simple docstring'''




			lowerCamelCase    		=  sentencepiece.SentencePieceProcessor(**lowerCamelCase__      )
			spm.Load(str(lowerCamelCase__      )      )
			return spm






def 	__lowerCamelCase						(							lowerCamelCase__		:						str      ):





			'''simple docstring'''




			with open(lowerCamelCase__  ,							"""r"""      ) as f:
						return json.load(lowerCamelCase__      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						Union[str, Any]  ,							lowerCamelCase__		:						str      ):





			'''simple docstring'''




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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase   :    Any     =		{
    "configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
    "tokenization_roc_bert": ["RoCBertTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    Union[str, Any]     =		[
		    "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "RoCBertForCausalLM",
		    "RoCBertForMaskedLM",
		    "RoCBertForMultipleChoice",
		    "RoCBertForPreTraining",
		    "RoCBertForQuestionAnswering",
		    "RoCBertForSequenceClassification",
		    "RoCBertForTokenClassification",
		    "RoCBertLayer",
		    "RoCBertModel",
		    "RoCBertPreTrainedModel",
		    "load_tf_weights_in_roc_bert",
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

		UpperCAmelCase   :    Tuple     =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

