



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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_mbartaa import MBartaaTokenizer
else:
				_lowercase:					Dict      =						None


_lowercase:					Dict      =						logging.get_logger(__name__)

_lowercase:					List[Any]      =						{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

_lowercase:					Optional[Any]      =						{
    "vocab_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json"
        ),
    },
}

_lowercase:					Tuple      =						{
    "facebook/mbart-large-50-one-to-many-mmt": 1024,
}

# fmt: off
_lowercase:					List[str]      =						["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", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"]



class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""
	__A		      =				VOCAB_FILES_NAMES
	__A		      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__A		      =				PRETRAINED_VOCAB_FILES_MAP
	__A		      =				["input_ids", "attention_mask"]
	__A		      =				MBartaaTokenizer

	__A		      =				[]
	__A		      =				[]




	def __init__(self						,			lowerCamelCase_=None						,			lowerCamelCase_=None						,			lowerCamelCase_=None						,			lowerCamelCase_=None						,			lowerCamelCase_="</s>"						,			lowerCamelCase_="</s>"						,			lowerCamelCase_="<s>"						,			lowerCamelCase_="<unk>"						,			lowerCamelCase_="<pad>"						,			lowerCamelCase_="<mask>"						,			**lowerCamelCase_						,			):
						"""simple docstring"""

						a   	=  AddedToken(lowerCamelCase_						,			lstrip=lowerCamelCase_						,			rstrip=lowerCamelCase_						) if isinstance(lowerCamelCase_						,			lowerCamelCase_						) else mask_token

						a   	=  kwargs.get("additional_special_tokens"						,			[]						)
						kwargs["additional_special_tokens"] += [
						    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
						]

						super().__init__(
						    lowerCamelCase_						,			src_lang=lowerCamelCase_						,			tgt_lang=lowerCamelCase_						,			tokenizer_file=lowerCamelCase_						,			eos_token=lowerCamelCase_						,			sep_token=lowerCamelCase_						,			cls_token=lowerCamelCase_						,			unk_token=lowerCamelCase_						,			pad_token=lowerCamelCase_						,			mask_token=lowerCamelCase_						,			**lowerCamelCase_						,			)

						a   	=  vocab_file
						a   	=  False if not self.vocab_file else True

						a   	=  {
						    lang_code: self.convert_tokens_to_ids(lowerCamelCase_						) for lang_code in FAIRSEQ_LANGUAGE_CODES
						}

						a   	=  src_lang if src_lang is not None else "en_XX"
						a   	=  tgt_lang
						a   	=  self.lang_code_to_id[self._src_lang]
						self.set_src_lang_special_tokens(self._src_lang						)




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						return self._src_lang




	@src_lang.setter
	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  new_src_lang
						self.set_src_lang_special_tokens(self._src_lang						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_ = 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 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_ = "en_XX"						,			lowerCamelCase_ = None						,			lowerCamelCase_ = "ro_RO"						,			**lowerCamelCase_						,			):
						"""simple docstring"""

						a   	=  src_lang
						a   	=  tgt_lang
						return super().prepare_seqaseq_batch(lowerCamelCase_						,			lowerCamelCase_						,			**lowerCamelCase_						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						return self.set_src_lang_special_tokens(self.src_lang						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						return self.set_tgt_lang_special_tokens(self.tgt_lang						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  self.convert_tokens_to_ids(lowerCamelCase_						)
						a   	=  [self.cur_lang_code_id]
						a   	=  [self.eos_token_id]

						a   	=  self.convert_ids_to_tokens(self.prefix_tokens						)
						a   	=  self.convert_ids_to_tokens(self.suffix_tokens						)

						a   	=  processors.TemplateProcessing(
						    single=prefix_tokens_str + ["$A"] + suffix_tokens_str						,			pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str						,			special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str						,			self.prefix_tokens + self.suffix_tokens						)						)						,			)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  self.convert_tokens_to_ids(lowerCamelCase_						)
						a   	=  [self.cur_lang_code_id]
						a   	=  [self.eos_token_id]

						a   	=  self.convert_ids_to_tokens(self.prefix_tokens						)
						a   	=  self.convert_ids_to_tokens(self.suffix_tokens						)

						a   	=  processors.TemplateProcessing(
						    single=prefix_tokens_str + ["$A"] + suffix_tokens_str						,			pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str						,			special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str						,			self.prefix_tokens + self.suffix_tokens						)						)						,			)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			**lowerCamelCase_						):
						"""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"						)
						a   	=  src_lang
						a   	=  self(lowerCamelCase_						,			add_special_tokens=lowerCamelCase_						,			return_tensors=lowerCamelCase_						,			**lowerCamelCase_						)
						a   	=  self.convert_tokens_to_ids(lowerCamelCase_						)
						a   	=  tgt_lang_id
						return inputs




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_ = None						):
						"""simple docstring"""

						if not self.can_save_slow_tokenizer:
											raise ValueError(
											    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
											    "tokenizer."						)

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

						if os.path.abspath(self.vocab_file						) != os.path.abspath(lowerCamelCase_						):
											copyfile(self.vocab_file						,			lowerCamelCase_						)

						return (out_vocab_file,)





from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


_lowercase:					List[str]      =						logging.get_logger(__name__)

_lowercase:					str      =						{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowercase:					Union[str, Any]      =						{
    "vocab_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/vocab.json"},
    "merges_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/merges.txt"},
    "tokenizer_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/tokenizer.json"},
}

_lowercase:					int      =						{
    "openai-gpt": 512,
}



class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""
	__A		      =				VOCAB_FILES_NAMES
	__A		      =				PRETRAINED_VOCAB_FILES_MAP
	__A		      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__A		      =				["input_ids", "attention_mask"]
	__A		      =				OpenAIGPTTokenizer




	def __init__(self						,			lowerCamelCase_=None						,			lowerCamelCase_=None						,			lowerCamelCase_=None						,			lowerCamelCase_="<unk>"						,			**lowerCamelCase_						):
						"""simple docstring"""

						super().__init__(lowerCamelCase_						,			lowerCamelCase_						,			tokenizer_file=lowerCamelCase_						,			unk_token=lowerCamelCase_						,			**lowerCamelCase_						)




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						return True




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_ = None						):
						"""simple docstring"""

						a   	=  self._tokenizer.model.save(lowerCamelCase_						,			name=lowerCamelCase_						)
						return tuple(lowerCamelCase_						)
