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_mbart import MBartTokenizer
else:
	lowerCamelCase_    :		str							=	None


lowerCamelCase_    :		int							=	logging.get_logger(__name__)


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

lowerCamelCase_    :		Union[str, Any]							=	{
    """vocab_file""": {
        """facebook/mbart-large-en-ro""": (
            """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"""
        ),
        """facebook/mbart-large-cc25""": (
            """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/mbart-large-en-ro""": """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json""",
        """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json""",
    },
}

lowerCamelCase_    :		Optional[Any]							=	{
    """facebook/mbart-large-en-ro""": 1_024,
    """facebook/mbart-large-cc25""": 1_024,
}

# fmt: off
lowerCamelCase_    :		Optional[int]							=	["""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"""]


class 						a__							(       a__		):
			A__    :       List[str]        =      VOCAB_FILES_NAMES
			A__    :       Union[str, Any]        =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A__    :       Any        =      PRETRAINED_VOCAB_FILES_MAP
			A__    :       List[str]        =      ['input_ids', 'attention_mask']
			A__    :       int        =      MBartTokenizer

			A__    :       Union[str, Any]        =      []
			A__    :       Union[str, Any]        =      []



			def __init__(						self				,						UpperCAmelCase=None				,						UpperCAmelCase=None				,						UpperCAmelCase="<s>"				,						UpperCAmelCase="</s>"				,						UpperCAmelCase="</s>"				,						UpperCAmelCase="<s>"				,						UpperCAmelCase="<unk>"				,						UpperCAmelCase="<pad>"				,						UpperCAmelCase="<mask>"				,						UpperCAmelCase=None				,						UpperCAmelCase=None				,						UpperCAmelCase=None				,						**UpperCAmelCase				,						)			->   Optional[Any]:
								__a		 =       AddedToken(_lowerCamelCase				,						lstrip=_lowerCamelCase				,						rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase				,						_lowerCamelCase   ) else mask_token

								super().__init__(
								    vocab_file=_lowerCamelCase				,						tokenizer_file=_lowerCamelCase				,						bos_token=_lowerCamelCase				,						eos_token=_lowerCamelCase				,						sep_token=_lowerCamelCase				,						cls_token=_lowerCamelCase				,						unk_token=_lowerCamelCase				,						pad_token=_lowerCamelCase				,						mask_token=_lowerCamelCase				,						src_lang=_lowerCamelCase				,						tgt_lang=_lowerCamelCase				,						additional_special_tokens=_lowerCamelCase				,						**_lowerCamelCase				,						)

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

								__a		 =       FAIRSEQ_LANGUAGE_CODES.copy()

								if additional_special_tokens is not None:
													# Only add those special tokens if they are not already there.
													_additional_special_tokens.extend(
													    [t for t in additional_special_tokens if t not in _additional_special_tokens]   )

								self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}   )
								__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		 =       self.convert_tokens_to_ids(self._src_lang   )
								__a		 =       tgt_lang
								self.set_src_lang_special_tokens(self._src_lang   )



			@property
			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								return self._src_lang



			@src_lang.setter
			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Optional[Any]:
								__a		 =       new_src_lang
								self.set_src_lang_special_tokens(self._src_lang   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   Optional[Any]:







								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 __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   Union[str, Any]:
								__a		 =       [self.sep_token_id]
								__a		 =       [self.cls_token_id]

								if token_ids_a is None:
													return len(cls + token_ids_a + sep   ) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						**UpperCAmelCase   )			->   Tuple:







								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 __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = "en_XX"				,						UpperCAmelCase = None				,						UpperCAmelCase = "ro_RO"				,						**UpperCAmelCase				,						)			->   Optional[int]:
								__a		 =       src_lang
								__a		 =       tgt_lang
								return super().prepare_seqaseq_batch(_lowerCamelCase				,						_lowerCamelCase				,						**_lowerCamelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								return self.set_src_lang_special_tokens(self.src_lang   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								return self.set_tgt_lang_special_tokens(self.tgt_lang   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   List[Any]:
								__a		 =       self.convert_tokens_to_ids(_lowerCamelCase   )
								__a		 =       []
								__a		 =       [self.eos_token_id, self.cur_lang_code]

								__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 __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Dict:
								__a		 =       self.convert_tokens_to_ids(_lowerCamelCase   )
								__a		 =       []
								__a		 =       [self.eos_token_id, self.cur_lang_code]

								__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 __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   Tuple:







								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 TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_    :		Optional[int]							=	{"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		str							=	["""XLNetTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Optional[Any]							=	["""XLNetTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Union[str, Any]							=	[
	    """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XLNetForMultipleChoice""",
	    """XLNetForQuestionAnswering""",
	    """XLNetForQuestionAnsweringSimple""",
	    """XLNetForSequenceClassification""",
	    """XLNetForTokenClassification""",
	    """XLNetLMHeadModel""",
	    """XLNetModel""",
	    """XLNetPreTrainedModel""",
	    """load_tf_weights_in_xlnet""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Optional[int]							=	[
	    """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFXLNetForMultipleChoice""",
	    """TFXLNetForQuestionAnsweringSimple""",
	    """TFXLNetForSequenceClassification""",
	    """TFXLNetForTokenClassification""",
	    """TFXLNetLMHeadModel""",
	    """TFXLNetMainLayer""",
	    """TFXLNetModel""",
	    """TFXLNetPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet import XLNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet_fast import XLNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlnet import (
		    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLNetForMultipleChoice,
		    XLNetForQuestionAnswering,
		    XLNetForQuestionAnsweringSimple,
		    XLNetForSequenceClassification,
		    XLNetForTokenClassification,
		    XLNetLMHeadModel,
		    XLNetModel,
		    XLNetPreTrainedModel,
		    load_tf_weights_in_xlnet,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlnet import (
		    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLNetForMultipleChoice,
		    TFXLNetForQuestionAnsweringSimple,
		    TFXLNetForSequenceClassification,
		    TFXLNetForTokenClassification,
		    TFXLNetLMHeadModel,
		    TFXLNetMainLayer,
		    TFXLNetModel,
		    TFXLNetPreTrainedModel,
		)

else:
	import sys

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




