

'''simple docstring'''





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

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


if is_sentencepiece_available():
							from .tokenization_albert import AlbertTokenizer
else:
							UpperCamelCase_    						=       None

UpperCamelCase_    						=       logging.get_logger(__name__)
UpperCamelCase_    						=       {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

UpperCamelCase_    						=       {
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json""",
    },
}

UpperCamelCase_    						=       {
    """albert-base-v1""": 512,
    """albert-large-v1""": 512,
    """albert-xlarge-v1""": 512,
    """albert-xxlarge-v1""": 512,
    """albert-base-v2""": 512,
    """albert-large-v2""": 512,
    """albert-xlarge-v2""": 512,
    """albert-xxlarge-v2""": 512,
}

UpperCamelCase_    						=       """▁"""





class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):
				lowerCamelCase_		       =    VOCAB_FILES_NAMES
				lowerCamelCase_		       =    PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase_		       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase_		       =    AlbertTokenizer







				def __init__(       self     :		List[str]		,					UpperCAmelCase__     :		List[Any]=None		,					UpperCAmelCase__     :		Union[str, Any]=None		,					UpperCAmelCase__     :		str=True		,					UpperCAmelCase__     :		Optional[int]=True		,					UpperCAmelCase__     :		Union[str, Any]=False		,					UpperCAmelCase__     :		List[Any]="[CLS]"		,					UpperCAmelCase__     :		Any="[SEP]"		,					UpperCAmelCase__     :		List[Any]="<unk>"		,					UpperCAmelCase__     :		List[str]="[SEP]"		,					UpperCAmelCase__     :		Optional[Any]="<pad>"		,					UpperCAmelCase__     :		Optional[int]="[CLS]"		,					UpperCAmelCase__     :		List[Any]="[MASK]"		,					**UpperCAmelCase__     :		Optional[Any]		,					):
										'''simple docstring'''
										# Mask token behave like a normal word, i.e. include the space before it and
										# is included in the raw text, there should be a match in a non-normalized sentence.
										lowercase :							Any    		=(
										    AddedToken(UpperCAmelCase__		,					lstrip=UpperCAmelCase__		,					rstrip=UpperCAmelCase__		,					normalized=UpperCAmelCase__   )
										    if isinstance(UpperCAmelCase__		,					UpperCAmelCase__   )
										    else mask_token
										)

										super().__init__(
										    UpperCAmelCase__		,					tokenizer_file=UpperCAmelCase__		,					do_lower_case=UpperCAmelCase__		,					remove_space=UpperCAmelCase__		,					keep_accents=UpperCAmelCase__		,					bos_token=UpperCAmelCase__		,					eos_token=UpperCAmelCase__		,					unk_token=UpperCAmelCase__		,					sep_token=UpperCAmelCase__		,					pad_token=UpperCAmelCase__		,					cls_token=UpperCAmelCase__		,					mask_token=UpperCAmelCase__		,					**UpperCAmelCase__		,					)

										lowercase :							Any    		=do_lower_case
										lowercase :							Optional[Any]    		=remove_space
										lowercase :							Dict    		=keep_accents
										lowercase :							Optional[Any]    		=vocab_file
										lowercase :							Union[str, Any]    		=False if not self.vocab_file else True







				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		List[int]		,					UpperCAmelCase__     :		Optional[List[int]] = None   ):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		=[self.sep_token_id]
										lowercase :							Tuple    		=[self.cls_token_id]
										if token_ids_a is None:
																return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep







				def  lowerCamelCase_	(       self     :		str		,					UpperCAmelCase__     :		List[int]		,					UpperCAmelCase__     :		Optional[List[int]] = None   ):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		=[self.sep_token_id]
										lowercase :							Optional[Any]    		=[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   ) * [0] + len(token_ids_a + sep   ) * [1]







				def  lowerCamelCase_	(       self     :		Dict		,					UpperCAmelCase__     :		str		,					UpperCAmelCase__     :		Optional[str] = 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(UpperCAmelCase__   ):
																logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
																return
										lowercase :							int    		=os.path.join(
										    UpperCAmelCase__		,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

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

										return (out_vocab_file,)








'''simple docstring'''





from typing import TYPE_CHECKING

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


UpperCamelCase_    						=       {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       ["""MBartTokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       ["""MBartTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       [
							    """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MBartForCausalLM""",
							    """MBartForConditionalGeneration""",
							    """MBartForQuestionAnswering""",
							    """MBartForSequenceClassification""",
							    """MBartModel""",
							    """MBartPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       [
							    """TFMBartForConditionalGeneration""",
							    """TFMBartModel""",
							    """TFMBartPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       [
							    """FlaxMBartForConditionalGeneration""",
							    """FlaxMBartForQuestionAnswering""",
							    """FlaxMBartForSequenceClassification""",
							    """FlaxMBartModel""",
							    """FlaxMBartPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_mbart import MBartTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_mbart_fast import MBartTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mbart import (
														    MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MBartForCausalLM,
														    MBartForConditionalGeneration,
														    MBartForQuestionAnswering,
														    MBartForSequenceClassification,
														    MBartModel,
														    MBartPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_mbart import (
														    FlaxMBartForConditionalGeneration,
														    FlaxMBartForQuestionAnswering,
														    FlaxMBartForSequenceClassification,
														    FlaxMBartModel,
														    FlaxMBartPreTrainedModel,
														)

else:
							import sys

							UpperCamelCase_    						=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





