



import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


snake_case    :						Dict  =						logging.get_logger(__name__)

snake_case    :						List[str]  =						'▁'

snake_case    :						Union[str, Any]  =						{'vocab_file': 'prophetnet.tokenizer'}

snake_case    :						List[Any]  =						{
    'vocab_file': {
        'microsoft/xprophetnet-large-wiki100-cased': (
            'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
        ),
    }
}

snake_case    :						Tuple  =						{
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

snake_case    :						Optional[int]  =						{
    'microsoft/xprophetnet-large-wiki100-cased': 5_1_2,
}
def 	snake_case__       (					__lowercase       )		->		Dict:







						"""simple docstring"""

						A__	:							List[Any]						    = collections.OrderedDict()
						with open(__A							,     "r"							,     encoding="utf-8"       ) as reader:
												A__	:							List[str]						    = reader.readlines()
						for index, token in enumerate(__A       ):
												A__	:							Tuple						    = token.rstrip("\n"       )
												A__	:							Union[str, Any]						    = index
						return vocab




class 							lowerCAmelCase__  (	_snake_case						):
					__A     :    Union[str, Any]           =						VOCAB_FILES_NAMES
					__A     :    Any           =						PRETRAINED_VOCAB_FILES_MAP
					__A     :    int           =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__A     :    Union[str, Any]           =						['input_ids', 'attention_mask']







					def __init__(       self      :     List[str]			,		_A      :     Any			,		_A      :     Optional[Any]="[SEP]"			,		_A      :     Any="[SEP]"			,		_A      :     Tuple="[SEP]"			,		_A      :     List[Any]="[UNK]"			,		_A      :     Dict="[PAD]"			,		_A      :     Tuple="[CLS]"			,		_A      :     str="[MASK]"			,		_A      :     Optional[Dict[str, Any]] = None			,		**_A      :     int			,		):
											A__	:							Optional[int]						    = {} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    bos_token=lowerCAmelCase__			,		eos_token=lowerCAmelCase__			,		sep_token=lowerCAmelCase__			,		unk_token=lowerCAmelCase__			,		pad_token=lowerCAmelCase__			,		cls_token=lowerCAmelCase__			,		mask_token=lowerCAmelCase__			,		sp_model_kwargs=self.sp_model_kwargs			,		**lowerCAmelCase__			,		)

											try:
																	import sentencepiece as spm
											except ImportError:
																	logger.warning(
																	    "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
																	    " pip install sentencepiece")
																	raise

											A__	:							Optional[int]						    = spm.SentencePieceProcessor(**self.sp_model_kwargs)
											self.sp_model.Load(str(lowerCAmelCase__))
											A__	:							Union[str, Any]						    = vocab_file

											# Original fairseq vocab and spm vocab must be "aligned":
											# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
											# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
											# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
											# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

											# put special tokens and [unused] tokens into the vocab
											A__	:							Dict						    = {"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

											for i in range(10):
																	A__	:							Tuple						    = F'[unused{i}]'
																	A__	:							Any						    = 5 + i

											# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
											A__	:							str						    = 12
											A__	:							List[Any]						    = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
											for k in self.fairseq_tokens_to_ids.keys():
																	self.unique_no_split_tokens.append(lowerCAmelCase__)







					def __getstate__(       self      :     Any):
											A__	:							List[str]						    = self.__dict__.copy()
											A__	:							Tuple						    = None
											return state







					def __setstate__(       self      :     List[Any]			,		_A      :     str):
											A__	:							List[str]						    = d
											try:
																	import sentencepiece as spm
											except ImportError:
																	logger.warning(
																	    "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
																	    " pip install sentencepiece")
																	raise

											# for backward compatibility
											if not hasattr(self			,		"sp_model_kwargs"):
																	A__	:							Union[str, Any]						    = {}

											A__	:							List[Any]						    = spm.SentencePieceProcessor(**self.sp_model_kwargs)
											self.sp_model.Load(self.vocab_file)







					def  _lowercase			(       self      :     Optional[Any]			,		_A      :     List[int]			,		_A      :     Optional[List[int]] = None			,		_A      :     bool = False):

											if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=lowerCAmelCase__			,		token_ids_a=lowerCAmelCase__			,		already_has_special_tokens=lowerCAmelCase__)

											if token_ids_a is None:
																	return ([0] * len(lowerCAmelCase__)) + [1]
											return ([0] * len(lowerCAmelCase__)) + [1] + ([0] * len(lowerCAmelCase__)) + [1]







					def  _lowercase			(       self      :     int			,		_A      :     List[int]			,		_A      :     Optional[List[int]] = None):
											A__	:							str						    = [self.sep_token_id]

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







					@property
					def  _lowercase			(       self      :     int):
											return len(self.sp_model) + self.fairseq_offset







					def  _lowercase			(       self      :     Tuple):
											A__	:							Optional[Any]						    = {self.convert_ids_to_tokens(lowerCAmelCase__): i for i in range(self.vocab_size)}
											vocab.update(self.added_tokens_encoder)
											return vocab







					def  _lowercase			(       self      :     Optional[Any]			,		_A      :     str):
											return self.sp_model.encode(lowerCAmelCase__			,		out_type=lowerCAmelCase__)







					def  _lowercase			(       self      :     Any			,		_A      :     int):
											if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
											A__	:							Optional[Any]						    = self.sp_model.PieceToId(lowerCAmelCase__)

											# Need to return unknown token if the SP model returned 0
											return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







					def  _lowercase			(       self      :     str			,		_A      :     int):
											if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
											return self.sp_model.IdToPiece(index - self.fairseq_offset)







					def  _lowercase			(       self      :     Union[str, Any]			,		_A      :     Any):
											A__	:							Dict						    = "".join(lowerCAmelCase__).replace(lowerCAmelCase__			,		" ").strip()
											return out_string







					def  _lowercase			(       self      :     Any			,		_A      :     str			,		_A      :     Optional[str] = None):
											if not os.path.isdir(lowerCAmelCase__):
																	logger.error(F'Vocabulary path ({save_directory}) should be a directory')
																	return
											A__	:							Union[str, Any]						    = 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__) and os.path.isfile(self.vocab_file):
																	copyfile(self.vocab_file			,		lowerCAmelCase__)
											elif not os.path.isfile(self.vocab_file):
																	with open(lowerCAmelCase__			,		"wb") as fi:
																							A__	:							Dict						    = self.sp_model.serialized_model_proto()
																							fi.write(lowerCAmelCase__)

											return (out_vocab_file,)







					def  _lowercase			(       self      :     Dict			,		_A      :     List[int]			,		_A      :     Optional[List[int]] = None):

											if token_ids_a is None:
																	return token_ids_a + [self.sep_token_id]
											A__	:							Tuple						    = [self.sep_token_id]
											return token_ids_a + sep + token_ids_a + sep





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,
)


snake_case    :						str  =						{
    'configuration_albert': ['ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'AlbertConfig', 'AlbertOnnxConfig'],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						Any  =						['AlbertTokenizer']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						List[str]  =						['AlbertTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						List[str]  =						[
		    'ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'AlbertForMaskedLM',
		    'AlbertForMultipleChoice',
		    'AlbertForPreTraining',
		    'AlbertForQuestionAnswering',
		    'AlbertForSequenceClassification',
		    'AlbertForTokenClassification',
		    'AlbertModel',
		    'AlbertPreTrainedModel',
		    'load_tf_weights_in_albert',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						Union[str, Any]  =						[
		    'TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFAlbertForMaskedLM',
		    'TFAlbertForMultipleChoice',
		    'TFAlbertForPreTraining',
		    'TFAlbertForQuestionAnswering',
		    'TFAlbertForSequenceClassification',
		    'TFAlbertForTokenClassification',
		    'TFAlbertMainLayer',
		    'TFAlbertModel',
		    'TFAlbertPreTrainedModel',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						Any  =						[
		    'FlaxAlbertForMaskedLM',
		    'FlaxAlbertForMultipleChoice',
		    'FlaxAlbertForPreTraining',
		    'FlaxAlbertForQuestionAnswering',
		    'FlaxAlbertForSequenceClassification',
		    'FlaxAlbertForTokenClassification',
		    'FlaxAlbertModel',
		    'FlaxAlbertPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_albert import AlbertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_albert_fast import AlbertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_albert import (
				    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    AlbertForMaskedLM,
				    AlbertForMultipleChoice,
				    AlbertForPreTraining,
				    AlbertForQuestionAnswering,
				    AlbertForSequenceClassification,
				    AlbertForTokenClassification,
				    AlbertModel,
				    AlbertPreTrainedModel,
				    load_tf_weights_in_albert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_albert import (
				    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFAlbertForMaskedLM,
				    TFAlbertForMultipleChoice,
				    TFAlbertForPreTraining,
				    TFAlbertForQuestionAnswering,
				    TFAlbertForSequenceClassification,
				    TFAlbertForTokenClassification,
				    TFAlbertMainLayer,
				    TFAlbertModel,
				    TFAlbertPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_albert import (
				    FlaxAlbertForMaskedLM,
				    FlaxAlbertForMultipleChoice,
				    FlaxAlbertForPreTraining,
				    FlaxAlbertForQuestionAnswering,
				    FlaxAlbertForSequenceClassification,
				    FlaxAlbertForTokenClassification,
				    FlaxAlbertModel,
				    FlaxAlbertPreTrainedModel,
				)
else:
		import sys

		snake_case    :						Any  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)