


from typing import TYPE_CHECKING

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


lowerCAmelCase__						:Optional[int]         =							{
    '''configuration_layoutlm''': ['''LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMConfig''', '''LayoutLMOnnxConfig'''],
    '''tokenization_layoutlm''': ['''LayoutLMTokenizer'''],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__						:Union[str, Any]         =							['''LayoutLMTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__						:List[str]         =							[
			    '''LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''LayoutLMForMaskedLM''',
			    '''LayoutLMForSequenceClassification''',
			    '''LayoutLMForTokenClassification''',
			    '''LayoutLMForQuestionAnswering''',
			    '''LayoutLMModel''',
			    '''LayoutLMPreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__						:List[str]         =							[
			    '''TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFLayoutLMForMaskedLM''',
			    '''TFLayoutLMForSequenceClassification''',
			    '''TFLayoutLMForTokenClassification''',
			    '''TFLayoutLMForQuestionAnswering''',
			    '''TFLayoutLMMainLayer''',
			    '''TFLayoutLMModel''',
			    '''TFLayoutLMPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
			from .tokenization_layoutlm import LayoutLMTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_layoutlm import (
						    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LayoutLMForMaskedLM,
						    LayoutLMForQuestionAnswering,
						    LayoutLMForSequenceClassification,
						    LayoutLMForTokenClassification,
						    LayoutLMModel,
						    LayoutLMPreTrainedModel,
						)
			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_layoutlm import (
						    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFLayoutLMForMaskedLM,
						    TFLayoutLMForQuestionAnswering,
						    TFLayoutLMForSequenceClassification,
						    TFLayoutLMForTokenClassification,
						    TFLayoutLMMainLayer,
						    TFLayoutLMModel,
						    TFLayoutLMPreTrainedModel,
						)

else:
			import sys

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




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

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
		from .tokenization_deberta_va import DebertaVaTokenizer
else:
		lowerCAmelCase__						:int         =							None

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

lowerCAmelCase__						:List[str]         =							{'''vocab_file''': '''spm.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase__						:List[str]         =							{
    '''vocab_file''': {
        '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model''',
        '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model''',
        '''microsoft/deberta-v2-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'''
        ),
        '''microsoft/deberta-v2-xxlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'''
        ),
    }
}

lowerCAmelCase__						:List[str]         =							{
    '''microsoft/deberta-v2-xlarge''': 5_1_2,
    '''microsoft/deberta-v2-xxlarge''': 5_1_2,
    '''microsoft/deberta-v2-xlarge-mnli''': 5_1_2,
    '''microsoft/deberta-v2-xxlarge-mnli''': 5_1_2,
}

lowerCAmelCase__						:Any         =							{
    '''microsoft/deberta-v2-xlarge''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xxlarge''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xlarge-mnli''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xxlarge-mnli''': {'''do_lower_case''': False},
}

class 	__a   (       UpperCAmelCase    ):
		_a			:						Tuple    			=			VOCAB_FILES_NAMES
		_a			:						int    			=			PRETRAINED_VOCAB_FILES_MAP
		_a			:						Optional[Any]    			=			PRETRAINED_INIT_CONFIGURATION
		_a			:						str    			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a			:						Optional[int]    			=			DebertaVaTokenizer
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=False    ,					_SCREAMING_SNAKE_CASE=False    ,					_SCREAMING_SNAKE_CASE="[CLS]"    ,					_SCREAMING_SNAKE_CASE="[SEP]"    ,					_SCREAMING_SNAKE_CASE="[UNK]"    ,					_SCREAMING_SNAKE_CASE="[SEP]"    ,					_SCREAMING_SNAKE_CASE="[PAD]"    ,					_SCREAMING_SNAKE_CASE="[CLS]"    ,					_SCREAMING_SNAKE_CASE="[MASK]"    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> None:




									"""simple docstring"""





									super().__init__(
									    _SCREAMING_SNAKE_CASE    ,					tokenizer_file=_SCREAMING_SNAKE_CASE    ,					do_lower_case=_SCREAMING_SNAKE_CASE    ,					bos_token=_SCREAMING_SNAKE_CASE    ,					eos_token=_SCREAMING_SNAKE_CASE    ,					unk_token=_SCREAMING_SNAKE_CASE    ,					sep_token=_SCREAMING_SNAKE_CASE    ,					pad_token=_SCREAMING_SNAKE_CASE    ,					cls_token=_SCREAMING_SNAKE_CASE    ,					mask_token=_SCREAMING_SNAKE_CASE    ,					split_by_punct=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE    ,					)

									_UpperCAmelCase					=    do_lower_case
									_UpperCAmelCase					=    split_by_punct
									_UpperCAmelCase					=    vocab_file
									_UpperCAmelCase					=    False if not self.vocab_file else True
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None		)				-> str:




									"""simple docstring"""





									if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									_UpperCAmelCase					=    [self.cls_token_id]
									_UpperCAmelCase					=    [self.sep_token_id]
									return cls + token_ids_a + sep + token_ids_a + sep
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=False		)				-> Any:




									"""simple docstring"""





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

									if token_ids_a is not None:
																return [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
									return [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None		)				-> Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    [self.sep_token_id]
									_UpperCAmelCase					=    [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 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE = None		)				-> Tuple[str]:




									"""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(_SCREAMING_SNAKE_CASE		):
																logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
																return
									_UpperCAmelCase					=    os.path.join(
									    _SCREAMING_SNAKE_CASE    ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

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

									return (out_vocab_file,)
