



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__	:  Union[str, Any] 		=None

lowerCAmelCase__	:  Any 		=logging.get_logger(__name__)

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

lowerCAmelCase__	:  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__	:  Any 		={
    '''microsoft/deberta-v2-xlarge''': 512,
    '''microsoft/deberta-v2-xxlarge''': 512,
    '''microsoft/deberta-v2-xlarge-mnli''': 512,
    '''microsoft/deberta-v2-xxlarge-mnli''': 512,
}

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      UpperCAmelCase_						(     UpperCamelCase_ ):

						'''simple docstring'''




						UpperCamelCase__    :				Optional[int]       						=						VOCAB_FILES_NAMES
						UpperCamelCase__    :				int       						=						PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase__    :				List[str]       						=						PRETRAINED_INIT_CONFIGURATION
						UpperCamelCase__    :				int       						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase__    :				Union[str, Any]       						=						DebertaVaTokenizer






						def __init__(					self   ,							_A=None   ,							_A=None   ,							_A=False   ,							_A=False   ,							_A="[CLS]"   ,							_A="[SEP]"   ,							_A="[UNK]"   ,							_A="[SEP]"   ,							_A="[PAD]"   ,							_A="[CLS]"   ,							_A="[MASK]"   ,							**_A   ,							):







													'''simple docstring'''

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

													__SCREAMING_SNAKE_CASE								=      do_lower_case
													__SCREAMING_SNAKE_CASE								=      split_by_punct
													__SCREAMING_SNAKE_CASE								=      vocab_file
													__SCREAMING_SNAKE_CASE								=      False if not self.vocab_file else True






						def  _A			(					self   ,							_A   ,							_A=None	):







													'''simple docstring'''

													if token_ids_a is None:
																				return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
													__SCREAMING_SNAKE_CASE								=      [self.cls_token_id]
													__SCREAMING_SNAKE_CASE								=      [self.sep_token_id]
													return cls + token_ids_a + sep + token_ids_a + sep






						def  _A			(					self   ,							_A   ,							_A=None   ,							_A=False	):







													'''simple docstring'''

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

													if token_ids_a is not None:
																				return [1] + ([0] * len(_A	)) + [1] + ([0] * len(_A	)) + [1]
													return [1] + ([0] * len(_A	)) + [1]






						def  _A			(					self   ,							_A   ,							_A=None	):







													'''simple docstring'''

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

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

													return (out_vocab_file,)











from __future__ import annotations

class      UpperCAmelCase_						:

						'''simple docstring'''




						def __init__(					self   ,							_A	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      value
													__SCREAMING_SNAKE_CASE								=      None
													__SCREAMING_SNAKE_CASE								=      None







class      UpperCAmelCase_						:

						'''simple docstring'''




						UpperCamelCase__    :				int






						def __init__(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      0






						def  _A			(					self   ,							_A   ,							_A	):







													'''simple docstring'''

													if node is None:
																				return

													if path_sum == self.target:
																				self.paths += 1

													if node.left:
																				self.depth_first_search(node.left   ,							path_sum + node.left.value	)
													if node.right:
																				self.depth_first_search(node.right   ,							path_sum + node.right.value	)






						def  _A			(					self   ,							_A   ,							_A = None	):







													'''simple docstring'''

													if node is None:
																				return 0
													if target is not None:
																				__SCREAMING_SNAKE_CASE								=      target

													self.depth_first_search(_A   ,							node.value	)
													self.path_sum(node.left	)
													self.path_sum(node.right	)

													return self.paths


if __name__ == "__main__":
			import doctest

			doctest.testmod()






