


'''simple docstring'''
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:
							__SCREAMING_SNAKE_CASE				:							str		       =		None

__SCREAMING_SNAKE_CASE				:							Tuple		       =		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE				:							Dict		       =		{'''vocab_file''': '''spm.model''', '''tokenizer_file''': '''tokenizer.json'''}

__SCREAMING_SNAKE_CASE				:							Any		       =		{
    '''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'''
        ),
    }
}

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

__SCREAMING_SNAKE_CASE				:							Optional[int]		       =		{
    '''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 				__lowerCAmelCase      (      lowercase	):

						"""simple docstring"""






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






						def __init__(     self       :			Dict   ,     lowerCAmelCase       :			List[str]=None   ,     lowerCAmelCase       :			List[Any]=None   ,     lowerCAmelCase       :			str=False   ,     lowerCAmelCase       :			Optional[Any]=False   ,     lowerCAmelCase       :			List[str]="[CLS]"   ,     lowerCAmelCase       :			List[Any]="[SEP]"   ,     lowerCAmelCase       :			List[Any]="[UNK]"   ,     lowerCAmelCase       :			Optional[Any]="[SEP]"   ,     lowerCAmelCase       :			str="[PAD]"   ,     lowerCAmelCase       :			Union[str, Any]="[CLS]"   ,     lowerCAmelCase       :			Tuple="[MASK]"   ,     **lowerCAmelCase       :			List[Any]   ,     ):
													super().__init__(
													    lowerCAmelCase   ,     tokenizer_file=lowerCAmelCase   ,     do_lower_case=lowerCAmelCase   ,     bos_token=lowerCAmelCase   ,     eos_token=lowerCAmelCase   ,     unk_token=lowerCAmelCase   ,     sep_token=lowerCAmelCase   ,     pad_token=lowerCAmelCase   ,     cls_token=lowerCAmelCase   ,     mask_token=lowerCAmelCase   ,     split_by_punct=lowerCAmelCase   ,     **lowerCAmelCase   ,     )

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






						def 							_UpperCAmelCase					(     self       :			List[Any]   ,     lowerCAmelCase       :			Dict   ,     lowerCAmelCase       :			int=None  ):

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






						def 							_UpperCAmelCase					(     self       :			Any   ,     lowerCAmelCase       :			str   ,     lowerCAmelCase       :			List[Any]=None   ,     lowerCAmelCase       :			int=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 not None:
																				return [1] + ([0] * len(lowerCAmelCase  )) + [1] + ([0] * len(lowerCAmelCase  )) + [1]
													return [1] + ([0] * len(lowerCAmelCase  )) + [1]






						def 							_UpperCAmelCase					(     self       :			str   ,     lowerCAmelCase       :			int   ,     lowerCAmelCase       :			Any=None  ):
													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  ) * [0] + len(token_ids_a + sep  ) * [1]






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]   ,     lowerCAmelCase       :			str   ,     lowerCAmelCase       :			Optional[str] = None  ):
													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,)









'''simple docstring'''
import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
							import tensorflow as tf

							from transformers.activations_tf import get_tf_activation






@require_tf
class 				__lowerCAmelCase      (      unittest.TestCase	):

						"""simple docstring"""






						def 							_UpperCAmelCase					(     self       :			List[Any]  ):
													A_ 						=		tf.constant([-1_00, -1.0, -0.1, 0, 0.1, 1.0, 1_0_0.0]  )
													A_ 						=		get_tf_activation("gelu"  )
													A_ 						=		get_tf_activation("gelu_10"  )

													A_ 						=		gelu(lowerCAmelCase  )
													A_ 						=		geluaa(lowerCAmelCase  )

													A_ 						=		tf.where(y_gelu_aa < 1_0.0   ,     1.0   ,     0.0  )

													self.assertEqual(tf.math.reduce_max(lowerCAmelCase  ).numpy().item()   ,     1_0.0  )
													self.assertTrue(np.allclose(y_gelu * clipped_mask   ,     y_gelu_aa * clipped_mask  )  )






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]  ):
													get_tf_activation("gelu"  )
													get_tf_activation("gelu_10"  )
													get_tf_activation("gelu_fast"  )
													get_tf_activation("gelu_new"  )
													get_tf_activation("glu"  )
													get_tf_activation("mish"  )
													get_tf_activation("quick_gelu"  )
													get_tf_activation("relu"  )
													get_tf_activation("sigmoid"  )
													get_tf_activation("silu"  )
													get_tf_activation("swish"  )
													get_tf_activation("tanh"  )
													with self.assertRaises(lowerCAmelCase  ):
																				get_tf_activation("bogus"  )
													with self.assertRaises(lowerCAmelCase  ):
																				get_tf_activation(lowerCAmelCase  )





