





import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class       A__     ( _a ,     unittest.TestCase    ):
      _UpperCAmelCase			:Dict							=     DebertaTokenizer
      _UpperCAmelCase			:Optional[int]							=     True
      _UpperCAmelCase			:Optional[Any]							=     DebertaTokenizerFast





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       UpperCamelCase			:      Dict 			=     [
           "l",
           "o",
           "w",
           "e",
           "r",
           "s",
           "t",
           "i",
           "d",
           "n",
           "\u0120",
           "\u0120l",
           "\u0120n",
           "\u0120lo",
           "\u0120low",
           "er",
           "\u0120lowest",
           "\u0120newer",
           "\u0120wider",
           "[UNK]",
       ]
       UpperCamelCase			:      str 			=     dict(zip(snake_case_ ,							range(len(snake_case_     )     )     )     )
       UpperCamelCase			:      int 			=     ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
       UpperCamelCase			:      Tuple 			=     {"unk_token": "[UNK]"}

       UpperCamelCase			:      Optional[int] 			=     os.path.join(self.tmpdirname ,							VOCAB_FILES_NAMES["vocab_file"]     )
       UpperCamelCase			:      Optional[Any] 			=     os.path.join(self.tmpdirname ,							VOCAB_FILES_NAMES["merges_file"]     )
       with open(self.vocab_file ,							"w" ,							encoding="utf-8"     ) as fp:
        fp.write(json.dumps(snake_case_     ) + "\n"     )
       with open(self.merges_file ,							"w" ,							encoding="utf-8"     ) as fp:
        fp.write("\n".join(snake_case_     )     )





      def  __UpperCamelCase(				self ,							**A_     ):
       '''simple docstring'''




       kwargs.update(self.special_tokens_map     )
       return self.tokenizer_class.from_pretrained(self.tmpdirname ,							**snake_case_     )





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     "lower newer"
       UpperCamelCase			:      List[str] 			=     "lower newer"
       return input_text, output_text





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     self.get_tokenizer()
       UpperCamelCase			:      Any 			=     "lower newer"
       UpperCamelCase			:      Any 			=     ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
       UpperCamelCase			:      Tuple 			=     tokenizer.tokenize(snake_case_     )
       self.assertListEqual(snake_case_ ,							snake_case_     )

       UpperCamelCase			:      List[str] 			=     tokens + [tokenizer.unk_token]
       UpperCamelCase			:      Dict 			=     [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_     ) ,							snake_case_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     self.get_tokenizer()
       UpperCamelCase			:      List[Any] 			=     tokenizer("Hello" ,							"World"     )
       UpperCamelCase			:      Optional[Any] 			=     [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
       self.assertListEqual(tokd["token_type_ids"] ,							snake_case_     )





      @slow
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     self.tokenizer_class.from_pretrained("microsoft/deberta-base"     )

       UpperCamelCase			:      Union[str, Any] 			=     tokenizer.encode("sequence builders" ,							add_special_tokens=snake_case_     )
       UpperCamelCase			:      Union[str, Any] 			=     tokenizer.encode("multi-sequence build" ,							add_special_tokens=snake_case_     )

       UpperCamelCase			:      int 			=     tokenizer.encode(
           "sequence builders" ,							add_special_tokens=snake_case_ ,							add_prefix_space=snake_case_     )
       UpperCamelCase			:      Optional[Any] 			=     tokenizer.encode(
           "sequence builders" ,							"multi-sequence build" ,							add_special_tokens=snake_case_ ,							add_prefix_space=snake_case_     )

       UpperCamelCase			:      Optional[int] 			=     tokenizer.build_inputs_with_special_tokens(snake_case_     )
       UpperCamelCase			:      List[str] 			=     tokenizer.build_inputs_with_special_tokens(snake_case_ ,							snake_case_     )

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode





      @slow
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     [self.tokenizer_class]
       if self.test_rust_tokenizer:
        tokenizer_classes.append(self.rust_tokenizer_class     )

       for tokenizer_class in tokenizer_classes:
        UpperCamelCase			:      str 			=     tokenizer_class.from_pretrained("microsoft/deberta-base"     )

        UpperCamelCase			:      Optional[int] 			=     [
            "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
            "ALBERT incorporates two parameter reduction techniques",
            "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
            " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
            " vocabulary embedding.",
        ]

        UpperCamelCase			:      Any 			=     tokenizer(snake_case_ ,							padding=snake_case_     )
        UpperCamelCase			:      Any 			=     [tokenizer.decode(snake_case_ ,							skip_special_tokens=snake_case_     ) for seq in encoding["input_ids"]]

        # fmt: off
        UpperCamelCase			:      Tuple 			=     {
            "input_ids": [
                [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 2118, 1_1126, 565, 2_4536, 80, 4_3797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 133, 78, 65, 16, 10, 3724, 1538, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 4, 2]
            ],
            "token_type_ids": [
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
            ],
            "attention_mask": [
                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
            ]
        }
        # fmt: on

        UpperCamelCase			:      Union[str, Any] 			=     [
            "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
            "ALBERT incorporates two parameter reduction techniques",
            "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
            " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
            " vocabulary embedding.",
        ]

        self.assertDictEqual(encoding.data ,							snake_case_     )

        for expected, decoded in zip(snake_case_ ,							snake_case_     ):
         self.assertEqual(snake_case_ ,							snake_case_     )







import math

import tensorflow as tf
from packaging import version



def 	A_       (  _lowerCAmelCase  )		->				Any:
 UpperCamelCase			:      List[Any] 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      Any 			=     0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0  )							,     x.dtype  )  ))

 return x * cdf



def 	A_       (  _lowerCAmelCase  )		->				Dict:
 UpperCamelCase			:      Union[str, Any] 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      List[Any] 			=     tf.cast(math.pi							,     x.dtype  )
 UpperCamelCase			:      Optional[Any] 			=     tf.cast(0.044_715							,     x.dtype  )
 UpperCamelCase			:      int 			=     0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi  ) * (x + coeff * tf.pow(_lowerCAmelCase							,     3  ))  ))

 return x * cdf



def 	A_       (  _lowerCAmelCase  )		->				List[Any]:
 UpperCamelCase			:      str 			=     tf.convert_to_tensor(_lowerCAmelCase  )

 return x * tf.tanh(tf.math.softplus(_lowerCAmelCase  )  )



def 	A_       (  _lowerCAmelCase  )		->				List[Any]:
 UpperCamelCase			:      Tuple 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      List[Any] 			=     tf.cast(0.044_715							,     x.dtype  )
 UpperCamelCase			:      Optional[Any] 			=     tf.cast(0.7_978_845_608							,     x.dtype  )

 return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)  ))



def 	A_       (  _lowerCAmelCase  )		->				Optional[Any]:
 UpperCamelCase			:      Any 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      List[Any] 			=     tf.cast(1.702							,     x.dtype  )
 return x * tf.math.sigmoid(coeff * x  )



def 	A_       (  _lowerCAmelCase  )		->				List[Any]:
 return tf.clip_by_value(_gelu(_lowerCAmelCase  )							,     -10							,     10  )



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase=-1  )		->				str:
 UpperCamelCase			,					UpperCamelCase			:      List[Any] 			=     tf.split(_lowerCAmelCase							,     2							,     axis=_lowerCAmelCase  )
 return a * tf.math.sigmoid(_lowerCAmelCase  )


if version.parse(tf.version.VERSION) >= version.parse("""2.4"""):



    def 	A_       (  _lowerCAmelCase  )		->				Any:
     return tf.keras.activations.gelu(_lowerCAmelCase							,     approximate=_lowerCAmelCase  )

    __lowerCamelCase :  Optional[int]										=  tf.keras.activations.gelu


    __lowerCamelCase :  int										=  approximate_gelu_wrap
else:
    __lowerCamelCase :  List[Any]										=  _gelu
    __lowerCamelCase :  Optional[Any]										=  _gelu_new


__lowerCamelCase :  Any										=  {
    """gelu""": gelu,
    """gelu_10""": gelu_aa,
    """gelu_fast""": gelu_fast,
    """gelu_new""": gelu_new,
    """glu""": glu,
    """mish""": mish,
    """quick_gelu""": quick_gelu,
    """relu""": tf.keras.activations.relu,
    """sigmoid""": tf.keras.activations.sigmoid,
    """silu""": tf.keras.activations.swish,
    """swish""": tf.keras.activations.swish,
    """tanh""": tf.keras.activations.tanh,
}



def 	A_       (  _lowerCAmelCase  )		->				Optional[Any]:
 if activation_string in ACTaFN:
  return ACTaFN[activation_string]
 else:
  raise KeyError(F"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()  )}"""  )
