




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 						__lowercase		(UpperCamelCase__					,		unittest.TestCase							):




      """simple docstring"""





      _snake_case												=						DebertaTokenizer
      _snake_case												=						True
      _snake_case												=						DebertaTokenizerFast

      def      UpperCAmelCase				(				self     )		->    Dict:
         super().setUp()

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

         snake_case				:    int           =	os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES["""vocab_file"""]     )
         snake_case				:    Dict           =	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(A     ) + """\n"""     )
         with open(self.merges_file    ,				"""w"""    ,				encoding="""utf-8"""     ) as fp:
            fp.write("""\n""".join(A     )     )

      def      UpperCAmelCase				(				self    ,				**A     )		->    Dict:
         kwargs.update(self.special_tokens_map     )
         return self.tokenizer_class.from_pretrained(self.tmpdirname    ,				**A     )

      def      UpperCAmelCase				(				self    ,				A     )		->    Optional[int]:
         snake_case				:    Tuple           =	"""lower newer"""
         snake_case				:    Optional[Any]           =	"""lower newer"""
         return input_text, output_text

      def      UpperCAmelCase				(				self     )		->    Dict:
         snake_case				:    str           =	self.get_tokenizer()
         snake_case				:    Any           =	"""lower newer"""
         snake_case				:    Optional[Any]           =	["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
         snake_case				:    int           =	tokenizer.tokenize(A     )
         self.assertListEqual(A    ,				A     )

         snake_case				:    Tuple           =	tokens + [tokenizer.unk_token]
         snake_case				:    Union[str, Any]           =	[0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(A     )    ,				A     )

      def      UpperCAmelCase				(				self     )		->    Dict:
         snake_case				:    int           =	self.get_tokenizer()
         snake_case				:    Optional[int]           =	tokenizer("""Hello"""    ,				"""World"""     )
         snake_case				:    str           =	[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
         self.assertListEqual(tokd["""token_type_ids"""]    ,				A     )

      @slow
      def      UpperCAmelCase				(				self     )		->    Optional[int]:
         snake_case				:    int           =	self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""     )

         snake_case				:    Any           =	tokenizer.encode("""sequence builders"""    ,				add_special_tokens=A     )
         snake_case				:    Tuple           =	tokenizer.encode("""multi-sequence build"""    ,				add_special_tokens=A     )

         snake_case				:    Tuple           =	tokenizer.encode(
             """sequence builders"""    ,				add_special_tokens=A    ,				add_prefix_space=A     )
         snake_case				:    List[str]           =	tokenizer.encode(
             """sequence builders"""    ,				"""multi-sequence build"""    ,				add_special_tokens=A    ,				add_prefix_space=A     )

         snake_case				:    int           =	tokenizer.build_inputs_with_special_tokens(A     )
         snake_case				:    Tuple           =	tokenizer.build_inputs_with_special_tokens(A    ,				A     )

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode

      @slow
      def      UpperCAmelCase				(				self     )		->    Any:
         snake_case				:    List[Any]           =	[self.tokenizer_class]
         if self.test_rust_tokenizer:
            tokenizer_classes.append(self.rust_tokenizer_class     )

         for tokenizer_class in tokenizer_classes:
            snake_case				:    List[str]           =	tokenizer_class.from_pretrained("""microsoft/deberta-base"""     )

            snake_case				:    Tuple           =	[
                """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.""",
            ]

            snake_case				:    List[str]           =	tokenizer(A    ,				padding=A     )
            snake_case				:    List[str]           =	[tokenizer.decode(A    ,				skip_special_tokens=A     ) for seq in encoding["""input_ids"""]]

            # fmt: off
            snake_case				:    Union[str, Any]           =	{
                """input_ids""": [
                    [1, 2_1_1_8, 1_1_1_2_6, 5_6_5, 3_5, 8_3, 2_5_1_9_1, 1_6_3, 1_8_8_5_4, 1_3, 1_2_1_5_6, 1_2, 1_6_1_0_1, 2_5_3_7_6, 1_3_8_0_7, 9, 2_2_2_0_5, 2_7_8_9_3, 1_6_3_5, 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, 2_1_1_8, 1_1_1_2_6, 5_6_5, 2_4_5_3_6, 8_0, 4_3_7_9_7, 4_8_7_8, 7_3_7_3, 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, 1_3_3, 7_8, 6_5, 1_6, 1_0, 3_7_2_4, 1_5_3_8, 3_3_1_8_3, 1_1_3_0_3, 4_3_7_9_7, 1_9_3_8, 4, 8_7_0, 2_4_1_6_5, 2_9_1_0_5, 5, 7_3_9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 3_6_1_7_3, 8_8, 8_0, 6_5_0, 7_8_2_1, 4_5_9_4_0, 6, 5_2, 2_5_5_9, 5, 1_8_3_6, 9, 5, 7_3_9_7, 1_3_1_7_1, 3_1, 5, 1_8_3_6, 9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 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

            snake_case				:    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.""",
            ]

            self.assertDictEqual(encoding.data    ,				A     )

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











from typing import TYPE_CHECKING

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


lowerCamelCase	:	Optional[Any]				=   {
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Tuple				=   ['RobertaTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	List[str]				=   [
           'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'RobertaForCausalLM',
           'RobertaForMaskedLM',
           'RobertaForMultipleChoice',
           'RobertaForQuestionAnswering',
           'RobertaForSequenceClassification',
           'RobertaForTokenClassification',
           'RobertaModel',
           'RobertaPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Dict				=   [
           'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFRobertaForCausalLM',
           'TFRobertaForMaskedLM',
           'TFRobertaForMultipleChoice',
           'TFRobertaForQuestionAnswering',
           'TFRobertaForSequenceClassification',
           'TFRobertaForTokenClassification',
           'TFRobertaMainLayer',
           'TFRobertaModel',
           'TFRobertaPreTrainedModel',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Any				=   [
           'FlaxRobertaForCausalLM',
           'FlaxRobertaForMaskedLM',
           'FlaxRobertaForMultipleChoice',
           'FlaxRobertaForQuestionAnswering',
           'FlaxRobertaForSequenceClassification',
           'FlaxRobertaForTokenClassification',
           'FlaxRobertaModel',
           'FlaxRobertaPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
       from .tokenization_roberta import RobertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roberta_fast import RobertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roberta import (
                  ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RobertaForCausalLM,
                  RobertaForMaskedLM,
                  RobertaForMultipleChoice,
                  RobertaForQuestionAnswering,
                  RobertaForSequenceClassification,
                  RobertaForTokenClassification,
                  RobertaModel,
                  RobertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roberta import (
                  TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRobertaForCausalLM,
                  TFRobertaForMaskedLM,
                  TFRobertaForMultipleChoice,
                  TFRobertaForQuestionAnswering,
                  TFRobertaForSequenceClassification,
                  TFRobertaForTokenClassification,
                  TFRobertaMainLayer,
                  TFRobertaModel,
                  TFRobertaPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roberta import (
                  FlaxRobertaForCausalLM,
                  FlaxRobertaForMaskedLM,
                  FlaxRobertaForMultipleChoice,
                  FlaxRobertaForQuestionAnswering,
                  FlaxRobertaForSequenceClassification,
                  FlaxRobertaForTokenClassification,
                  FlaxRobertaModel,
                  FlaxRobertaPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase	:	str				=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





