




'''simple docstring'''

import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class 						a				(   lowerCamelCase__   ,     unittest.TestCase     ):
				snake_case_      =		CTRLTokenizer
				snake_case_      =		False
				snake_case_      =		False


				def   A_ (		self    :					List[str]					):
									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									snake_case_									=				['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
									snake_case_									=				dict(zip(lowercase_       ,				range(len(lowercase_					)					)					)					)
									snake_case_									=				['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
									snake_case_									=				{'''unk_token''': '''<unk>'''}

									snake_case_									=				os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES['''vocab_file''']					)
									snake_case_									=				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(lowercase_					) + '''\n'''					)
									with open(self.merges_file       ,				'''w'''       ,				encoding='''utf-8'''					) as fp:
														fp.write('''\n'''.join(lowercase_					)					)


				def   A_ (		self    :					Optional[Any]       ,				**lowercase_    :					List[str]					):
									kwargs.update(self.special_tokens_map					)
									return CTRLTokenizer.from_pretrained(self.tmpdirname       ,				**lowercase_					)


				def   A_ (		self    :					Optional[int]       ,				lowercase_    :					Optional[int]					):
									snake_case_									=				'''adapt react readapt apt'''
									snake_case_									=				'''adapt react readapt apt'''
									return input_text, output_text


				def   A_ (		self    :					str					):
									snake_case_									=				CTRLTokenizer(self.vocab_file       ,				self.merges_file       ,				**self.special_tokens_map					)
									snake_case_									=				'''adapt react readapt apt'''
									snake_case_									=				'''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
									snake_case_									=				tokenizer.tokenize(lowercase_					)
									self.assertListEqual(lowercase_       ,				lowercase_					)

									snake_case_									=				tokens + [tokenizer.unk_token]

									snake_case_									=				[0, 1, 2, 4, 5, 1, 0, 3, 6]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_					)       ,				lowercase_					)




from typing import TYPE_CHECKING

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


lowerCamelCase           =					{
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''DebertaForMaskedLM''',
           '''DebertaForQuestionAnswering''',
           '''DebertaForSequenceClassification''',
           '''DebertaForTokenClassification''',
           '''DebertaModel''',
           '''DebertaPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFDebertaForMaskedLM''',
           '''TFDebertaForQuestionAnswering''',
           '''TFDebertaForSequenceClassification''',
           '''TFDebertaForTokenClassification''',
           '''TFDebertaModel''',
           '''TFDebertaPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
       from .tokenization_deberta import DebertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_deberta_fast import DebertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deberta import (
                  DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DebertaForMaskedLM,
                  DebertaForQuestionAnswering,
                  DebertaForSequenceClassification,
                  DebertaForTokenClassification,
                  DebertaModel,
                  DebertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_deberta import (
                  TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFDebertaForMaskedLM,
                  TFDebertaForQuestionAnswering,
                  TFDebertaForSequenceClassification,
                  TFDebertaForTokenClassification,
                  TFDebertaModel,
                  TFDebertaPreTrainedModel,
              )


else:
       import sys

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