




from typing import TYPE_CHECKING

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


lowerCamelCase_  :	List[str] 				=					{
    """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_  :	Tuple 				=					["""DebertaTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_  :	Optional[Any] 				=					[
							    """DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """DebertaForMaskedLM""",
							    """DebertaForQuestionAnswering""",
							    """DebertaForSequenceClassification""",
							    """DebertaForTokenClassification""",
							    """DebertaModel""",
							    """DebertaPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_  :	Tuple 				=					[
							    """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_  :	int 				=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








import json
import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin

class     _UpperCamelCase			(   _A       ,    unittest.TestCase					):






			'''simple docstring'''

			__UpperCamelCase :  Tuple    =	CTRLTokenizer
			__UpperCamelCase :  int    =	False
			__UpperCamelCase :  List[str]    =	False

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

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							UpperCamelCase_:							int		=  ["""adapt""", """re@@""", """a@@""", """apt""", """c@@""", """t""", """<unk>"""]
							UpperCamelCase_:							int		=  dict(zip(snake_case_  ,		range(len(snake_case_ ) ) ) )
							UpperCamelCase_:							Union[str, Any]		=  ["""#version: 0.2""", """a p""", """ap t</w>""", """r e""", """a d""", """ad apt</w>""", """"""]
							UpperCamelCase_:							Tuple		=  {"""unk_token""": """<unk>"""}

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

			def  lowerCAmelCase__		(    self	:    Optional[int]  ,		**snake_case_	:    int ):
							kwargs.update(self.special_tokens_map )
							return CTRLTokenizer.from_pretrained(self.tmpdirname  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Optional[int]  ,		snake_case_	:    List[str] ):
							UpperCamelCase_:							Dict		=  """adapt react readapt apt"""
							UpperCamelCase_:							List[str]		=  """adapt react readapt apt"""
							return input_text, output_text




			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							str		=  CTRLTokenizer(self.vocab_file  ,		self.merges_file  ,		**self.special_tokens_map )
							UpperCamelCase_:							List[Any]		=  """adapt react readapt apt"""
							UpperCamelCase_:							Optional[int]		=  """adapt re@@ a@@ c@@ t re@@ adapt apt""".split()
							UpperCamelCase_:							int		=  tokenizer.tokenize(snake_case_ )
							self.assertListEqual(snake_case_  ,		snake_case_ )

							UpperCamelCase_:							List[Any]		=  tokens + [tokenizer.unk_token]

							UpperCamelCase_:							Union[str, Any]		=  [0, 1, 2, 4, 5, 1, 0, 3, 6]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_ )  ,		snake_case_ )






