from typing import TYPE_CHECKING

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


UpperCAmelCase							   =      {
    """configuration_deberta_v2""": ["""DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaV2Config""", """DebertaV2OnnxConfig"""],
    """tokenization_deberta_v2""": ["""DebertaV2Tokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase							   =      ["""DebertaV2TokenizerFast"""]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase							   =      [
      """TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFDebertaV2ForMaskedLM""",
      """TFDebertaV2ForQuestionAnswering""",
      """TFDebertaV2ForSequenceClassification""",
      """TFDebertaV2ForTokenClassification""",
      """TFDebertaV2Model""",
      """TFDebertaV2PreTrainedModel""",
  ]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase							   =      [
      """DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """DebertaV2ForMaskedLM""",
      """DebertaV2ForMultipleChoice""",
      """DebertaV2ForQuestionAnswering""",
      """DebertaV2ForSequenceClassification""",
      """DebertaV2ForTokenClassification""",
      """DebertaV2Model""",
      """DebertaV2PreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_deberta_va import (
      DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DebertaVaConfig,
      DebertaVaOnnxConfig,
  )
  from .tokenization_deberta_va import DebertaVaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_deberta_va import (
        TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDebertaVaForMaskedLM,
        TFDebertaVaForQuestionAnswering,
        TFDebertaVaForSequenceClassification,
        TFDebertaVaForTokenClassification,
        TFDebertaVaModel,
        TFDebertaVaPreTrainedModel,
    )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_deberta_va import (
        DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
        DebertaVaForMaskedLM,
        DebertaVaForMultipleChoice,
        DebertaVaForQuestionAnswering,
        DebertaVaForSequenceClassification,
        DebertaVaForTokenClassification,
        DebertaVaModel,
        DebertaVaPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase							   =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property






class 							_SCREAMING_SNAKE_CASE(   unittest.TestCase	):
   @cached_property
   def _lowercase(    self    )	->List[str]:
        return BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''    )
   @cached_property
   def _lowercase(    self    )	->str:
        return BlenderbotTokenizerFast.from_pretrained('''facebook/blenderbot-3B'''    )
   def _lowercase(    self    )	->str:
        a  :    Optional[int]				=						self.tokenizer_ab
        a  :    List[Any]				=						''' I am a small frog.'''
        a  :    Optional[Any]				=						tok([src_text]							,     padding=lowerCAmelCase_							,     truncation=lowerCAmelCase_    )['''input_ids''']
        a  :    int				=						tok.batch_decode(lowerCAmelCase_							,     skip_special_tokens=lowerCAmelCase_							,     clean_up_tokenization_spaces=lowerCAmelCase_    )[0]
        assert src_text == decoded
   def _lowercase(    self    )	->Dict:
        a  :    List[Any]				=						self.rust_tokenizer_ab
        a  :    str				=						''' I am a small frog.'''
        a  :    Tuple				=						tok([src_text]							,     padding=lowerCAmelCase_							,     truncation=lowerCAmelCase_    )['''input_ids''']
        a  :    Optional[Any]				=						tok.batch_decode(lowerCAmelCase_							,     skip_special_tokens=lowerCAmelCase_							,     clean_up_tokenization_spaces=lowerCAmelCase_    )[0]
        assert src_text == decoded
   def _lowercase(    self    )	->Tuple:
        assert self.tokenizer_ab.add_prefix_space
        assert self.tokenizer_ab([''' Sam''', '''Sam''']    ).input_ids == [[55_02, 2], [55_02, 2]]


   def _lowercase(    self    )	->Optional[Any]:
        assert self.rust_tokenizer_ab.add_prefix_space
        assert self.rust_tokenizer_ab([''' Sam''', '''Sam''']    ).input_ids == [[55_02, 2], [55_02, 2]]