


'''simple docstring'''

from typing import TYPE_CHECKING

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


__lowercase:		int							=     {"configuration_t5": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5Config", "T5OnnxConfig"]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase:		Optional[Any]							=     ["T5Tokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase:		List[Any]							=     ["T5TokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase:		List[Any]							=     [
           "T5_PRETRAINED_MODEL_ARCHIVE_LIST",
           "T5EncoderModel",
           "T5ForConditionalGeneration",
           "T5Model",
           "T5PreTrainedModel",
           "load_tf_weights_in_t5",
           "T5ForQuestionAnswering",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase:		Tuple							=     [
           "TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFT5EncoderModel",
           "TFT5ForConditionalGeneration",
           "TFT5Model",
           "TFT5PreTrainedModel",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase:		List[Any]							=     [
           "FlaxT5EncoderModel",
           "FlaxT5ForConditionalGeneration",
           "FlaxT5Model",
           "FlaxT5PreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_ta import TaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_ta_fast import TaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_ta import (
                  T5_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TaEncoderModel,
                  TaForConditionalGeneration,
                  TaForQuestionAnswering,
                  TaModel,
                  TaPreTrainedModel,
                  load_tf_weights_in_ta,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_ta import (
                  TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFTaEncoderModel,
                  TFTaForConditionalGeneration,
                  TFTaModel,
                  TFTaPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_ta import (
                  FlaxTaEncoderModel,
                  FlaxTaForConditionalGeneration,
                  FlaxTaModel,
                  FlaxTaPreTrainedModel,
              )


else:
       import sys

       __lowercase:		Tuple							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



'''simple docstring'''

from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest







@require_tokenizers
class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
 _lowerCamelCase :					Optional[int]    					=					SqueezeBertTokenizer
 _lowerCamelCase :					List[Any]    					=					SqueezeBertTokenizerFast
 _lowerCamelCase :					int    					=					True

 def 		lowercase_						( self :	List[str],					**a_ :	str					):





    """simple docstring"""



    return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname,					**a_					)

 @slow
 def 		lowercase_						( self :	int					):





    """simple docstring"""



    UpperCamelCase__ 						=						SqueezeBertTokenizer.from_pretrained("squeezebert/squeezebert-mnli-headless"					)

    UpperCamelCase__ 						=						tokenizer.encode("sequence builders",					add_special_tokens=a_					)
    UpperCamelCase__ 						=						tokenizer.encode("multi-sequence build",					add_special_tokens=a_					)

    UpperCamelCase__ 						=						tokenizer.build_inputs_with_special_tokens(a_					)
    UpperCamelCase__ 						=						tokenizer.build_inputs_with_special_tokens(a_,					a_					)

    assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
    assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
        tokenizer.sep_token_id
    ]