





from typing import TYPE_CHECKING

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


__lowerCamelCase	:					Any			  =					{
    """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	:					str			  =					["""DebertaTokenizerFast"""]

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

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







def 		A_    (					_lowerCAmelCase = "The quick brown fox jumps over the lazy dog"							,       )						->			bool:
 UpperCamelCase     : Union[str, Any]	     =       set()

 # Replace all the whitespace in our sentence
 UpperCamelCase     : Union[str, Any]	     =       input_str.replace(" "							,       ""					)
 for alpha in input_str:
  if "a" <= alpha.lower() <= "z":
   frequency.add(alpha.lower()					)
 return len(_lowerCAmelCase					) == 26



def 		A_    (					_lowerCAmelCase = "The quick brown fox jumps over the lazy dog"							,       )						->			bool:
 UpperCamelCase     : List[Any]	     =       [False] * 26
 for char in input_str:
  if char.islower():
   UpperCamelCase     : Tuple	     =       True
  elif char.isupper():
   UpperCamelCase     : str	     =       True
 return all(_lowerCAmelCase					)



def 		A_    (					_lowerCAmelCase = "The quick brown fox jumps over the lazy dog"							,       )						->			bool:
 return len({char for char in input_str.lower() if char.isalpha()}					) == 26



def 		A_    (					)						->			None:
 from timeit import timeit

 UpperCamelCase     : Tuple	     =       "from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
 print(timeit("is_pangram()"							,       setup=_lowerCAmelCase					)					)
 print(timeit("is_pangram_faster()"							,       setup=_lowerCAmelCase					)					)
 print(timeit("is_pangram_fastest()"							,       setup=_lowerCAmelCase					)					)
 # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
 # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()
