




'''simple docstring'''





def       __a      (  _UpperCamelCase:       str = "The quick brown fox jumps over the lazy dog"   ,		)		->					bool:

     """simple docstring"""




     _snake_case				= set()

     # Replace all the whitespace in our sentence
     _snake_case				= input_str.replace(" "   ,		""       )
     for alpha in input_str:
          if "a" <= alpha.lower() <= "z":
               frequency.add(alpha.lower()       )
     return len(_UpperCamelCase       ) == 26





def       __a      (  _UpperCamelCase:       str = "The quick brown fox jumps over the lazy dog"   ,		)		->					bool:

     """simple docstring"""




     _snake_case				= [False] * 26
     for char in input_str:
          if char.islower():
               _snake_case				= True
          elif char.isupper():
               _snake_case				= True
     return all(_UpperCamelCase       )





def       __a      (  _UpperCamelCase:       str = "The quick brown fox jumps over the lazy dog"   ,		)		->					bool:

     """simple docstring"""




     return len({char for char in input_str.lower() if char.isalpha()}       ) == 26





def       __a      (  )		->					None:

     """simple docstring"""




     from timeit import timeit

     _snake_case				= "from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
     print(timeit("is_pangram()"   ,		setup=_UpperCamelCase       )       )
     print(timeit("is_pangram_faster()"   ,		setup=_UpperCamelCase       )       )
     print(timeit("is_pangram_fastest()"   ,		setup=_UpperCamelCase       )       )
     # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
     # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   benchmark()








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase_		:     Optional[Any]          =		{
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   pass

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_		:     Union[str, Any]          =		[
       '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RoCBertForCausalLM''',
       '''RoCBertForMaskedLM''',
       '''RoCBertForMultipleChoice''',
       '''RoCBertForPreTraining''',
       '''RoCBertForQuestionAnswering''',
       '''RoCBertForSequenceClassification''',
       '''RoCBertForTokenClassification''',
       '''RoCBertLayer''',
       '''RoCBertModel''',
       '''RoCBertPreTrainedModel''',
       '''load_tf_weights_in_roc_bert''',
   ]

if TYPE_CHECKING:
   from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
   from .tokenization_roc_bert import RoCBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      raise OptionalDependencyNotAvailable()

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roc_bert import (
          ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          RoCBertForCausalLM,
          RoCBertForMaskedLM,
          RoCBertForMultipleChoice,
          RoCBertForPreTraining,
          RoCBertForQuestionAnswering,
          RoCBertForSequenceClassification,
          RoCBertForTokenClassification,
          RoCBertLayer,
          RoCBertModel,
          RoCBertPreTrainedModel,
          load_tf_weights_in_roc_bert,
      )


else:
   import sys

   UpperCamelCase_		:     int          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


