


from typing import TYPE_CHECKING

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


A							:					Union[str, 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:
	A							:					int     						=     [
	    '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

	A							:					Any     						=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









def 	_lowerCAmelCase       (    _lowerCAmelCase = "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(_lowerCAmelCase ) == 26





def 	_lowerCAmelCase       (    _lowerCAmelCase = "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(_lowerCAmelCase )





def 	_lowerCAmelCase       (    _lowerCAmelCase = "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 	_lowerCAmelCase       (    )						->	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=_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()





