

def 					lowerCamelCase_     (							UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     bool = False	)						->				str:




	'''simple docstring'''




	if not isinstance(UpperCAmelCase_							,       UpperCAmelCase_	):
		_UpperCamelCase   :		Dict			=						F'''Expected string as input, found {type(UpperCAmelCase_	)}'''
		raise ValueError(UpperCAmelCase_	)
	if not isinstance(UpperCAmelCase_							,       UpperCAmelCase_	):
		_UpperCamelCase   :		Tuple			=						F'''Expected boolean as use_pascal parameter, found {type(UpperCAmelCase_	)}'''
		raise ValueError(UpperCAmelCase_	)

	_UpperCamelCase   :		Any			=						input_str.split('_'	)

	_UpperCamelCase   :		Dict			=						0 if use_pascal else 1

	_UpperCamelCase   :		Union[str, Any]			=						words[start_index:]

	_UpperCamelCase   :		str			=						[word[0].upper() + word[1:] for word in words_to_capitalize]

	_UpperCamelCase   :		Optional[int]			=						'' if use_pascal else words[0]

	return "".join([initial_word, *capitalized_words]	)


if __name__ == "__main__":
							from doctest import testmod

							testmod()







from typing import TYPE_CHECKING

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


lowerCAmelCase__								=		{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		[
							    """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """CanineForMultipleChoice""",
							    """CanineForQuestionAnswering""",
							    """CanineForSequenceClassification""",
							    """CanineForTokenClassification""",
							    """CanineLayer""",
							    """CanineModel""",
							    """CaninePreTrainedModel""",
							    """load_tf_weights_in_canine""",
							]


if TYPE_CHECKING:
							from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
							from .tokenization_canine import CanineTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_canine import (
														    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CanineForMultipleChoice,
														    CanineForQuestionAnswering,
														    CanineForSequenceClassification,
														    CanineForTokenClassification,
														    CanineLayer,
														    CanineModel,
														    CaninePreTrainedModel,
														    load_tf_weights_in_canine,
														)


else:
							import sys

							lowerCAmelCase__								=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




