

import os
from distutils.util import strtobool






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict							,       UpperCAmelCase_							:     List[str]	)						->				Optional[int]:




	'''simple docstring'''




	for e in env_keys:
		_UpperCamelCase   :		Tuple			=						int(os.environ.get(__snake_case							,       -1	)	)
		if val >= 0:
			return val
	return default






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Any							,       UpperCAmelCase_							:     Optional[Any]=False	)						->				Optional[Any]:




	'''simple docstring'''




	_UpperCamelCase   :		int			=						os.environ.get(__snake_case							,       str(__snake_case	)	)
	return strtobool(__snake_case	) == 1  # As its name indicates `strtobool` actually returns an int...







def 					lowerCamelCase_     (							UpperCAmelCase_							:     Union[str, Any]							,       UpperCAmelCase_							:     List[Any]="no"	)						->				List[str]:




	'''simple docstring'''




	_UpperCamelCase   :		Union[str, Any]			=						os.environ.get(__snake_case							,       str(__snake_case	)	)
	return value







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__)




