



'''simple docstring'''




from typing import TYPE_CHECKING

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


a     :    int      						=					{"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"""]}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    Union[str, Any]      						=					["""DPTFeatureExtractor"""]
							a     :    Optional[Any]      						=					["""DPTImageProcessor"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    int      						=					[
							    """DPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """DPTForDepthEstimation""",
							    """DPTForSemanticSegmentation""",
							    """DPTModel""",
							    """DPTPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_dpt import DPTFeatureExtractor
														from .image_processing_dpt import DPTImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_dpt import (
														    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DPTForDepthEstimation,
														    DPTForSemanticSegmentation,
														    DPTModel,
														    DPTPreTrainedModel,
														)


else:
							import sys

							a     :    List[Any]      						=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






'''simple docstring'''




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

							# Replace all the whitespace in our sentence
							UpperCAmelCase  :	List[str]							  =	input_str.replace(""" """			,       """"""	)
							for alpha in input_str:
														if "a" <= alpha.lower() <= "z":
																					frequency.add(alpha.lower()	)
							return len(_lowercase	) == 2_6



def    __lowerCamelCase						(      _lowercase = "The quick brown fox jumps over the lazy dog"			,       )   ->    bool:
							UpperCAmelCase  :	Tuple							  =	[False] * 2_6
							for char in input_str:
														if char.islower():
																					UpperCAmelCase  :	Any							  =	True
														elif char.isupper():
																					UpperCAmelCase  :	Union[str, Any]							  =	True
							return all(_lowercase	)



def    __lowerCamelCase						(      _lowercase = "The quick brown fox jumps over the lazy dog"			,       )   ->    bool:
							return len({char for char in input_str.lower() if char.isalpha()}	) == 2_6



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

							UpperCAmelCase  :	str							  =	"""from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"""
							print(timeit("""is_pangram()"""			,       setup=_lowercase	)	)
							print(timeit("""is_pangram_faster()"""			,       setup=_lowercase	)	)
							print(timeit("""is_pangram_fastest()"""			,       setup=_lowercase	)	)
							# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
							# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							benchmark()

