






def    UpperCamelCase						(	_A = "The quick brown fox jumps over the lazy dog",   ):






      """simple docstring"""







      __magic_name__      :       Optional[int]       						= set()

      # Replace all the whitespace in our sentence
      __magic_name__      :       Optional[Any]       						= input_str.replace(""" """,   """"""			)
      for alpha in input_str:
            if "a" <= alpha.lower() <= "z":
                  frequency.add(alpha.lower()			)
      return len(__UpperCamelCase			) == 26

def    UpperCamelCase						(	_A = "The quick brown fox jumps over the lazy dog",   ):






      """simple docstring"""







      __magic_name__      :       List[Any]       						= [False] * 26
      for char in input_str:
            if char.islower():
                  __magic_name__      :       Any       						= True
            elif char.isupper():
                  __magic_name__      :       Optional[int]       						= True
      return all(__UpperCamelCase			)

def    UpperCamelCase						(	_A = "The quick brown fox jumps over the lazy dog",   ):






      """simple docstring"""







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

def    UpperCamelCase						(	):






      """simple docstring"""







      from timeit import timeit

      __magic_name__      :       List[str]       						= """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()








from typing import TYPE_CHECKING

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


__magic_name__:					List[Any]							=     {
    "configuration_conditional_detr": [
        "CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ConditionalDetrConfig",
        "ConditionalDetrOnnxConfig",
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					List[str]							=     ["ConditionalDetrFeatureExtractor"]
		__magic_name__:					int							=     ["ConditionalDetrImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					List[Any]							=     [
		    "CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "ConditionalDetrForObjectDetection",
		    "ConditionalDetrForSegmentation",
		    "ConditionalDetrModel",
		    "ConditionalDetrPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_conditional_detr import (
		    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ConditionalDetrConfig,
		    ConditionalDetrOnnxConfig,
		)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
				from .image_processing_conditional_detr import ConditionalDetrImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_conditional_detr import (
				    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConditionalDetrForObjectDetection,
				    ConditionalDetrForSegmentation,
				    ConditionalDetrModel,
				    ConditionalDetrPreTrainedModel,
				)

else:
		import sys

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