





def lowerCamelCase__    (							snake_case_	:  str = "The quick brown fox jumps over the lazy dog"       ,      )      ->				bool:
   __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(snake_case_       ) == 26






def lowerCamelCase__    (							snake_case_	:  str = "The quick brown fox jumps over the lazy dog"       ,      )      ->				bool:
   __snake_case							       =   [False] * 26
   for char in input_str:
      if char.islower():
         __snake_case							       =   True
      elif char.isupper():
         __snake_case							       =   True
   return all(snake_case_       )






def lowerCamelCase__    (							snake_case_	:  str = "The quick brown fox jumps over the lazy dog"       ,      )      ->				bool:
   return len({char for char in input_str.lower() if char.isalpha()}       ) == 26






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

   __snake_case							       =   '''from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'''
   print(timeit('''is_pangram()'''       ,      setup=snake_case_       )       )
   print(timeit('''is_pangram_faster()'''       ,      setup=snake_case_       )       )
   print(timeit('''is_pangram_fastest()'''       ,      setup=snake_case_       )       )
   # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
   # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()








from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

snake_case_				=			logging.get_logger(__name__)

@add_end_docstrings(_UpperCAmelCase  )
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):


      def __init__(self       :      Optional[int]				,			*a__       :      Any				,			**a__       :      Dict      ):





         """simple docstring"""



         super().__init__(*a__				,			**a__      )
         requires_backends(self				,			'''vision'''      )
         self.check_model_type(a__      )


      def __call__(self       :      Optional[int]				,			a__       :      Union[str, List[str], "Image.Image", List["Image.Image"]]				,			**a__       :      Tuple      ):





         """simple docstring"""



         return super().__call__(a__				,			**a__      )


      def    a (self       :      Dict				,			**a__       :      Any      ):





         """simple docstring"""



         return {}, {}, {}


      def    a (self       :      List[str]				,			a__       :      Any      ):





         """simple docstring"""



         __snake_case							       =   load_image(a__      )
         __snake_case							       =   image.size
         __snake_case							       =   self.image_processor(images=a__				,			return_tensors=self.framework      )
         return model_inputs


      def    a (self       :      int				,			a__       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model(**a__      )
         return model_outputs


      def    a (self       :      int				,			a__       :      str      ):





         """simple docstring"""



         __snake_case							       =   model_outputs.predicted_depth
         __snake_case							       =   torch.nn.functional.interpolate(
             predicted_depth.unsqueeze(1      )				,			size=self.image_size[::-1]				,			mode='''bicubic'''				,			align_corners=a__      )
         __snake_case							       =   prediction.squeeze().cpu().numpy()
         __snake_case							       =   (output * 255 / np.max(a__      )).astype('''uint8'''      )
         __snake_case							       =   Image.fromarray(a__      )
         __snake_case							       =   {}
         __snake_case							       =   predicted_depth
         __snake_case							       =   depth
         return output_dict

