


def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			str:







 """simple docstring"""


 _lowercase							      =[[] for _ in range(__snake_case						)]
 _lowercase							      =key - 1

 if key <= 0:
  raise ValueError('''Height of grid can\'t be 0 or negative'''						)
 if key == 1 or len(__snake_case						) <= key:
  return input_string

 for position, character in enumerate(__snake_case						):
  _lowercase							      =position % (lowest * 2)  # puts it in bounds
  _lowercase							      =min(__snake_case     ,						lowest * 2 - num						)  # creates zigzag pattern
  temp_grid[num].append(__snake_case						)
 _lowercase							      =[''''''.join(__snake_case						) for row in temp_grid]
 _lowercase							      =''''''.join(__snake_case						)

 return output_string
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			str:







 """simple docstring"""


 _lowercase							      =[]
 _lowercase							      =key - 1

 if key <= 0:
  raise ValueError('''Height of grid can\'t be 0 or negative'''						)
 if key == 1:
  return input_string

 _lowercase							      =[[] for _ in range(__snake_case						)]  # generates template
 for position in range(len(__snake_case						)						):
  _lowercase							      =position % (lowest * 2)  # puts it in bounds
  _lowercase							      =min(__snake_case     ,						lowest * 2 - num						)  # creates zigzag pattern
  temp_grid[num].append('''*'''						)

 _lowercase							      =0
 for row in temp_grid:  # fills in the characters
  _lowercase							      =input_string[counter : counter + len(__snake_case						)]
  grid.append(list(__snake_case						)						)
  counter += len(__snake_case						)

 _lowercase							      =''''''  # reads as zigzag
 for position in range(len(__snake_case						)						):
  _lowercase							      =position % (lowest * 2)  # puts it in bounds
  _lowercase							      =min(__snake_case     ,						lowest * 2 - num						)  # creates zigzag pattern
  output_string += grid[num][0]
  grid[num].pop(0						)
 return output_string
def 				UpperCAmelCase_					(							__snake_case						)     ->			dict[int, str]:







 """simple docstring"""


 _lowercase							      ={}
 for key_guess in range(1     ,						len(__snake_case						)						):  # tries every key
  _lowercase							      =decrypt(__snake_case     ,						__snake_case						)
 return results


if __name__ == "__main__":
  import doctest

  doctest.testmod()



"""simple docstring"""




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

UpperCAmelCase__      :    List[str]    							= logging.get_logger(__name__)



@add_end_docstrings(a__  )
class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def __init__(self   ,       *SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											super().__init__(*SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )
											requires_backends(self   ,       """vision"""    )
											self.check_model_type(SCREAMING_SNAKE_CASE__    )



				def __call__(self   ,       SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		List[str]:



											"""simple docstring"""




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



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




											return {}, {}, {}



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   load_image(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Any           =   image.size
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.image_processor(images=SCREAMING_SNAKE_CASE__   ,       return_tensors=self.framework    )
											return model_inputs



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.model(**SCREAMING_SNAKE_CASE__    )
											return model_outputs



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   model_outputs.predicted_depth
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   torch.nn.functional.interpolate(
											    predicted_depth.unsqueeze(1    )   ,       size=self.image_size[::-1]   ,       mode="""bicubic"""   ,       align_corners=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   prediction.squeeze().cpu().numpy()
											SCREAMING_SNAKE_CASE__						:       Any           =   (output * 2_55 / np.max(SCREAMING_SNAKE_CASE__    )).astype("""uint8"""    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   Image.fromarray(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       str           =   {}
											SCREAMING_SNAKE_CASE__						:       Any           =   predicted_depth
											SCREAMING_SNAKE_CASE__						:       Dict           =   depth
											return output_dict




