





'''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

lowercase__       : int         =       logging.get_logger(__name__)


@add_end_docstrings(a__				)
class 					SCREAMING_SNAKE_CASE       (a__				):


     def __init__(		self		,      *_UpperCAmelCase		,      **_UpperCAmelCase):

          '''simple docstring'''






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


     def __call__(		self		,      _UpperCAmelCase		,      **_UpperCAmelCase):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self		,      **_UpperCAmelCase):

          '''simple docstring'''






          return {}, {}, {}


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					str						=			load_image(_UpperCAmelCase)
          __A		:					Optional[int]						=			image.size
          __A		:					Union[str, Any]						=			self.image_processor(images=_UpperCAmelCase		,      return_tensors=self.framework)
          return model_inputs


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Any						=			self.model(**_UpperCAmelCase)
          return model_outputs


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					List[str]						=			model_outputs.predicted_depth
          __A		:					Optional[int]						=			torch.nn.functional.interpolate(
              predicted_depth.unsqueeze(1)		,      size=self.image_size[::-1]		,      mode='bicubic'		,      align_corners=_UpperCAmelCase)
          __A		:					List[str]						=			prediction.squeeze().cpu().numpy()
          __A		:					Tuple						=			(output * 255 / np.max(_UpperCAmelCase)).astype('uint8')
          __A		:					int						=			Image.fromarray(_UpperCAmelCase)
          __A		:					Union[str, Any]						=			{}
          __A		:					Tuple						=			predicted_depth
          __A		:					List[str]						=			depth
          return output_dict






def 		lowerCAmelCase  (	UpperCamelCase__   :   List[Any]       ,			UpperCamelCase__   :   Dict					)      ->    List[Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				int           =   [0 for i in range(r + 1					)]
						# nc0 = 1
						__SCREAMING_SNAKE_CASE:				Dict           =   1
						for i in range(1       ,			n + 1					):
												# to compute current row from previous row.
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   min(UpperCamelCase__       ,			UpperCamelCase__					)
												while j > 0:
																		c[j] += c[j - 1]
																		j -= 1
						return c[r]




print(binomial_coefficient(n=10, r=5))



