


"""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_			     =  logging.get_logger(__name__)
@add_end_docstrings(__SCREAMING_SNAKE_CASE       )
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		def __init__(  self							, *_a							, **_a      ):
				super().__init__(*_a							, **_a      )
				requires_backends(self							, '''vision'''      )
				self.check_model_type(_a      )

		def __call__(  self							, _a							, **_a      ):
				return super().__call__(_a							, **_a      )

		def 			__UpperCAmelCase       (  self							, **_a      ):
				return {}, {}, {}

		def 			__UpperCAmelCase       (  self							, _a      ):
				__a           =			load_image(_a      )
				__a           =			image.size
				__a           =			self.image_processor(images=_a							, return_tensors=self.framework      )
				return model_inputs

		def 			__UpperCAmelCase       (  self							, _a      ):
				__a           =			self.model(**_a      )
				return model_outputs





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










"""simple docstring"""




def        lowercase							(		lowerCAmelCase__ :     list			) ->      bool:
		if not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''			)
		if len(lowerCAmelCase__			) == 0:
				raise ValueError('''Input list must be a non empty list'''			)
		if len(lowerCAmelCase__			) == 1:
				return True
		__a           =			series[1] - series[0]
		for index in range(len(lowerCAmelCase__			) - 1			):
				if series[index + 1] - series[index] != common_diff:
						return False
		return True

def        lowercase							(		lowerCAmelCase__ :     list			) ->      float:
		if not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''			)
		if len(lowerCAmelCase__			) == 0:
				raise ValueError('''Input list must be a non empty list'''			)
		__a           =			0
		for val in series:
				answer += val
		return answer / len(lowerCAmelCase__			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()






