

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

a_      =     logging.get_logger(__name__)






@add_end_docstrings(__A )
class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			def __init__(      self				:			List[Any]   ,   *a				:			Optional[Any]   ,   **a				:			Optional[Any]						)				->							List[Any]:

				"""simple docstring"""

				super().__init__(*a   ,   **a						)
				requires_backends(self   ,   "vision"						)
				self.check_model_type(a						)
			def __call__(      self				:			int   ,   a				:			Union[str, List[str], "Image.Image", List["Image.Image"]]   ,   **a				:			Optional[int]						)				->							List[Any]:

				"""simple docstring"""

				return super().__call__(a   ,   **a						)
			def 				__UpperCamelCase			(      self				:			Tuple   ,   **a				:			Any						)				->							Optional[Any]:

				"""simple docstring"""

				return {}, {}, {}
			def 				__UpperCamelCase			(      self				:			int   ,   a				:			List[Any]						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							load_image(a						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							image.size
				SCREAMING_SNAKE_CASE :   List[str]					  =							self.image_processor(images=a   ,   return_tensors=self.framework						)
				return model_inputs
			def 				__UpperCamelCase			(      self				:			Optional[int]   ,   a				:			Any						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							self.model(**a						)
				return model_outputs






			def 				__UpperCamelCase			(      self				:			List[str]   ,   a				:			Tuple						)				->							List[Any]:

				"""simple docstring"""

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


from math import factorial, pi

def 			lowerCamelCase__			(       _a				,      _a = 30):

	if not isinstance(_a				,      (int, float)):
		raise ValueError("maclaurin_sin() requires either an int or float for theta")

	if not isinstance(_a				,      _a) or accuracy <= 0:
		raise ValueError("maclaurin_sin() requires a positive int for accuracy")

	SCREAMING_SNAKE_CASE :   int					  =							float(_a)
	SCREAMING_SNAKE_CASE :   Dict					  =							theta // (2 * pi)
	theta -= 2 * div * pi
	return sum(
	    (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1) for r in range(_a))

def 			lowerCamelCase__			(       _a				,      _a = 30):

	if not isinstance(_a				,      (int, float)):
		raise ValueError("maclaurin_cos() requires either an int or float for theta")

	if not isinstance(_a				,      _a) or accuracy <= 0:
		raise ValueError("maclaurin_cos() requires a positive int for accuracy")

	SCREAMING_SNAKE_CASE :   str					  =							float(_a)
	SCREAMING_SNAKE_CASE :   Any					  =							theta // (2 * pi)
	theta -= 2 * div * pi
	return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r) for r in range(_a))


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			print(maclaurin_sin(10))
			print(maclaurin_sin(-10))
			print(maclaurin_sin(10, 15))
			print(maclaurin_sin(-10, 15))

			print(maclaurin_cos(5))
			print(maclaurin_cos(-5))
			print(maclaurin_cos(10, 15))
			print(maclaurin_cos(-10, 15))