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(__SCREAMING_SNAKE_CASE				)
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):







							def __init__(				self     :    Union[str, Any]	,					*__snake_case     :    Any	,					**__snake_case     :    Union[str, Any]		):
										super().__init__(*__lowerCAmelCase	,					**__lowerCAmelCase		)
										requires_backends(self	,					'''vision'''		)
										self.check_model_type(__lowerCAmelCase		)







							def __call__(				self     :    Any	,					__snake_case     :    Any	,					**__snake_case     :    List[str]		):
										return super().__call__(__lowerCAmelCase	,					**__lowerCAmelCase		)







							def 				snake_case  (				self     :    List[str]	,					**__snake_case     :    Optional[int]		):
										return {}, {}, {}







							def 				snake_case  (				self     :    List[Any]	,					__snake_case     :    List[Any]		):
										lowerCamelCase						:Optional[Any]						  =     load_image(__lowerCAmelCase		)
										lowerCamelCase						:Union[str, Any]						  =     image.size
										lowerCamelCase						:Optional[int]						  =     self.image_processor(images=__lowerCAmelCase	,					return_tensors=self.framework		)
										return model_inputs







							def 				snake_case  (				self     :    str	,					__snake_case     :    str		):
										lowerCamelCase						:Tuple						  =     self.model(**__lowerCAmelCase		)
										return model_outputs







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




from maths.prime_factors import prime_factors





def  _lowerCamelCase					(   a_				: int):
			if not isinstance(a_						,			a_):
						lowerCamelCase						:Tuple						  =     F"Input value of [number={number}] must be an integer"
						raise TypeError(a_)
			if number < 1:
						raise ValueError('''Input must be a positive integer''')
			return -1 if len(prime_factors(a_)) % 2 else 1


if __name__ == "__main__":
			import doctest

			doctest.testmod()



