

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

SCREAMING_SNAKE_CASE_          =   logging.get_logger(__name__)

@add_end_docstrings(__lowercase					)
class 					a	(						__lowercase					):


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







										'''simple docstring'''


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


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







										'''simple docstring'''


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


						def 							_UpperCAmelCase					(							self    ,       **A_						):







										'''simple docstring'''


										return {}, {}, {}


						def 							_UpperCAmelCase					(							self    ,       A_						):







										'''simple docstring'''


										_UpperCAmelCase							:		List[Any]					=     load_image(SCREAMING_SNAKE_CASE_						)
										_UpperCAmelCase							:		str					=     image.size
										_UpperCAmelCase							:		Tuple					=     self.image_processor(images=SCREAMING_SNAKE_CASE_    ,       return_tensors=self.framework						)
										return model_inputs


						def 							_UpperCAmelCase					(							self    ,       A_						):







										'''simple docstring'''


										_UpperCAmelCase							:		List[str]					=     self.model(**SCREAMING_SNAKE_CASE_						)
										return model_outputs





						def 							_UpperCAmelCase					(							self    ,       A_						):







										'''simple docstring'''


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




"""simple docstring"""




from ..utils import DummyObject, requires_backends





class 			snake_case  (				metaclass=__lowercase ):
							UpperCAmelCase__      =   ['''note_seq''']

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

									"""simple docstring"""







									requires_backends(self							,							['''note_seq''']     )

							@classmethod
							def _lowercase       (cls							,							*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									requires_backends(cls							,							['''note_seq''']     )







							@classmethod
							def _lowercase       (cls							,							*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									requires_backends(cls							,							['''note_seq''']     )