



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

snake_case__					:     Union[str, Any]    		=   logging.get_logger(__name__)

@add_end_docstrings(_lowercase	)
class   _A   ( _lowercase	):


						'''simple docstring'''





						def __init__(	self	:							int							,      *lowerCamelCase	:							Any							,      **lowerCamelCase	:							Dict	):






							'''simple docstring'''



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



						def __call__(	self	:							Optional[Any]							,      lowerCamelCase	:							Union[str, List[str], "Image.Image", List["Image.Image"]]							,      **lowerCamelCase	:							Any	):






							'''simple docstring'''



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



						def     _snake_case	(	self	:							Union[str, Any]							,      **lowerCamelCase	:							Any	):






							'''simple docstring'''



							return {}, {}, {}



						def     _snake_case	(	self	:							Dict							,      lowerCamelCase	:							Optional[int]	):






							'''simple docstring'''



							__lowercase		       =			load_image(lowerCamelCase	)
							__lowercase		       =			image.size
							__lowercase		       =			self.image_processor(images=lowerCamelCase							,      return_tensors=self.framework	)
							return model_inputs



						def     _snake_case	(	self	:							int							,      lowerCamelCase	:							Union[str, Any]	):






							'''simple docstring'''



							__lowercase		       =			self.model(**lowerCamelCase	)
							return model_outputs



						def     _snake_case	(	self	:							int							,      lowerCamelCase	:							Any	):






							'''simple docstring'''



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








from ....utils import logging


snake_case__					:     List[Any]    		=   logging.get_logger(__name__)

class   _A   ( _lowercase	):


						'''simple docstring'''





						def __init__(	self	:							List[str]							,      lowerCamelCase	:							Any							,      lowerCamelCase	:							Dict=None							,      lowerCamelCase	:							Dict=2_048	):






							'''simple docstring'''



							__lowercase		       =			config.__dict__
							__lowercase		       =			modal_hidden_size
							if num_labels:
								__lowercase		       =			num_labels



