"""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

__lowerCamelCase											=  logging.get_logger(__name__)
@add_end_docstrings(__A )
class     UpperCamelCase__(			__A ):





						def __init__(       self		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				int:
													super().__init__(*__UpperCAmelCase		,**__UpperCAmelCase     )
													requires_backends(self		,'vision'     )
													self.check_model_type(__UpperCAmelCase     )





						def __call__(       self		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				Any:
													return super().__call__(__UpperCAmelCase		,**__UpperCAmelCase     )





						def 				snake_case__	(       self		,**__UpperCAmelCase     )					->				Optional[Any]:
													return {}, {}, {}





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				Tuple:
													A__	     =							load_image(__UpperCAmelCase     )
													A__	     =							image.size
													A__	     =							self.image_processor(images=__UpperCAmelCase		,return_tensors=self.framework     )
													return model_inputs





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				Optional[Any]:
													A__	     =							self.model(**__UpperCAmelCase     )
													return model_outputs





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				int:
													A__	     =							model_outputs.predicted_depth
													A__	     =							torch.nn.functional.interpolate(
													    predicted_depth.unsqueeze(1     )		,size=self.image_size[::-1]		,mode='bicubic'		,align_corners=__UpperCAmelCase     )
													A__	     =							prediction.squeeze().cpu().numpy()
													A__	     =							(output * 2_55 / np.max(__UpperCAmelCase     )).astype('uint8'     )
													A__	     =							Image.fromarray(__UpperCAmelCase     )
													A__	     =							{}
													A__	     =							predicted_depth
													A__	     =							depth
													return output_dict






"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


__lowerCamelCase											=  logging.get_logger(__name__)
class     UpperCamelCase__(			__A ):





						def __init__(       self		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				None:
													warnings.warn(
													    'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
													    ' Please use ImageGPTImageProcessor instead.'		,__UpperCAmelCase		,)
													super().__init__(*__UpperCAmelCase		,**__UpperCAmelCase     )





