




"""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

a         =      logging.get_logger(__name__)





@add_end_docstrings(_A							)
class     lowercase_	(					_A							):



 '''simple docstring'''

 def __init__(			self	:	Union[str, Any]   ,							*_UpperCAmelCase	:	str   ,							**_UpperCAmelCase	:	List[Any]		):
       super().__init__(*__SCREAMING_SNAKE_CASE   ,							**__SCREAMING_SNAKE_CASE		)
       requires_backends(self   ,							'vision'		)
       self.check_model_type(__SCREAMING_SNAKE_CASE		)

 def __call__(			self	:	Optional[int]   ,							_UpperCAmelCase	:	Union[str, List[str], "Image.Image", List["Image.Image"]]   ,							**_UpperCAmelCase	:	List[Any]		):
       return super().__call__(__SCREAMING_SNAKE_CASE   ,							**__SCREAMING_SNAKE_CASE		)

 def       lowerCAmelCase_							(			self	:	Dict   ,							**_UpperCAmelCase	:	int		):
       return {}, {}, {}

 def       lowerCAmelCase_							(			self	:	Tuple   ,							_UpperCAmelCase	:	str		):
       _A        =       load_image(__SCREAMING_SNAKE_CASE		)
       _A        =       image.size
       _A        =       self.image_processor(images=__SCREAMING_SNAKE_CASE   ,							return_tensors=self.framework		)
       return model_inputs

 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	List[Any]		):
       _A        =       self.model(**__SCREAMING_SNAKE_CASE		)
       return model_outputs



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







"""simple docstring"""

import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


a         =      HUGGINGFACE_HUB_CACHE


a         =      '''config.json'''
a         =      '''diffusion_pytorch_model.bin'''
a         =      '''diffusion_flax_model.msgpack'''
a         =      '''model.onnx'''
a         =      '''diffusion_pytorch_model.safetensors'''
a         =      '''weights.pb'''
a         =      '''https://huggingface.co'''
a         =      default_cache_path
a         =      '''diffusers_modules'''
a         =      os.getenv('''HF_MODULES_CACHE''', os.path.join(hf_cache_home, '''modules'''))
a         =      ['''fp16''', '''non-ema''']
a         =      '''.self_attn'''

