





'''simple docstring'''


_SCREAMING_SNAKE_CASE	 =							"\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

_SCREAMING_SNAKE_CASE	 =							[{"type": "code", "content": INSTALL_CONTENT}]
_SCREAMING_SNAKE_CASE	 =							{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}











'''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

_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

@add_end_docstrings(__magic_name__		)
class    lowerCAmelCase_		(		__magic_name__		):




 def __init__(	self			,     *_lowerCAmelCase			,     **_lowerCAmelCase		)    ->							List[Any]:
     super().__init__(*_lowerCAmelCase			,     **_lowerCAmelCase		)
     requires_backends(self			,     "vision"		)
     self.check_model_type(_lowerCAmelCase		)




 def __call__(	self			,     _lowerCAmelCase			,     **_lowerCAmelCase		)    ->							str:
     return super().__call__(_lowerCAmelCase			,     **_lowerCAmelCase		)




 def 							_snake_case   (	self			,     **_lowerCAmelCase		)    ->							Tuple:
     return {}, {}, {}




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      load_image(_lowerCAmelCase		)
     _lowerCAmelCase				=      image.size
     _lowerCAmelCase				=      self.image_processor(images=_lowerCAmelCase			,     return_tensors=self.framework		)
     return model_inputs




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Tuple:
     _lowerCAmelCase				=      self.model(**_lowerCAmelCase		)
     return model_outputs





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


