

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      :					Dict					    =      logging.get_logger(__name__)





@add_end_docstrings(__magic_name__			)
class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







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







             '''simple docstring'''



             super().__init__(*lowercase__ ,   **lowercase__     )
             requires_backends(self ,   '''vision'''     )
             self.check_model_type(lowercase__     )

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







             '''simple docstring'''



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

       def     __UpperCamelCase     (							self ,   **lowercase__     ):







             '''simple docstring'''



             return {}, {}, {}

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             __A    =load_image(lowercase__     )
             __A    =image.size
             __A    =self.image_processor(images=lowercase__ ,   return_tensors=self.framework     )
             return model_inputs

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             __A    =self.model(**lowercase__     )
             return model_outputs


       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             __A    =model_outputs.predicted_depth
             __A    =torch.nn.functional.interpolate(
                 predicted_depth.unsqueeze(1     ) ,   size=self.image_size[::-1] ,   mode='''bicubic''' ,   align_corners=lowercase__     )
             __A    =prediction.squeeze().cpu().numpy()
             __A    =(output * 2_5_5 / np.max(lowercase__     )).astype('''uint8'''     )
             __A    =Image.fromarray(lowercase__     )
             __A    ={}
             __A    =predicted_depth
             __A    =depth
             return output_dict





from typing import TYPE_CHECKING

from ...utils import _LazyModule


_lowerCamelCase      :					Tuple					    =      {'''tokenization_byt5''': ['''ByT5Tokenizer''']}


if TYPE_CHECKING:
   from .tokenization_byta import ByTaTokenizer
else:
   import sys

   _lowerCamelCase      :					Optional[int]					    =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


