




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       a					(       _A      ):

     '''simple docstring'''





     def __init__(     self       :      int ,   *__snake_case       :      Tuple ,   **__snake_case       :      Tuple    ):
         super().__init__(*__snake_case ,   **__snake_case    )
         requires_backends(self ,   '''vision'''    )
         self.check_model_type(__snake_case    )
     def __call__(     self       :      List[str] ,   __snake_case       :      Union[str, List[str], "Image.Image", List["Image.Image"]] ,   **__snake_case       :      Union[str, Any]    ):
         return super().__call__(__snake_case ,   **__snake_case    )
     def   lowerCamelCase_       (     self       :      int ,   **__snake_case       :      Union[str, Any]    ):
         return {}, {}, {}
     def   lowerCamelCase_       (     self       :      List[Any] ,   __snake_case       :      str    ):
         UpperCAmelCase_										=					load_image(__snake_case    )
         UpperCAmelCase_										=					image.size
         UpperCAmelCase_										=					self.image_processor(images=__snake_case ,   return_tensors=self.framework    )
         return model_inputs
     def   lowerCamelCase_       (     self       :      int ,   __snake_case       :      Dict    ):
         UpperCAmelCase_										=					self.model(**__snake_case    )
         return model_outputs




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











import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate



@dataclass(frozen=_A      )
class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	str       		=  field(default='audio-classification'     ,       metadata={'include_in_asdict_even_if_is_default': True}      )
     lowerCAmelCase    :	ClassVar[Features]       		=  Features({'audio': Audio()}      )
     lowerCAmelCase    :	ClassVar[Features]       		=  Features({'labels': ClassLabel}      )
     lowerCAmelCase    :	str       		=  "audio"
     lowerCAmelCase    :	str       		=  "labels"
     def   lowerCamelCase_       (     self       :      Optional[Any] ,   __snake_case       :      List[Any]    ):
         if self.label_column not in features:
             raise ValueError(F'Column {self.label_column} is not present in features.'    )
         if not isinstance(features[self.label_column] ,   __snake_case    ):
             raise ValueError(F'Column {self.label_column} is not a ClassLabel.'    )
         UpperCAmelCase_										=					copy.deepcopy(self    )
         UpperCAmelCase_										=					self.label_schema.copy()
         UpperCAmelCase_										=					features[self.label_column]
         UpperCAmelCase_										=					label_schema
         return task_template




     @property
     def   lowerCamelCase_       (     self       :      Tuple    ):
         return {
             self.audio_column: "audio",
             self.label_column: "labels",
         }





