

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_					:	Any							  =						logging.get_logger(__name__)



@add_end_docstrings(_lowerCamelCase				)
class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    def __init__(     self: Optional[int]    ,						*UpperCamelCase: Any    ,						**UpperCamelCase: int       ):






         """simple docstring"""


         super().__init__(*UpperCamelCase    ,						**UpperCamelCase       )
         requires_backends(self    ,						"""vision"""       )
         self.check_model_type(UpperCamelCase       )

    def __call__(     self: List[Any]    ,						UpperCamelCase: Union[str, List[str], "Image.Image", List["Image.Image"]]    ,						**UpperCamelCase: Dict       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: Tuple    ,						**UpperCamelCase: Optional[Any]       ):






         """simple docstring"""


         return {}, {}, {}

    def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: int       ):






         """simple docstring"""


         A__										=  load_image(UpperCamelCase       )
         A__										=  image.size
         A__										=  self.image_processor(images=UpperCamelCase    ,						return_tensors=self.framework       )
         return model_inputs

    def 				UpperCamelCase	(     self: Tuple    ,						UpperCamelCase: Tuple       ):






         """simple docstring"""


         A__										=  self.model(**UpperCamelCase       )
         return model_outputs





    def 				UpperCamelCase	(     self: Optional[int]    ,						UpperCamelCase: Dict       ):






         """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=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"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_					:	Any							  =						{
    'configuration_jukebox': [
        'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'JukeboxConfig',
        'JukeboxPriorConfig',
        'JukeboxVQVAEConfig',
    ],
    'tokenization_jukebox': ['JukeboxTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						[
     'JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST',
     'JukeboxModel',
     'JukeboxPreTrainedModel',
     'JukeboxVQVAE',
     'JukeboxPrior',
 ]

if TYPE_CHECKING:
 from .configuration_jukebox import (
     JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
     JukeboxConfig,
     JukeboxPriorConfig,
     JukeboxVQVAEConfig,
 )
 from .tokenization_jukebox import JukeboxTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_jukebox import (
      JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
      JukeboxModel,
      JukeboxPreTrainedModel,
      JukeboxPrior,
      JukeboxVQVAE,
  )

else:
 import sys

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






