



"""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

lowercase__					:						List[str]							=			logging.get_logger(__name__)




@add_end_docstrings(lowerCAmelCase__)
class  _UpperCAmelCase (    lowerCAmelCase__):

 def __init__(  self	: Tuple						,  *lowercase_	: Any						,  **lowercase_	: Any       ):
     super().__init__(*lowercase_						,  **lowercase_       )
     requires_backends(self						,  '''vision'''       )
     self.check_model_type(lowercase_       )

 def __call__(  self	: str						,  lowercase_	: Union[str, List[str], "Image.Image", List["Image.Image"]]						,  **lowercase_	: Union[str, Any]       ):
     return super().__call__(lowercase_						,  **lowercase_       )

 def 		_snake_case      (  self	: Tuple						,  **lowercase_	: Tuple       ):
     return {}, {}, {}

 def 		_snake_case      (  self	: Tuple						,  lowercase_	: Dict       ):
     snake_case_				:  Dict       =			load_image(lowercase_       )
     snake_case_				:  List[Any]       =			image.size
     snake_case_				:  List[Any]       =			self.image_processor(images=lowercase_						,  return_tensors=self.framework       )
     return model_inputs

 def 		_snake_case      (  self	: Optional[Any]						,  lowercase_	: Union[str, Any]       ):
     snake_case_				:  str       =			self.model(**lowercase_       )
     return model_outputs






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










"""simple docstring"""

from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info




def __lowercase    (							_a					):
    return {key.lstrip('''-'''					): value for key, value in zip(unknown_args[::2]	,    unknown_args[1::2]					)}




def __lowercase    (							):
    snake_case_				:  List[str]       =			ArgumentParser(
        '''HuggingFace Datasets CLI tool'''	,    usage='''datasets-cli <command> [<args>]'''	,    allow_abbrev=_a					)
    snake_case_				:  List[Any]       =			parser.add_subparsers(help='''datasets-cli command helpers'''					)
    set_verbosity_info()

    # Register commands
    ConvertCommand.register_subcommand(_a					)
    EnvironmentCommand.register_subcommand(_a					)
    TestCommand.register_subcommand(_a					)
    RunBeamCommand.register_subcommand(_a					)
    DummyDataCommand.register_subcommand(_a					)

    # Parse args
    snake_case_,			snake_case_				:  Optional[Any]       =			parser.parse_known_args()
    if not hasattr(_a	,    '''func'''					):
        parser.print_help()
        exit(1					)
    snake_case_				:  Optional[int]       =			parse_unknown_args(_a					)

    # Run
    snake_case_				:  Optional[int]       =			args.func(_a	,    **_a					)
    service.run()


if __name__ == "__main__":
     main()





