




"""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

UpperCamelCase__						=			logging.get_logger(__name__)




@add_end_docstrings(UpperCAmelCase_  )
class   a__			(       UpperCAmelCase_  ):





						def __init__(							self      :	Dict     ,*a__      :	List[str]     ,**a__      :	Optional[Any])						->       List[Any]:




											"""simple docstring"""







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





						def __call__(							self      :	Tuple     ,a__      :	int     ,**a__      :	Optional[Any])						->       int:




											"""simple docstring"""







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





						def       __UpperCamelCase     (							self      :	Tuple     ,**a__      :	str)						->       Optional[int]:




											"""simple docstring"""







											return {}, {}, {}





						def       __UpperCamelCase     (							self      :	Optional[int]     ,a__      :	List[str])						->       Union[str, Any]:




											"""simple docstring"""







											_lowerCAmelCase:List[Any]									=  load_image(_lowercase)
											_lowerCAmelCase:str									=  image.size
											_lowerCAmelCase:Union[str, Any]									=  self.image_processor(images=_lowercase     ,return_tensors=self.framework)
											return model_inputs





						def       __UpperCamelCase     (							self      :	str     ,a__      :	List[Any])						->       Optional[Any]:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  self.model(**_lowercase)
											return model_outputs







						def       __UpperCamelCase     (							self      :	Dict     ,a__      :	Dict)						->       Tuple:




											"""simple docstring"""







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






"""simple docstring"""






from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:List[Any]									=  ArgumentParser('''Transformers CLI tool'''      ,       usage='''transformers-cli <command> [<args>]'''    )
					_lowerCAmelCase:Union[str, Any]									=  parser.add_subparsers(help='''transformers-cli command helpers'''    )

					# Register commands
					ConvertCommand.register_subcommand(snake_case    )
					DownloadCommand.register_subcommand(snake_case    )
					EnvironmentCommand.register_subcommand(snake_case    )
					RunCommand.register_subcommand(snake_case    )
					ServeCommand.register_subcommand(snake_case    )
					UserCommands.register_subcommand(snake_case    )
					AddNewModelCommand.register_subcommand(snake_case    )
					AddNewModelLikeCommand.register_subcommand(snake_case    )
					LfsCommands.register_subcommand(snake_case    )
					PTtoTFCommand.register_subcommand(snake_case    )

					# Let's go
					_lowerCAmelCase:Union[str, Any]									=  parser.parse_args()

					if not hasattr(snake_case      ,       '''func'''    ):
										parser.print_help()
										exit(1    )

					# Run
					_lowerCAmelCase:Any									=  args.func(snake_case    )
					service.run()


if __name__ == "__main__":
				main()






