

from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
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():
    from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

__A			:					Optional[Any]						=			logging.get_logger(__name__)





@add_end_docstrings(__snake_case		)
class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   def __init__(       self					:			Any     , *__lowerCamelCase					:			int     , **__lowerCamelCase					:			Union[str, Any]			):
       super().__init__(*_lowercase     , **_lowercase			)
       self.check_model_type(_lowercase			)





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			Tuple=None     , __lowerCamelCase					:			str=None     , __lowerCamelCase					:			Union[str, Any]=None     , **__lowerCamelCase					:			str			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   {}, {}
       if padding is not None:
           SCREAMING_SNAKE_CASE										=   padding
       if truncation is not None:
           SCREAMING_SNAKE_CASE										=   truncation
       if top_k is not None:
           SCREAMING_SNAKE_CASE										=   top_k
       return preprocess_params, {}, postprocess_params





   def __call__(       self					:			Any     , __lowerCamelCase					:			Union["Image.Image", str]     , __lowerCamelCase					:			str = None     , **__lowerCamelCase					:			int			):
       if isinstance(_lowercase     , (Image.Image, str)			) and isinstance(_lowercase     , _lowercase			):
           SCREAMING_SNAKE_CASE										=   {"image": image, "question": question}
       else:
           SCREAMING_SNAKE_CASE										=   image
       SCREAMING_SNAKE_CASE										=   super().__call__(_lowercase     , **_lowercase			)
       return results





   def      _snake_case		(       self					:			Union[str, Any]     , __lowerCamelCase					:			str     , __lowerCamelCase					:			Optional[int]=False     , __lowerCamelCase					:			Tuple=False			):
       SCREAMING_SNAKE_CASE										=   load_image(inputs["image"]			)
       SCREAMING_SNAKE_CASE										=   self.tokenizer(
           inputs["question"]     , return_tensors=self.framework     , padding=_lowercase     , truncation=_lowercase			)
       SCREAMING_SNAKE_CASE										=   self.image_processor(images=_lowercase     , return_tensors=self.framework			)
       model_inputs.update(_lowercase			)
       return model_inputs





   def      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			Tuple			):
       SCREAMING_SNAKE_CASE										=   self.model(**_lowercase			)
       return model_outputs





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			Union[str, Any]     , __lowerCamelCase					:			int=5			):
       if top_k > self.model.config.num_labels:
           SCREAMING_SNAKE_CASE										=   self.model.config.num_labels

       if self.framework == "pt":
           SCREAMING_SNAKE_CASE										=   model_outputs.logits.sigmoid()[0]
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   probs.topk(_lowercase			)
       else:
           raise ValueError(f"Unsupported framework: {self.framework}"			)

       SCREAMING_SNAKE_CASE										=   scores.tolist()
       SCREAMING_SNAKE_CASE										=   ids.tolist()
       return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(_lowercase     , _lowercase			)]





'''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 			lowerCamelCase(							)		->		int:
			A_									= ArgumentParser('Transformers CLI tool'       ,usage='transformers-cli <command> [<args>]'     )
			A_									= parser.add_subparsers(help='transformers-cli command helpers'     )

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

			# Let's go
			A_									= parser.parse_args()

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

			# Run
			A_									= args.func(SCREAMING_SNAKE_CASE_     )
			service.run()


if __name__ == "__main__":
							main()




