
"""simple docstring"""




import colorsys

from PIL import Image  # type: ignore


def     __magic_name__      ( __snake_case							:			float	,					__snake_case							:			float	,					__snake_case							:			int    )					->							float:
       lowercase       :							Dict									=				x
       lowercase       :							str									=				y
       for step in range(__snake_case    ):  # noqa: B007
              lowercase       :							List[Any]									=				a * a - b * b + x
              lowercase       :							Any									=				2 * a * b + y
              lowercase       :							Any									=				a_new

              # divergence happens for all complex number with an absolute value
              # greater than 4
              if a * a + b * b > 4:
                     break
       return step / (max_step - 1)


def     __magic_name__      ( __snake_case							:			float    )					->							tuple:
       if distance == 1:
              return (0, 0, 0)
       else:
              return (255, 255, 255)


def     __magic_name__      ( __snake_case							:			float    )					->							tuple:
       if distance == 1:
              return (0, 0, 0)
       else:
              return tuple(round(i * 255    ) for i in colorsys.hsv_to_rgb(__snake_case	,					1	,					1    )    )


def     __magic_name__      ( __snake_case							:			int = 800	,					__snake_case							:			int = 600	,					__snake_case							:			float = -0.6	,					__snake_case							:			float = 0	,					__snake_case							:			float = 3.2	,					__snake_case							:			int = 50	,					__snake_case							:			bool = True	,					)					->							Image.Image:
       lowercase       :							List[str]									=				Image.new("RGB"	,					(image_width, image_height)    )
       lowercase       :							List[str]									=				img.load()

       # loop through the image-coordinates
       for image_x in range(__snake_case    ):
              for image_y in range(__snake_case    ):
                     # determine the figure-coordinates based on the image-coordinates
                     lowercase       :							Tuple									=				figure_width / image_width * image_height
                     lowercase       :							List[Any]									=				figure_center_x + (image_x / image_width - 0.5) * figure_width
                     lowercase       :							Union[str, Any]									=				figure_center_y + (image_y / image_height - 0.5) * figure_height

                     lowercase       :							Tuple									=				get_distance(__snake_case	,					__snake_case	,					__snake_case    )

                     # color the corresponding pixel based on the selected coloring-function
                     if use_distance_color_coding:
                            lowercase       :							Optional[int]									=				get_color_coded_rgb(__snake_case    )
                     else:
                            lowercase       :							Union[str, Any]									=				get_black_and_white_rgb(__snake_case    )

       return img


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # colored version, full figure
   _A     :			Optional[Any]  						=			get_image()

   # uncomment for colored version, different section, zoomed in
   # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
   # figure_width = 0.8)

   # uncomment for black and white version, full figure
   # img = get_image(use_distance_color_coding = False)

   # uncomment to save the image
   # img.save("mandelbrot.png")

   img.show()




"""simple docstring"""




from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


_A     :			Union[str, Any]  						=			logging.get_logger(__name__)  # pylint: disable=invalid-name


def     __magic_name__      ( __snake_case							:			str    )					->							List[str]:
       if not path:
              return "pipe"

       for ext in PipelineDataFormat.SUPPORTED_FORMATS:
              if path.endswith(__snake_case    ):
                     return ext

       raise Exception(
           f"""Unable to determine file format from file extension {path}. """
           f"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"""    )


def     __magic_name__      ( __snake_case							:			str    )					->							Optional[int]:
       lowercase       :							int									=				pipeline(
           task=args.task	,					model=args.model if args.model else None	,					config=args.config	,					tokenizer=args.tokenizer	,					device=args.device	,					)
       lowercase       :							Optional[Any]									=				try_infer_format_from_ext(args.input    ) if args.format == "infer" else args.format
       lowercase       :							Optional[int]									=				PipelineDataFormat.from_str(
           format=__snake_case	,					output_path=args.output	,					input_path=args.input	,					column=args.column if args.column else nlp.default_input_names	,					overwrite=args.overwrite	,					)
       return RunCommand(__snake_case	,					__snake_case    )

class 						a__		( a_		):
   def __init__(     self      ,       _a      ,       _a							):
          lowercase       :							List[str]									=				nlp
          lowercase       :							Optional[Any]									=				reader
   @staticmethod
   def      __magic_name__     (     _a							):
          lowercase       :							Any									=				parser.add_parser("run"      ,       help="Run a pipeline through the CLI"							)
          run_parser.add_argument("--task"      ,       choices=get_supported_tasks()      ,       help="Task to run"							)
          run_parser.add_argument("--input"      ,       type=_a      ,       help="Path to the file to use for inference"							)
          run_parser.add_argument("--output"      ,       type=_a      ,       help="Path to the file that will be used post to write results."							)
          run_parser.add_argument("--model"      ,       type=_a      ,       help="Name or path to the model to instantiate."							)
          run_parser.add_argument("--config"      ,       type=_a      ,       help="Name or path to the model's config to instantiate."							)
          run_parser.add_argument(
              "--tokenizer"      ,       type=_a      ,       help="Name of the tokenizer to use. (default: same as the model name)"							)
          run_parser.add_argument(
              "--column"      ,       type=_a      ,       help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)"      ,       )
          run_parser.add_argument(
              "--format"      ,       type=_a      ,       default="infer"      ,       choices=PipelineDataFormat.SUPPORTED_FORMATS      ,       help="Input format to read from"      ,       )
          run_parser.add_argument(
              "--device"      ,       type=_a      ,       default=-1      ,       help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"      ,       )
          run_parser.add_argument("--overwrite"      ,       action="store_true"      ,       help="Allow overwriting the output file."							)
          run_parser.set_defaults(func=_a							)

   def      __magic_name__     (     self							):
          lowercase						,       lowercase       :							int									=				self._nlp, []

          for entry in self._reader:
                 lowercase       :							str									=				nlp(**_a							) if self._reader.is_multi_columns else nlp(_a							)
                 if isinstance(_a      ,       _a							):
                        outputs.append(_a							)
                 else:
                        outputs += output

        # Saving data
          if self._nlp.binary_output:
                 lowercase       :							Dict									=				self._reader.save_binary(_a							)
                 logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""							)
          else:
                 self._reader.save(_a							)


