

'''simple docstring'''



from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand


def       snake_case   (	snake_case      : Namespace			) ->  Optional[int]:


    """simple docstring"""


    return ConvertCommand(
        args.model_type					,		args.tf_checkpoint					,		args.pytorch_dump_output					,		args.config					,		args.finetuning_task_name			)


_UpperCamelCase						:  List[str]							       =				"\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"



class 				_snake_case					(   a_			):

  @staticmethod
  def   _SCREAMING_SNAKE_CASE				(				_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							parser.add_parser(
          'convert'      , help='CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'      , )
      train_parser.add_argument('--model_type'      , type=_SCREAMING_SNAKE_CASE      , required=_SCREAMING_SNAKE_CASE      , help='Model\'s type.'	)
      train_parser.add_argument(
          '--tf_checkpoint'      , type=_SCREAMING_SNAKE_CASE      , required=_SCREAMING_SNAKE_CASE      , help='TensorFlow checkpoint path or folder.'	)
      train_parser.add_argument(
          '--pytorch_dump_output'      , type=_SCREAMING_SNAKE_CASE      , required=_SCREAMING_SNAKE_CASE      , help='Path to the PyTorch saved model output.'	)
      train_parser.add_argument('--config'      , type=_SCREAMING_SNAKE_CASE      , default=''      , help='Configuration file path or folder.'	)
      train_parser.add_argument(
          '--finetuning_task_name'      , type=_SCREAMING_SNAKE_CASE      , default=_SCREAMING_SNAKE_CASE      , help='Optional fine-tuning task name if the TF model was a finetuned model.'      , )
      train_parser.set_defaults(func=_SCREAMING_SNAKE_CASE	)

  def __init__(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , *_SCREAMING_SNAKE_CASE      , ):





      '''simple docstring'''

      lowerCAmelCase 						=							logging.get_logger('transformers-cli/converting'	)

      self._logger.info(F'Loading model {model_type}'	)
      lowerCAmelCase 						=							model_type
      lowerCAmelCase 						=							tf_checkpoint
      lowerCAmelCase 						=							pytorch_dump_output
      lowerCAmelCase 						=							config
      lowerCAmelCase 						=							finetuning_task_name


  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      if self._model_type == "albert":
          try:
              from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
                  convert_tf_checkpoint_to_pytorch,
              )
          except ImportError:
              raise ImportError(_SCREAMING_SNAKE_CASE	)

          convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      , self._config      , self._pytorch_dump_output	)
      elif self._model_type == "bert":
          try:
              from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
                  convert_tf_checkpoint_to_pytorch,
              )
          except ImportError:
              raise ImportError(_SCREAMING_SNAKE_CASE	)

          convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      , self._config      , self._pytorch_dump_output	)
      elif self._model_type == "funnel":
          try:
              from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
                  convert_tf_checkpoint_to_pytorch,
              )
          except ImportError:
              raise ImportError(_SCREAMING_SNAKE_CASE	)

          convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      , self._config      , self._pytorch_dump_output	)
      elif self._model_type == "t5":
          try:
              from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
          except ImportError:
              raise ImportError(_SCREAMING_SNAKE_CASE	)

          convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      , self._config      , self._pytorch_dump_output	)
      elif self._model_type == "gpt":
          from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
              convert_openai_checkpoint_to_pytorch,
          )

          convert_openai_checkpoint_to_pytorch(self._tf_checkpoint      , self._config      , self._pytorch_dump_output	)
      elif self._model_type == "transfo_xl":
          try:
              from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
                  convert_transfo_xl_checkpoint_to_pytorch,
              )
          except ImportError:
              raise ImportError(_SCREAMING_SNAKE_CASE	)

          if "ckpt" in self._tf_checkpoint.lower():
              lowerCAmelCase 						=							self._tf_checkpoint
              lowerCAmelCase 						=							''
          else:
              lowerCAmelCase 						=							self._tf_checkpoint
              lowerCAmelCase 						=							''
          convert_transfo_xl_checkpoint_to_pytorch(
              _SCREAMING_SNAKE_CASE      , self._config      , self._pytorch_dump_output      , _SCREAMING_SNAKE_CASE	)
      elif self._model_type == "gpt2":
          try:
              from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
                  convert_gpta_checkpoint_to_pytorch,
              )
          except ImportError:
              raise ImportError(_SCREAMING_SNAKE_CASE	)

          convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint      , self._config      , self._pytorch_dump_output	)
      elif self._model_type == "xlnet":
          try:
              from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
                  convert_xlnet_checkpoint_to_pytorch,
              )
          except ImportError:
              raise ImportError(_SCREAMING_SNAKE_CASE	)

          convert_xlnet_checkpoint_to_pytorch(
              self._tf_checkpoint      , self._config      , self._pytorch_dump_output      , self._finetuning_task_name	)
      elif self._model_type == "xlm":
          from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
              convert_xlm_checkpoint_to_pytorch,
          )

          convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint      , self._pytorch_dump_output	)
      elif self._model_type == "lxmert":
          from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
              convert_lxmert_checkpoint_to_pytorch,
          )

          convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint      , self._pytorch_dump_output	)
      elif self._model_type == "rembert":
          from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
              convert_rembert_tf_checkpoint_to_pytorch,
          )

          convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint      , self._config      , self._pytorch_dump_output	)
      else:
          raise ValueError(
              '--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]'	)








'''simple docstring'''



import colorsys

from PIL import Image  # type: ignore


def       snake_case   (	snake_case      : float					,		snake_case      : float					,		snake_case      : int			) ->  float:


    """simple docstring"""


    lowerCAmelCase 						=							x
    lowerCAmelCase 						=							y
    for step in range(snake_case			):  # noqa: B007
        lowerCAmelCase 						=							a * a - b * b + x
        lowerCAmelCase 						=							2 * a * b + y
        lowerCAmelCase 						=							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       snake_case   (	snake_case      : float			) ->  tuple:


    """simple docstring"""


    if distance == 1:
        return (0, 0, 0)
    else:
        return (255, 255, 255)


def       snake_case   (	snake_case      : float			) ->  tuple:


    """simple docstring"""


    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       snake_case   (	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:


    """simple docstring"""


    lowerCAmelCase 						=							Image.new('RGB'					,		(image_width, image_height)			)
    lowerCAmelCase 						=							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
            lowerCAmelCase 						=							figure_width / image_width * image_height
            lowerCAmelCase 						=							figure_center_x + (image_x / image_width - 0.5) * figure_width
            lowerCAmelCase 						=							figure_center_y + (image_y / image_height - 0.5) * figure_height

            lowerCAmelCase 						=							get_distance(snake_case					,		snake_case					,		snake_case			)

            # color the corresponding pixel based on the selected coloring-function
            if use_distance_color_coding:
                lowerCAmelCase 						=							get_color_coded_rgb(snake_case			)
            else:
                lowerCAmelCase 						=							get_black_and_white_rgb(snake_case			)

    return img


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # colored version, full figure
     _UpperCamelCase						:  Dict							       =				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()





