


'''simple docstring'''






import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
 raise RuntimeError("At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training")

# TF training parameters
a_   :      Union[str, Any]				    =      False
a_   :      str				    =      False






def 		_A			(lowerCAmelCase__					:Namespace						)				->					List[str]:




     '''simple docstring'''



     return TrainCommand(lowerCAmelCase__						)






class 	a       (       _SCREAMING_SNAKE_CASE    ):
 @staticmethod
 def 							__UpperCAmelCase						(	__magic_name__  )   ->							Dict:
      _a      =							parser.add_parser('train' ,			help='CLI tool to train a model on a task.'  )

      train_parser.add_argument(
          '--train_data' ,			type=__magic_name__ ,			required=__magic_name__ ,			help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.' ,			)
      train_parser.add_argument(
          '--column_label' ,			type=__magic_name__ ,			default=0 ,			help='Column of the dataset csv file with example labels.'  )
      train_parser.add_argument(
          '--column_text' ,			type=__magic_name__ ,			default=1 ,			help='Column of the dataset csv file with example texts.'  )
      train_parser.add_argument(
          '--column_id' ,			type=__magic_name__ ,			default=2 ,			help='Column of the dataset csv file with example ids.'  )
      train_parser.add_argument(
          '--skip_first_row' ,			action='store_true' ,			help='Skip the first row of the csv file (headers).'  )

      train_parser.add_argument('--validation_data' ,			type=__magic_name__ ,			default='' ,			help='path to validation dataset.'  )
      train_parser.add_argument(
          '--validation_split' ,			type=__magic_name__ ,			default=0.1 ,			help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.' ,			)

      train_parser.add_argument('--output' ,			type=__magic_name__ ,			default='./' ,			help='path to saved the trained model.'  )

      train_parser.add_argument(
          '--task' ,			type=__magic_name__ ,			default='text_classification' ,			help='Task to train the model on.'  )
      train_parser.add_argument(
          '--model' ,			type=__magic_name__ ,			default='bert-base-uncased' ,			help='Model\'s name or path to stored model.'  )
      train_parser.add_argument('--train_batch_size' ,			type=__magic_name__ ,			default=32 ,			help='Batch size for training.'  )
      train_parser.add_argument('--valid_batch_size' ,			type=__magic_name__ ,			default=64 ,			help='Batch size for validation.'  )
      train_parser.add_argument('--learning_rate' ,			type=__magic_name__ ,			default=3e-5 ,			help='Learning rate.'  )
      train_parser.add_argument('--adam_epsilon' ,			type=__magic_name__ ,			default=1e-08 ,			help='Epsilon for Adam optimizer.'  )
      train_parser.set_defaults(func=__magic_name__  )
 def __init__(	self ,			__magic_name__  )   ->							str:
      _a      =							logging.get_logger('transformers-cli/training'  )

      _a      =							'tf' if is_tf_available() else 'torch'

      os.makedirs(args.output ,			exist_ok=__magic_name__  )
      _a      =							args.output

      _a      =							args.column_label
      _a      =							args.column_text
      _a      =							args.column_id

      self.logger.info(f'Loading {args.task} pipeline for {args.model}'  )
      if args.task == "text_classification":
           _a      =							TextClassificationPipeline.from_pretrained(args.model  )
      elif args.task == "token_classification":
           raise NotImplementedError
      elif args.task == "question_answering":
           raise NotImplementedError

      self.logger.info(f'Loading dataset from {args.train_data}'  )
      _a      =							Processor.create_from_csv(
          args.train_data ,			column_label=args.column_label ,			column_text=args.column_text ,			column_id=args.column_id ,			skip_first_row=args.skip_first_row ,			)
      _a      =							None
      if args.validation_data:
           self.logger.info(f'Loading validation dataset from {args.validation_data}'  )
           _a      =							Processor.create_from_csv(
               args.validation_data ,			column_label=args.column_label ,			column_text=args.column_text ,			column_id=args.column_id ,			skip_first_row=args.skip_first_row ,			)

      _a      =							args.validation_split
      _a      =							args.train_batch_size
      _a      =							args.valid_batch_size
      _a      =							args.learning_rate
      _a      =							args.adam_epsilon
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      if self.framework == "tf":
           return self.run_tf()
      return self.run_torch()
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      raise NotImplementedError




 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      self.pipeline.fit(
          self.train_dataset ,			validation_data=self.valid_dataset ,			validation_split=self.validation_split ,			learning_rate=self.learning_rate ,			adam_epsilon=self.adam_epsilon ,			train_batch_size=self.train_batch_size ,			valid_batch_size=self.valid_batch_size ,			)

      # Save trained pipeline
      self.pipeline.save_pretrained(self.output  )





'''simple docstring'''






import colorsys

from PIL import Image  # type: ignore






def 		_A			(lowerCAmelCase__					:float			,  lowerCAmelCase__					:float			,  lowerCAmelCase__					:int						)				->					float:




     '''simple docstring'''



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




     '''simple docstring'''



     if distance == 1:
          return (0, 0, 0)
     else:
          return (2_55, 2_55, 2_55)






def 		_A			(lowerCAmelCase__					:float						)				->					tuple:




     '''simple docstring'''



     if distance == 1:
          return (0, 0, 0)
     else:
          return tuple(round(i * 2_55						) for i in colorsys.hsv_to_rgb(lowerCAmelCase__			,  1			,  1						)						)






def 		_A			(lowerCAmelCase__					:int = 8_00			,  lowerCAmelCase__					:int = 6_00			,  lowerCAmelCase__					:float = -0.6			,  lowerCAmelCase__					:float = 0			,  lowerCAmelCase__					:float = 3.2			,  lowerCAmelCase__					:int = 50			,  lowerCAmelCase__					:bool = True			,  )				->					Image.Image:




     '''simple docstring'''



     _a      =							Image.new('RGB'			,  (image_width, image_height)						)
     _a      =							img.load()

     # loop through the image-coordinates
     for image_x in range(lowerCAmelCase__						):
          for image_y in range(lowerCAmelCase__						):
               # determine the figure-coordinates based on the image-coordinates
               _a      =							figure_width / image_width * image_height
               _a      =							figure_center_x + (image_x / image_width - 0.5) * figure_width
               _a      =							figure_center_y + (image_y / image_height - 0.5) * figure_height

               _a      =							get_distance(lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__						)

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

     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()

