


'''simple docstring'''


import colorsys

from PIL import Image  # type: ignore






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   ):
		_UpperCAmelCase		:       Union[str, Any]								=      x
		_UpperCAmelCase		:       int								=      y
		for step in range(UpperCAmelCase__   ):  # noqa: B007
				_UpperCAmelCase		:       Tuple								=      a * a - b * b + x
				_UpperCAmelCase		:       int								=      2 * a * b + y
				_UpperCAmelCase		:       List[str]								=      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  __lowerCAmelCase (__lowerCAmelCase   ):
		if distance == 1:
				return (0, 0, 0)
		else:
				return (255, 255, 255)






def  __lowerCAmelCase (__lowerCAmelCase   ):
		if distance == 1:
				return (0, 0, 0)
		else:
				return tuple(round(i * 255   ) for i in colorsys.hsv_to_rgb(UpperCAmelCase__      , 1      , 1   )   )






def  __lowerCAmelCase (__lowerCAmelCase = 800      , __lowerCAmelCase = 600      , __lowerCAmelCase = -0.6      , __lowerCAmelCase = 0      , __lowerCAmelCase = 3.2      , __lowerCAmelCase = 50      , __lowerCAmelCase = True      , ):
		_UpperCAmelCase		:       str								=      Image.new("RGB"      , (image_width, image_height)   )
		_UpperCAmelCase		:       int								=      img.load()

		# loop through the image-coordinates
		for image_x in range(UpperCAmelCase__   ):
				for image_y in range(UpperCAmelCase__   ):
						# determine the figure-coordinates based on the image-coordinates
						_UpperCAmelCase		:       Dict								=      figure_width / image_width * image_height
						_UpperCAmelCase		:       Any								=      figure_center_x + (image_x / image_width - 0.5) * figure_width
						_UpperCAmelCase		:       Optional[int]								=      figure_center_y + (image_y / image_height - 0.5) * figure_height

						_UpperCAmelCase		:       List[Any]								=      get_distance(UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__   )

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

		return img


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	# colored version, full figure
	lowerCamelCase__    	=     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'''


import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
						lowerCamelCase__    	=     argparse.ArgumentParser(
						    description=(
						        'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
						        ' Distillation'
						    )
						)
						parser.add_argument('--model_type', default='bert', choices=['bert'])
						parser.add_argument('--model_name', default='bert-base-uncased', type=str)
						parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
						parser.add_argument('--vocab_transform', action='store_true')
						lowerCamelCase__    	=     parser.parse_args()

						if args.model_type == "bert":
												lowerCamelCase__    	=     BertForMaskedLM.from_pretrained(args.model_name)
												lowerCamelCase__    	=     'bert'
						else:
												raise ValueError('args.model_type should be "bert".')

						lowerCamelCase__    	=     model.state_dict()
						lowerCamelCase__    	=     {}

						for w in ["word_embeddings", "position_embeddings"]:
												lowerCamelCase__    	=     state_dict[F'''{prefix}.embeddings.{w}.weight''']
						for w in ["weight", "bias"]:
												lowerCamelCase__    	=     state_dict[F'''{prefix}.embeddings.LayerNorm.{w}''']

						lowerCamelCase__    	=     0
						for teacher_idx in [0, 2, 4, 7, 9, 11]:
												for w in ["weight", "bias"]:
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
																		]
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
																		]
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
																		]

																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
																		]
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
																		]

																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
																		]
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
																		]
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
																		]
												std_idx += 1

						lowerCamelCase__    	=     state_dict['cls.predictions.decoder.weight']
						lowerCamelCase__    	=     state_dict['cls.predictions.bias']
						if args.vocab_transform:
												for w in ["weight", "bias"]:
																		lowerCamelCase__    	=     state_dict[F'''cls.predictions.transform.dense.{w}''']
																		lowerCamelCase__    	=     state_dict[F'''cls.predictions.transform.LayerNorm.{w}''']

						print(F'''N layers selected for distillation: {std_idx}''')
						print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

						print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
						torch.save(compressed_sd, args.dump_checkpoint)



