



"""simple docstring"""






import colorsys

from PIL import Image  # type: ignore


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case      )		->  float:
			__lowerCAmelCase :    Optional[int]				=			x
			__lowerCAmelCase :    Union[str, Any]				=			y
			for step in range(__snake_case      ):  # noqa: B007
						__lowerCAmelCase :    Optional[int]				=			a * a - b * b + x
						__lowerCAmelCase :    Any				=			2 * a * b + y
						__lowerCAmelCase :    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      _lowercase							(    __snake_case      )		->  tuple:
			if distance == 1:
						return (0, 0, 0)
			else:
						return (255, 255, 255)


def      _lowercase							(    __snake_case      )		->  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      _lowercase							(    __snake_case = 800   ,__snake_case = 600   ,__snake_case = -0.6   ,__snake_case = 0   ,__snake_case = 3.2   ,__snake_case = 50   ,__snake_case = True   ,)		->  Image.Image:
			__lowerCAmelCase :    List[Any]				=			Image.new("RGB"   ,(image_width, image_height)      )
			__lowerCAmelCase :    List[Any]				=			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 :    Any				=			figure_width / image_width * image_height
									__lowerCAmelCase :    Optional[int]				=			figure_center_x + (image_x / image_width - 0.5) * figure_width
									__lowerCAmelCase :    List[str]				=			figure_center_y + (image_y / image_height - 0.5) * figure_height

									__lowerCAmelCase :    List[str]				=			get_distance(__snake_case   ,__snake_case   ,__snake_case      )

									# color the corresponding pixel based on the selected coloring-function
									if use_distance_color_coding:
												__lowerCAmelCase :    List[Any]				=			get_color_coded_rgb(__snake_case      )
									else:
												__lowerCAmelCase :    int				=			get_black_and_white_rgb(__snake_case      )

			return img


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	# colored version, full figure
	__snake_case				:  Union[str, 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"""






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class      A__  (    __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	@register_to_config
	def __init__(	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						float			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						bool = False			,     )							->			Any:

				"""simple docstring"""

				super().__init__()

				__lowerCAmelCase :    str				=			nn.Embedding(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			nn.Embedding(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    str				=			False

				__lowerCAmelCase :    Optional[Any]				=			nn.Dropout(p=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			TaConfig(
				    vocab_size=_SCREAMING_SNAKE_CASE			,     d_model=_SCREAMING_SNAKE_CASE			,     num_heads=_SCREAMING_SNAKE_CASE			,     d_kv=_SCREAMING_SNAKE_CASE			,     d_ff=_SCREAMING_SNAKE_CASE			,     dropout_rate=_SCREAMING_SNAKE_CASE			,     feed_forward_proj=_SCREAMING_SNAKE_CASE			,     is_decoder=_SCREAMING_SNAKE_CASE			,     is_encoder_decoder=_SCREAMING_SNAKE_CASE			,     )

				__lowerCAmelCase :    str				=			nn.ModuleList()
				for lyr_num in range(_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    int				=			TaBlock(_SCREAMING_SNAKE_CASE)
							self.encoders.append(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			TaLayerNorm(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Any				=			nn.Dropout(p=_SCREAMING_SNAKE_CASE)







	def 				_SCREAMING_SNAKE_CASE  (	self:						Any			,     _SCREAMING_SNAKE_CASE:						Tuple			,     _SCREAMING_SNAKE_CASE:						str)							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			self.token_embedder(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Optional[Any]				=			encoder_input_tokens.shape[1]
				__lowerCAmelCase :    List[Any]				=			torch.arange(_SCREAMING_SNAKE_CASE			,     device=encoder_input_tokens.device)
				x += self.position_encoding(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    List[Any]				=			self.dropout_pre(_SCREAMING_SNAKE_CASE)

				# inverted the attention mask
				__lowerCAmelCase :    List[Any]				=			encoder_input_tokens.size()
				__lowerCAmelCase :    Any				=			self.get_extended_attention_mask(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				for lyr in self.encoders:
							__lowerCAmelCase :    Union[str, Any]				=			lyr(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)[0]
				__lowerCAmelCase :    int				=			self.layer_norm(_SCREAMING_SNAKE_CASE)

				return self.dropout_post(_SCREAMING_SNAKE_CASE), encoder_inputs_mask