





import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase						:   Union[str, Any]			  =     logging.get_logger(__name__)

UpperCAmelCase						:   Optional[int]			  =     {
    '''facebook/data2vec-base-960h''': '''https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json''',
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}





class 						_A(					snake_case__   ):


				"""simple docstring"""


				UpperCamelCase    :  Any					    =	'''data2vec-audio'''



				def __init__(			self     ,					_A=32     ,					_A=768     ,					_A=12     ,					_A=12     ,					_A=3072     ,					_A="gelu"     ,					_A=0.1     ,					_A=0.1     ,					_A=0.1     ,					_A=0.0     ,					_A=0.1     ,					_A=0.1     ,					_A=0.0_2     ,					_A=1e-5     ,					_A="gelu"     ,					_A=(512, 512, 512, 512, 512, 512, 512)     ,					_A=(5, 2, 2, 2, 2, 2, 2)     ,					_A=(10, 3, 3, 3, 3, 2, 2)     ,					_A=False     ,					_A=16     ,					_A=19     ,					_A=5     ,					_A=0.0_5     ,					_A=10     ,					_A=2     ,					_A=0.0     ,					_A=10     ,					_A=0     ,					_A="sum"     ,					_A=False     ,					_A=False     ,					_A=256     ,					_A=(512, 512, 512, 512, 1500)     ,					_A=(5, 3, 3, 1, 1)     ,					_A=(1, 2, 3, 1, 1)     ,					_A=512     ,					_A=0     ,					_A=1     ,					_A=2     ,					_A=False     ,					_A=3     ,					_A=2     ,					_A=3     ,					_A=None     ,					**_A     ,					):
									super().__init__(**_A     ,					pad_token_id=_A     ,					bos_token_id=_A     ,					eos_token_id=_A     )
									__A	:						str       =				hidden_size
									__A	:						Optional[Any]       =				feat_extract_activation
									__A	:						Optional[Any]       =				list(_A     )
									__A	:						Any       =				list(_A     )
									__A	:						Optional[Any]       =				list(_A     )
									__A	:						Dict       =				conv_bias
									__A	:						Optional[int]       =				num_conv_pos_embeddings
									__A	:						int       =				num_conv_pos_embedding_groups
									__A	:						int       =				conv_pos_kernel_size
									__A	:						List[Any]       =				len(self.conv_dim     )
									__A	:						Any       =				num_hidden_layers
									__A	:						List[str]       =				intermediate_size
									__A	:						List[Any]       =				hidden_act
									__A	:						Tuple       =				num_attention_heads
									__A	:						Tuple       =				hidden_dropout
									__A	:						List[Any]       =				attention_dropout
									__A	:						Tuple       =				activation_dropout
									__A	:						Union[str, Any]       =				feat_proj_dropout
									__A	:						List[Any]       =				final_dropout
									__A	:						Optional[Any]       =				layerdrop
									__A	:						Tuple       =				layer_norm_eps
									__A	:						str       =				initializer_range
									__A	:						Optional[int]       =				vocab_size
									__A	:						Any       =				use_weighted_layer_sum

									if (
									    (len(self.conv_stride     ) != self.num_feat_extract_layers)
									    or (len(self.conv_kernel     ) != self.num_feat_extract_layers)
									    or (len(self.conv_dim     ) != self.num_feat_extract_layers)
									):
														raise ValueError(
														    'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
														    ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
														    F""" {len(self.conv_dim     )}`, `len(config.conv_stride) = {len(self.conv_stride     )}`,"""
														    F""" `len(config.conv_kernel) = {len(self.conv_kernel     )}`."""     )

									# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
									__A	:						Optional[int]       =				mask_time_prob
									__A	:						Optional[int]       =				mask_time_length
									__A	:						Union[str, Any]       =				mask_time_min_masks
									__A	:						Optional[int]       =				mask_feature_prob
									__A	:						List[str]       =				mask_feature_length
									__A	:						Tuple       =				mask_feature_min_masks

									# ctc loss
									__A	:						Dict       =				ctc_loss_reduction
									__A	:						List[Any]       =				ctc_zero_infinity

									# adapter
									__A	:						Tuple       =				add_adapter
									__A	:						Union[str, Any]       =				adapter_kernel_size
									__A	:						str       =				adapter_stride
									__A	:						List[Any]       =				num_adapter_layers
									__A	:						List[str]       =				output_hidden_size or hidden_size

									# SequenceClassification-specific parameter. Feel free to ignore for other classes.
									__A	:						Union[str, Any]       =				classifier_proj_size

									# XVector-specific parameters. Feel free to ignore for other classes.
									__A	:						Optional[int]       =				list(_A     )
									__A	:						Optional[int]       =				list(_A     )
									__A	:						Union[str, Any]       =				list(_A     )
									__A	:						Union[str, Any]       =				xvector_output_dim





				@property
				def 					UpperCAmelCase_		(			self     ):
									return math.prod(self.conv_stride     )











import colorsys

from PIL import Image  # type: ignore







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      float:
					__A	:						List[str]       =				x
					__A	:						str       =				y
					for step in range(a  ):  # noqa: B007
										__A	:						Union[str, Any]       =				a * a - b * b + x
										__A	:						Optional[int]       =				2 * a * b + y
										__A	:						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  _SCREAMING_SNAKE_CASE				(   a  )      ->      tuple:
					if distance == 1:
										return (0, 0, 0)
					else:
										return (2_55, 2_55, 2_55)







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      tuple:
					if distance == 1:
										return (0, 0, 0)
					else:
										return tuple(round(i * 2_55  ) for i in colorsys.hsv_to_rgb(a					,						1					,						1  )  )







def  _SCREAMING_SNAKE_CASE				(   a = 8_00					,						a = 6_00					,						a = -0.6					,						a = 0					,						a = 3.2					,						a = 50					,						a = True					,						)      ->      Image.Image:
					__A	:						str       =				Image.new('RGB'					,						(image_width, image_height)  )
					__A	:						Dict       =				img.load()

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

															__A	:						Union[str, Any]       =				get_distance(a					,						a					,						a  )

															# color the corresponding pixel based on the selected coloring-function
															if use_distance_color_coding:
																				__A	:						Optional[Any]       =				get_color_coded_rgb(a  )
															else:
																				__A	:						Dict       =				get_black_and_white_rgb(a  )

					return img


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				# colored version, full figure
				UpperCAmelCase						:   str			  =     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()




