



"""simple docstring"""







from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean

def       _lowerCamelCase				(							UpperCAmelCase_ :       int     )	-> Optional[Any]:






	"""simple docstring"""





	def is_in_circle(UpperCAmelCase_ :       float,	UpperCAmelCase_ :       float     ) -> bool:
		A__											=					sqrt((x**2) + (y**2)     )
		# Our circle has a radius of 1, so a distance
		# greater than 1 would land outside the circle.
		return distance_from_centre <= 1

	# The proportion of guesses that landed in the circle
	A__											=					mean(
	    int(is_in_circle(uniform(-1.0,	1.0     ),	uniform(-1.0,	1.0     )     )     )
	    for _ in range(UpperCAmelCase_     )     )
	# The ratio of the area for circle to square is pi/4.
	A__											=					proportion * 4
	print(F"""The estimated value of pi is {pi_estimate}"""     )
	print(F"""The numpy value of pi is {pi}"""     )
	print(F"""The total error is {abs(pi - pi_estimate     )}"""     )

def       _lowerCamelCase				(							UpperCAmelCase_ :       int,	UpperCAmelCase_ :       Callable[[float], float],	UpperCAmelCase_ :       float = 0.0,	UpperCAmelCase_ :       float = 1.0,	)	-> float:






	"""simple docstring"""





	return mean(
	    function_to_integrate(uniform(UpperCAmelCase_,	UpperCAmelCase_     )     ) for _ in range(UpperCAmelCase_     )     ) * (max_value - min_value)

def       _lowerCamelCase				(							UpperCAmelCase_ :       int,	UpperCAmelCase_ :       float = 0.0,	UpperCAmelCase_ :       float = 1.0     )	-> None:






	"""simple docstring"""





	def identity_function(UpperCAmelCase_ :       float     ) -> float:
		return x

	A__											=					area_under_curve_estimator(
	    UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_     )
	A__											=					(max_value * max_value - min_value * min_value) / 2

	print("******************"     )
	print(F"""Estimating area under y=x where x varies from {min_value} to {max_value}"""     )
	print(F"""Estimated value is {estimated_value}"""     )
	print(F"""Expected value is {expected_value}"""     )
	print(F"""Total error is {abs(estimated_value - expected_value     )}"""     )
	print("******************"     )

def       _lowerCamelCase				(							UpperCAmelCase_ :       int     )	-> None:






	"""simple docstring"""





	def function_to_integrate(UpperCAmelCase_ :       float     ) -> float:
		return sqrt(4.0 - x * x     )

	A__											=					area_under_curve_estimator(
	    UpperCAmelCase_,	UpperCAmelCase_,	0.0,	2.0     )

	print("******************"     )
	print("Estimating pi using area_under_curve_estimator"     )
	print(F"""Estimated value is {estimated_value}"""     )
	print(F"""Expected value is {pi}"""     )
	print(F"""Total error is {abs(estimated_value - pi     )}"""     )
	print("******************"     )


if __name__ == "__main__":
			import doctest

			doctest.testmod()









"""simple docstring"""







import math

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


UpperCamelCase     =							logging.get_logger(__name__)

UpperCamelCase     =							{
    """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       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					List[str]							  =     "data2vec-audio"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=32						,	SCREAMING_SNAKE_CASE__=768						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=3072						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.0_2						,	SCREAMING_SNAKE_CASE__=1e-5						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=(512, 512, 512, 512, 512, 512, 512)						,	SCREAMING_SNAKE_CASE__=(5, 2, 2, 2, 2, 2, 2)						,	SCREAMING_SNAKE_CASE__=(10, 3, 3, 3, 3, 2, 2)						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=19						,	SCREAMING_SNAKE_CASE__=5						,	SCREAMING_SNAKE_CASE__=0.0_5						,	SCREAMING_SNAKE_CASE__=10						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=10						,	SCREAMING_SNAKE_CASE__=0						,	SCREAMING_SNAKE_CASE__="sum"						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=256						,	SCREAMING_SNAKE_CASE__=(512, 512, 512, 512, 1500)						,	SCREAMING_SNAKE_CASE__=(5, 3, 3, 1, 1)						,	SCREAMING_SNAKE_CASE__=(1, 2, 3, 1, 1)						,	SCREAMING_SNAKE_CASE__=512						,	SCREAMING_SNAKE_CASE__=0						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=None						,	**SCREAMING_SNAKE_CASE__						,	)      ->	Dict:
				super().__init__(**SCREAMING_SNAKE_CASE__						,	pad_token_id=SCREAMING_SNAKE_CASE__						,	bos_token_id=SCREAMING_SNAKE_CASE__						,	eos_token_id=SCREAMING_SNAKE_CASE__		)
				A__											=					hidden_size
				A__											=					feat_extract_activation
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					conv_bias
				A__											=					num_conv_pos_embeddings
				A__											=					num_conv_pos_embedding_groups
				A__											=					conv_pos_kernel_size
				A__											=					len(self.conv_dim		)
				A__											=					num_hidden_layers
				A__											=					intermediate_size
				A__											=					hidden_act
				A__											=					num_attention_heads
				A__											=					hidden_dropout
				A__											=					attention_dropout
				A__											=					activation_dropout
				A__											=					feat_proj_dropout
				A__											=					final_dropout
				A__											=					layerdrop
				A__											=					layer_norm_eps
				A__											=					initializer_range
				A__											=					vocab_size
				A__											=					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__											=					mask_time_prob
				A__											=					mask_time_length
				A__											=					mask_time_min_masks
				A__											=					mask_feature_prob
				A__											=					mask_feature_length
				A__											=					mask_feature_min_masks

				# ctc loss
				A__											=					ctc_loss_reduction
				A__											=					ctc_zero_infinity

				# adapter
				A__											=					add_adapter
				A__											=					adapter_kernel_size
				A__											=					adapter_stride
				A__											=					num_adapter_layers
				A__											=					output_hidden_size or hidden_size

				# SequenceClassification-specific parameter. Feel free to ignore for other classes.
				A__											=					classifier_proj_size

				# XVector-specific parameters. Feel free to ignore for other classes.
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					xvector_output_dim







			@property
			def   snake_case__	(      self		)      ->	List[str]:
				return math.prod(self.conv_stride		)




