




from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter
def 	snake_case_					(UpperCamelCase :						int				,		UpperCamelCase :						int				,		UpperCamelCase :						float = 1 / sqrt(2   )   ):





							'''simple docstring'''


							_a			  =       tau * frequency / samplerate
							_a			  =       sin(snake_case__   )
							_a			  =       cos(snake_case__   )
							_a			  =       _sin / (2 * q_factor)

							_a			  =       (1 - _cos) / 2
							_a			  =       1 - _cos

							_a			  =       1 + alpha
							_a			  =       -2 * _cos
							_a			  =       1 - alpha

							_a			  =       IIRFilter(2   )
							filt.set_coefficients([aa, aa, aa]				,		[ba, ba, ba]   )
							return filt
def 	snake_case_					(UpperCamelCase :						int				,		UpperCamelCase :						int				,		UpperCamelCase :						float = 1 / sqrt(2   )   ):





							'''simple docstring'''


							_a			  =       tau * frequency / samplerate
							_a			  =       sin(snake_case__   )
							_a			  =       cos(snake_case__   )
							_a			  =       _sin / (2 * q_factor)

							_a			  =       (1 + _cos) / 2
							_a			  =       -1 - _cos

							_a			  =       1 + alpha
							_a			  =       -2 * _cos
							_a			  =       1 - alpha

							_a			  =       IIRFilter(2   )
							filt.set_coefficients([aa, aa, aa]				,		[ba, ba, ba]   )
							return filt
def 	snake_case_					(UpperCamelCase :						int				,		UpperCamelCase :						int				,		UpperCamelCase :						float = 1 / sqrt(2   )   ):





							'''simple docstring'''


							_a			  =       tau * frequency / samplerate
							_a			  =       sin(snake_case__   )
							_a			  =       cos(snake_case__   )
							_a			  =       _sin / (2 * q_factor)

							_a			  =       _sin / 2
							_a			  =       0
							_a			  =       -ba

							_a			  =       1 + alpha
							_a			  =       -2 * _cos
							_a			  =       1 - alpha

							_a			  =       IIRFilter(2   )
							filt.set_coefficients([aa, aa, aa]				,		[ba, ba, ba]   )
							return filt
def 	snake_case_					(UpperCamelCase :						int				,		UpperCamelCase :						int				,		UpperCamelCase :						float = 1 / sqrt(2   )   ):





							'''simple docstring'''


							_a			  =       tau * frequency / samplerate
							_a			  =       sin(snake_case__   )
							_a			  =       cos(snake_case__   )
							_a			  =       _sin / (2 * q_factor)

							_a			  =       1 - alpha
							_a			  =       -2 * _cos
							_a			  =       1 + alpha

							_a			  =       IIRFilter(2   )
							filt.set_coefficients([ba, ba, ba]				,		[ba, ba, ba]   )
							return filt
def 	snake_case_					(UpperCamelCase :						int				,		UpperCamelCase :						int				,		UpperCamelCase :						float				,		UpperCamelCase :						float = 1 / sqrt(2   )				,		):





							'''simple docstring'''


							_a			  =       tau * frequency / samplerate
							_a			  =       sin(snake_case__   )
							_a			  =       cos(snake_case__   )
							_a			  =       _sin / (2 * q_factor)
							_a			  =       10 ** (gain_db / 40)

							_a			  =       1 + alpha * big_a
							_a			  =       -2 * _cos
							_a			  =       1 - alpha * big_a
							_a			  =       1 + alpha / big_a
							_a			  =       -2 * _cos
							_a			  =       1 - alpha / big_a

							_a			  =       IIRFilter(2   )
							filt.set_coefficients([aa, aa, aa]				,		[ba, ba, ba]   )
							return filt
def 	snake_case_					(UpperCamelCase :						int				,		UpperCamelCase :						int				,		UpperCamelCase :						float				,		UpperCamelCase :						float = 1 / sqrt(2   )				,		):





							'''simple docstring'''


							_a			  =       tau * frequency / samplerate
							_a			  =       sin(snake_case__   )
							_a			  =       cos(snake_case__   )
							_a			  =       _sin / (2 * q_factor)
							_a			  =       10 ** (gain_db / 40)
							_a			  =       (big_a + 1) - (big_a - 1) * _cos
							_a			  =       (big_a + 1) + (big_a - 1) * _cos
							_a			  =       (big_a - 1) - (big_a + 1) * _cos
							_a			  =       (big_a - 1) + (big_a + 1) * _cos
							_a			  =       2 * sqrt(snake_case__   ) * alpha

							_a			  =       big_a * (pmc + aaa)
							_a			  =       2 * big_a * mpc
							_a			  =       big_a * (pmc - aaa)
							_a			  =       ppmc + aaa
							_a			  =       -2 * pmpc
							_a			  =       ppmc - aaa

							_a			  =       IIRFilter(2   )
							filt.set_coefficients([aa, aa, aa]				,		[ba, ba, ba]   )
							return filt

def 	snake_case_					(UpperCamelCase :						int				,		UpperCamelCase :						int				,		UpperCamelCase :						float				,		UpperCamelCase :						float = 1 / sqrt(2   )				,		):





							'''simple docstring'''


							_a			  =       tau * frequency / samplerate
							_a			  =       sin(snake_case__   )
							_a			  =       cos(snake_case__   )
							_a			  =       _sin / (2 * q_factor)
							_a			  =       10 ** (gain_db / 40)
							_a			  =       (big_a + 1) - (big_a - 1) * _cos
							_a			  =       (big_a + 1) + (big_a - 1) * _cos
							_a			  =       (big_a - 1) - (big_a + 1) * _cos
							_a			  =       (big_a - 1) + (big_a + 1) * _cos
							_a			  =       2 * sqrt(snake_case__   ) * alpha

							_a			  =       big_a * (ppmc + aaa)
							_a			  =       -2 * big_a * pmpc
							_a			  =       big_a * (ppmc - aaa)
							_a			  =       pmc + aaa
							_a			  =       2 * mpc
							_a			  =       pmc - aaa

							_a			  =       IIRFilter(2   )
							filt.set_coefficients([aa, aa, aa]				,		[ba, ba, ba]   )
							return filt







'''simple docstring'''


import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def 	snake_case_					(UpperCamelCase :						BertModel				,		UpperCamelCase :						str				,		UpperCamelCase :						str   ):





							'''simple docstring'''


							_a			  =       ('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

							_a			  =       (
							    ('''layer.''', '''layer_'''),
							    ('''word_embeddings.weight''', '''word_embeddings'''),
							    ('''position_embeddings.weight''', '''position_embeddings'''),
							    ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
							    ('''.''', '''/'''),
							    ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
							    ('''LayerNorm/bias''', '''LayerNorm/beta'''),
							    ('''weight''', '''kernel'''),
							)

							if not os.path.isdir(UpperCamelCase   ):
														os.makedirs(UpperCamelCase   )

							_a			  =       model.state_dict()

							def to_tf_var_name(UpperCamelCase :						str   ):
														for patt, repl in iter(UpperCamelCase   ):
																					_a			  =       name.replace(UpperCamelCase				,		UpperCamelCase   )
														return f'bert/{name}'

							def create_tf_var(UpperCamelCase :						np.ndarray				,		UpperCamelCase :						str				,		UpperCamelCase :						tf.Session   ):
														_a			  =       tf.dtypes.as_dtype(tensor.dtype   )
														_a			  =       tf.get_variable(dtype=UpperCamelCase				,		shape=tensor.shape				,		name=UpperCamelCase				,		initializer=tf.zeros_initializer()   )
														session.run(tf.variables_initializer([tf_var]   )   )
														session.run(UpperCamelCase   )
														return tf_var

							tf.reset_default_graph()
							with tf.Session() as session:
														for var_name in state_dict:
																					_a			  =       to_tf_var_name(UpperCamelCase   )
																					_a			  =       state_dict[var_name].numpy()
																					if any(x in var_name for x in tensors_to_transpose   ):
																												_a			  =       torch_tensor.T
																					_a			  =       create_tf_var(tensor=UpperCamelCase				,		name=UpperCamelCase				,		session=UpperCamelCase   )
																					tf.keras.backend.set_value(UpperCamelCase				,		UpperCamelCase   )
																					_a			  =       session.run(UpperCamelCase   )
																					print(f'Successfully created {tf_name}: {np.allclose(UpperCamelCase				,		UpperCamelCase   )}'   )

														_a			  =       tf.train.Saver(tf.trainable_variables()   )
														saver.save(UpperCamelCase				,		os.path.join(UpperCamelCase				,		model_name.replace('''-'''				,		'''_'''   ) + '''.ckpt'''   )   )
def 	snake_case_					(UpperCamelCase :						Tuple=None   ):





							'''simple docstring'''


							_a			  =       argparse.ArgumentParser()
							parser.add_argument('''--model_name'''				,		type=UpperCamelCase				,		required=UpperCamelCase				,		help='''model name e.g. bert-base-uncased'''   )
							parser.add_argument(
							    '''--cache_dir'''				,		type=UpperCamelCase				,		default=UpperCamelCase				,		required=UpperCamelCase				,		help='''Directory containing pytorch model'''   )
							parser.add_argument('''--pytorch_model_path'''				,		type=UpperCamelCase				,		required=UpperCamelCase				,		help='''/path/to/<pytorch-model-name>.bin'''   )
							parser.add_argument('''--tf_cache_dir'''				,		type=UpperCamelCase				,		required=UpperCamelCase				,		help='''Directory in which to save tensorflow model'''   )
							_a			  =       parser.parse_args(UpperCamelCase   )

							_a			  =       BertModel.from_pretrained(
							    pretrained_model_name_or_path=args.model_name				,		state_dict=torch.load(args.pytorch_model_path   )				,		cache_dir=args.cache_dir				,		)

							convert_pytorch_checkpoint_to_tf(model=UpperCamelCase				,		ckpt_dir=args.tf_cache_dir				,		model_name=args.model_name   )


if __name__ == "__main__":
				main()

