
import mpmath  # for roots of unity
import numpy as np
class  lowercase__:



	"""simple docstring"""




	def __init__(       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			str=None       ,							SCREAMING_SNAKE_CASE_  :			Tuple=None       )       ->     List[Any]:
					# Input as list
					lowercase_       =					list(poly_a or [0]       )[:]
					lowercase_       =					list(poly_b or [0]       )[:]

					# Remove leading zero coefficients
					while self.polyA[-1] == 0:
									self.polyA.pop()
					lowercase_       =					len(self.polyA       )

					while self.polyB[-1] == 0:
									self.polyB.pop()
					lowercase_       =					len(self.polyB       )

					# Add 0 to make lengths equal a power of 2
					lowercase_       =					int(
					    2 ** np.ceil(np.loga(len(self.polyA       ) + len(self.polyB       ) - 1       )       )       )

					while len(self.polyA       ) < self.c_max_length:
									self.polyA.append(0       )
					while len(self.polyB       ) < self.c_max_length:
									self.polyB.append(0       )
					# A complex root used for the fourier transform
					lowercase_       =					complex(mpmath.root(x=1       ,							n=self.c_max_length       ,							k=1       )       )

					# The product
					lowercase_       =					self.__multiply()
	def 						_lowercase  (       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			str       )       ->     List[Any]:
					lowercase_       =					[[x] for x in self.polyA] if which == '''A''' else [[x] for x in self.polyB]
					# Corner case
					if len(SCREAMING_SNAKE_CASE_       ) <= 1:
									return dft[0]
					#
					lowercase_       =					self.c_max_length // 2
					while next_ncol > 0:
									lowercase_       =					[[] for i in range(SCREAMING_SNAKE_CASE_       )]
									lowercase_       =					self.root**next_ncol

									# First half of next step
									lowercase_       =					1
									for j in range(self.c_max_length // (next_ncol * 2)       ):
													for i in range(SCREAMING_SNAKE_CASE_       ):
																	new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]       )
													current_root *= root
									# Second half of next step
									lowercase_       =					1
									for j in range(self.c_max_length // (next_ncol * 2)       ):
													for i in range(SCREAMING_SNAKE_CASE_       ):
																	new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]       )
													current_root *= root
									# Update
									lowercase_       =					new_dft
									lowercase_       =					next_ncol // 2
					return dft[0]
	def 						_lowercase  (       self  :			int       )       ->     Dict:
					lowercase_       =					self.__dft('''A'''       )
					lowercase_       =					self.__dft('''B'''       )
					lowercase_       =					[[dft_a[i] * dft_b[i] for i in range(self.c_max_length       )]]
					del dft_a
					del dft_b

					# Corner Case
					if len(inverce_c[0]       ) <= 1:
									return inverce_c[0]
					# Inverse DFT
					lowercase_       =					2
					while next_ncol <= self.c_max_length:
									lowercase_       =					[[] for i in range(SCREAMING_SNAKE_CASE_       )]
									lowercase_       =					self.root ** (next_ncol // 2)
									lowercase_       =					1
									# First half of next step
									for j in range(self.c_max_length // next_ncol       ):
													for i in range(next_ncol // 2       ):
																	# Even positions
																	new_inverse_c[i].append(
																	    (
																	        inverce_c[i][j]
																	        + inverce_c[i][j + self.c_max_length // next_ncol]
																	    )
																	    / 2       )
																	# Odd positions
																	new_inverse_c[i + next_ncol // 2].append(
																	    (
																	        inverce_c[i][j]
																	        - inverce_c[i][j + self.c_max_length // next_ncol]
																	    )
																	    / (2 * current_root)       )
													current_root *= root
									# Update
									lowercase_       =					new_inverse_c
									next_ncol *= 2
					# Unpack
					lowercase_       =					[round(x[0].real       ,							8       ) + round(x[0].imag       ,							8       ) * 1J for x in inverce_c]

					# Remove leading 0's
					while inverce_c[-1] == 0:
									inverce_c.pop()
					return inverce_c




	def __str__(       self  :			str       )       ->     Optional[int]:
					lowercase_       =					'''A = ''' + ''' + '''.join(
					    f'''{coef}*x^{i}''' for coef, i in enumerate(self.polyA[: self.len_A]       )       )
					lowercase_       =					'''B = ''' + ''' + '''.join(
					    f'''{coef}*x^{i}''' for coef, i in enumerate(self.polyB[: self.len_B]       )       )
					lowercase_       =					'''A*B = ''' + ''' + '''.join(
					    f'''{coef}*x^{i}''' for coef, i in enumerate(self.product       )       )

					return f'''{a}\n{b}\n{c}'''


# Unit tests
if __name__ == "__main__":
						import doctest

						doctest.testmod()








"""simple docstring"""




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def     lowercase			(SCREAMING_SNAKE_CASE_		:				BertModel ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				str  )   ->						Dict:
						SCREAMING_SNAKE_CASE													=					('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

						SCREAMING_SNAKE_CASE													=					(
						    ('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(SCREAMING_SNAKE_CASE_  ):
												os.makedirs(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					model.state_dict()

						def to_tf_var_name(SCREAMING_SNAKE_CASE_		:				str  ):
												for patt, repl in iter(SCREAMING_SNAKE_CASE_  ):
																		SCREAMING_SNAKE_CASE													=					name.replace(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
												return F'bert/{name}'

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

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

												SCREAMING_SNAKE_CASE													=					tf.train.Saver(tf.trainable_variables()  )
												saver.save(SCREAMING_SNAKE_CASE_ ,			os.path.join(SCREAMING_SNAKE_CASE_ ,			model_name.replace('-' ,			'_'  ) + '.ckpt'  )  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any]=None  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					argparse.ArgumentParser()
						parser.add_argument('--model_name' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='model name e.g. bert-base-uncased'  )
						parser.add_argument(
						    '--cache_dir' ,			type=SCREAMING_SNAKE_CASE_ ,			default=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='Directory containing pytorch model'  )
						parser.add_argument('--pytorch_model_path' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='/path/to/<pytorch-model-name>.bin'  )
						parser.add_argument('--tf_cache_dir' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='Directory in which to save tensorflow model'  )
						SCREAMING_SNAKE_CASE													=					parser.parse_args(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					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=SCREAMING_SNAKE_CASE_ ,			ckpt_dir=args.tf_cache_dir ,			model_name=args.model_name  )


if __name__ == "__main__":
	main()


