

import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_       	=	logging.get_logger(__name__)


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	os.path.abspath(SCREAMING_SNAKE_CASE__       )
						logger.info(F'''Converting TensorFlow checkpoint from {tf_path}'''       )
						# Load weights from TF model
						snake_case_   =	tf.train.list_variables(SCREAMING_SNAKE_CASE__       )
						snake_case_   =	[]
						snake_case_   =	[]
						snake_case_   =	[]
						for full_name, shape in init_vars:
												# logger.info(f"Loading TF weight {name} with shape {shape}")
												snake_case_   =	full_name.split('''/'''       )
												if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
																		logger.info(F'''Skipping non-model layer {full_name}'''       )
																		continue
												if "optimizer" in full_name:
																		logger.info(F'''Skipping optimization layer {full_name}'''       )
																		continue
												if name[0] == "model":
																		# ignore initial 'model'
																		snake_case_   =	name[1:]
												# figure out how many levels deep the name is
												snake_case_   =	0
												for _name in name:
																		if _name.startswith('''layer_with_weights'''       ):
																								depth += 1
																		else:
																								break
												layer_depth.append(SCREAMING_SNAKE_CASE__       )
												# read data
												snake_case_   =	tf.train.load_variable(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
												names.append('''/'''.join(SCREAMING_SNAKE_CASE__       )       )
												arrays.append(SCREAMING_SNAKE_CASE__       )
						logger.info(F'''Read a total of {len(SCREAMING_SNAKE_CASE__       ):,} layers'''       )

						# Sanity check
						if len(set(SCREAMING_SNAKE_CASE__       )       ) != 1:
												raise ValueError(F'''Found layer names with different depths (layer depth {list(set(SCREAMING_SNAKE_CASE__       )       )})'''       )
						snake_case_   =	list(set(SCREAMING_SNAKE_CASE__       )       )[0]
						if layer_depth != 1:
												raise ValueError(
												    '''The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'''
												    ''' heads.'''       )

						# convert layers
						logger.info('''Converting weights...'''       )
						for full_name, array in zip(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
												snake_case_   =	full_name.split('''/'''       )
												snake_case_   =	model
												snake_case_   =	[]
												for i, m_name in enumerate(SCREAMING_SNAKE_CASE__       ):
																		if m_name == ".ATTRIBUTES":
																								# variable names end with .ATTRIBUTES/VARIABLE_VALUE
																								break
																		if m_name.startswith('''layer_with_weights'''       ):
																								snake_case_   =	int(m_name.split('''-'''       )[-1]       )
																								if layer_num <= 2:
																														# embedding layers
																														# layer_num 0: word_embeddings
																														# layer_num 1: position_embeddings
																														# layer_num 2: token_type_embeddings
																														continue
																								elif layer_num == 3:
																														# embedding LayerNorm
																														trace.extend(['''embeddings''', '''LayerNorm''']       )
																														snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''embeddings'''       )
																														snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''LayerNorm'''       )
																								elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
																														# encoder layers
																														trace.extend(['''encoder''', '''layer''', str(layer_num - 4       )]       )
																														snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''encoder'''       )
																														snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''layer'''       )
																														snake_case_   =	pointer[layer_num - 4]
																								elif layer_num == config.num_hidden_layers + 4:
																														# pooler layer
																														trace.extend(['''pooler''', '''dense''']       )
																														snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''pooler'''       )
																														snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''dense'''       )
																		elif m_name == "embeddings":
																								trace.append('''embeddings'''       )
																								snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''embeddings'''       )
																								if layer_num == 0:
																														trace.append('''word_embeddings'''       )
																														snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''word_embeddings'''       )
																								elif layer_num == 1:
																														trace.append('''position_embeddings'''       )
																														snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''position_embeddings'''       )
																								elif layer_num == 2:
																														trace.append('''token_type_embeddings'''       )
																														snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''token_type_embeddings'''       )
																								else:
																														raise ValueError(F'''Unknown embedding layer with name {full_name}'''       )
																								trace.append('''weight'''       )
																								snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''weight'''       )
																		elif m_name == "_attention_layer":
																								# self-attention layer
																								trace.extend(['''attention''', '''self''']       )
																								snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''attention'''       )
																								snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''self'''       )
																		elif m_name == "_attention_layer_norm":
																								# output attention norm
																								trace.extend(['''attention''', '''output''', '''LayerNorm''']       )
																								snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''attention'''       )
																								snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''output'''       )
																								snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''LayerNorm'''       )
																		elif m_name == "_attention_output_dense":
																								# output attention dense
																								trace.extend(['''attention''', '''output''', '''dense''']       )
																								snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''attention'''       )
																								snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''output'''       )
																								snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''dense'''       )
																		elif m_name == "_output_dense":
																								# output dense
																								trace.extend(['''output''', '''dense''']       )
																								snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''output'''       )
																								snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''dense'''       )
																		elif m_name == "_output_layer_norm":
																								# output dense
																								trace.extend(['''output''', '''LayerNorm''']       )
																								snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''output'''       )
																								snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''LayerNorm'''       )
																		elif m_name == "_key_dense":
																								# attention key
																								trace.append('''key'''       )
																								snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''key'''       )
																		elif m_name == "_query_dense":
																								# attention query
																								trace.append('''query'''       )
																								snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''query'''       )
																		elif m_name == "_value_dense":
																								# attention value
																								trace.append('''value'''       )
																								snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''value'''       )
																		elif m_name == "_intermediate_dense":
																								# attention intermediate dense
																								trace.extend(['''intermediate''', '''dense''']       )
																								snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''intermediate'''       )
																								snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''dense'''       )
																		elif m_name == "_output_layer_norm":
																								# output layer norm
																								trace.append('''output'''       )
																								snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''output'''       )
																		# weights & biases
																		elif m_name in ["bias", "beta"]:
																								trace.append('''bias'''       )
																								snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''bias'''       )
																		elif m_name in ["kernel", "gamma"]:
																								trace.append('''weight'''       )
																								snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		'''weight'''       )
																		else:
																								logger.warning(F'''Ignored {m_name}'''       )
        # for certain layers reshape is necessary
												snake_case_   =	'''.'''.join(SCREAMING_SNAKE_CASE__       )
												if re.match(R'''(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'''  ,		SCREAMING_SNAKE_CASE__       ) or re.match(
												    R'''(\S+)\.attention\.output\.dense\.weight'''  ,		SCREAMING_SNAKE_CASE__       ):
																		snake_case_   =	array.reshape(pointer.data.shape       )
												if "kernel" in full_name:
																		snake_case_   =	array.transpose()
												if pointer.shape == array.shape:
																		snake_case_   =	torch.from_numpy(SCREAMING_SNAKE_CASE__       )
												else:
																		raise ValueError(
																		    F'''Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'''
																		    F''' {array.shape}'''       )
												logger.info(F'''Successfully set variable {full_name} to PyTorch layer {trace}'''       )
						return model


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						# Instantiate model
						logger.info(F'''Loading model based on config from {config_path}...'''       )
						snake_case_   =	BertConfig.from_json_file(SCREAMING_SNAKE_CASE__       )
						snake_case_   =	BertModel(SCREAMING_SNAKE_CASE__       )

						# Load weights from checkpoint
						logger.info(F'''Loading weights from checkpoint {tf_checkpoint_path}...'''       )
						load_tfa_weights_in_bert(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )

						# Save pytorch-model
						logger.info(F'''Saving PyTorch model to {pytorch_dump_path}...'''       )
						torch.save(model.state_dict()  ,		SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
					lowerCAmelCase_       	=	argparse.ArgumentParser()
					parser.add_argument(
					    '''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow 2.x checkpoint path.'''
					)
					parser.add_argument(
					    '''--bert_config_file''',
					    type=str,
					    required=True,
					    help='''The config json file corresponding to the BERT model. This specifies the model architecture.''',
					)
					parser.add_argument(
					    '''--pytorch_dump_path''',
					    type=str,
					    required=True,
					    help='''Path to the output PyTorch model (must include filename).''',
					)
					lowerCAmelCase_       	=	parser.parse_args()
					convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)


from __future__ import annotations

from math import pi, sqrt


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):

						if inductance <= 0:
												raise ValueError('''Inductance cannot be 0 or negative'''       )

						elif capacitance <= 0:
												raise ValueError('''Capacitance cannot be 0 or negative'''       )

						else:
												return (
												    "Resonant frequency",
												    float(1 / (2 * pi * (sqrt(inductance * capacitance       )))       ),
												)


if __name__ == "__main__":
					import doctest

					doctest.testmod()