






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       :       Union[str, Any]        =				logging.get_logger(__name__)
def 					snake_case_						(   lowerCAmelCase_				:  Optional[int]				,				lowerCAmelCase_				:  Any				,				lowerCAmelCase_				:  Tuple					):
							__lowercase					:							List[str]			=   os.path.abspath(lowerCAmelCase_					)
							logger.info(F"Converting TensorFlow checkpoint from {tf_path}"					)
							# Load weights from TF model
							__lowercase					:							Union[str, Any]			=   tf.train.list_variables(lowerCAmelCase_					)
							__lowercase					:							Union[str, Any]			=   []
							__lowercase					:							List[Any]			=   []
							__lowercase					:							Any			=   []
							for full_name, shape in init_vars:
														# logger.info(f"Loading TF weight {name} with shape {shape}")
														__lowercase					:							List[str]			=   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'
																					__lowercase					:							str			=   name[1:]
														# figure out how many levels deep the name is
														__lowercase					:							Any			=   0
														for _name in name:
																					if _name.startswith("""layer_with_weights"""					):
																												depth += 1
																					else:
																												break
														layer_depth.append(lowerCAmelCase_					)
														# read data
														__lowercase					:							Tuple			=   tf.train.load_variable(lowerCAmelCase_				,				lowerCAmelCase_					)
														names.append("""/""".join(lowerCAmelCase_					)					)
														arrays.append(lowerCAmelCase_					)
							logger.info(F"Read a total of {len(lowerCAmelCase_					):,} layers"					)

							# Sanity check
							if len(set(lowerCAmelCase_					)					) != 1:
														raise ValueError(F"Found layer names with different depths (layer depth {list(set(lowerCAmelCase_					)					)})"					)
							__lowercase					:							Tuple			=   list(set(lowerCAmelCase_					)					)[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(lowerCAmelCase_				,				lowerCAmelCase_					):
														__lowercase					:							Optional[Any]			=   full_name.split("""/"""					)
														__lowercase					:							Optional[Any]			=   model
														__lowercase					:							List[Any]			=   []
														for i, m_name in enumerate(lowerCAmelCase_					):
																					if m_name == ".ATTRIBUTES":
																												# variable names end with .ATTRIBUTES/VARIABLE_VALUE
																												break
																					if m_name.startswith("""layer_with_weights"""					):
																												__lowercase					:							Dict			=   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"""]					)
																																			__lowercase					:							List[Any]			=   getattr(lowerCAmelCase_				,				"""embeddings"""					)
																																			__lowercase					:							Any			=   getattr(lowerCAmelCase_				,				"""LayerNorm"""					)
																												elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
																																			# encoder layers
																																			trace.extend(["""encoder""", """layer""", str(layer_num - 4					)]					)
																																			__lowercase					:							Dict			=   getattr(lowerCAmelCase_				,				"""encoder"""					)
																																			__lowercase					:							Tuple			=   getattr(lowerCAmelCase_				,				"""layer"""					)
																																			__lowercase					:							Dict			=   pointer[layer_num - 4]
																												elif layer_num == config.num_hidden_layers + 4:
																																			# pooler layer
																																			trace.extend(["""pooler""", """dense"""]					)
																																			__lowercase					:							Optional[int]			=   getattr(lowerCAmelCase_				,				"""pooler"""					)
																																			__lowercase					:							str			=   getattr(lowerCAmelCase_				,				"""dense"""					)
																					elif m_name == "embeddings":
																												trace.append("""embeddings"""					)
																												__lowercase					:							List[Any]			=   getattr(lowerCAmelCase_				,				"""embeddings"""					)
																												if layer_num == 0:
																																			trace.append("""word_embeddings"""					)
																																			__lowercase					:							List[Any]			=   getattr(lowerCAmelCase_				,				"""word_embeddings"""					)
																												elif layer_num == 1:
																																			trace.append("""position_embeddings"""					)
																																			__lowercase					:							List[Any]			=   getattr(lowerCAmelCase_				,				"""position_embeddings"""					)
																												elif layer_num == 2:
																																			trace.append("""token_type_embeddings"""					)
																																			__lowercase					:							Optional[int]			=   getattr(lowerCAmelCase_				,				"""token_type_embeddings"""					)
																												else:
																																			raise ValueError(F"Unknown embedding layer with name {full_name}"					)
																												trace.append("""weight"""					)
																												__lowercase					:							str			=   getattr(lowerCAmelCase_				,				"""weight"""					)
																					elif m_name == "_attention_layer":
																												# self-attention layer
																												trace.extend(["""attention""", """self"""]					)
																												__lowercase					:							Any			=   getattr(lowerCAmelCase_				,				"""attention"""					)
																												__lowercase					:							List[Any]			=   getattr(lowerCAmelCase_				,				"""self"""					)
																					elif m_name == "_attention_layer_norm":
																												# output attention norm
																												trace.extend(["""attention""", """output""", """LayerNorm"""]					)
																												__lowercase					:							Optional[Any]			=   getattr(lowerCAmelCase_				,				"""attention"""					)
																												__lowercase					:							str			=   getattr(lowerCAmelCase_				,				"""output"""					)
																												__lowercase					:							str			=   getattr(lowerCAmelCase_				,				"""LayerNorm"""					)
																					elif m_name == "_attention_output_dense":
																												# output attention dense
																												trace.extend(["""attention""", """output""", """dense"""]					)
																												__lowercase					:							Dict			=   getattr(lowerCAmelCase_				,				"""attention"""					)
																												__lowercase					:							List[str]			=   getattr(lowerCAmelCase_				,				"""output"""					)
																												__lowercase					:							str			=   getattr(lowerCAmelCase_				,				"""dense"""					)
																					elif m_name == "_output_dense":
																												# output dense
																												trace.extend(["""output""", """dense"""]					)
																												__lowercase					:							List[str]			=   getattr(lowerCAmelCase_				,				"""output"""					)
																												__lowercase					:							Any			=   getattr(lowerCAmelCase_				,				"""dense"""					)
																					elif m_name == "_output_layer_norm":
																												# output dense
																												trace.extend(["""output""", """LayerNorm"""]					)
																												__lowercase					:							Union[str, Any]			=   getattr(lowerCAmelCase_				,				"""output"""					)
																												__lowercase					:							Dict			=   getattr(lowerCAmelCase_				,				"""LayerNorm"""					)
																					elif m_name == "_key_dense":
																												# attention key
																												trace.append("""key"""					)
																												__lowercase					:							Optional[Any]			=   getattr(lowerCAmelCase_				,				"""key"""					)
																					elif m_name == "_query_dense":
																												# attention query
																												trace.append("""query"""					)
																												__lowercase					:							Tuple			=   getattr(lowerCAmelCase_				,				"""query"""					)
																					elif m_name == "_value_dense":
																												# attention value
																												trace.append("""value"""					)
																												__lowercase					:							Optional[Any]			=   getattr(lowerCAmelCase_				,				"""value"""					)
																					elif m_name == "_intermediate_dense":
																												# attention intermediate dense
																												trace.extend(["""intermediate""", """dense"""]					)
																												__lowercase					:							Tuple			=   getattr(lowerCAmelCase_				,				"""intermediate"""					)
																												__lowercase					:							str			=   getattr(lowerCAmelCase_				,				"""dense"""					)
																					elif m_name == "_output_layer_norm":
																												# output layer norm
																												trace.append("""output"""					)
																												__lowercase					:							Union[str, Any]			=   getattr(lowerCAmelCase_				,				"""output"""					)
																					# weights & biases
																					elif m_name in ["bias", "beta"]:
																												trace.append("""bias"""					)
																												__lowercase					:							Tuple			=   getattr(lowerCAmelCase_				,				"""bias"""					)
																					elif m_name in ["kernel", "gamma"]:
																												trace.append("""weight"""					)
																												__lowercase					:							Dict			=   getattr(lowerCAmelCase_				,				"""weight"""					)
																					else:
																												logger.warning(F"Ignored {m_name}"					)
        # for certain layers reshape is necessary
														__lowercase					:							Optional[Any]			=   """.""".join(lowerCAmelCase_					)
														if re.match(r"""(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"""				,				lowerCAmelCase_					) or re.match(
														    r"""(\S+)\.attention\.output\.dense\.weight"""				,				lowerCAmelCase_					):
																					__lowercase					:							List[str]			=   array.reshape(pointer.data.shape					)
														if "kernel" in full_name:
																					__lowercase					:							Optional[int]			=   array.transpose()
														if pointer.shape == array.shape:
																					__lowercase					:							Optional[Any]			=   torch.from_numpy(lowerCAmelCase_					)
														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 					snake_case_						(   lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  Any				,				lowerCAmelCase_				:  Dict					):
							# Instantiate model
							logger.info(F"Loading model based on config from {config_path}..."					)
							__lowercase					:							Dict			=   BertConfig.from_json_file(lowerCAmelCase_					)
							__lowercase					:							Union[str, Any]			=   BertModel(lowerCAmelCase_					)

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

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


if __name__ == "__main__":
	lowerCamelCase       :       Optional[int]        =				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       :       Any        =				parser.parse_args()
	convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







from __future__ import annotations

import math

lowerCamelCase       :       List[Any]        =				'''2020.9.26'''
lowerCamelCase       :       str        =				'''xcodz-dot, cclaus, dhruvmanila'''
def 					snake_case_						(   lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float					):
							if not all(isinstance(lowerCAmelCase_				,				(float, int)					) for val in locals().values()					):
														__lowercase					:							str			=   F"Input values must either be float or int: {list(locals().values()					)}"
														raise TypeError(lowerCAmelCase_					)
							__lowercase					:							List[Any]			=   ((x * distance) / (z + distance)) * scale
							__lowercase					:							Tuple			=   ((y * distance) / (z + distance)) * scale
							return projected_x, projected_y
def 					snake_case_						(   lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  float					):
							if not isinstance(lowerCAmelCase_				,				lowerCAmelCase_					):
														raise TypeError("""Axis must be a str"""					)
							__lowercase					:							Optional[int]			=   locals()
							del input_variables["axis"]
							if not all(isinstance(lowerCAmelCase_				,				(float, int)					) for val in input_variables.values()					):
														__lowercase					:							List[str]			=   (
														    """Input values except axis must either be float or int: """
														    F"{list(input_variables.values()					)}"
														)
														raise TypeError(lowerCAmelCase_					)
							__lowercase					:							Tuple			=   (angle % 360) / 450 * 180 / math.pi
							if axis == "z":
														__lowercase					:							int			=   x * math.cos(lowerCAmelCase_					) - y * math.sin(lowerCAmelCase_					)
														__lowercase					:							Tuple			=   y * math.cos(lowerCAmelCase_					) + x * math.sin(lowerCAmelCase_					)
														__lowercase					:							Union[str, Any]			=   z
							elif axis == "x":
														__lowercase					:							str			=   y * math.cos(lowerCAmelCase_					) - z * math.sin(lowerCAmelCase_					)
														__lowercase					:							Dict			=   z * math.cos(lowerCAmelCase_					) + y * math.sin(lowerCAmelCase_					)
														__lowercase					:							List[str]			=   x
							elif axis == "y":
														__lowercase					:							List[str]			=   x * math.cos(lowerCAmelCase_					) - z * math.sin(lowerCAmelCase_					)
														__lowercase					:							List[str]			=   z * math.cos(lowerCAmelCase_					) + x * math.sin(lowerCAmelCase_					)
														__lowercase					:							List[Any]			=   y
							else:
														raise ValueError("""not a valid axis, choose one of 'x', 'y', 'z'"""					)

							return new_x, new_y, new_z


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(f'''{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }''')
	print(f'''{rotate(1.0, 2.0, 3.0, "y", 90.0) = }''')