
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()
_A   =			logging.get_logger(__name__)



def 						lowercase_      (       A__			,		A__			,		A__		)							->							Optional[Any]:
				"""simple docstring"""

				snake_case								= os.path.abspath(A__		)
				logger.info(F'Converting TensorFlow checkpoint from {tf_path}'		)
				# Load weights from TF model
				snake_case								= tf.train.list_variables(A__		)
				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(A__		)
								# read data
								snake_case								= tf.train.load_variable(A__			,		A__		)
								names.append("/".join(A__		)		)
								arrays.append(A__		)
				logger.info(F'Read a total of {len(A__		):,} layers'		)

				# Sanity check
				if len(set(A__		)		) != 1:
								raise ValueError(F'Found layer names with different depths (layer depth {list(set(A__		)		)})'		)
				snake_case								= list(set(A__		)		)[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(A__			,		A__		):
								snake_case								= full_name.split("/"		)
								snake_case								= model
								snake_case								= []
								for i, m_name in enumerate(A__		):
												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(A__			,		"embeddings"		)
																				snake_case								= getattr(A__			,		"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(A__			,		"encoder"		)
																				snake_case								= getattr(A__			,		"layer"		)
																				snake_case								= pointer[layer_num - 4]
																elif layer_num == config.num_hidden_layers + 4:
																				# pooler layer
																				trace.extend(["pooler", "dense"]		)
																				snake_case								= getattr(A__			,		"pooler"		)
																				snake_case								= getattr(A__			,		"dense"		)
												elif m_name == "embeddings":
																trace.append("embeddings"		)
																snake_case								= getattr(A__			,		"embeddings"		)
																if layer_num == 0:
																				trace.append("word_embeddings"		)
																				snake_case								= getattr(A__			,		"word_embeddings"		)
																elif layer_num == 1:
																				trace.append("position_embeddings"		)
																				snake_case								= getattr(A__			,		"position_embeddings"		)
																elif layer_num == 2:
																				trace.append("token_type_embeddings"		)
																				snake_case								= getattr(A__			,		"token_type_embeddings"		)
																else:
																				raise ValueError(F'Unknown embedding layer with name {full_name}'		)
																trace.append("weight"		)
																snake_case								= getattr(A__			,		"weight"		)
												elif m_name == "_attention_layer":
																# self-attention layer
																trace.extend(["attention", "self"]		)
																snake_case								= getattr(A__			,		"attention"		)
																snake_case								= getattr(A__			,		"self"		)
												elif m_name == "_attention_layer_norm":
																# output attention norm
																trace.extend(["attention", "output", "LayerNorm"]		)
																snake_case								= getattr(A__			,		"attention"		)
																snake_case								= getattr(A__			,		"output"		)
																snake_case								= getattr(A__			,		"LayerNorm"		)
												elif m_name == "_attention_output_dense":
																# output attention dense
																trace.extend(["attention", "output", "dense"]		)
																snake_case								= getattr(A__			,		"attention"		)
																snake_case								= getattr(A__			,		"output"		)
																snake_case								= getattr(A__			,		"dense"		)
												elif m_name == "_output_dense":
																# output dense
																trace.extend(["output", "dense"]		)
																snake_case								= getattr(A__			,		"output"		)
																snake_case								= getattr(A__			,		"dense"		)
												elif m_name == "_output_layer_norm":
																# output dense
																trace.extend(["output", "LayerNorm"]		)
																snake_case								= getattr(A__			,		"output"		)
																snake_case								= getattr(A__			,		"LayerNorm"		)
												elif m_name == "_key_dense":
																# attention key
																trace.append("key"		)
																snake_case								= getattr(A__			,		"key"		)
												elif m_name == "_query_dense":
																# attention query
																trace.append("query"		)
																snake_case								= getattr(A__			,		"query"		)
												elif m_name == "_value_dense":
																# attention value
																trace.append("value"		)
																snake_case								= getattr(A__			,		"value"		)
												elif m_name == "_intermediate_dense":
																# attention intermediate dense
																trace.extend(["intermediate", "dense"]		)
																snake_case								= getattr(A__			,		"intermediate"		)
																snake_case								= getattr(A__			,		"dense"		)
												elif m_name == "_output_layer_norm":
																# output layer norm
																trace.append("output"		)
																snake_case								= getattr(A__			,		"output"		)
												# weights & biases
												elif m_name in ["bias", "beta"]:
																trace.append("bias"		)
																snake_case								= getattr(A__			,		"bias"		)
												elif m_name in ["kernel", "gamma"]:
																trace.append("weight"		)
																snake_case								= getattr(A__			,		"weight"		)
												else:
																logger.warning(F'Ignored {m_name}'		)
																    # for certain layers reshape is necessary
								snake_case								= """.""".join(A__		)
								if re.match(r"(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"			,		A__		) or re.match(
								    r"(\S+)\.attention\.output\.dense\.weight"			,		A__		):
												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(A__		)
								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 						lowercase_      (       A__			,		A__			,		A__		)							->							List[Any]:
				"""simple docstring"""

				logger.info(F'Loading model based on config from {config_path}...'		)
				snake_case								= BertConfig.from_json_file(A__		)
				snake_case								= BertModel(A__		)

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

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


if __name__ == "__main__":
			_A   =			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).",
			)
			_A   =			parser.parse_args()
			convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)



def 						lowercase_      (       A__ = 1000		)							->							int:
    """simple docstring"""

    return sum(2 * a * ((a - 1) // 2) for a in range(3			,		n + 1		)		)


if __name__ == "__main__":
   print(solution())

