




"""simple docstring"""






import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()





def 							UpperCAmelCase		(		snake_case			:					str      ,       snake_case			:					str      ,       snake_case			:					str    ):
					def get_masked_lm_array(snake_case			:					str    ):
										_lowerCAmelCase:Optional[int]									=  F'masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'
										_lowerCAmelCase:Optional[int]									=  tf.train.load_variable(snake_case      ,       snake_case    )

										if "kernel" in name:
															_lowerCAmelCase:str									=  array.transpose()

										return torch.from_numpy(snake_case    )

					def get_encoder_array(snake_case			:					str    ):
										_lowerCAmelCase:Optional[Any]									=  F'encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'
										_lowerCAmelCase:int									=  tf.train.load_variable(snake_case      ,       snake_case    )

										if "kernel" in name:
															_lowerCAmelCase:int									=  array.transpose()

										return torch.from_numpy(snake_case    )

					def get_encoder_layer_array(snake_case			:					int      ,       snake_case			:					str    ):
										_lowerCAmelCase:str									=  F'encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'
										_lowerCAmelCase:Union[str, Any]									=  tf.train.load_variable(snake_case      ,       snake_case    )

										if "kernel" in name:
															_lowerCAmelCase:Tuple									=  array.transpose()

										return torch.from_numpy(snake_case    )

					def get_encoder_attention_layer_array(snake_case			:					int      ,       snake_case			:					str      ,       snake_case			:					Tuple    ):
										_lowerCAmelCase:List[Any]									=  F'encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'
										_lowerCAmelCase:Any									=  tf.train.load_variable(snake_case      ,       snake_case    )
										_lowerCAmelCase:int									=  array.reshape(snake_case    )

										if "kernel" in name:
															_lowerCAmelCase:Optional[Any]									=  array.transpose()

										return torch.from_numpy(snake_case    )

					print(F'Loading model based on config from {config_path}...'    )
					_lowerCAmelCase:List[str]									=  BertConfig.from_json_file(snake_case    )
					_lowerCAmelCase:str									=  BertForMaskedLM(snake_case    )

					# Layers
					for layer_index in range(0      ,       config.num_hidden_layers    ):
										_lowerCAmelCase:BertLayer									=  model.bert.encoder.layer[layer_index]

										# Self-attention
										_lowerCAmelCase:BertSelfAttention									=  layer.attention.self

										_lowerCAmelCase:Tuple									=  get_encoder_attention_layer_array(
										    snake_case      ,       '''_query_dense/kernel'''      ,       self_attn.query.weight.data.shape    )
										_lowerCAmelCase:Any									=  get_encoder_attention_layer_array(
										    snake_case      ,       '''_query_dense/bias'''      ,       self_attn.query.bias.data.shape    )
										_lowerCAmelCase:Tuple									=  get_encoder_attention_layer_array(
										    snake_case      ,       '''_key_dense/kernel'''      ,       self_attn.key.weight.data.shape    )
										_lowerCAmelCase:List[Any]									=  get_encoder_attention_layer_array(
										    snake_case      ,       '''_key_dense/bias'''      ,       self_attn.key.bias.data.shape    )
										_lowerCAmelCase:Dict									=  get_encoder_attention_layer_array(
										    snake_case      ,       '''_value_dense/kernel'''      ,       self_attn.value.weight.data.shape    )
										_lowerCAmelCase:Dict									=  get_encoder_attention_layer_array(
										    snake_case      ,       '''_value_dense/bias'''      ,       self_attn.value.bias.data.shape    )

										# Self-attention Output
										_lowerCAmelCase:BertSelfOutput									=  layer.attention.output

										_lowerCAmelCase:str									=  get_encoder_attention_layer_array(
										    snake_case      ,       '''_output_dense/kernel'''      ,       self_output.dense.weight.data.shape    )
										_lowerCAmelCase:str									=  get_encoder_attention_layer_array(
										    snake_case      ,       '''_output_dense/bias'''      ,       self_output.dense.bias.data.shape    )

										_lowerCAmelCase:Dict									=  get_encoder_layer_array(snake_case      ,       '''_attention_layer_norm/gamma'''    )
										_lowerCAmelCase:Optional[int]									=  get_encoder_layer_array(snake_case      ,       '''_attention_layer_norm/beta'''    )

										# Intermediate
										_lowerCAmelCase:BertIntermediate									=  layer.intermediate

										_lowerCAmelCase:List[str]									=  get_encoder_layer_array(snake_case      ,       '''_intermediate_dense/kernel'''    )
										_lowerCAmelCase:Tuple									=  get_encoder_layer_array(snake_case      ,       '''_intermediate_dense/bias'''    )

										# Output
										_lowerCAmelCase:BertOutput									=  layer.output

										_lowerCAmelCase:List[str]									=  get_encoder_layer_array(snake_case      ,       '''_output_dense/kernel'''    )
										_lowerCAmelCase:Tuple									=  get_encoder_layer_array(snake_case      ,       '''_output_dense/bias'''    )

										_lowerCAmelCase:int									=  get_encoder_layer_array(snake_case      ,       '''_output_layer_norm/gamma'''    )
										_lowerCAmelCase:int									=  get_encoder_layer_array(snake_case      ,       '''_output_layer_norm/beta'''    )

					# Embeddings
					_lowerCAmelCase:List[str]									=  get_encoder_array('''_position_embedding_layer/embeddings'''    )
					_lowerCAmelCase:Any									=  get_encoder_array('''_type_embedding_layer/embeddings'''    )
					_lowerCAmelCase:Union[str, Any]									=  get_encoder_array('''_embedding_norm_layer/gamma'''    )
					_lowerCAmelCase:str									=  get_encoder_array('''_embedding_norm_layer/beta'''    )

					# LM Head
					_lowerCAmelCase:Any									=  model.cls.predictions.transform

					_lowerCAmelCase:str									=  get_masked_lm_array('''dense/kernel'''    )
					_lowerCAmelCase:Optional[int]									=  get_masked_lm_array('''dense/bias'''    )

					_lowerCAmelCase:Dict									=  get_masked_lm_array('''layer_norm/gamma'''    )
					_lowerCAmelCase:List[Any]									=  get_masked_lm_array('''layer_norm/beta'''    )

					_lowerCAmelCase:Any									=  get_masked_lm_array('''embedding_table'''    )

					# Pooling
					_lowerCAmelCase:int									=  BertPooler(config=snake_case    )
					_lowerCAmelCase:BertPooler									=  get_encoder_array('''_pooler_layer/kernel'''    )
					_lowerCAmelCase:BertPooler									=  get_encoder_array('''_pooler_layer/bias'''    )

					# Export final model
					model.save_pretrained(snake_case    )

					# Integration test - should load without any errors ;)
					_lowerCAmelCase:List[Any]									=  BertForMaskedLM.from_pretrained(snake_case    )
					print(new_model.eval()    )

					print('''Model conversion was done sucessfully!'''    )


if __name__ == "__main__":
				UpperCamelCase__						=			argparse.ArgumentParser()
				parser.add_argument(
				    '''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow Token Dropping 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.''',
				)
				UpperCamelCase__						=			parser.parse_args()
				convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)












"""simple docstring"""






import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:Optional[int]									=  ArgumentParser(
					    description=(
					        '''PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'''
					    )    )

					# Optional arguments for the launch helper
					parser.add_argument('''--num_cores'''      ,       type=snake_case      ,       default=1      ,       help='''Number of TPU cores to use (1 or 8).'''    )

					# positional
					parser.add_argument(
					    '''training_script'''      ,       type=snake_case      ,       help=(
					        '''The full path to the single TPU training '''
					        '''program/script to be launched in parallel, '''
					        '''followed by all the arguments for the '''
					        '''training script'''
					    )      ,       )

					# rest from the training program
					parser.add_argument('''training_script_args'''      ,       nargs=snake_case    )

					return parser.parse_args()





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:int									=  parse_args()

					# Import training_script as a module.
					_lowerCAmelCase:Optional[int]									=  Path(args.training_script    )
					sys.path.append(str(script_fpath.parent.resolve()    )    )
					_lowerCAmelCase:Optional[int]									=  script_fpath.stem
					_lowerCAmelCase:Any									=  importlib.import_module(snake_case    )

					# Patch sys.argv
					_lowerCAmelCase:Dict									=  [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores    )]

					xmp.spawn(mod._mp_fn      ,       args=()      ,       nprocs=args.num_cores    )


if __name__ == "__main__":
				main()






