





import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def   a_    ( lowerCAmelCase_      :			BertModel,	lowerCAmelCase_      :			str,	lowerCAmelCase_      :			str	):
   __lowerCAmelCase								=	('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

   __lowerCAmelCase								=	(
       ('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(lowerCAmelCase_	):
      os.makedirs(lowerCAmelCase_	)

   __lowerCAmelCase								=	model.state_dict()

   def to_tf_var_name(lowerCAmelCase_      :			str	):
      for patt, repl in iter(lowerCAmelCase_	):
         __lowerCAmelCase								=	name.replace(lowerCAmelCase_,	lowerCAmelCase_	)
      return F"""bert/{name}"""

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

   tf.reset_default_graph()
   with tf.Session() as session:
      for var_name in state_dict:
         __lowerCAmelCase								=	to_tf_var_name(lowerCAmelCase_	)
         __lowerCAmelCase								=	state_dict[var_name].numpy()
         if any(x in var_name for x in tensors_to_transpose	):
            __lowerCAmelCase								=	torch_tensor.T
         __lowerCAmelCase								=	create_tf_var(tensor=lowerCAmelCase_,	name=lowerCAmelCase_,	session=lowerCAmelCase_	)
         tf.keras.backend.set_value(lowerCAmelCase_,	lowerCAmelCase_	)
         __lowerCAmelCase								=	session.run(lowerCAmelCase_	)
         print(F"""Successfully created {tf_name}: {np.allclose(lowerCAmelCase_,	lowerCAmelCase_	)}"""	)

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

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

   __lowerCAmelCase								=	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=lowerCAmelCase_,	ckpt_dir=args.tf_cache_dir,	model_name=args.model_name	)


if __name__ == "__main__":
     main()








import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json

def   a_    ( lowerCAmelCase_      :			Optional[int]	):
   __lowerCAmelCase								=	filter(lambda lowerCAmelCase_	: p.requires_grad,	model.parameters()	)
   __lowerCAmelCase								=	sum([np.prod(p.size()	) for p in model_parameters]	)
   return params


_snake_case  :    Dict						    =  logging.getLogger(__name__)

def   a_    ( lowerCAmelCase_      :			Optional[int],	lowerCAmelCase_      :			Optional[int]	):
   if metric == "rouge2":
      __lowerCAmelCase								=	'{val_avg_rouge2:.4f}-{step_count}'
   elif metric == "bleu":
      __lowerCAmelCase								=	'{val_avg_bleu:.4f}-{step_count}'
   elif metric == "em":
      __lowerCAmelCase								=	'{val_avg_em:.4f}-{step_count}'
   else:
      raise NotImplementedError(
          F"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
          ' function.'	)

   __lowerCAmelCase								=	ModelCheckpoint(
       dirpath=lowerCAmelCase_,	filename=lowerCAmelCase_,	monitor=F"""val_{metric}""",	mode='max',	save_top_k=3,	every_n_epochs=1,	)
   return checkpoint_callback

def   a_    ( lowerCAmelCase_      :			Union[str, Any],	lowerCAmelCase_      :			Any	):
   return EarlyStopping(
       monitor=F"""val_{metric}""",	mode='min' if 'loss' in metric else 'max',	patience=lowerCAmelCase_,	verbose=lowerCAmelCase_,	)




class 				_UpperCAmelCase    (    pl.Callback    ):

  """simple docstring"""


  def 				lowercase		(				self :		Tuple					,  lowerCAmelCase_ :		List[str]					,  lowerCAmelCase_ :		int     )							->						Any:
     __lowerCAmelCase								=	{f"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups     )}
     pl_module.logger.log_metrics(lowerCAmelCase_     )






  @rank_zero_only
  def 				lowercase		(				self :		Optional[int]					,  lowerCAmelCase_ :		pl.Trainer					,  lowerCAmelCase_ :		pl.LightningModule					,  lowerCAmelCase_ :		str					,  lowerCAmelCase_ :		List[Any]=True     )							->						None:
     logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""     )
     __lowerCAmelCase								=	trainer.callback_metrics
     trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}     )
     # Log results
     __lowerCAmelCase								=	Path(pl_module.hparams.output_dir     )
     if type_path == "test":
        __lowerCAmelCase								=	od / 'test_results.txt'
        __lowerCAmelCase								=	od / 'test_generations.txt'
     else:
        # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
        # If people want this it will be easy enough to add back.
        __lowerCAmelCase								=	od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
        __lowerCAmelCase								=	od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
        results_file.parent.mkdir(exist_ok=lowerCAmelCase_     )
        generations_file.parent.mkdir(exist_ok=lowerCAmelCase_     )
     with open(lowerCAmelCase_					,  'a+'     ) as writer:
        for key in sorted(lowerCAmelCase_     ):
           if key in ["log", "progress_bar", "preds"]:
              continue
           __lowerCAmelCase								=	metrics[key]
           if isinstance(lowerCAmelCase_					,  torch.Tensor     ):
              __lowerCAmelCase								=	val.item()
           __lowerCAmelCase								=	f"""{key}: {val:.6f}\n"""
           writer.write(lowerCAmelCase_     )

     if not save_generations:
        return

     if "preds" in metrics:
        __lowerCAmelCase								=	'\n'.join(metrics['preds']     )
        generations_file.open('w+'     ).write(lowerCAmelCase_     )






  @rank_zero_only
  def 				lowercase		(				self :		Union[str, Any]					,  lowerCAmelCase_ :		Union[str, Any]					,  lowerCAmelCase_ :		List[str]     )							->						Dict:
     try:
        __lowerCAmelCase								=	pl_module.model.model.num_parameters()
     except AttributeError:
        __lowerCAmelCase								=	pl_module.model.num_parameters()

     __lowerCAmelCase								=	count_trainable_parameters(lowerCAmelCase_     )
     # mp stands for million parameters
     trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1e6, 'grad_mp': n_trainable_pars / 1e6}     )






  @rank_zero_only
  def 				lowercase		(				self :		int					,  lowerCAmelCase_ :		pl.Trainer					,  lowerCAmelCase_ :		pl.LightningModule     )							->						Any:
     save_json(pl_module.metrics					,  pl_module.metrics_save_path     )
     return self._write_logs(lowerCAmelCase_					,  lowerCAmelCase_					,  'test'     )






  @rank_zero_only
  def 				lowercase		(				self :		List[Any]					,  lowerCAmelCase_ :		pl.Trainer					,  lowerCAmelCase_ :		Any     )							->						int:
     save_json(pl_module.metrics					,  pl_module.metrics_save_path     )
     # Uncommenting this will save val generations
     # return self._write_logs(trainer, pl_module, "valid")

