


'''simple docstring'''

import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
lowercase__				     =		logging.getLogger(__name__)


lowercase__				     =		"Hello world! cécé herlolip"


lowercase__				     =		namedtuple(
    "BertAbsConfig",
    [
        "temp_dir",
        "large",
        "use_bert_emb",
        "finetune_bert",
        "encoder",
        "share_emb",
        "max_pos",
        "enc_layers",
        "enc_hidden_size",
        "enc_heads",
        "enc_ff_size",
        "enc_dropout",
        "dec_layers",
        "dec_hidden_size",
        "dec_heads",
        "dec_ff_size",
        "dec_dropout",
    ],
)






def __UpperCamelCase   (			__lowerCamelCase		: Tuple      ,  __lowerCamelCase		: List[str]				)      ->     Dict:







     '''simple docstring'''






     _a						=		BertAbsConfig(
         temp_dir="."      ,  finetune_bert=__lowerCamelCase      ,  large=__lowerCamelCase      ,  share_emb=__lowerCamelCase      ,  use_bert_emb=__lowerCamelCase      ,  encoder="bert"      ,  max_pos=512      ,  enc_layers=6      ,  enc_hidden_size=512      ,  enc_heads=8      ,  enc_ff_size=512      ,  enc_dropout=0.2      ,  dec_layers=6      ,  dec_hidden_size=768      ,  dec_heads=8      ,  dec_ff_size=2048      ,  dec_dropout=0.2      ,  )
     _a						=		torch.load(__lowerCamelCase      ,  lambda __lowerCamelCase      ,  __lowerCamelCase				: storage				)
     _a						=		AbsSummarizer(__lowerCamelCase      ,  torch.device("cpu"				)      ,  __lowerCamelCase				)
     original.eval()

     _a						=		BertAbsSummarizer(__lowerCamelCase      ,  torch.device("cpu"				)				)
     new_model.eval()

     # -------------------
     # Convert the weights
     # -------------------

     logging.info("convert the model"				)
     new_model.bert.load_state_dict(original.bert.state_dict()				)
     new_model.decoder.load_state_dict(original.decoder.state_dict()				)
     new_model.generator.load_state_dict(original.generator.state_dict()				)

     # ----------------------------------
     # Make sure the outpus are identical
     # ----------------------------------

     logging.info("Make sure that the models' outputs are identical"				)
     _a						=		BertTokenizer.from_pretrained("bert-base-uncased"				)

     # prepare the model inputs
     _a						=		tokenizer.encode("This is sample éàalj'-."				)
     encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__lowerCamelCase				))				)
     _a						=		torch.tensor(__lowerCamelCase				).unsqueeze(0				)
     _a						=		tokenizer.encode("This is sample 3 éàalj'-."				)
     decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__lowerCamelCase				))				)
     _a						=		torch.tensor(__lowerCamelCase				).unsqueeze(0				)

     # failsafe to make sure the weights reset does not affect the
     # loaded weights.
     assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight				)				) == 0

     # forward pass
     _a						=		encoder_input_ids
     _a						=		decoder_input_ids
     _a						=		_a						=		None
     _a						=		None
     _a						=		_a						=		None
     _a						=		_a						=		None
     _a						=		None

     # The original model does not apply the geneator layer immediatly but rather in
     # the beam search (where it combines softmax + linear layer). Since we already
     # apply the softmax in our generation process we only apply the linear layer here.
     # We make sure that the outputs of the full stack are identical
     _a						=		original(__lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase				)[0]
     _a						=		original.generator(__lowerCamelCase				)

     _a						=		new_model(
         __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase				)[0]
     _a						=		new_model.generator(__lowerCamelCase				)

     _a						=		torch.max(torch.abs(output_converted_model - output_original_model				)				).item()
     print("Maximum absolute difference beween weights: {:.2f}".format(__lowerCamelCase				)				)
     _a						=		torch.max(torch.abs(output_converted_generator - output_original_generator				)				).item()
     print("Maximum absolute difference beween weights: {:.2f}".format(__lowerCamelCase				)				)

     _a						=		torch.allclose(__lowerCamelCase      ,  __lowerCamelCase      ,  atol=1E-3				)
     if are_identical:
          logging.info("all weights are equal up to 1e-3"				)
     else:
          raise ValueError("the weights are different. The new model is likely different from the original one."				)

     # The model has been saved with torch.save(model) and this is bound to the exact
     # directory structure. We save the state_dict instead.
     logging.info("saving the model's state dictionary"				)
     torch.save(
         new_model.state_dict()      ,  "./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin"				)


if __name__ == "__main__":
   lowercase__				     =		argparse.ArgumentParser()
   parser.add_argument(
       "--bertabs_checkpoint_path",
       default=None,
       type=str,
       required=True,
       help="Path the official PyTorch dump.",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path",
       default=None,
       type=str,
       required=True,
       help="Path to the output PyTorch model.",
   )
   lowercase__				     =		parser.parse_args()

   convert_bertabs_checkpoints(
       args.bertabs_checkpoint_path,
       args.pytorch_dump_folder_path,
   )





'''simple docstring'''

import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def __UpperCamelCase   (			__lowerCamelCase		: BertModel      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: str				)      ->     List[str]:







     '''simple docstring'''






     _a						=		("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

     _a						=		(
         ("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				)

     _a						=		model.state_dict()

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

     def create_tf_var(__lowerCamelCase		: np.ndarray      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: tf.Session				):
          _a						=		tf.dtypes.as_dtype(tensor.dtype				)
          _a						=		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:
               _a						=		to_tf_var_name(__lowerCamelCase				)
               _a						=		state_dict[var_name].numpy()
               if any(x in var_name for x in tensors_to_transpose				):
                    _a						=		torch_tensor.T
               _a						=		create_tf_var(tensor=__lowerCamelCase      ,  name=__lowerCamelCase      ,  session=__lowerCamelCase				)
               tf.keras.backend.set_value(__lowerCamelCase      ,  __lowerCamelCase				)
               _a						=		session.run(__lowerCamelCase				)
               print(F"Successfully created {tf_name}: {np.allclose(__lowerCamelCase      ,  __lowerCamelCase				)}"				)

          _a						=		tf.train.Saver(tf.trainable_variables()				)
          saver.save(__lowerCamelCase      ,  os.path.join(__lowerCamelCase      ,  model_name.replace("-"      ,  "_"				) + ".ckpt"				)				)






def __UpperCamelCase   (			__lowerCamelCase		: str=None				)      ->     Optional[int]:







     '''simple docstring'''






     _a						=		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"				)
     _a						=		parser.parse_args(__lowerCamelCase				)

     _a						=		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()

