

"""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)
__UpperCAmelCase			=					logging.getLogger(__name__)


__UpperCAmelCase			=					'Hello world! cécé herlolip'


__UpperCAmelCase			=					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 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 UpperCAmelCase__       :	List[Any]        =       BertAbsConfig(
     temp_dir="""."""				,     finetune_bert=__UpperCamelCase				,     large=__UpperCamelCase				,     share_emb=__UpperCamelCase				,     use_bert_emb=__UpperCamelCase				,     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				,     )
 UpperCAmelCase__       :	Dict        =       torch.load(__UpperCamelCase				,     lambda __UpperCamelCase				,     __UpperCamelCase   : storage   )
 UpperCAmelCase__       :	int        =       AbsSummarizer(__UpperCamelCase				,     torch.device("""cpu"""   )				,     __UpperCamelCase   )
 original.eval()

 UpperCAmelCase__       :	int        =       BertAbsSummarizer(__UpperCamelCase				,     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"""   )
 UpperCAmelCase__       :	List[Any]        =       BertTokenizer.from_pretrained("""bert-base-uncased"""   )

 # prepare the model inputs
 UpperCAmelCase__       :	List[Any]        =       tokenizer.encode("""This is sample éàalj'-."""   )
 encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__UpperCamelCase   ))   )
 UpperCAmelCase__       :	List[Any]        =       torch.tensor(__UpperCamelCase   ).unsqueeze(0   )
 UpperCAmelCase__       :	Any        =       tokenizer.encode("""This is sample 3 éàalj'-."""   )
 decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__UpperCamelCase   ))   )
 UpperCAmelCase__       :	List[str]        =       torch.tensor(__UpperCamelCase   ).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
 UpperCAmelCase__       :	List[Any]        =       encoder_input_ids
 UpperCAmelCase__       :	Optional[Any]        =       decoder_input_ids
 UpperCAmelCase__       :	str        =       None
 UpperCAmelCase__       :	Optional[int]        =       None
 UpperCAmelCase__       :	List[str]        =       None
 UpperCAmelCase__       :	Tuple        =       None
 UpperCAmelCase__       :	int        =       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
 UpperCAmelCase__       :	Tuple        =       original(__UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase   )[0]
 UpperCAmelCase__       :	Dict        =       original.generator(__UpperCamelCase   )

 UpperCAmelCase__       :	List[Any]        =       new_model(
     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase   )[0]
 UpperCAmelCase__       :	Any        =       new_model.generator(__UpperCamelCase   )

 UpperCAmelCase__       :	int        =       torch.max(torch.abs(output_converted_model - output_original_model   )   ).item()
 print("""Maximum absolute difference beween weights: {:.2f}""".format(__UpperCamelCase   )   )
 UpperCAmelCase__       :	Union[str, Any]        =       torch.max(torch.abs(output_converted_generator - output_original_generator   )   ).item()
 print("""Maximum absolute difference beween weights: {:.2f}""".format(__UpperCamelCase   )   )

 UpperCAmelCase__       :	Union[str, Any]        =       torch.allclose(__UpperCamelCase				,     __UpperCamelCase				,     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__":
      __UpperCAmelCase			=					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.',
      )
      __UpperCAmelCase			=					parser.parse_args()

      convert_bertabs_checkpoints(
          args.bertabs_checkpoint_path,
          args.pytorch_dump_folder_path,
      )












import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
       _lowerCamelCase      							=   argparse.ArgumentParser(
           description=(
               'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
               ' Distillation'
           )
       )
       parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
       parser.add_argument('--model_name', default='roberta-large', type=str)
       parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
       parser.add_argument('--vocab_transform', action='store_true')
       _lowerCamelCase      							=   parser.parse_args()

       if args.model_type == "roberta":
              _lowerCamelCase      							=   RobertaForMaskedLM.from_pretrained(args.model_name)
              _lowerCamelCase      							=   'roberta'
       elif args.model_type == "gpt2":
              _lowerCamelCase      							=   GPTaLMHeadModel.from_pretrained(args.model_name)
              _lowerCamelCase      							=   'transformer'

       _lowerCamelCase      							=   model.state_dict()
       _lowerCamelCase      							=   {}

       # Embeddings #
       if args.model_type == "gpt2":
              for param_name in ["wte.weight", "wpe.weight"]:
                     _lowerCamelCase      							=   state_dict[F'''{prefix}.{param_name}''']
       else:
              for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
                     _lowerCamelCase      							=   F'''{prefix}.embeddings.{w}.weight'''
                     _lowerCamelCase      							=   state_dict[param_name]
              for w in ["weight", "bias"]:
                     _lowerCamelCase      							=   F'''{prefix}.embeddings.LayerNorm.{w}'''
                     _lowerCamelCase      							=   state_dict[param_name]

    # Transformer Blocks #
       _lowerCamelCase      							=   0
       for teacher_idx in [0, 2, 4, 7, 9, 11]:
              if args.model_type == "gpt2":
                     for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
                            for w in ["weight", "bias"]:
                                   _lowerCamelCase      							=   state_dict[
                                       F'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
                                   ]
                     _lowerCamelCase      							=   state_dict[F'''{prefix}.h.{teacher_idx}.attn.bias''']
              else:
                     for layer in [
                         "attention.self.query",
                         "attention.self.key",
                         "attention.self.value",
                         "attention.output.dense",
                         "attention.output.LayerNorm",
                         "intermediate.dense",
                         "output.dense",
                         "output.LayerNorm",
                     ]:
                            for w in ["weight", "bias"]:
                                   _lowerCamelCase      							=   state_dict[
                                       F'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
                                   ]
              std_idx += 1

       # Language Modeling Head ###s
       if args.model_type == "roberta":
              for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
                     _lowerCamelCase      							=   state_dict[F'''{layer}''']
              if args.vocab_transform:
                     for w in ["weight", "bias"]:
                            _lowerCamelCase      							=   state_dict[F'''lm_head.dense.{w}''']
                            _lowerCamelCase      							=   state_dict[F'''lm_head.layer_norm.{w}''']
       elif args.model_type == "gpt2":
              for w in ["weight", "bias"]:
                     _lowerCamelCase      							=   state_dict[F'''{prefix}.ln_f.{w}''']
              _lowerCamelCase      							=   state_dict['lm_head.weight']

       print(F'''N layers selected for distillation: {std_idx}''')
       print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

       print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
       torch.save(compressed_sd, args.dump_checkpoint)

