

"""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__					:  Optional[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__					:  Optional[Any]					   =					torch.load(__UpperCamelCase							,    lambda __UpperCamelCase							,    __UpperCamelCase      : storage      )
 UpperCAmelCase__					:  int					   =					AbsSummarizer(__UpperCamelCase							,    torch.device("""cpu"""      )							,    __UpperCamelCase      )
 original.eval()

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

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

 UpperCAmelCase__					:  List[Any]					   =					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__					:  Optional[int]					   =					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,
      )








"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase =				logging.get_logger(__name__)

__UpperCAmelCase =				{
    'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json',
    'microsoft/markuplm-large': 'https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json',
}







class      __lowercase			(    __lowerCamelCase					):
  snake_case_				       =       """markuplm"""

  def __init__(  self		:			str					,A		:			List[Any]=30_522					,A		:			Tuple=768					,A		:			str=12					,A		:			int=12					,A		:			int=3_072					,A		:			Optional[int]="gelu"					,A		:			Optional[int]=0.1					,A		:			Optional[int]=0.1					,A		:			Any=512					,A		:			Any=2					,A		:			str=0.0_2					,A		:			int=1e-12					,A		:			int=0					,A		:			str=0					,A		:			List[Any]=2					,A		:			List[str]=256					,A		:			Union[str, Any]=1_024					,A		:			List[Any]=216					,A		:			Any=1_001					,A		:			Optional[int]=32					,A		:			Any=50					,A		:			int="absolute"					,A		:			Dict=True					,A		:			int=None					,**A		:			Optional[int]					,):






   '''simple docstring'''
   super().__init__(
       pad_token_id=A					,bos_token_id=A					,eos_token_id=A					,**A					,)
   UpperCAmelCase__					:  Dict					   =					vocab_size
   UpperCAmelCase__					:  Union[str, Any]					   =					hidden_size
   UpperCAmelCase__					:  Optional[int]					   =					num_hidden_layers
   UpperCAmelCase__					:  Tuple					   =					num_attention_heads
   UpperCAmelCase__					:  Optional[Any]					   =					hidden_act
   UpperCAmelCase__					:  Any					   =					intermediate_size
   UpperCAmelCase__					:  Dict					   =					hidden_dropout_prob
   UpperCAmelCase__					:  Optional[Any]					   =					attention_probs_dropout_prob
   UpperCAmelCase__					:  Optional[int]					   =					max_position_embeddings
   UpperCAmelCase__					:  Dict					   =					type_vocab_size
   UpperCAmelCase__					:  Optional[Any]					   =					initializer_range
   UpperCAmelCase__					:  str					   =					layer_norm_eps
   UpperCAmelCase__					:  Any					   =					position_embedding_type
   UpperCAmelCase__					:  int					   =					use_cache
   UpperCAmelCase__					:  List[str]					   =					classifier_dropout
   # additional properties
   UpperCAmelCase__					:  Optional[int]					   =					max_depth
   UpperCAmelCase__					:  List[str]					   =					max_xpath_tag_unit_embeddings
   UpperCAmelCase__					:  Any					   =					max_xpath_subs_unit_embeddings
   UpperCAmelCase__					:  str					   =					tag_pad_id
   UpperCAmelCase__					:  Dict					   =					subs_pad_id
   UpperCAmelCase__					:  List[str]					   =					xpath_unit_hidden_size





