




'''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_		:     str          =		logging.getLogger(__name__)


UpperCamelCase_		:     Union[str, Any]          =		'''Hello world! cécé herlolip'''


UpperCamelCase_		:     Dict          =		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       __a      (  _UpperCamelCase:       List[str]   ,		_UpperCamelCase:       Dict       )		->					str:

     """simple docstring"""




     _snake_case				= 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=2_048   ,		dec_dropout=0.2   ,		)
     _snake_case				= torch.load(_UpperCamelCase   ,		lambda _UpperCamelCase   ,		_UpperCamelCase       : storage       )
     _snake_case				= AbsSummarizer(_UpperCamelCase   ,		torch.device("cpu"       )   ,		_UpperCamelCase       )
     original.eval()

     _snake_case				= 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"       )
     _snake_case				= BertTokenizer.from_pretrained("bert-base-uncased"       )

     # prepare the model inputs
     _snake_case				= tokenizer.encode("This is sample éàalj'-."       )
     encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_UpperCamelCase       ))       )
     _snake_case				= torch.tensor(_UpperCamelCase       ).unsqueeze(0       )
     _snake_case				= tokenizer.encode("This is sample 3 éàalj'-."       )
     decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_UpperCamelCase       ))       )
     _snake_case				= 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
     _snake_case				= encoder_input_ids
     _snake_case				= decoder_input_ids
     _snake_case				= _snake_case				= None
     _snake_case				= None
     _snake_case				= _snake_case				= None
     _snake_case				= _snake_case				= None
     _snake_case				= 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
     _snake_case				= original(_UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase       )[0]
     _snake_case				= original.generator(_UpperCamelCase       )

     _snake_case				= new_model(
         _UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase       )[0]
     _snake_case				= new_model.generator(_UpperCamelCase       )

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

     _snake_case				= 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_		:     List[Any]          =		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_		:     Optional[int]          =		parser.parse_args()

   convert_bertabs_checkpoints(
       args.bertabs_checkpoint_path,
       args.pytorch_dump_folder_path,
   )








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase_		:     List[Any]          =		{
    '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
    '''processing_git''': ['''GitProcessor'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_		:     Optional[Any]          =		[
       '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''GitForCausalLM''',
       '''GitModel''',
       '''GitPreTrainedModel''',
       '''GitVisionModel''',
   ]

if TYPE_CHECKING:
   from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
   from .processing_git import GitProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_git import (
          GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          GitForCausalLM,
          GitModel,
          GitPreTrainedModel,
          GitVisionModel,
      )

else:
   import sys

   UpperCamelCase_		:     List[Any]          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


