





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


a_ =			"""Hello world! cécé herlolip"""


a_ =			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 		__lowercase     (   snake_case_  :  str				,snake_case_  :  Tuple						)   ->str:



    '''simple docstring'''







    __A    :						Any						 =     BertAbsConfig(
        temp_dir='''.'''				,finetune_bert=snake_case_				,large=snake_case_				,share_emb=snake_case_				,use_bert_emb=snake_case_				,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    :						Union[str, Any]						 =     torch.load(snake_case_				,lambda snake_case_				,snake_case_						: storage						)
    __A    :						Tuple						 =     AbsSummarizer(snake_case_				,torch.device('''cpu'''						)				,snake_case_						)
    original.eval()

    __A    :						Optional[int]						 =     BertAbsSummarizer(snake_case_				,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    :						Optional[Any]						 =     BertTokenizer.from_pretrained('''bert-base-uncased'''						)

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

    __A    :						Any						 =     new_model(
        snake_case_				,snake_case_				,snake_case_				,snake_case_				,snake_case_						)[0]
    __A    :						Dict						 =     new_model.generator(snake_case_						)

    __A    :						Dict						 =     torch.max(torch.abs(output_converted_model - output_original_model						)						).item()
    print('''Maximum absolute difference beween weights: {:.2f}'''.format(snake_case_						)						)
    __A    :						List[Any]						 =     torch.max(torch.abs(output_converted_generator - output_original_generator						)						).item()
    print('''Maximum absolute difference beween weights: {:.2f}'''.format(snake_case_						)						)

    __A    :						int						 =     torch.allclose(snake_case_				,snake_case_				,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__":
       a_ =			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.""",
       )
       a_ =			parser.parse_args()

       convert_bertabs_checkpoints(
           args.bertabs_checkpoint_path,
           args.pytorch_dump_folder_path,
       )











"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_ =			logging.get_logger(__name__)

a_ =			{
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    """bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/config.json""",
    """bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/config.json""",
    """bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/config.json""",
    """bert-base-multilingual-uncased""": """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json""",
    """bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json""",
    """bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/config.json""",
    """bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/config.json""",
    """bert-large-uncased-whole-word-masking""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-base-cased-finetuned-mrpc""": """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json""",
    """bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json""",
    """bert-base-german-dbmdz-uncased""": """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese""": """https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-cased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-uncased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"""
    ),
    """wietsedv/bert-base-dutch-cased""": """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json""",
    # See all BERT models at https://huggingface.co/models?filter=bert
}






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """bert"""

 def __init__(    self					,						__lowerCamelCase=3_0522					,						__lowerCamelCase=768					,						__lowerCamelCase=12					,						__lowerCamelCase=12					,						__lowerCamelCase=3072					,						__lowerCamelCase="gelu"					,						__lowerCamelCase=0.1					,						__lowerCamelCase=0.1					,						__lowerCamelCase=512					,						__lowerCamelCase=2					,						__lowerCamelCase=0.0_2					,						__lowerCamelCase=1e-1_2					,						__lowerCamelCase=0					,						__lowerCamelCase="absolute"					,						__lowerCamelCase=True					,						__lowerCamelCase=None					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     super().__init__(pad_token_id=__lowerCamelCase					,						**__lowerCamelCase  )

     __A    :						Dict						 =     vocab_size
     __A    :						Any						 =     hidden_size
     __A    :						str						 =     num_hidden_layers
     __A    :						int						 =     num_attention_heads
     __A    :						Optional[int]						 =     hidden_act
     __A    :						List[Any]						 =     intermediate_size
     __A    :						Tuple						 =     hidden_dropout_prob
     __A    :						Optional[int]						 =     attention_probs_dropout_prob
     __A    :						Optional[int]						 =     max_position_embeddings
     __A    :						Optional[Any]						 =     type_vocab_size
     __A    :						Optional[Any]						 =     initializer_range
     __A    :						Dict						 =     layer_norm_eps
     __A    :						Any						 =     position_embedding_type
     __A    :						Optional[int]						 =     use_cache
     __A    :						str						 =     classifier_dropout






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     if self.task == "multiple-choice":
         __A    :						str						 =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
         __A    :						Tuple						 =     {0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
             ('''token_type_ids''', dynamic_axis),
         ]  )




