

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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     List[Any]   ,      SCREAMING_SNAKE_CASE__      :     int				)							->				Optional[int]:





    '''simple docstring'''






    A__   				=       BertAbsConfig(
        temp_dir='.'   ,      finetune_bert=SCREAMING_SNAKE_CASE__   ,      large=SCREAMING_SNAKE_CASE__   ,      share_emb=SCREAMING_SNAKE_CASE__   ,      use_bert_emb=SCREAMING_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__   				=       torch.load(SCREAMING_SNAKE_CASE__   ,      lambda SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				: storage				)
    A__   				=       AbsSummarizer(SCREAMING_SNAKE_CASE__   ,      torch.device('cpu'				)   ,      SCREAMING_SNAKE_CASE__				)
    original.eval()

    A__   				=       BertAbsSummarizer(SCREAMING_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__   				=       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(SCREAMING_SNAKE_CASE__				))				)
    A__   				=       torch.tensor(SCREAMING_SNAKE_CASE__				).unsqueeze(0				)
    A__   				=       tokenizer.encode('This is sample 3 éàalj\'-.'				)
    decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(SCREAMING_SNAKE_CASE__				))				)
    A__   				=       torch.tensor(SCREAMING_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__   				=       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(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)[0]
    A__   				=       original.generator(SCREAMING_SNAKE_CASE__				)

    A__   				=       new_model(
        SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)[0]
    A__   				=       new_model.generator(SCREAMING_SNAKE_CASE__				)

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

    A__   				=       torch.allclose(SCREAMING_SNAKE_CASE__   ,      SCREAMING_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__":
     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,
     )




import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowercase_			=  logging.get_logger(__name__)

lowercase_			=  {
    "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/config.json",
    # See all BART models at https://huggingface.co/models?filter=bart
}


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							'bart'
    lowerCamelCase		     =							['past_key_values']
    lowerCamelCase		     =							{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


    def __init__(				self			:	Tuple,lowercase_			:	Optional[int]=5_0_2_6_5,lowercase_			:	List[str]=1_0_2_4,lowercase_			:	Any=1_2,lowercase_			:	Optional[Any]=4_0_9_6,lowercase_			:	str=1_6,lowercase_			:	int=1_2,lowercase_			:	Optional[Any]=4_0_9_6,lowercase_			:	Any=1_6,lowercase_			:	Any=0.0,lowercase_			:	str=0.0,lowercase_			:	Optional[Any]="gelu",lowercase_			:	List[str]=1_0_2_4,lowercase_			:	List[Any]=0.1,lowercase_			:	Union[str, Any]=0.0,lowercase_			:	Optional[int]=0.0,lowercase_			:	List[Any]=0.02,lowercase_			:	int=0.0,lowercase_			:	Optional[Any]=False,lowercase_			:	List[Any]=True,lowercase_			:	Union[str, Any]=3,lowercase_			:	int=1,lowercase_			:	int=0,lowercase_			:	List[str]=2,lowercase_			:	Optional[int]=True,lowercase_			:	Tuple=2,lowercase_			:	List[str]=2,**lowercase_			:	Dict,)->   List[Any]:





        '''simple docstring'''







        A__   				=       vocab_size
        A__   				=       max_position_embeddings
        A__   				=       d_model
        A__   				=       encoder_ffn_dim
        A__   				=       encoder_layers
        A__   				=       encoder_attention_heads
        A__   				=       decoder_ffn_dim
        A__   				=       decoder_layers
        A__   				=       decoder_attention_heads
        A__   				=       dropout
        A__   				=       attention_dropout
        A__   				=       activation_dropout
        A__   				=       activation_function
        A__   				=       init_std
        A__   				=       encoder_layerdrop
        A__   				=       decoder_layerdrop
        A__   				=       classifier_dropout
        A__   				=       use_cache
        A__   				=       encoder_layers
        A__   				=       scale_embedding  # scale factor will be sqrt(d_model) if True

        super().__init__(
            num_labels=lowercase_,pad_token_id=lowercase_,bos_token_id=lowercase_,eos_token_id=lowercase_,is_encoder_decoder=lowercase_,decoder_start_token_id=lowercase_,forced_eos_token_id=lowercase_,**lowercase_,)

        # ensure backward compatibility for BART CNN models
        if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated',lowercase_		):
            A__   				=       self.bos_token_id
            warnings.warn(
                F'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
                'The config can simply be saved and uploaded again to be fixed.'		)




class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    @property
    def 						snake_case__					(				self			:	Dict		)->   Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
            A__   				=       OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                ]		)

            if self.use_past:
                A__   				=       {0: 'batch'}
                A__   				=       {0: 'batch', 1: 'past_decoder_sequence + sequence'}
            else:
                A__   				=       {0: 'batch', 1: 'decoder_sequence'}
                A__   				=       {0: 'batch', 1: 'decoder_sequence'}

            if self.use_past:
                self.fill_with_past_key_values_(lowercase_,direction='inputs'		)
        elif self.task == "causal-lm":
            # TODO: figure this case out.
            A__   				=       OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                ]		)
            if self.use_past:
                A__				,						A__   				=       self.num_layers
                for i in range(lowercase_		):
                    A__   				=       {0: 'batch', 2: 'past_sequence + sequence'}
                    A__   				=       {0: 'batch', 2: 'past_sequence + sequence'}
        else:
            A__   				=       OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                    ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                    ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                ]		)

        return common_inputs


    @property
    def 						snake_case__					(				self			:	Optional[Any]		)->   Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
            A__   				=       super().outputs
        else:
            A__   				=       super(lowercase_,self		).outputs
            if self.use_past:
                A__				,						A__   				=       self.num_layers
                for i in range(lowercase_		):
                    A__   				=       {0: 'batch', 2: 'past_sequence + sequence'}
                    A__   				=       {0: 'batch', 2: 'past_sequence + sequence'}
        return common_outputs


    def 						snake_case__					(				self			:	Tuple,lowercase_			:	PreTrainedTokenizer,lowercase_			:	int = -1,lowercase_			:	int = -1,lowercase_			:	bool = False,lowercase_			:	Optional[TensorType] = None,)->   Mapping[str, Any]:





        '''simple docstring'''







        A__   				=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase_,lowercase_,lowercase_,lowercase_,lowercase_		)

        # Generate decoder inputs
        A__   				=       seq_length if not self.use_past else 1
        A__   				=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase_,lowercase_,lowercase_,lowercase_,lowercase_		)
        A__   				=       {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
        A__   				=       dict(**lowercase_,**lowercase_		)

        if self.use_past:
            if not is_torch_available():
                raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'		)
            else:
                import torch
            A__				,						A__   				=       common_inputs['input_ids'].shape
            A__   				=       common_inputs['decoder_input_ids'].shape[1]
            A__				,						A__   				=       self.num_attention_heads
            A__   				=       (
                batch,
                num_encoder_attention_heads,
                encoder_seq_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
            A__   				=       decoder_seq_length + 3
            A__   				=       (
                batch,
                num_decoder_attention_heads,
                decoder_past_length,
                self._config.hidden_size // num_decoder_attention_heads,
            )

            A__   				=       torch.cat(
                [common_inputs['decoder_attention_mask'], torch.ones(lowercase_,lowercase_		)],dim=1		)

            A__   				=       []
            # If the number of encoder and decoder layers are present in the model configuration, both are considered
            A__				,						A__   				=       self.num_layers
            A__   				=       min(lowercase_,lowercase_		)
            A__   				=       max(lowercase_,lowercase_		) - min_num_layers
            A__   				=       'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

            for _ in range(lowercase_		):
                common_inputs["past_key_values"].append(
                    (
                        torch.zeros(lowercase_		),
                        torch.zeros(lowercase_		),
                        torch.zeros(lowercase_		),
                        torch.zeros(lowercase_		),
                    )		)
            # TODO: test this.
            A__   				=       encoder_shape if remaining_side_name == 'encoder' else decoder_shape
            for _ in range(lowercase_,lowercase_		):
                common_inputs["past_key_values"].append((torch.zeros(lowercase_		), torch.zeros(lowercase_		))		)
        return common_inputs


    def 						snake_case__					(				self			:	List[str],lowercase_			:	PreTrainedTokenizer,lowercase_			:	int = -1,lowercase_			:	int = -1,lowercase_			:	bool = False,lowercase_			:	Optional[TensorType] = None,)->   Mapping[str, Any]:





        '''simple docstring'''







        A__   				=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase_,lowercase_,lowercase_,lowercase_,lowercase_		)

        if self.use_past:
            if not is_torch_available():
                raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'		)
            else:
                import torch
            A__				,						A__   				=       common_inputs['input_ids'].shape
            # Not using the same length for past_key_values
            A__   				=       seqlen + 2
            A__				,						A__   				=       self.num_layers
            A__				,						A__   				=       self.num_attention_heads
            A__   				=       (
                batch,
                num_encoder_attention_heads,
                past_key_values_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )

            A__   				=       common_inputs['attention_mask'].dtype
            A__   				=       torch.cat(
                [common_inputs['attention_mask'], torch.ones(lowercase_,lowercase_,dtype=lowercase_		)],dim=1		)
            A__   				=       [
                (torch.zeros(lowercase_		), torch.zeros(lowercase_		)) for _ in range(lowercase_		)
            ]
        return common_inputs


    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	PreTrainedTokenizer,lowercase_			:	int = -1,lowercase_			:	int = -1,lowercase_			:	bool = False,lowercase_			:	Optional[TensorType] = None,)->   Mapping[str, Any]:





        '''simple docstring'''







        A__   				=       compute_effective_axis_dimension(
            lowercase_,fixed_dimension=OnnxConfig.default_fixed_batch,num_token_to_add=0		)

        # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
        A__   				=       tokenizer.num_special_tokens_to_add(lowercase_		)
        A__   				=       compute_effective_axis_dimension(
            lowercase_,fixed_dimension=OnnxConfig.default_fixed_sequence,num_token_to_add=lowercase_		)

        # Generate dummy inputs according to compute batch and sequence
        A__   				=       [' '.join([tokenizer.unk_token]		) * seq_length] * batch_size
        A__   				=       dict(tokenizer(lowercase_,return_tensors=lowercase_		)		)
        return common_inputs


    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	PreTrainedTokenizer,lowercase_			:	int = -1,lowercase_			:	int = -1,lowercase_			:	bool = False,lowercase_			:	Optional[TensorType] = None,)->   Mapping[str, Any]:





        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
            A__   				=       self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                lowercase_,batch_size=lowercase_,seq_length=lowercase_,is_pair=lowercase_,framework=lowercase_		)

        elif self.task == "causal-lm":
            A__   				=       self._generate_dummy_inputs_for_causal_lm(
                lowercase_,batch_size=lowercase_,seq_length=lowercase_,is_pair=lowercase_,framework=lowercase_		)
        else:
            A__   				=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                lowercase_,batch_size=lowercase_,seq_length=lowercase_,is_pair=lowercase_,framework=lowercase_		)

        return common_inputs


    def 						snake_case__					(				self			:	int,lowercase_			:	Tuple,lowercase_			:	int,lowercase_			:	int,lowercase_			:	str		)->   str:





        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
            A__   				=       super()._flatten_past_key_values_(lowercase_,lowercase_,lowercase_,lowercase_		)
        else:
            A__   				=       super(lowercase_,self		)._flatten_past_key_values_(
                lowercase_,lowercase_,lowercase_,lowercase_		)

