






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 				_a     (   UpperCamelCase_    : Tuple			,      UpperCamelCase_    : Dict      )       ->      Dict:
						"""simple docstring"""







						lowerCAmelCase__       					=  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			,      )
						lowerCAmelCase__       					=  torch.load(UpperCamelCase_			,      lambda UpperCamelCase_			,      UpperCamelCase_      : storage      )
						lowerCAmelCase__       					=  AbsSummarizer(UpperCamelCase_			,      torch.device("cpu"      )			,      UpperCamelCase_      )
						original.eval()

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

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

						lowerCAmelCase__       					=  new_model(
						    UpperCamelCase_			,      UpperCamelCase_			,      UpperCamelCase_			,      UpperCamelCase_			,      UpperCamelCase_      )[0]
						lowerCAmelCase__       					=  new_model.generator(UpperCamelCase_      )

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

						lowerCAmelCase__       					=  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__":
	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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case    						=logging.get_logger(__name__)

__snake_case    						={
    """facebook/s2t-wav2vec2-large-en-de""": (
        """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	Union[str, Any]		=     '''speech_to_text_2'''
     lowerCamelCase				:	Any		=     ['''past_key_values''']
     lowerCamelCase				:	Optional[Any]		=     {'''num_attention_heads''': '''decoder_attention_heads''', '''hidden_size''': '''d_model'''}





     def __init__( self       :      Optional[int]    ,      UpperCAmelCase__       :      Optional[Any]=1_0_0_0_0    ,      UpperCAmelCase__       :      int=6    ,      UpperCAmelCase__       :      Optional[Any]=2_0_4_8    ,      UpperCAmelCase__       :      Union[str, Any]=4    ,      UpperCAmelCase__       :      List[Any]=0.0    ,      UpperCAmelCase__       :      str=True    ,      UpperCAmelCase__       :      str="relu"    ,      UpperCAmelCase__       :      Any=2_5_6    ,      UpperCAmelCase__       :      Optional[Any]=0.1    ,      UpperCAmelCase__       :      Any=0.0    ,      UpperCAmelCase__       :      Tuple=0.0    ,      UpperCAmelCase__       :      List[Any]=0.02    ,      UpperCAmelCase__       :      List[Any]=2    ,      UpperCAmelCase__       :      str=True    ,      UpperCAmelCase__       :      List[str]=1    ,      UpperCAmelCase__       :      Any=0    ,      UpperCAmelCase__       :      Dict=2    ,      UpperCAmelCase__       :      int=1_0_2_4    ,      **UpperCAmelCase__       :      Optional[Any]    ,      )				->  Dict:
        lowerCAmelCase     =			vocab_size
        lowerCAmelCase     =			d_model
        lowerCAmelCase     =			decoder_ffn_dim
        lowerCAmelCase     =			decoder_layers
        lowerCAmelCase     =			decoder_attention_heads
        lowerCAmelCase     =			dropout
        lowerCAmelCase     =			attention_dropout
        lowerCAmelCase     =			activation_dropout
        lowerCAmelCase     =			activation_function
        lowerCAmelCase     =			init_std
        lowerCAmelCase     =			decoder_layerdrop
        lowerCAmelCase     =			use_cache
        lowerCAmelCase     =			decoder_layers
        lowerCAmelCase     =			scale_embedding  # scale factor will be sqrt(d_model) if True
        lowerCAmelCase     =			max_target_positions

        super().__init__(
            pad_token_id=UpperCAmelCase__    ,      bos_token_id=UpperCAmelCase__    ,      eos_token_id=UpperCAmelCase__    ,      decoder_start_token_id=UpperCAmelCase__    ,      **UpperCAmelCase__    ,      )





