
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)
__magic_name__  							=	logging.getLogger(__name__)


__magic_name__  							=	'''Hello world! cécé herlolip'''


__magic_name__  							=	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 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase				):
    snake_case__    =					BertAbsConfig(
        temp_dir="."     ,		finetune_bert=__lowerCAmelCase     ,		large=__lowerCAmelCase     ,		share_emb=__lowerCAmelCase     ,		use_bert_emb=__lowerCAmelCase     ,		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(__lowerCAmelCase     ,		lambda __lowerCAmelCase     ,		__lowerCAmelCase				: storage				)
    snake_case__    =					AbsSummarizer(__lowerCAmelCase     ,		torch.device("cpu"				)     ,		__lowerCAmelCase				)
    original.eval()

    snake_case__    =					BertAbsSummarizer(__lowerCAmelCase     ,		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(__lowerCAmelCase				))				)
    snake_case__    =					torch.tensor(__lowerCAmelCase				).unsqueeze(0				)
    snake_case__    =					tokenizer.encode("This is sample 3 éàalj'-."				)
    decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__lowerCAmelCase				))				)
    snake_case__    =					torch.tensor(__lowerCAmelCase				).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(__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				)[0]
    snake_case__    =					original.generator(__lowerCAmelCase				)

    snake_case__    =					new_model(
        __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				)[0]
    snake_case__    =					new_model.generator(__lowerCAmelCase				)

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

    snake_case__    =					torch.allclose(__lowerCAmelCase     ,		__lowerCAmelCase     ,		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__":
     __magic_name__  							=	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.''',
     )
     __magic_name__  							=	parser.parse_args()

     convert_bertabs_checkpoints(
         args.bertabs_checkpoint_path,
         args.pytorch_dump_folder_path,
     )





from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
__magic_name__  							=	299_792_458

# Symbols
__magic_name__							,   __magic_name__							,   __magic_name__							,   __magic_name__  							=	symbols('''ct x y z''')





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    if velocity > c:
        raise ValueError("Speed must not exceed light speed 299,792,458 [m/s]!"				)
    elif velocity < 1:
        # Usually the speed should be much higher than 1 (c order of magnitude)
        raise ValueError("Speed must be greater than or equal to 1!"				)

    return velocity / c





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    return 1 / sqrt(1 - beta(__lowerCAmelCase				) ** 2				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    return np.array(
        [
            [gamma(__lowerCAmelCase				), -gamma(__lowerCAmelCase				) * beta(__lowerCAmelCase				), 0, 0],
            [-gamma(__lowerCAmelCase				) * beta(__lowerCAmelCase				), gamma(__lowerCAmelCase				), 0, 0],
            [0, 0, 1, 0],
            [0, 0, 0, 1],
        ]				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase = None				):
    # Ensure event is not empty
    if event is None:
        snake_case__    =					np.array([ct, x, y, z]				)  # Symbolic four vector
    else:
        event[0] *= c  # x0 is ct (speed of light * time)

    return transformation_matrix(__lowerCAmelCase				) @ event


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # Example of symbolic vector:
     __magic_name__  							=	transform(29_979_245)
     print('''Example of four vector: ''')
     print(F'''ct\' = {four_vector[0]}''')
     print(F'''x\' = {four_vector[1]}''')
     print(F'''y\' = {four_vector[2]}''')
     print(F'''z\' = {four_vector[3]}''')

     # Substitute symbols with numerical values
     __magic_name__  							=	{ct: c, x: 1, y: 1, z: 1}
     __magic_name__  							=	[four_vector[i].subs(sub_dict) for i in range(4)]

     print(F'''\n{numerical_vector}''')



