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


SCREAMING_SNAKE_CASE									=						'Hello world! cécé herlolip'


SCREAMING_SNAKE_CASE									=						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      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Union[str, Any]							=							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     ,)
	_lowercase :    Any							=							torch.load(__UpperCAmelCase     ,lambda __UpperCAmelCase     ,__UpperCAmelCase			: storage			)
	_lowercase :    Any							=							AbsSummarizer(__UpperCAmelCase     ,torch.device('cpu'			)     ,__UpperCAmelCase			)
	original.eval()

	_lowercase :    Optional[int]							=							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'			)
	_lowercase :    Tuple							=							BertTokenizer.from_pretrained('bert-base-uncased'			)

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

	_lowercase :    Tuple							=							new_model(
	    __UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			)[0]
	_lowercase :    Dict							=							new_model.generator(__UpperCAmelCase			)

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

	_lowercase :    int							=							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__":
						SCREAMING_SNAKE_CASE									=						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.',
						)
						SCREAMING_SNAKE_CASE									=						parser.parse_args()

						convert_bertabs_checkpoints(
						    args.bertabs_checkpoint_path,
						    args.pytorch_dump_folder_path,
						)




"""simple docstring"""







from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


SCREAMING_SNAKE_CASE									=						logging.get_logger(__name__)



class  _lowerCamelCase			(__lowerCamelCase     ):
				_snake_case     =  ["input_features", "attention_mask"]







				def __init__( self    :       int			,					lowerCamelCase_    :       List[str]=8_0			,					lowerCamelCase_    :       Tuple=1_6_0_0_0			,					lowerCamelCase_    :       Union[str, Any]=0.0			,					lowerCamelCase_    :       List[Any]=1_0			,					lowerCamelCase_    :       List[str]=2_5			,					lowerCamelCase_    :       List[Any]="hamming_window"			,					lowerCamelCase_    :       Tuple=3_2768.0			,					lowerCamelCase_    :       int=0.97			,					lowerCamelCase_    :       Optional[int]=1.0			,					lowerCamelCase_    :       Optional[Any]=True			,					lowerCamelCase_    :       Union[str, Any]=True			,					lowerCamelCase_    :       Optional[int]=False			,					**lowerCamelCase_    :       Optional[Any]			,					):

					"""simple docstring"""






					super().__init__(feature_size=lowerCamelCase_			,					sampling_rate=lowerCamelCase_			,					padding_value=lowerCamelCase_			,					**lowerCamelCase_						)

					_lowercase :    Dict							=							feature_size
					_lowercase :    Dict							=							sampling_rate
					_lowercase :    Tuple							=							padding_value
					_lowercase :    int							=							hop_length
					_lowercase :    Any							=							win_length
					_lowercase :    Union[str, Any]							=							frame_signal_scale
					_lowercase :    Tuple							=							preemphasis_coeff
					_lowercase :    Tuple							=							mel_floor
					_lowercase :    Tuple							=							normalize_means
					_lowercase :    List[Any]							=							normalize_vars
					_lowercase :    List[str]							=							win_function
					_lowercase :    int							=							return_attention_mask

					_lowercase :    Optional[Any]							=							win_length * sampling_rate // 1_0_0_0
					_lowercase :    Tuple							=							hop_length * sampling_rate // 1_0_0_0

					_lowercase :    str							=							optimal_fft_length(self.sample_size						)
					_lowercase :    Dict							=							(self.n_fft // 2) + 1







				def 							__UpperCAmelCase					( self    :       List[str]			,					lowerCamelCase_    :       np.array						):

					"""simple docstring"""






					if self.win_function == "hamming_window":
						_lowercase :    List[Any]							=							window_function(window_length=self.sample_size			,					name=self.win_function			,					periodic=lowerCamelCase_						)
					else:
						_lowercase :    Union[str, Any]							=							window_function(window_length=self.sample_size			,					name=self.win_function						)

					_lowercase :    Tuple							=							mel_filter_bank(
					    num_frequency_bins=self.n_freqs			,					num_mel_filters=self.feature_size			,					min_frequency=0.0			,					max_frequency=self.sampling_rate / 2.0			,					sampling_rate=self.sampling_rate			,					)

					_lowercase :    Tuple							=							spectrogram(
					    one_waveform * self.frame_signal_scale			,					window=lowerCamelCase_			,					frame_length=self.sample_size			,					hop_length=self.sample_stride			,					fft_length=self.n_fft			,					center=lowerCamelCase_			,					preemphasis=self.preemphasis_coeff			,					mel_filters=lowerCamelCase_			,					mel_floor=self.mel_floor			,					log_mel='log'			,					)
					return msfc_features.T







				def 							__UpperCAmelCase					( self    :       Optional[Any]			,					lowerCamelCase_    :       Dict			,					lowerCamelCase_    :       Dict			,					lowerCamelCase_    :       Tuple						):

					"""simple docstring"""






					if self.normalize_means:
						_lowercase :    Optional[int]							=							x[:input_length].mean(axis=0						)
						_lowercase :    int							=							np.subtract(lowerCamelCase_			,					lowerCamelCase_						)
					if self.normalize_vars:
						_lowercase :    int							=							x[:input_length].std(axis=0						)
						_lowercase :    Optional[Any]							=							np.divide(lowerCamelCase_			,					lowerCamelCase_						)

					if input_length < x.shape[0]:
						_lowercase :    Dict							=							padding_value

					# make sure array is in float32
					_lowercase :    Tuple							=							x.astype(np.floataa						)

					return x







				def 							__UpperCAmelCase					( self    :       List[Any]			,					lowerCamelCase_    :       List[np.ndarray]			,					lowerCamelCase_    :       Optional[np.ndarray] = None						):

					"""simple docstring"""






					_lowercase :    Dict							=							attention_mask.sum(-1						) if attention_mask is not None else [x.shape[0] for x in input_features]
					return [self._normalize_one(lowerCamelCase_			,					lowerCamelCase_			,					self.padding_value						) for x, n in zip(lowerCamelCase_			,					lowerCamelCase_						)]







				def __call__( self    :       Dict			,					lowerCamelCase_    :       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,					lowerCamelCase_    :       Union[bool, str, PaddingStrategy] = False			,					lowerCamelCase_    :       Optional[int] = None			,					lowerCamelCase_    :       bool = False			,					lowerCamelCase_    :       Optional[int] = None			,					lowerCamelCase_    :       Optional[bool] = None			,					lowerCamelCase_    :       Optional[Union[str, TensorType]] = None			,					lowerCamelCase_    :       Optional[int] = None			,					**lowerCamelCase_    :       Optional[int]			,					):

					"""simple docstring"""






					if sampling_rate is not None:
						if sampling_rate != self.sampling_rate:
							raise ValueError(
							    F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
							    F''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
							    F''' {self.sampling_rate} and not {sampling_rate}.'''						)
					else:
						logger.warning(
						    'It is strongly recommended to pass the ``sampling_rate`` argument to this function. '
						    'Failing to do so can result in silent errors that might be hard to debug.'						)

					_lowercase :    Optional[Any]							=							isinstance(lowerCamelCase_			,					np.ndarray						) and len(raw_speech.shape						) > 1
					if is_batched_numpy and len(raw_speech.shape						) > 2:
						raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''						)
					_lowercase :    Optional[int]							=							is_batched_numpy or (
					    isinstance(lowerCamelCase_			,					(list, tuple)						) and (isinstance(raw_speech[0]			,					(np.ndarray, tuple, list)						))
					)

					if is_batched:
						_lowercase :    str							=							[np.asarray(lowerCamelCase_			,					dtype=np.floataa						) for speech in raw_speech]
					elif not is_batched and not isinstance(lowerCamelCase_			,					np.ndarray						):
						_lowercase :    Tuple							=							np.asarray(lowerCamelCase_			,					dtype=np.floataa						)
					elif isinstance(lowerCamelCase_			,					np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
						_lowercase :    Optional[int]							=							raw_speech.astype(np.floataa						)

					# always return batch
					if not is_batched:
						_lowercase :    str							=							[raw_speech]

					# extract fbank features
					_lowercase :    Optional[Any]							=							[self._extract_mfsc_features(lowerCamelCase_						) for one_waveform in raw_speech]

					# convert into correct format for padding
					_lowercase :    Optional[int]							=							BatchFeature({'input_features': features}						)

					_lowercase :    Tuple							=							self.pad(
					    lowerCamelCase_			,					padding=lowerCamelCase_			,					max_length=lowerCamelCase_			,					truncation=lowerCamelCase_			,					pad_to_multiple_of=lowerCamelCase_			,					return_attention_mask=lowerCamelCase_			,					**lowerCamelCase_			,					)
					# make sure list is in array format
					_lowercase :    Dict							=							padded_inputs.get('input_features'						)
					if isinstance(input_features[0]			,					lowerCamelCase_						):
						_lowercase :    List[str]							=							[np.asarray(lowerCamelCase_			,					dtype=np.floataa						) for feature in input_features]

					_lowercase :    List[Any]							=							padded_inputs.get('attention_mask'						)
					if attention_mask is not None:
						_lowercase :    Union[str, Any]							=							[np.asarray(lowerCamelCase_			,					dtype=np.intaa						) for array in attention_mask]

					if self.normalize_means or self.normalize_vars:
						_lowercase :    int							=							(
						    np.array(lowerCamelCase_			,					dtype=np.intaa						)
						    if self._get_padding_strategies(lowerCamelCase_			,					max_length=lowerCamelCase_						) is not PaddingStrategy.DO_NOT_PAD
						    and padding
						    else None
						)
						_lowercase :    List[Any]							=							self.normalize(
						    padded_inputs['input_features']			,					attention_mask=lowerCamelCase_						)

					if return_tensors is not None:
						_lowercase :    Union[str, Any]							=							padded_inputs.convert_to_tensors(lowerCamelCase_						)

					return padded_inputs



