
"""simple docstring"""




import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

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


A_							:			List[str]											= logging.get_logger(__name__)





class    lowerCamelCase						(UpperCAmelCase__			):
			lowerCamelCase__   :		List[str]							=							["input_features", "is_longer"]

			def __init__(					self				:						List[str]    ,				__UpperCAmelCase				:						Optional[Any]=6_4    ,				__UpperCAmelCase				:						int=4_8_0_0_0    ,				__UpperCAmelCase				:						str=4_8_0    ,				__UpperCAmelCase				:						str=1_0    ,				__UpperCAmelCase				:						List[str]=1_0_2_4    ,				__UpperCAmelCase				:						Any=0.0    ,				__UpperCAmelCase				:						int=False    ,				__UpperCAmelCase				:						float = 0    ,				__UpperCAmelCase				:						float = 1_4_0_0_0    ,				__UpperCAmelCase				:						int = None    ,				__UpperCAmelCase				:						str = "fusion"    ,				__UpperCAmelCase				:						str = "repeatpad"    ,				**__UpperCAmelCase				:						Optional[Any]    ,				)   ->    str:
						super().__init__(
						    feature_size=lowerCamelCase__    ,				sampling_rate=lowerCamelCase__    ,				padding_value=lowerCamelCase__    ,				return_attention_mask=lowerCamelCase__    ,				**lowerCamelCase__    ,				)
						SCREAMING_SNAKE_CASE__								=      top_db
						SCREAMING_SNAKE_CASE__								=      truncation
						SCREAMING_SNAKE_CASE__								=      padding
						SCREAMING_SNAKE_CASE__								=      fft_window_size
						SCREAMING_SNAKE_CASE__								=      (fft_window_size >> 1) + 1
						SCREAMING_SNAKE_CASE__								=      hop_length
						SCREAMING_SNAKE_CASE__								=      max_length_s
						SCREAMING_SNAKE_CASE__								=      max_length_s * sampling_rate
						SCREAMING_SNAKE_CASE__								=      sampling_rate
						SCREAMING_SNAKE_CASE__								=      frequency_min
						SCREAMING_SNAKE_CASE__								=      frequency_max
						SCREAMING_SNAKE_CASE__								=      mel_filter_bank(
						    num_frequency_bins=self.nb_frequency_bins    ,				num_mel_filters=lowerCamelCase__    ,				min_frequency=lowerCamelCase__    ,				max_frequency=lowerCamelCase__    ,				sampling_rate=lowerCamelCase__    ,				norm=lowerCamelCase__    ,				mel_scale="""htk"""    ,				)
						SCREAMING_SNAKE_CASE__								=      mel_filter_bank(
						    num_frequency_bins=self.nb_frequency_bins    ,				num_mel_filters=lowerCamelCase__    ,				min_frequency=lowerCamelCase__    ,				max_frequency=lowerCamelCase__    ,				sampling_rate=lowerCamelCase__    ,				norm="""slaney"""    ,				mel_scale="""slaney"""    ,				)

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Dict[str, Any]:
						SCREAMING_SNAKE_CASE__								=      copy.deepcopy(self.__dict__       )
						SCREAMING_SNAKE_CASE__								=      self.__class__.__name__
						if "mel_filters" in output:
									del output["mel_filters"]
						if "mel_filters_slaney" in output:
									del output["mel_filters_slaney"]
						return output

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						np.array    ,				__UpperCAmelCase				:						Optional[np.array] = None       )   ->    np.ndarray:
						SCREAMING_SNAKE_CASE__								=      spectrogram(
						    lowerCamelCase__    ,				window_function(self.fft_window_size    ,				"""hann"""       )    ,				frame_length=self.fft_window_size    ,				hop_length=self.hop_length    ,				power=2.0    ,				mel_filters=lowerCamelCase__    ,				log_mel="""dB"""    ,				)
						return log_mel_spectrogram.T

			def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						Any    ,				__UpperCAmelCase				:						str       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      np.array_split(list(range(0    ,				total_frames - chunk_frames + 1       )       )    ,				3       )
						if len(ranges[1]       ) == 0:
									# if the audio is too short, we just use the first chunk
									SCREAMING_SNAKE_CASE__								=      [0]
						if len(ranges[2]       ) == 0:
									# if the audio is too short, we just use the first chunk
									SCREAMING_SNAKE_CASE__								=      [0]
						# randomly choose index for each part
						SCREAMING_SNAKE_CASE__								=      np.random.choice(ranges[0]       )
						SCREAMING_SNAKE_CASE__								=      np.random.choice(ranges[1]       )
						SCREAMING_SNAKE_CASE__								=      np.random.choice(ranges[2]       )

						SCREAMING_SNAKE_CASE__								=      mel[idx_front : idx_front + chunk_frames, :]
						SCREAMING_SNAKE_CASE__								=      mel[idx_middle : idx_middle + chunk_frames, :]
						SCREAMING_SNAKE_CASE__								=      mel[idx_back : idx_back + chunk_frames, :]

						SCREAMING_SNAKE_CASE__								=      torch.tensor(mel[None, None, :]       )
						SCREAMING_SNAKE_CASE__								=      torch.nn.functional.interpolate(
						    lowerCamelCase__    ,				size=[chunk_frames, 6_4]    ,				mode="""bilinear"""    ,				align_corners=lowerCamelCase__       )
						SCREAMING_SNAKE_CASE__								=      mel_shrink[0][0].numpy()
						SCREAMING_SNAKE_CASE__								=      np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]    ,				axis=0       )
						return mel_fusion

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						np.array    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						str       )   ->    np.array:







						if waveform.shape[0] > max_length:
									if truncation == "rand_trunc":
												SCREAMING_SNAKE_CASE__								=      True
												# random crop to max_length (for compatibility) -> this should be handled by self.pad
												SCREAMING_SNAKE_CASE__								=      len(lowerCamelCase__       ) - max_length
												SCREAMING_SNAKE_CASE__								=      np.random.randint(0    ,				overflow + 1       )
												SCREAMING_SNAKE_CASE__								=      waveform[idx : idx + max_length]
												SCREAMING_SNAKE_CASE__								=      self._np_extract_fbank_features(lowerCamelCase__    ,				self.mel_filters_slaney       )[None, :]
									elif truncation == "fusion":
												SCREAMING_SNAKE_CASE__								=      self._np_extract_fbank_features(lowerCamelCase__    ,				self.mel_filters       )
												SCREAMING_SNAKE_CASE__								=      max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
												SCREAMING_SNAKE_CASE__								=      mel.shape[0]
												if chunk_frames == total_frames:
															# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
															# In this case, we just use the whole audio.
															SCREAMING_SNAKE_CASE__								=      np.stack([mel, mel, mel, mel]    ,				axis=0       )
															SCREAMING_SNAKE_CASE__								=      False
												else:
															SCREAMING_SNAKE_CASE__								=      self._random_mel_fusion(lowerCamelCase__    ,				lowerCamelCase__    ,				lowerCamelCase__       )
															SCREAMING_SNAKE_CASE__								=      True
									else:
												raise NotImplementedError(F"""data_truncating {truncation} not implemented"""       )

						else:
									SCREAMING_SNAKE_CASE__								=      False
									# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
									if waveform.shape[0] < max_length:
												if padding == "repeat":
															SCREAMING_SNAKE_CASE__								=      int(max_length / len(lowerCamelCase__       )       )
															SCREAMING_SNAKE_CASE__								=      np.stack(np.tile(lowerCamelCase__    ,				n_repeat + 1       )       )[:max_length]
												if padding == "repeatpad":
															SCREAMING_SNAKE_CASE__								=      int(max_length / len(lowerCamelCase__       )       )
															SCREAMING_SNAKE_CASE__								=      np.stack(np.tile(lowerCamelCase__    ,				lowerCamelCase__       )       )
												SCREAMING_SNAKE_CASE__								=      np.pad(lowerCamelCase__    ,				(0, max_length - waveform.shape[0])    ,				mode="""constant"""    ,				constant_values=0       )

									if truncation == "fusion":
												SCREAMING_SNAKE_CASE__								=      self._np_extract_fbank_features(lowerCamelCase__    ,				self.mel_filters       )
												SCREAMING_SNAKE_CASE__								=      np.stack([input_mel, input_mel, input_mel, input_mel]    ,				axis=0       )
									else:
												SCREAMING_SNAKE_CASE__								=      self._np_extract_fbank_features(lowerCamelCase__    ,				self.mel_filters_slaney       )[None, :]

						return input_mel, longer





			def __call__(					self				:						str    ,				__UpperCAmelCase				:						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]    ,				__UpperCAmelCase				:						str = None    ,				__UpperCAmelCase				:						Optional[str] = None    ,				__UpperCAmelCase				:						Optional[int] = None    ,				__UpperCAmelCase				:						Optional[int] = None    ,				__UpperCAmelCase				:						Optional[Union[str, TensorType]] = None    ,				**__UpperCAmelCase				:						str    ,				)   ->    BatchFeature:
						SCREAMING_SNAKE_CASE__								=      truncation if truncation is not None else self.truncation
						SCREAMING_SNAKE_CASE__								=      padding if padding else self.padding

						if sampling_rate is not None:
									if sampling_rate != self.sampling_rate:
												raise ValueError(
												    F"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
												    F""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
												    F""" was sampled with {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."""       )

						SCREAMING_SNAKE_CASE__								=      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}"""       )
						SCREAMING_SNAKE_CASE__								=      is_batched_numpy or (
						    isinstance(lowerCamelCase__    ,				(list, tuple)       ) and (isinstance(raw_speech[0]    ,				(np.ndarray, tuple, list)       ))
						)

						if is_batched:
									SCREAMING_SNAKE_CASE__								=      [np.asarray(lowerCamelCase__    ,				dtype=np.floataa       ) for speech in raw_speech]
						elif not is_batched and not isinstance(lowerCamelCase__    ,				np.ndarray       ):
									SCREAMING_SNAKE_CASE__								=      np.asarray(lowerCamelCase__    ,				dtype=np.floataa       )
						elif isinstance(lowerCamelCase__    ,				np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
									SCREAMING_SNAKE_CASE__								=      raw_speech.astype(np.floataa       )

						# always return batch
						if not is_batched:
									SCREAMING_SNAKE_CASE__								=      [np.asarray(lowerCamelCase__       )]

						# convert to mel spectrogram, truncate and pad if needed.
						SCREAMING_SNAKE_CASE__								=      [
						    self._get_input_mel(lowerCamelCase__    ,				max_length if max_length else self.nb_max_samples    ,				lowerCamelCase__    ,				lowerCamelCase__       )
						    for waveform in raw_speech
						]

						SCREAMING_SNAKE_CASE__								=      []
						SCREAMING_SNAKE_CASE__								=      []
						for mel, longer in padded_inputs:
									input_mel.append(lowerCamelCase__       )
									is_longer.append(lowerCamelCase__       )

						if truncation == "fusion" and sum(lowerCamelCase__       ) == 0:
									# if no audio is longer than 10s, then randomly select one audio to be longer
									SCREAMING_SNAKE_CASE__								=      np.random.randint(0    ,				len(lowerCamelCase__       )       )
									SCREAMING_SNAKE_CASE__								=      True

						if isinstance(input_mel[0]    ,				lowerCamelCase__       ):
									SCREAMING_SNAKE_CASE__								=      [np.asarray(lowerCamelCase__    ,				dtype=np.floataa       ) for feature in input_mel]

						# is_longer is a list of bool
						SCREAMING_SNAKE_CASE__								=      [[longer] for longer in is_longer]

						SCREAMING_SNAKE_CASE__								=      {"input_features": input_mel, "is_longer": is_longer}
						SCREAMING_SNAKE_CASE__								=      BatchFeature(lowerCamelCase__       )

						if return_tensors is not None:
									SCREAMING_SNAKE_CASE__								=      input_features.convert_to_tensors(lowerCamelCase__       )

						return input_features





"""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_							:			Tuple											= logging.getLogger(__name__)


A_							:			Tuple											= "Hello world! cécé herlolip"


A_							:			Dict											= 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       (     snake_case__		,				snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      BertAbsConfig(
			    temp_dir="""."""		,				finetune_bert=snake_case__		,				large=snake_case__		,				share_emb=snake_case__		,				use_bert_emb=snake_case__		,				encoder="""bert"""		,				max_pos=5_12		,				enc_layers=6		,				enc_hidden_size=5_12		,				enc_heads=8		,				enc_ff_size=5_12		,				enc_dropout=0.2		,				dec_layers=6		,				dec_hidden_size=7_68		,				dec_heads=8		,				dec_ff_size=20_48		,				dec_dropout=0.2		,				)
			SCREAMING_SNAKE_CASE__								=      torch.load(snake_case__		,				lambda snake_case__		,				snake_case__    : storage    )
			SCREAMING_SNAKE_CASE__								=      AbsSummarizer(snake_case__		,				torch.device("""cpu"""    )		,				snake_case__    )
			original.eval()

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

			# prepare the model inputs
			SCREAMING_SNAKE_CASE__								=      tokenizer.encode("""This is sample éàalj'-."""    )
			encoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(snake_case__    ))    )
			SCREAMING_SNAKE_CASE__								=      torch.tensor(snake_case__    ).unsqueeze(0    )
			SCREAMING_SNAKE_CASE__								=      tokenizer.encode("""This is sample 3 éàalj'-."""    )
			decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(snake_case__    ))    )
			SCREAMING_SNAKE_CASE__								=      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
			SCREAMING_SNAKE_CASE__								=      encoder_input_ids
			SCREAMING_SNAKE_CASE__								=      decoder_input_ids
			SCREAMING_SNAKE_CASE__								=      SCREAMING_SNAKE_CASE__								=      None
			SCREAMING_SNAKE_CASE__								=      None
			SCREAMING_SNAKE_CASE__								=      SCREAMING_SNAKE_CASE__								=      None
			SCREAMING_SNAKE_CASE__								=      SCREAMING_SNAKE_CASE__								=      None
			SCREAMING_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
			SCREAMING_SNAKE_CASE__								=      original(snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )[0]
			SCREAMING_SNAKE_CASE__								=      original.generator(snake_case__    )

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

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

			SCREAMING_SNAKE_CASE__								=      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_							:			Dict											= 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_							:			Tuple											= parser.parse_args()

						convert_bertabs_checkpoints(
						    args.bertabs_checkpoint_path,
						    args.pytorch_dump_folder_path,
						)


