

import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


_SCREAMING_SNAKE_CASE					:  str             =      [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['''memory_attention''', '''encoder_attn'''],
    ['''attention''', '''attn'''],
    ['''/''', '''.'''],
    ['''.LayerNorm.gamma''', '''_layer_norm.weight'''],
    ['''.LayerNorm.beta''', '''_layer_norm.bias'''],
    ['''r.layer_''', '''r.layers.'''],
    ['''output_proj''', '''out_proj'''],
    ['''ffn.dense_1.''', '''fc2.'''],
    ['''ffn.dense.''', '''fc1.'''],
    ['''ffn_layer_norm''', '''final_layer_norm'''],
    ['''kernel''', '''weight'''],
    ['''encoder_layer_norm.''', '''encoder.layer_norm.'''],
    ['''decoder_layer_norm.''', '''decoder.layer_norm.'''],
    ['''embeddings.weights''', '''shared.weight'''],
]
def     UpperCAmelCase_       (						_A    ):





       '''simple docstring'''




       for pegasus_name, hf_name in PATTERNS:
              SCREAMING_SNAKE_CASE__   =   k.replace(_A				,		_A    )
       return k
def     UpperCAmelCase_       (						_A				,		_A    ):





       '''simple docstring'''




       SCREAMING_SNAKE_CASE__   =   DEFAULTS.copy()
       cfg_kwargs.update(_A    )
       SCREAMING_SNAKE_CASE__   =   PegasusConfig(**_A    )
       SCREAMING_SNAKE_CASE__   =   PegasusForConditionalGeneration(_A    )
       SCREAMING_SNAKE_CASE__   =   torch_model.model.state_dict()
       SCREAMING_SNAKE_CASE__   =   {}
       for k, v in tf_weights.items():
              SCREAMING_SNAKE_CASE__   =   rename_state_dict_key(_A    )
              if new_k not in sd:
                     raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})'''    )

              if "dense" in k or "proj" in new_k:
                     SCREAMING_SNAKE_CASE__   =   v.T
              SCREAMING_SNAKE_CASE__   =   torch.tensor(_A				,		dtype=sd[new_k].dtype    )
              assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
       # make sure embedding.padding_idx is respected
       SCREAMING_SNAKE_CASE__   =   torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1]    )
       SCREAMING_SNAKE_CASE__   =   mapping['''shared.weight''']
       SCREAMING_SNAKE_CASE__   =   mapping['''shared.weight''']
       SCREAMING_SNAKE_CASE__   =   {k: torch.zeros_like(_A    ) for k, v in sd.items() if k.endswith('''bias'''    ) and k not in mapping}
       mapping.update(**_A    )
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   torch_model.model.load_state_dict(_A				,		strict=_A    )
       SCREAMING_SNAKE_CASE__   =   [
           k for k in missing if k not in ['''encoder.embed_positions.weight''', '''decoder.embed_positions.weight''']
       ]
       assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
       assert extra == [], F'''no matches found for the following tf keys {extra}'''
       return torch_model
def     UpperCAmelCase_       (						_A="./ckpt/aeslc/model.ckpt-32000"    ):





       '''simple docstring'''




       SCREAMING_SNAKE_CASE__   =   tf.train.list_variables(_A    )
       SCREAMING_SNAKE_CASE__   =   {}
       SCREAMING_SNAKE_CASE__   =   ['''Adafactor''', '''global_step''']
       for name, shape in tqdm(_A				,		desc='''converting tf checkpoint to dict'''    ):
              SCREAMING_SNAKE_CASE__   =   any(pat in name for pat in ignore_name    )
              if skip_key:
                     continue
              SCREAMING_SNAKE_CASE__   =   tf.train.load_variable(_A				,		_A    )
              SCREAMING_SNAKE_CASE__   =   array
       return tf_weights
def     UpperCAmelCase_       (						_A				,		_A    ):





       '''simple docstring'''




       SCREAMING_SNAKE_CASE__   =   Path(_A    ).parent.name
       SCREAMING_SNAKE_CASE__   =   task_specific_params[F'''summarization_{dataset}''']['''max_position_embeddings''']
       SCREAMING_SNAKE_CASE__   =   PegasusTokenizer.from_pretrained('''sshleifer/pegasus'''				,		model_max_length=_A    )
       assert tok.model_max_length == desired_max_model_length
       tok.save_pretrained(_A    )

       # convert model
       SCREAMING_SNAKE_CASE__   =   get_tf_weights_as_numpy(_A    )
       SCREAMING_SNAKE_CASE__   =   task_specific_params[F'''summarization_{dataset}''']
       if dataset == "large":
              SCREAMING_SNAKE_CASE__   =   task_specific_params
       SCREAMING_SNAKE_CASE__   =   convert_pegasus(_A				,		_A    )
       torch_model.save_pretrained(_A    )
       SCREAMING_SNAKE_CASE__   =   torch_model.state_dict()
       sd.pop('''model.decoder.embed_positions.weight'''    )
       sd.pop('''model.encoder.embed_positions.weight'''    )
       torch.save(_A				,		Path(_A    ) / '''pytorch_model.bin'''    )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE					:  Optional[Any]             =      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
       parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
       _SCREAMING_SNAKE_CASE					:  Optional[Any]             =      parser.parse_args()
       if args.save_dir is None:
              _SCREAMING_SNAKE_CASE					:  Optional[Any]             =      Path(args.tf_ckpt_path).parent.name
              _SCREAMING_SNAKE_CASE					:  List[Any]             =      os.path.join('''pegasus''', dataset)
       convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)





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

import numpy as np

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


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






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= ["input_features"]






  def __init__(							self						:		Dict     ,       __lowerCamelCase						:		Tuple=80     ,       __lowerCamelCase						:		List[Any]=1_6000     ,       __lowerCamelCase						:		Optional[int]=160     ,       __lowerCamelCase						:		List[str]=30     ,       __lowerCamelCase						:		List[Any]=400     ,       __lowerCamelCase						:		Union[str, Any]=0.0     ,       __lowerCamelCase						:		str=False     ,       **__lowerCamelCase						:		List[str]     ,       )		->							Any:
       super().__init__(
           feature_size=__lowerCamelCase     ,       sampling_rate=__lowerCamelCase     ,       padding_value=__lowerCamelCase     ,       return_attention_mask=__lowerCamelCase     ,       **__lowerCamelCase     ,       )
       SCREAMING_SNAKE_CASE__   =   n_fft
       SCREAMING_SNAKE_CASE__   =   hop_length
       SCREAMING_SNAKE_CASE__   =   chunk_length
       SCREAMING_SNAKE_CASE__   =   chunk_length * sampling_rate
       SCREAMING_SNAKE_CASE__   =   self.n_samples // hop_length
       SCREAMING_SNAKE_CASE__   =   sampling_rate
       SCREAMING_SNAKE_CASE__   =   mel_filter_bank(
           num_frequency_bins=1 + n_fft // 2     ,       num_mel_filters=__lowerCamelCase     ,       min_frequency=0.0     ,       max_frequency=8000.0     ,       sampling_rate=__lowerCamelCase     ,       norm='''slaney'''     ,       mel_scale='''slaney'''     ,       )






  def 				lowercase_		(							self						:		int     ,       __lowerCamelCase						:		np.array )		->							np.ndarray:
       SCREAMING_SNAKE_CASE__   =   spectrogram(
           __lowerCamelCase     ,       window_function(self.n_fft     ,       '''hann''' )     ,       frame_length=self.n_fft     ,       hop_length=self.hop_length     ,       power=2.0     ,       mel_filters=self.mel_filters     ,       log_mel='''log10'''     ,       )
       SCREAMING_SNAKE_CASE__   =   log_spec[:, :-1]
       SCREAMING_SNAKE_CASE__   =   np.maximum(__lowerCamelCase     ,       log_spec.max() - 8.0 )
       SCREAMING_SNAKE_CASE__   =   (log_spec + 4.0) / 4.0
       return log_spec






  @staticmethod
  # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
  def 				lowercase_		(							__lowerCamelCase						:		List[np.ndarray]     ,       __lowerCamelCase						:		List[np.ndarray]     ,       __lowerCamelCase						:		float = 0.0 )		->							List[np.ndarray]:
       if attention_mask is not None:
            SCREAMING_SNAKE_CASE__   =   np.array(__lowerCamelCase     ,       np.intaa )
            SCREAMING_SNAKE_CASE__   =   []

            for vector, length in zip(__lowerCamelCase     ,       attention_mask.sum(-1 ) ):
                 SCREAMING_SNAKE_CASE__   =   (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7 )
                 if length < normed_slice.shape[0]:
                      SCREAMING_SNAKE_CASE__   =   padding_value

                 normed_input_values.append(__lowerCamelCase )
       else:
            SCREAMING_SNAKE_CASE__   =   [(x - x.mean()) / np.sqrt(x.var() + 1e-7 ) for x in input_values]

       return normed_input_values






  def __call__(							self						:		List[str]     ,       __lowerCamelCase						:		Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,       __lowerCamelCase						:		bool = True     ,       __lowerCamelCase						:		Optional[int] = None     ,       __lowerCamelCase						:		Optional[Union[str, TensorType]] = None     ,       __lowerCamelCase						:		Optional[bool] = None     ,       __lowerCamelCase						:		Optional[str] = "max_length"     ,       __lowerCamelCase						:		Optional[int] = None     ,       __lowerCamelCase						:		Optional[int] = None     ,       __lowerCamelCase						:		Optional[bool] = None     ,       **__lowerCamelCase						:		List[str]     ,       )		->							BatchFeature:

       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([speech]     ,       dtype=np.floataa ).T 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([raw_speech] ).T]

       SCREAMING_SNAKE_CASE__   =   BatchFeature({'''input_features''': raw_speech} )

       # convert into correct format for padding

       SCREAMING_SNAKE_CASE__   =   self.pad(
           __lowerCamelCase     ,       padding=__lowerCamelCase     ,       max_length=max_length if max_length else self.n_samples     ,       truncation=__lowerCamelCase     ,       pad_to_multiple_of=__lowerCamelCase     ,       return_attention_mask=return_attention_mask or do_normalize     ,       )

       # zero-mean and unit-variance normalization
       if do_normalize:
            SCREAMING_SNAKE_CASE__   =   self.zero_mean_unit_var_norm(
                padded_inputs['''input_features''']     ,       attention_mask=padded_inputs['''attention_mask''']     ,       padding_value=self.padding_value     ,       )
            SCREAMING_SNAKE_CASE__   =   np.stack(padded_inputs['''input_features''']     ,       axis=0 )

       # make sure list is in array format
       SCREAMING_SNAKE_CASE__   =   padded_inputs.get('''input_features''' ).transpose(2     ,       0     ,       1 )

       SCREAMING_SNAKE_CASE__   =   [self._np_extract_fbank_features(__lowerCamelCase ) for waveform in input_features[0]]

       if isinstance(input_features[0]     ,       __lowerCamelCase ):
            SCREAMING_SNAKE_CASE__   =   [np.asarray(__lowerCamelCase     ,       dtype=np.floataa ) for feature in input_features]
       else:
            SCREAMING_SNAKE_CASE__   =   input_features

       if return_attention_mask:
            # rescale from sample (48000) to feature (3000)
            SCREAMING_SNAKE_CASE__   =   padded_inputs['''attention_mask'''][:, :: self.hop_length]

       if return_tensors is not None:
            SCREAMING_SNAKE_CASE__   =   padded_inputs.convert_to_tensors(__lowerCamelCase )

       return padded_inputs







  def 				lowercase_		(							self						:		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"]
       return output


