


"""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


UpperCAmelCase      		=			logging.get_logger(__name__)
class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      List[Any]        =   ["input_features", "attention_mask"]






       def __init__(   self  :    int  ,     snake_case__  :    Tuple=8_0  ,     snake_case__  :    List[str]=1_6_0_0_0  ,     snake_case__  :    Optional[Any]=0.0  ,     snake_case__  :    Optional[Any]=1_0  ,     snake_case__  :    Tuple=2_5  ,     snake_case__  :    Union[str, Any]="hamming_window"  ,     snake_case__  :    Optional[int]=3_27_68.0  ,     snake_case__  :    List[str]=0.97  ,     snake_case__  :    List[str]=1.0  ,     snake_case__  :    Optional[Any]=True  ,     snake_case__  :    List[str]=True  ,     snake_case__  :    List[str]=False  ,     **snake_case__  :    str  ,     ):

              '''simple docstring'''






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

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

              lowercase		:List[str]       							=				win_length * sampling_rate // 1_0_0_0
              lowercase		:Any       							=				hop_length * sampling_rate // 1_0_0_0

              lowercase		:Tuple       							=				optimal_fft_length(self.sample_size				)
              lowercase		:str       							=				(self.n_fft // 2) + 1






       def  __snake_case						(   self  :    int  ,     snake_case__  :    np.array				):

              '''simple docstring'''






              if self.win_function == "hamming_window":
                     lowercase		:Optional[Any]       							=				window_function(window_length=self.sample_size  ,     name=self.win_function  ,     periodic=snake_case__				)
              else:
                     lowercase		:Dict       							=				window_function(window_length=self.sample_size  ,     name=self.win_function				)

              lowercase		:Optional[Any]       							=				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		:List[str]       							=				spectrogram(
                  one_waveform * self.frame_signal_scale  ,     window=snake_case__  ,     frame_length=self.sample_size  ,     hop_length=self.sample_stride  ,     fft_length=self.n_fft  ,     center=snake_case__  ,     preemphasis=self.preemphasis_coeff  ,     mel_filters=snake_case__  ,     mel_floor=self.mel_floor  ,     log_mel='''log'''  ,     )
              return msfc_features.T






       def  __snake_case						(   self  :    Union[str, Any]  ,     snake_case__  :    List[str]  ,     snake_case__  :    Any  ,     snake_case__  :    Optional[int]				):

              '''simple docstring'''






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

              if input_length < x.shape[0]:
                     lowercase		:List[str]       							=				padding_value

              # make sure array is in float32
              lowercase		:Union[str, Any]       							=				x.astype(np.floataa				)

              return x






       def  __snake_case						(   self  :    Dict  ,     snake_case__  :    List[np.ndarray]  ,     snake_case__  :    Optional[np.ndarray] = None				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				attention_mask.sum(-1				) if attention_mask is not None else [x.shape[0] for x in input_features]
              return [self._normalize_one(snake_case__  ,     snake_case__  ,     self.padding_value				) for x, n in zip(snake_case__  ,     snake_case__				)]







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

              '''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		:List[Any]       							=				isinstance(snake_case__  ,     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		:Any       							=				is_batched_numpy or (
                  isinstance(snake_case__  ,     (list, tuple)				) and (isinstance(raw_speech[0]  ,     (np.ndarray, tuple, list)				))
              )

              if is_batched:
                     lowercase		:int       							=				[np.asarray(snake_case__  ,     dtype=np.floataa				) for speech in raw_speech]
              elif not is_batched and not isinstance(snake_case__  ,     np.ndarray				):
                     lowercase		:Optional[Any]       							=				np.asarray(snake_case__  ,     dtype=np.floataa				)
              elif isinstance(snake_case__  ,     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		:Optional[int]       							=				[raw_speech]

              # extract fbank features
              lowercase		:Union[str, Any]       							=				[self._extract_mfsc_features(snake_case__				) for one_waveform in raw_speech]

              # convert into correct format for padding
              lowercase		:Any       							=				BatchFeature({'''input_features''': features}				)

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

              lowercase		:Dict       							=				padded_inputs.get('''attention_mask'''				)
              if attention_mask is not None:
                     lowercase		:Any       							=				[np.asarray(snake_case__  ,     dtype=np.intaa				) for array in attention_mask]

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

              if return_tensors is not None:
                     lowercase		:List[Any]       							=				padded_inputs.convert_to_tensors(snake_case__				)

              return padded_inputs





"""simple docstring"""



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


UpperCAmelCase      		=			[
    # 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       lowerCamelCase					(a_						:Union[str, Any])     ->							Optional[Any]:
       for pegasus_name, hf_name in PATTERNS:
              lowercase		:List[Any]       							=				k.replace(a_				,				a_)
       return k






def       lowerCamelCase					(a_						:dict				,				a_						:dict)     ->							PegasusForConditionalGeneration:
       lowercase		:Union[str, Any]       							=				DEFAULTS.copy()
       cfg_kwargs.update(a_)
       lowercase		:Optional[Any]       							=				PegasusConfig(**a_)
       lowercase		:int       							=				PegasusForConditionalGeneration(a_)
       lowercase		:Optional[int]       							=				torch_model.model.state_dict()
       lowercase		:Tuple       							=				{}
       for k, v in tf_weights.items():
              lowercase		:Dict       							=				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:
                     lowercase		:Union[str, Any]       							=				v.T
              lowercase		:List[str]       							=				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
       lowercase		:Optional[int]       							=				torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1])
       lowercase		:Union[str, Any]       							=				mapping['''shared.weight''']
       lowercase		:Dict       							=				mapping['''shared.weight''']
       lowercase		:int       							=				{k: torch.zeros_like(a_) for k, v in sd.items() if k.endswith('''bias''') and k not in mapping}
       mapping.update(**a_)
       lowercase							,  lowercase		:Tuple       							=				torch_model.model.load_state_dict(a_				,				strict=a_)
       lowercase		:Dict       							=				[
           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       lowerCamelCase					(a_						:Union[str, Any]="./ckpt/aeslc/model.ckpt-32000")     ->							Dict:
       lowercase		:Any       							=				tf.train.list_variables(a_)
       lowercase		:str       							=				{}
       lowercase		:Optional[Any]       							=				['''Adafactor''', '''global_step''']
       for name, shape in tqdm(a_				,				desc='''converting tf checkpoint to dict'''):
              lowercase		:str       							=				any(pat in name for pat in ignore_name)
              if skip_key:
                     continue
              lowercase		:str       							=				tf.train.load_variable(a_				,				a_)
              lowercase		:Dict       							=				array
       return tf_weights






def       lowerCamelCase					(a_						:str				,				a_						:str)     ->							List[str]:
       # save tokenizer first
       lowercase		:List[str]       							=				Path(a_).parent.name
       lowercase		:List[Any]       							=				task_specific_params[F"""summarization_{dataset}"""]['''max_position_embeddings''']
       lowercase		:List[Any]       							=				PegasusTokenizer.from_pretrained('''sshleifer/pegasus'''				,				model_max_length=a_)
       assert tok.model_max_length == desired_max_model_length
       tok.save_pretrained(a_)

       # convert model
       lowercase		:Any       							=				get_tf_weights_as_numpy(a_)
       lowercase		:str       							=				task_specific_params[F"""summarization_{dataset}"""]
       if dataset == "large":
              lowercase		:int       							=				task_specific_params
       lowercase		:List[Any]       							=				convert_pegasus(a_				,				a_)
       torch_model.save_pretrained(a_)
       lowercase		:Union[str, Any]       							=				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__":
 UpperCAmelCase      		=			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.''')
 UpperCAmelCase      		=			parser.parse_args()
 if args.save_dir is None:
  UpperCAmelCase      		=			Path(args.tf_ckpt_path).parent.name
  UpperCAmelCase      		=			os.path.join('''pegasus''', dataset)
 convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)

