


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 : Dict 			=				[
    # 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      a	(							SCREAMING_SNAKE_CASE_      :		Dict       ):






       """simple docstring"""





       for pegasus_name, hf_name in PATTERNS:
              UpperCamelCase    :	str   	=		k.replace(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       return k


def      a	(							SCREAMING_SNAKE_CASE_      :		dict							,			SCREAMING_SNAKE_CASE_      :		dict       ):






       """simple docstring"""





       UpperCamelCase    :	Optional[int]   	=		DEFAULTS.copy()
       cfg_kwargs.update(SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	int   	=		PegasusConfig(**SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	int   	=		PegasusForConditionalGeneration(SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	str   	=		torch_model.model.state_dict()
       UpperCamelCase    :	Optional[Any]   	=		{}
       for k, v in tf_weights.items():
              UpperCamelCase    :	str   	=		rename_state_dict_key(SCREAMING_SNAKE_CASE_       )
              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:
                     UpperCamelCase    :	List[Any]   	=		v.T
              UpperCamelCase    :	Any   	=		torch.tensor(SCREAMING_SNAKE_CASE_							,			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
       UpperCamelCase    :	Union[str, Any]   	=		torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1]       )
       UpperCamelCase    :	Union[str, Any]   	=		mapping['''shared.weight''']
       UpperCamelCase    :	Union[str, Any]   	=		mapping['''shared.weight''']
       UpperCamelCase    :	List[str]   	=		{k: torch.zeros_like(SCREAMING_SNAKE_CASE_       ) for k, v in sd.items() if k.endswith('''bias'''       ) and k not in mapping}
       mapping.update(**SCREAMING_SNAKE_CASE_       )
       UpperCamelCase	, UpperCamelCase    :	str   	=		torch_model.model.load_state_dict(SCREAMING_SNAKE_CASE_							,			strict=SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	Union[str, Any]   	=		[
           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      a	(							SCREAMING_SNAKE_CASE_      :		Any="./ckpt/aeslc/model.ckpt-32000"       ):






       """simple docstring"""





       UpperCamelCase    :	Optional[Any]   	=		tf.train.list_variables(SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	List[Any]   	=		{}
       UpperCamelCase    :	Union[str, Any]   	=		['''Adafactor''', '''global_step''']
       for name, shape in tqdm(SCREAMING_SNAKE_CASE_							,			desc='''converting tf checkpoint to dict'''       ):
              UpperCamelCase    :	Dict   	=		any(pat in name for pat in ignore_name       )
              if skip_key:
                     continue
              UpperCamelCase    :	Union[str, Any]   	=		tf.train.load_variable(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
              UpperCamelCase    :	Any   	=		array
       return tf_weights


def      a	(							SCREAMING_SNAKE_CASE_      :		str							,			SCREAMING_SNAKE_CASE_      :		str       ):






       """simple docstring"""





       UpperCamelCase    :	Any   	=		Path(SCREAMING_SNAKE_CASE_       ).parent.name
       UpperCamelCase    :	Tuple   	=		task_specific_params[F"""summarization_{dataset}"""]['''max_position_embeddings''']
       UpperCamelCase    :	str   	=		PegasusTokenizer.from_pretrained('''sshleifer/pegasus'''							,			model_max_length=SCREAMING_SNAKE_CASE_       )
       assert tok.model_max_length == desired_max_model_length
       tok.save_pretrained(SCREAMING_SNAKE_CASE_       )

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


if __name__ == "__main__":
  __UpperCAmelCase : Tuple 			=				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 : List[Any] 			=				parser.parse_args()
  if args.save_dir is None:
    __UpperCAmelCase : Optional[int] 			=				Path(args.tf_ckpt_path).parent.name
    __UpperCAmelCase : Optional[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


__UpperCAmelCase : Dict 			=				logging.get_logger(__name__)



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     Any							=					["input_features"]






       def __init__(     self ,					__SCREAMING_SNAKE_CASE=80 ,					__SCREAMING_SNAKE_CASE=16_000 ,					__SCREAMING_SNAKE_CASE=160 ,					__SCREAMING_SNAKE_CASE=30 ,					__SCREAMING_SNAKE_CASE=400 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=False ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              super().__init__(
                  feature_size=__SCREAMING_SNAKE_CASE ,					sampling_rate=__SCREAMING_SNAKE_CASE ,					padding_value=__SCREAMING_SNAKE_CASE ,					return_attention_mask=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE ,					)
              UpperCamelCase    :	List[str]   	=		n_fft
              UpperCamelCase    :	Dict   	=		hop_length
              UpperCamelCase    :	Dict   	=		chunk_length
              UpperCamelCase    :	List[str]   	=		chunk_length * sampling_rate
              UpperCamelCase    :	Dict   	=		self.n_samples // hop_length
              UpperCamelCase    :	str   	=		sampling_rate
              UpperCamelCase    :	Union[str, Any]   	=		mel_filter_bank(
                  num_frequency_bins=1 + n_fft // 2 ,					num_mel_filters=__SCREAMING_SNAKE_CASE ,					min_frequency=0.0 ,					max_frequency=8_000.0 ,					sampling_rate=__SCREAMING_SNAKE_CASE ,					norm='''slaney''' ,					mel_scale='''slaney''' ,					)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		spectrogram(
                  __SCREAMING_SNAKE_CASE ,					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''' ,					)
              UpperCamelCase    :	int   	=		log_spec[:, :-1]
              UpperCamelCase    :	int   	=		np.maximum(__SCREAMING_SNAKE_CASE ,					log_spec.max() - 8.0						)
              UpperCamelCase    :	Any   	=		(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   (     __SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = 0.0						):




              """simple docstring"""




              if attention_mask is not None:
                     UpperCamelCase    :	List[Any]   	=		np.array(__SCREAMING_SNAKE_CASE ,					np.intaa						)
                     UpperCamelCase    :	Optional[Any]   	=		[]

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

                            normed_input_values.append(__SCREAMING_SNAKE_CASE						)
              else:
                     UpperCamelCase    :	Union[str, Any]   	=		[(x - x.mean()) / np.sqrt(x.var() + 1e-7						) for x in input_values]

              return normed_input_values






       def __call__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = True ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = "max_length" ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					**__SCREAMING_SNAKE_CASE ,					):




              """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.__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.'''						)

              UpperCamelCase    :	Tuple   	=		isinstance(__SCREAMING_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}"""						)
              UpperCamelCase    :	Union[str, Any]   	=		is_batched_numpy or (
                  isinstance(__SCREAMING_SNAKE_CASE ,					(list, tuple)						) and (isinstance(raw_speech[0] ,					(np.ndarray, tuple, list)						))
              )

              if is_batched:
                     UpperCamelCase    :	List[Any]   	=		[np.asarray([speech] ,					dtype=np.floataa						).T for speech in raw_speech]
              elif not is_batched and not isinstance(__SCREAMING_SNAKE_CASE ,					np.ndarray						):
                     UpperCamelCase    :	int   	=		np.asarray(__SCREAMING_SNAKE_CASE ,					dtype=np.floataa						)
              elif isinstance(__SCREAMING_SNAKE_CASE ,					np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
                     UpperCamelCase    :	Union[str, Any]   	=		raw_speech.astype(np.floataa						)

              # always return batch
              if not is_batched:
                     UpperCamelCase    :	Optional[int]   	=		[np.asarray([raw_speech]						).T]

              UpperCamelCase    :	Optional[int]   	=		BatchFeature({'''input_features''': raw_speech}						)

              # convert into correct format for padding

              UpperCamelCase    :	Optional[Any]   	=		self.pad(
                  __SCREAMING_SNAKE_CASE ,					padding=__SCREAMING_SNAKE_CASE ,					max_length=max_length if max_length else self.n_samples ,					truncation=__SCREAMING_SNAKE_CASE ,					pad_to_multiple_of=__SCREAMING_SNAKE_CASE ,					return_attention_mask=return_attention_mask or do_normalize ,					)

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

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

              UpperCamelCase    :	Tuple   	=		[self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE						) for waveform in input_features[0]]

              if isinstance(input_features[0] ,					__SCREAMING_SNAKE_CASE						):
                     UpperCamelCase    :	Optional[int]   	=		[np.asarray(__SCREAMING_SNAKE_CASE ,					dtype=np.floataa						) for feature in input_features]
              else:
                     UpperCamelCase    :	Dict   	=		input_features

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

              if return_tensors is not None:
                     UpperCamelCase    :	Dict   	=		padded_inputs.convert_to_tensors(__SCREAMING_SNAKE_CASE						)

              return padded_inputs






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[Any]   	=		copy.deepcopy(self.__dict__						)
              UpperCamelCase    :	List[str]   	=		self.__class__.__name__
              if "mel_filters" in output:
                     del output["mel_filters"]
              return output

