

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  :						Tuple         =    [
    # 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  :						str         =    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  :						str         =    parser.parse_args()
       if args.save_dir is None:
              _SCREAMING_SNAKE_CASE  :						Dict         =    Path(args.tf_ckpt_path).parent.name
              _SCREAMING_SNAKE_CASE  :						Optional[Any]         =    os.path.join('''pegasus''', dataset)
       convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)





from typing import List

import datasets
from datasets.tasks import AudioClassification

from ..folder_based_builder import folder_based_builder


_SCREAMING_SNAKE_CASE  :						Optional[Any]         =    datasets.utils.logging.get_logger(__name__)






class  UpperCAmelCase__	( folder_based_builder.FolderBasedBuilderConfig ):






  """simple docstring"""


  a     =      None
  a     =      None






class  UpperCAmelCase__	( folder_based_builder.FolderBasedBuilder ):






  """simple docstring"""


  a     =      datasets.Audio()
  a     =      "audio"
  a     =      AudioFolderConfig
  a     =      42  # definition at the bottom of the script
  a     =      AudioClassification(audio_column="audio"		,  label_column="label" )



_SCREAMING_SNAKE_CASE  :						str         =    [
    '''.aiff''',
    '''.au''',
    '''.avr''',
    '''.caf''',
    '''.flac''',
    '''.htk''',
    '''.svx''',
    '''.mat4''',
    '''.mat5''',
    '''.mpc2k''',
    '''.ogg''',
    '''.paf''',
    '''.pvf''',
    '''.raw''',
    '''.rf64''',
    '''.sd2''',
    '''.sds''',
    '''.ircam''',
    '''.voc''',
    '''.w64''',
    '''.wav''',
    '''.nist''',
    '''.wavex''',
    '''.wve''',
    '''.xi''',
    '''.mp3''',
    '''.opus''',
]
_SCREAMING_SNAKE_CASE  :						List[Any]         =    AUDIO_EXTENSIONS


