

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 argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   args.log_outputs
     SCREAMING_SNAKE_CASE__   =   '''_'''.join(args.dataset.split('''/'''    ) + [args.config, args.split]    )

     # load metric
     SCREAMING_SNAKE_CASE__   =   load_metric('''wer'''    )
     SCREAMING_SNAKE_CASE__   =   load_metric('''cer'''    )

     # compute metrics
     SCREAMING_SNAKE_CASE__   =   wer.compute(references=result['''target''']				,		predictions=result['''prediction''']    )
     SCREAMING_SNAKE_CASE__   =   cer.compute(references=result['''target''']				,		predictions=result['''prediction''']    )

     # print & log results
     SCREAMING_SNAKE_CASE__   =   F'''WER: {wer_result}\nCER: {cer_result}'''
     print(_A    )

     with open(F'''{dataset_id}_eval_results.txt'''				,		'''w'''    ) as f:
          f.write(_A    )

     # log all results in text file. Possibly interesting for analysis
     if log_outputs is not None:
          SCREAMING_SNAKE_CASE__   =   F'''log_{dataset_id}_predictions.txt'''
          SCREAMING_SNAKE_CASE__   =   F'''log_{dataset_id}_targets.txt'''

          with open(_A				,		'''w'''    ) as p, open(_A				,		'''w'''    ) as t:
               # mapping function to write output
               def write_to_file(_A				,		_A    ):
                    p.write(F'''{i}''' + '''\n'''    )
                    p.write(batch['''prediction'''] + '''\n'''    )
                    t.write(F'''{i}''' + '''\n'''    )
                    t.write(batch['''target'''] + '''\n'''    )

               result.map(_A				,		with_indices=_A    )
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   '''[,?.!\-\;\:"“%‘”�—’…–]'''  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

     SCREAMING_SNAKE_CASE__   =   re.sub(_A				,		''''''				,		text.lower()    )

     # In addition, we can normalize the target text, e.g. removing new lines characters etc...
     # note that order is important here!
     SCREAMING_SNAKE_CASE__   =   ['''\n\n''', '''\n''', '''   ''', '''  ''']

     for t in token_sequences_to_ignore:
          SCREAMING_SNAKE_CASE__   =   ''' '''.join(text.split(_A    )    )

     return text
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   load_dataset(args.dataset				,		args.config				,		split=args.split				,		use_auth_token=_A    )

     # for testing: only process the first two examples as a test
     # dataset = dataset.select(range(10))

     # load processor
     SCREAMING_SNAKE_CASE__   =   AutoFeatureExtractor.from_pretrained(args.model_id    )
     SCREAMING_SNAKE_CASE__   =   feature_extractor.sampling_rate

     # resample audio
     SCREAMING_SNAKE_CASE__   =   dataset.cast_column('''audio'''				,		Audio(sampling_rate=_A    )    )

     # load eval pipeline
     if args.device is None:
          SCREAMING_SNAKE_CASE__   =   0 if torch.cuda.is_available() else -1
     SCREAMING_SNAKE_CASE__   =   pipeline('''automatic-speech-recognition'''				,		model=args.model_id				,		device=args.device    )

     # map function to decode audio
     def map_to_pred(_A    ):
          SCREAMING_SNAKE_CASE__   =   asr(
              batch['''audio''']['''array''']				,		chunk_length_s=args.chunk_length_s				,		stride_length_s=args.stride_length_s    )

          SCREAMING_SNAKE_CASE__   =   prediction['''text''']
          SCREAMING_SNAKE_CASE__   =   normalize_text(batch['''sentence''']    )
          return batch

     # run inference on all examples
     SCREAMING_SNAKE_CASE__   =   dataset.map(_A				,		remove_columns=dataset.column_names    )

     # compute and log_results
     # do not change function below
     log_results(_A				,		_A    )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE					:  List[str]             =      argparse.ArgumentParser()

       parser.add_argument(
           '''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
       )
       parser.add_argument(
           '''--dataset''',
           type=str,
           required=True,
           help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
       )
       parser.add_argument(
           '''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'`  for Common Voice'''
       )
       parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
       parser.add_argument(
           '''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
       )
       parser.add_argument(
           '''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
       )
       parser.add_argument(
           '''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
       )
       parser.add_argument(
           '''--device''',
           type=int,
           default=None,
           help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
       )
       _SCREAMING_SNAKE_CASE					:  List[Any]             =      parser.parse_args()

       main(args)


