




import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase	:		str							       =   logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Union[str, Any]:



       """simple docstring"""

       A__       =    torch.load(lowercase_ ,				map_location='''cpu'''						)
       if "model" in sd.keys():
              A__       =    torch.load(lowercase_ ,				map_location='''cpu'''						)['''model''']

       # pop unnecessary weights
       A__       =    [
           '''decoder.version''',
           '''decoder.output_projection.weight''',
       ]
       for key in keys_to_delete:
              if key in sd:
                     sd.pop(lowercase_						)

       A__       =    {
           '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
           '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
           '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
           '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
       }
       for old_key, new_key in keys_to_rename.items():
              if old_key in sd:
                     A__       =    sd.pop(lowercase_						)

       A__       =    list(sd.keys()						)
       for key in keys:
              if ".qkv_proj." in key:
                     A__       =    sd[key]
                     # We split QKV in separate Q,K,V

                     A__       =    key.replace('''.qkv_proj.''' ,				'''.q_proj.'''						)
                     A__       =    key.replace('''.qkv_proj.''' ,				'''.k_proj.'''						)
                     A__       =    key.replace('''.qkv_proj.''' ,				'''.v_proj.'''						)

                     A__       =    value.shape[0]
                     assert depth % 3 == 0
                     # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                     # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                     A__ ,							A__ ,							A__       =    torch.split(lowercase_ ,				depth // 3 ,				dim=0						)

                     A__       =    q
                     A__       =    k
                     A__       =    v
                     del sd[key]

       return sd







@torch.no_grad()
def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_=None						)   ->       Optional[int]:



       """simple docstring"""

       A__       =    load_checkpoint(lowercase_						)

       if config is not None:
              A__       =    OPTConfig.from_pretrained(lowercase_						)
       else:
              A__       =    OPTConfig()

       A__       =    OPTModel(lowercase_						).half().eval()
       model.load_state_dict(lowercase_						)

       # Check results
       Path(lowercase_						).mkdir(exist_ok=lowercase_						)
       model.save_pretrained(lowercase_						)


if __name__ == "__main__":
  _lowerCamelCase	:		List[Any]							       =   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--fairseq_path""",
      type=str,
      help=(
          """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
          """ https://huggingface.co/models?other=opt_metasq"""
      ),
  )
  parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
  parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
  _lowerCamelCase	:		str							       =   parser.parse_args()
  convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)








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 SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       Optional[Any]:



       """simple docstring"""

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

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

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

       # print & log results
       A__       =    f"""WER: {wer_result}\nCER: {cer_result}"""
       print(lowercase_						)

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

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

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

                     result.map(lowercase_ ,				with_indices=lowercase_						)







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       str:



       """simple docstring"""

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

       A__       =    re.sub(lowercase_ ,				'''''' ,				text.lower()						)

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

       for t in token_sequences_to_ignore:
              A__       =    ''' '''.join(text.split(lowercase_						)						)

       return text







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       List[str]:



       """simple docstring"""

       A__       =    load_dataset(args.dataset ,				args.config ,				split=args.split ,				use_auth_token=lowercase_						)

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

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

       # resample audio
       A__       =    dataset.cast_column('''audio''' ,				Audio(sampling_rate=lowercase_						)						)

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

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

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

       # run inference on all examples
       A__       =    dataset.map(lowercase_ ,				remove_columns=dataset.column_names						)

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


if __name__ == "__main__":
  _lowerCamelCase	:		List[Any]							       =   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.""",
  )
  _lowerCamelCase	:		str							       =   parser.parse_args()

  main(args)


