
import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


_A		     =    {
    """169M""": 12,
    """430M""": 24,
    """1B5""": 24,
    """3B""": 32,
    """7B""": 32,
    """14B""": 40,
}

_A		     =    {
    """169M""": 768,
    """430M""": 1_024,
    """1B5""": 2_048,
    """3B""": 2_560,
    """7B""": 4_096,
    """14B""": 5_120,
}




def  lowerCamelCase__ (  a__     :     Tuple   )						->		int:
       UpperCamelCase_											=				list(state_dict.keys()   )
       for name in state_dict_keys:
              UpperCamelCase_											=				state_dict.pop(lowercase_   )
              # emb -> embedding
              if name.startswith("""emb."""   ):
                     UpperCamelCase_											=				name.replace("""emb."""		,					"""embeddings."""   )
              # ln_0 -> pre_ln (only present at block 0)
              if name.startswith("""blocks.0.ln0"""   ):
                     UpperCamelCase_											=				name.replace("""blocks.0.ln0"""		,					"""blocks.0.pre_ln"""   )
              # att -> attention
              UpperCamelCase_											=				re.sub(r"""blocks\.(\d+)\.att"""		,					r"""blocks.\1.attention"""		,					lowercase_   )
              # ffn -> feed_forward
              UpperCamelCase_											=				re.sub(r"""blocks\.(\d+)\.ffn"""		,					r"""blocks.\1.feed_forward"""		,					lowercase_   )
              # time_mix_k -> time_mix_key and reshape
              if name.endswith(""".time_mix_k"""   ):
                     UpperCamelCase_											=				name.replace(""".time_mix_k"""		,					""".time_mix_key"""   )
              # time_mix_v -> time_mix_value and reshape
              if name.endswith(""".time_mix_v"""   ):
                     UpperCamelCase_											=				name.replace(""".time_mix_v"""		,					""".time_mix_value"""   )
              # time_mix_r -> time_mix_key and reshape
              if name.endswith(""".time_mix_r"""   ):
                     UpperCamelCase_											=				name.replace(""".time_mix_r"""		,					""".time_mix_receptance"""   )

              if name != "head.weight":
                     UpperCamelCase_											=				"""rwkv.""" + name

              UpperCamelCase_											=				weight
       return state_dict




def  lowerCamelCase__ (  a__     :     Tuple		,					a__     :     Optional[int]		,					a__     :     List[str]		,					a__     :     Tuple=None		,					a__     :     List[str]=None		,					a__     :     Dict=False		,					a__     :     str=None   )						->		Any:

       if tokenizer_file is None:
              print("""No `--tokenizer_file` provided, we will use the default tokenizer."""   )
              UpperCamelCase_											=				5_0277
              UpperCamelCase_											=				AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""   )
       else:
              UpperCamelCase_											=				PreTrainedTokenizerFast(tokenizer_file=lowercase_   )
              UpperCamelCase_											=				len(lowercase_   )
       tokenizer.save_pretrained(lowercase_   )

       # 2. Build the config
       UpperCamelCase_											=				list(NUM_HIDDEN_LAYERS_MAPPING.keys()   )
       if size is None:
              # Try to infer size from the checkpoint name
              for candidate in possible_sizes:
                     if candidate in checkpoint_file:
                            UpperCamelCase_											=				candidate
                            break
              if size is None:
                     raise ValueError("""Could not infer the size, please provide it with the `--size` argument."""   )
       if size not in possible_sizes:
              raise ValueError(f'''`size` should be one of {possible_sizes}, got {size}.'''   )

       UpperCamelCase_											=				RwkvConfig(
           vocab_size=lowercase_		,					num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]		,					hidden_size=HIDEN_SIZE_MAPPING[size]		,					)
       config.save_pretrained(lowercase_   )

       # 3. Download model file then convert state_dict
       UpperCamelCase_											=				hf_hub_download(lowercase_		,					lowercase_   )
       UpperCamelCase_											=				torch.load(lowercase_		,					map_location="""cpu"""   )
       UpperCamelCase_											=				convert_state_dict(lowercase_   )

       # 4. Split in shards and save
       UpperCamelCase_				, UpperCamelCase_											=				shard_checkpoint(lowercase_   )
       for shard_file, shard in shards.items():
              torch.save(lowercase_		,					os.path.join(lowercase_		,					lowercase_   )   )

       if index is not None:
              UpperCamelCase_											=				os.path.join(lowercase_		,					lowercase_   )
              # Save the index as well
              with open(lowercase_		,					"""w"""		,					encoding="""utf-8"""   ) as f:
                     UpperCamelCase_											=				json.dumps(lowercase_		,					indent=2		,					sort_keys=lowercase_   ) + """\n"""
                     f.write(lowercase_   )

              # 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
              print(
                  """Cleaning up shards. This may error with an OOM error, it this is the case don\'t worry you still have converted the model."""   )
              UpperCamelCase_											=				list(shards.keys()   )

              del state_dict
              del shards
              gc.collect()

              for shard_file in shard_files:
                     UpperCamelCase_											=				torch.load(os.path.join(lowercase_		,					lowercase_   )   )
                     torch.save({k: v.cpu().clone() for k, v in state_dict.items()}		,					os.path.join(lowercase_		,					lowercase_   )   )

       del state_dict
       gc.collect()

       if push_to_hub:
              if model_name is None:
                     raise ValueError("""Please provide a `model_name` to push the model to the Hub."""   )
              UpperCamelCase_											=				AutoModelForCausalLM.from_pretrained(lowercase_   )
              model.push_to_hub(lowercase_		,					max_shard_size="""2GB"""   )
              tokenizer.push_to_hub(lowercase_   )


if __name__ == "__main__":
      _A		     =    argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--repo_id''', default=None, type=str, required=True, help='''Repo ID from which to pull the checkpoint.'''
      )
      parser.add_argument(
          '''--checkpoint_file''', default=None, type=str, required=True, help='''Name of the checkpoint file in the repo.'''
      )
      parser.add_argument(
          '''--output_dir''', default=None, type=str, required=True, help='''Where to save the converted model.'''
      )
      parser.add_argument(
          '''--tokenizer_file''',
          default=None,
          type=str,
          help='''Path to the tokenizer file to use (if not provided, only the model is converted).''',
      )
      parser.add_argument(
          '''--size''',
          default=None,
          type=str,
          help='''Size of the model. Will be inferred from the `checkpoint_file` if not passed.''',
      )
      parser.add_argument(
          '''--push_to_hub''',
          action='''store_true''',
          help='''Push to the Hub the converted model.''',
      )
      parser.add_argument(
          '''--model_name''',
          default=None,
          type=str,
          help='''Name of the pushed model on the Hub, including the username / organization.''',
      )

      _A		     =    parser.parse_args()
      convert_rmkv_checkpoint_to_hf_format(
          args.repo_id,
          args.checkpoint_file,
          args.output_dir,
          size=args.size,
          tokenizer_file=args.tokenizer_file,
          push_to_hub=args.push_to_hub,
          model_name=args.model_name,
      )









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)


