



'''simple docstring'''





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


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

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




def lowerCAmelCase						(__A):



     """simple docstring"""



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

          if name != "head.weight":
               _a											=						'''rwkv.''' + name

          _a											=						weight
     return state_dict




def lowerCAmelCase						(__A						,			__A						,			__A						,			__A=None						,			__A=None						,			__A=False						,			__A=None):



     """simple docstring"""



     if tokenizer_file is None:
          print('''No `--tokenizer_file` provided, we will use the default tokenizer.''')
          _a											=						50_277
          _a											=						AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b''')
     else:
          _a											=						PreTrainedTokenizerFast(tokenizer_file=__A)
          _a											=						len(__A)
     tokenizer.save_pretrained(__A)

     # 2. Build the config
     _a											=						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:
                    _a											=						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}.''')

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

     # 3. Download model file then convert state_dict
     _a											=						hf_hub_download(__A						,			__A)
     _a											=						torch.load(__A						,			map_location='''cpu''')
     _a											=						convert_state_dict(__A)

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

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

          # 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.''')
          _a											=						list(shards.keys())

          del state_dict
          del shards
          gc.collect()

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

     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.''')
          _a											=						AutoModelForCausalLM.from_pretrained(__A)
          model.push_to_hub(__A						,			max_shard_size='''2GB''')
          tokenizer.push_to_hub(__A)


if __name__ == "__main__":
  lowercase_	 =						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.",
  )

  lowercase_	 =						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,
  )








'''simple docstring'''





import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


lowercase_	 =						"\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\n    author = \"Lin, Chin-Yew  and\n      Och, Franz Josef\",\n    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\n    month = \"aug 23{--}aug 27\",\n    year = \"2004\",\n    address = \"Geneva, Switzerland\",\n    publisher = \"COLING\",\n    url = \"https://www.aclweb.org/anthology/C04-1072\",\n    pages = \"501--507\",\n}\n"

lowercase_	 =						"\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n"

lowercase_	 =						"\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    'bleu': bleu score,\n    'precisions': geometric mean of n-gram precisions,\n    'brevity_penalty': brevity penalty,\n    'length_ratio': ratio of lengths,\n    'translation_length': translation_length,\n    'reference_length': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample\n    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)\n    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric(\"bleu\")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results[\"bleu\"])\n    1.0\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,     _KWARGS_DESCRIPTION		)
class __A    (						datasets.Metric		):



 '''simple docstring'''







 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      return datasets.MetricInfo(
          description=_DESCRIPTION				,     citation=_CITATION				,     inputs_description=_KWARGS_DESCRIPTION				,     features=datasets.Features(
              {
                  '''predictions''': datasets.Sequence(datasets.Value('''string'''				,     id='''token''' )				,     id='''sequence''' ),
                  '''references''': datasets.Sequence(
                      datasets.Sequence(datasets.Value('''string'''				,     id='''token''' )				,     id='''sequence''' )				,     id='''references''' ),
              } )				,     codebase_urls=['''https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py''']				,     reference_urls=[
              '''https://en.wikipedia.org/wiki/BLEU''',
              '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
          ]				,     )



 def       a__						(self				,     A				,     A				,     A=4				,     A=False )					->  Tuple:


      """simple docstring"""



      _a											=						compute_bleu(
          reference_corpus=A				,     translation_corpus=A				,     max_order=A				,     smooth=A )
      ((_a)						,					(_a)						,					(_a)						,					(_a)						,					(_a)						,					(_a))											=						score
      return {
          "bleu": bleu,
          "precisions": precisions,
          "brevity_penalty": bp,
          "length_ratio": ratio,
          "translation_length": translation_length,
          "reference_length": reference_length,
      }



