




'''simple docstring'''





from sklearn.metrics import recall_score

import datasets


UpperCamelCase_						:      int       =						'''
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
'''


UpperCamelCase_						:      List[str]       =						'''
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.
- **pos_label** (`int`): The class label to use as the \'positive class\' when calculating the recall. Defaults to `1`.
- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.
    - `\'binary\'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.
    - `\'micro\'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.
    - `\'macro\'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
    - `\'weighted\'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.
    - `\'samples\'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
    - `\'warn\'`: If there is a zero division, the return value is `0`, but warnings are also raised.
    - `0`: If there is a zero division, the return value is `0`.
    - `1`: If there is a zero division, the return value is `1`.

Returns:
- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.

Examples:

    Example 1-A simple example with some errors
        >>> recall_metric = datasets.load_metric(\'recall\')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
        >>> print(results)
        {\'recall\': 0.6666666666666666}

    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
        >>> recall_metric = datasets.load_metric(\'recall\')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
        >>> print(results)
        {\'recall\': 0.5}

    Example 3-The same example as Example 1, but with `sample_weight` included.
        >>> recall_metric = datasets.load_metric(\'recall\')
        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
        >>> print(results)
        {\'recall\': 0.55}

    Example 4-A multiclass example, using different averages.
        >>> recall_metric = datasets.load_metric(\'recall\')
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'macro\')
        >>> print(results)
        {\'recall\': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'micro\')
        >>> print(results)
        {\'recall\': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'weighted\')
        >>> print(results)
        {\'recall\': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {\'recall\': array([1., 0., 0.])}
'''


UpperCamelCase_						:      Dict       =						'''
@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}
'''

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






     def     _lowercase     (			self   )							->      Union[str, Any]:
          return datasets.MetricInfo(
              description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
                  {
                      "predictions": datasets.Sequence(datasets.Value("int32"   )   ),
                      "references": datasets.Sequence(datasets.Value("int32"   )   ),
                  }
                  if self.config_name == "multilabel"
                  else {
                      "predictions": datasets.Value("int32"   ),
                      "references": datasets.Value("int32"   ),
                  }   ) ,reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html"] ,)






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=1 ,_SCREAMING_SNAKE_CASE="binary" ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE="warn" ,)							->      Tuple:
          _snake_case							=				recall_score(
              _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,labels=_SCREAMING_SNAKE_CASE ,pos_label=_SCREAMING_SNAKE_CASE ,average=_SCREAMING_SNAKE_CASE ,sample_weight=_SCREAMING_SNAKE_CASE ,zero_division=_SCREAMING_SNAKE_CASE ,)
          return {"recall": float(_SCREAMING_SNAKE_CASE   ) if score.size == 1 else score}








'''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


UpperCamelCase_						:      int       =						{
    '''169M''': 12,
    '''430M''': 24,
    '''1B5''': 24,
    '''3B''': 32,
    '''7B''': 32,
    '''14B''': 40,
}

UpperCamelCase_						:      str       =						{
    '''169M''': 768,
    '''430M''': 1024,
    '''1B5''': 2048,
    '''3B''': 2560,
    '''7B''': 4096,
    '''14B''': 5120,
}





def      __a     (       _UpperCamelCase:   str   )						->			Any:

     """simple docstring"""




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

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

          _snake_case							=				weight
     return state_dict





def      __a     (       _UpperCamelCase:   Any ,						_UpperCamelCase:   List[Any] ,						_UpperCamelCase:   List[Any] ,						_UpperCamelCase:   str=None ,						_UpperCamelCase:   Optional[Any]=None ,						_UpperCamelCase:   List[str]=False ,						_UpperCamelCase:   Dict=None   )						->			Dict:

     """simple docstring"""




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

     # 2. Build the config
     _snake_case							=				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:
                    _snake_case							=				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}."""   )

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

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

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

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

          # 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."   )
          _snake_case							=				list(shards.keys()   )

          del state_dict
          del shards
          gc.collect()

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

     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."   )
          _snake_case							=				AutoModelForCausalLM.from_pretrained(_UpperCamelCase   )
          model.push_to_hub(_UpperCamelCase ,						max_shard_size="2GB"   )
          tokenizer.push_to_hub(_UpperCamelCase   )


if __name__ == "__main__":
   UpperCamelCase_						:      Any       =						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.''',
   )

   UpperCamelCase_						:      int       =						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,
   )


