



import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score

import datasets


UpperCAmelCase_							:      Dict      =  '''\
    @inproceedings{kakwani2020indicnlpsuite,
    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
    year={2020},
    booktitle={Findings of EMNLP},
}
'''

UpperCAmelCase_							:      str      =  '''\
    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide
    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.
'''

UpperCAmelCase_							:      List[Any]      =  '''
Compute IndicGLUE evaluation metric associated to each IndicGLUE dataset.
Args:
    predictions: list of predictions to score (as int64),
        except for \'cvit-mkb-clsr\' where each prediction is a vector (of float32).
    references: list of ground truth labels corresponding to the predictions (as int64),
        except for \'cvit-mkb-clsr\' where each reference is a vector (of float32).
Returns: depending on the IndicGLUE subset, one or several of:
    "accuracy": Accuracy
    "f1": F1 score
    "precision": Precision@10
Examples:

    >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wnli\')  # \'wnli\' or any of ["copa", "sna", "csqa", "wstp", "inltkh", "bbca", "iitp-mr", "iitp-pr", "actsa-sc", "md"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}

    >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wiki-ner\')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0, \'f1\': 1.0}

    >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'cvit-mkb-clsr\')
    >>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
    >>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'precision@10\': 1.0}

'''
def  UpperCamelCase						(       _A			:				Dict			,							_A			:				Tuple    )->       List[str]:





      """simple docstring"""





      return float((preds == labels).mean()    )
def  UpperCamelCase						(       _A			:				Optional[int]			,							_A			:				Union[str, Any]    )->       Dict:





      """simple docstring"""





      A__							=       simple_accuracy(lowercase_			,							lowercase_    )
      A__							=       float(fa_score(y_true=lowercase_			,							y_pred=lowercase_    )    )
      return {
          "accuracy": acc,
          "f1": fa,
      }
def  UpperCamelCase						(       _A			:				Optional[int]			,							_A			:				Any    )->       Optional[int]:





      """simple docstring"""





      A__							=       np.array(lowercase_    )
      A__							=       np.array(lowercase_    )
      A__							=       en_sentvecs.shape[0]

      # mean centering
      A__							=       en_sentvecs - np.mean(lowercase_			,							axis=0    )
      A__							=       in_sentvecs - np.mean(lowercase_			,							axis=0    )

      A__							=       cdist(lowercase_			,							lowercase_			,							"cosine"    )
      A__							=       np.array(range(lowercase_    )    )
      A__							=       sim.argsort(axis=1    )[:, :10]
      A__							=       np.any(preds == actual[:, None]			,							axis=1    )
      return float(matches.mean()    )






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



 def 			__A							(       self		):
       if self.config_name not in [
           "wnli",
           "copa",
           "sna",
           "csqa",
           "wstp",
           "inltkh",
           "bbca",
           "cvit-mkb-clsr",
           "iitp-mr",
           "iitp-pr",
           "actsa-sc",
           "md",
           "wiki-ner",
       ]:
             raise KeyError(
                 "You should supply a configuration name selected in "
                 "[\"wnli\", \"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", "
                 "\"cvit-mkb-clsr\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\", "
                 "\"wiki-ner\"]"		)
       return datasets.MetricInfo(
           description=_DESCRIPTION			,	citation=_CITATION			,	inputs_description=_KWARGS_DESCRIPTION			,	features=datasets.Features(
               {
                   "predictions": datasets.Value("int64"		)
                   if self.config_name != "cvit-mkb-clsr"
                   else datasets.Sequence(datasets.Value("float32"		)		),
                   "references": datasets.Value("int64"		)
                   if self.config_name != "cvit-mkb-clsr"
                   else datasets.Sequence(datasets.Value("float32"		)		),
               }		)			,	codebase_urls=[]			,	reference_urls=[]			,	format="numpy" if self.config_name != "cvit-mkb-clsr" else None			,	)



 def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__		):
       if self.config_name == "cvit-mkb-clsr":
             return {"precision@10": precision_at_aa(lowerCamelCase__			,	lowerCamelCase__		)}
       elif self.config_name in ["wiki-ner"]:
             return acc_and_fa(lowerCamelCase__			,	lowerCamelCase__		)
       elif self.config_name in [
           "wnli",
           "copa",
           "sna",
           "csqa",
           "wstp",
           "inltkh",
           "bbca",
           "iitp-mr",
           "iitp-pr",
           "actsa-sc",
           "md",
       ]:
             return {"accuracy": simple_accuracy(lowerCamelCase__			,	lowerCamelCase__		)}
       else:
             raise KeyError(
                 "You should supply a configuration name selected in "
                 "[\"wnli\", \"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", "
                 "\"cvit-mkb-clsr\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\", "
                 "\"wiki-ner\"]"		)










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

UpperCAmelCase_							:      Any      =  {
    "169M": 768,
    "430M": 1_024,
    "1B5": 2_048,
    "3B": 2_560,
    "7B": 4_096,
    "14B": 5_120,
}
def  UpperCamelCase						(       _A			:				Dict    )->       Optional[int]:





						"""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  UpperCamelCase						(       _A			:				str			,							_A			:				List[Any]			,							_A			:				List[Any]			,							_A			:				int=None			,							_A			:				List[str]=None			,							_A			:				Dict=False			,							_A			:				List[Any]=None    )->       str:





						"""simple docstring"""





						if tokenizer_file is None:
												print("No `--tokenizer_file` provided, we will use the default tokenizer."    )
												A__							=       50277
												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__":
						UpperCAmelCase_							:      Union[str, 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_							:      Optional[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,
						)





