






"""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


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

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






def       __UpperCamelCase						(		snake_case__       ):
	A_		:    Tuple						     = list(state_dict.keys()       )
	for name in state_dict_keys:
		A_		:    List[Any]						     = state_dict.pop(snake_case__       )
		# emb -> embedding
		if name.startswith("""emb."""       ):
			A_		:    Optional[Any]						     = name.replace("""emb.""" , """embeddings."""       )
		# ln_0 -> pre_ln (only present at block 0)
		if name.startswith("""blocks.0.ln0"""       ):
			A_		:    str						     = name.replace("""blocks.0.ln0""" , """blocks.0.pre_ln"""       )
		# att -> attention
		A_		:    Any						     = re.sub(R"""blocks\.(\d+)\.att""" , R"""blocks.\1.attention""" , snake_case__       )
		# ffn -> feed_forward
		A_		:    Union[str, Any]						     = re.sub(R"""blocks\.(\d+)\.ffn""" , R"""blocks.\1.feed_forward""" , snake_case__       )
		# time_mix_k -> time_mix_key and reshape
		if name.endswith(""".time_mix_k"""       ):
			A_		:    List[str]						     = name.replace(""".time_mix_k""" , """.time_mix_key"""       )
		# time_mix_v -> time_mix_value and reshape
		if name.endswith(""".time_mix_v"""       ):
			A_		:    Any						     = name.replace(""".time_mix_v""" , """.time_mix_value"""       )
		# time_mix_r -> time_mix_key and reshape
		if name.endswith(""".time_mix_r"""       ):
			A_		:    int						     = name.replace(""".time_mix_r""" , """.time_mix_receptance"""       )

		if name != "head.weight":
			A_		:    int						     = """rwkv.""" + name

		A_		:    List[str]						     = weight
	return state_dict






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ , snake_case__=None , snake_case__=None , snake_case__=False , snake_case__=None       ):
	# 1. If possible, build the tokenizer.
	if tokenizer_file is None:
		print("""No `--tokenizer_file` provided, we will use the default tokenizer."""       )
		A_		:    str						     = 50_277
		A_		:    int						     = AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""       )
	else:
		A_		:    List[str]						     = PreTrainedTokenizerFast(tokenizer_file=snake_case__       )
		A_		:    Optional[Any]						     = len(snake_case__       )
	tokenizer.save_pretrained(snake_case__       )

	# 2. Build the config
	A_		:    str						     = 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_		:    Optional[int]						     = 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_		:    List[Any]						     = RwkvConfig(
	    vocab_size=snake_case__ , num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size] , hidden_size=HIDEN_SIZE_MAPPING[size] , )
	config.save_pretrained(snake_case__       )

	# 3. Download model file then convert state_dict
	A_		:    Tuple						     = hf_hub_download(snake_case__ , snake_case__       )
	A_		:    int						     = torch.load(snake_case__ , map_location="""cpu"""       )
	A_		:    Dict						     = convert_state_dict(snake_case__       )

	# 4. Split in shards and save
	A_     ,  A_		:    List[str]						     = shard_checkpoint(snake_case__       )
	for shard_file, shard in shards.items():
		torch.save(snake_case__ , os.path.join(snake_case__ , snake_case__       )       )

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

		# 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_		:    Any						     = list(shards.keys()       )

		del state_dict
		del shards
		gc.collect()

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

	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_		:    Dict						     = AutoModelForCausalLM.from_pretrained(snake_case__       )
		model.push_to_hub(snake_case__ , max_shard_size="""2GB"""       )
		tokenizer.push_to_hub(snake_case__       )


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

							_lowerCAmelCase       =				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 math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


_lowerCAmelCase       =				"\\n@article{hendrycksmath2021,\n  title={Measuring Mathematical Problem Solving With the MATH Dataset},\n  author={Dan Hendrycks\n    and Collin Burns\n    and Saurav Kadavath\n    and Akul Arora\n    and Steven Basart\n    and Eric Tang\n    and Dawn Song\n    and Jacob Steinhardt},\n  journal={arXiv preprint arXiv:2103.03874},\n  year={2021}\n}\n"


_lowerCAmelCase       =				"\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting \"1/2\" to \"\\frac{1}{2}\") and then computes accuracy.\n"


_lowerCAmelCase       =				r"\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n    predictions: list of predictions to score. Each prediction\n        is a string that contains natural language and LaTex.\n    references: list of reference for each prediction. Each\n        reference is a string that contains natural language\n        and LaTex.\nReturns:\n    accuracy: accuracy after canonicalizing inputs\n        (e.g., converting \"1/2\" to \"\\frac{1}{2}\")\n\nExamples:\n    >>> metric = datasets.load_metric(\"competition_math\")\n    >>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])\n    >>> print(results)\n    {'accuracy': 1.0}\n"



@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION							,							_KWARGS_DESCRIPTION    )
class   SCREAMING_SNAKE_CASE	(					datasets.Metric    ):





	"""simple docstring"""




	def  lowerCamelCase(self				):
		return datasets.MetricInfo(
		    description=_DESCRIPTION  ,  citation=_CITATION  ,  inputs_description=_KWARGS_DESCRIPTION  ,  features=datasets.Features(
		        {
		            """predictions""": datasets.Value("""string"""				),
		            """references""": datasets.Value("""string"""				),
		        }				)  ,  homepage="""https://github.com/hendrycks/math"""  ,  codebase_urls=["""https://github.com/hendrycks/math"""]  ,  )





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    Optional[Any]						     = 0.0
		for i, j in zip(lowerCAmelCase_  ,  lowerCAmelCase_				):
			n_correct += 1.0 if math_equivalence.is_equiv(lowerCAmelCase_  ,  lowerCAmelCase_				) else 0.0
		A_		:    List[str]						     = n_correct / len(lowerCAmelCase_				)
		return {
		    "accuracy": accuracy,
		}

