



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

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






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

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

														__SCREAMING_SNAKE_CASE										=   weight
							return state_dict






def       __lowercase  (					a__				,  a__				,  a__				,  a__=None				,  a__=None				,  a__=False				,  a__=None  )							->					Optional[Any]:


							if tokenizer_file is None:
														print('No `--tokenizer_file` provided, we will use the default tokenizer.'  )
														__SCREAMING_SNAKE_CASE										=   5_02_77
														__SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b'  )
							else:
														__SCREAMING_SNAKE_CASE										=   PreTrainedTokenizerFast(tokenizer_file=__lowerCAmelCase  )
														__SCREAMING_SNAKE_CASE										=   len(__lowerCAmelCase  )
							tokenizer.save_pretrained(__lowerCAmelCase  )

							# 2. Build the config
							__SCREAMING_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:
																												__SCREAMING_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}."""  )

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

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

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

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

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

														del state_dict
														del shards
														gc.collect()

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

							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.'  )
														__SCREAMING_SNAKE_CASE										=   AutoModelForCausalLM.from_pretrained(__lowerCAmelCase  )
														model.push_to_hub(__lowerCAmelCase				,  max_shard_size='2GB'  )
														tokenizer.push_to_hub(__lowerCAmelCase  )


if __name__ == "__main__":
			lowerCAmelCase__						:	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.''',
			)

			lowerCAmelCase__						:	Tuple					=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 sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


_A  =	"\\n@inproceedings{snover-etal-2006-study,\n    title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n    author = \"Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John\",\n    booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",\n    month = aug # \" 8-12\",\n    year = \"2006\",\n    address = \"Cambridge, Massachusetts, USA\",\n    publisher = \"Association for Machine Translation in the Americas\",\n    url = \"https://aclanthology.org/2006.amta-papers.25\",\n    pages = \"223--231\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

_A  =	"\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n"

_A  =	"\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    'score' (float): TER score (num_edits / sum_ref_lengths * 100)\n    'num_edits' (int): The cumulative number of edits\n    'ref_length' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\",\n        ...                     \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}\n\n    Example 2:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}\n\n    Example 3:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}\n\n    Example 4:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}\n\n    Example 5:\n        >>> predictions = [\"does this sentence match??\",\n        ...                    \"what about this sentence?\",\n        ...                    \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}\n"





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




				def 	__a		(   self	)							-> str:
							if version.parse(scb.__version__	) < version.parse("1.4.12"	):
										raise ImportWarning(
										    "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
										    "You can install it with `pip install \"sacrebleu>=1.4.12\"`."	)
							return datasets.MetricInfo(
							    description=_DESCRIPTION							,							citation=_CITATION							,							homepage="http://www.cs.umd.edu/~snover/tercom/"							,							inputs_description=_KWARGS_DESCRIPTION							,							features=datasets.Features(
							        {
							            "predictions": datasets.Value("string"							,							id="sequence"	),
							            "references": datasets.Sequence(datasets.Value("string"							,							id="sequence"	)							,							id="references"	),
							        }	)							,							codebase_urls=["https://github.com/mjpost/sacreBLEU#ter"]							,							reference_urls=[
							        "https://github.com/jhclark/tercom",
							    ]							,							)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase = False							,							_UpperCamelCase = False							,							_UpperCamelCase = False							,							_UpperCamelCase = False							,							)							-> Any:
							lowerCAmelCase_         =      len(references[0]	)
							if any(len(_UpperCamelCase	) != references_per_prediction for refs in references	):
										raise ValueError("Sacrebleu requires the same number of references for each prediction"	)
							lowerCAmelCase_         =      [[refs[i] for refs in references] for i in range(_UpperCamelCase	)]

							lowerCAmelCase_         =      TER(
							    normalized=_UpperCamelCase							,							no_punct=_UpperCamelCase							,							asian_support=_UpperCamelCase							,							case_sensitive=_UpperCamelCase							,							)
							lowerCAmelCase_         =      sb_ter.corpus_score(_UpperCamelCase							,							_UpperCamelCase	)

							return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}



