





'''simple docstring'''



import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


_SCREAMING_SNAKE_CASE   =      datasets.logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE   =      "\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n"

_SCREAMING_SNAKE_CASE   =      "\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project's README at https://github.com/google-research/bleurt#readme for more information.\n"

_SCREAMING_SNAKE_CASE   =      "\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    'scores': List of scores.\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bleurt = datasets.load_metric(\"bleurt\")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [1.03, 1.04]\n"

_SCREAMING_SNAKE_CASE   =      {
    "bleurt-tiny-128": "https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip",
    "bleurt-tiny-512": "https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip",
    "bleurt-base-128": "https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip",
    "bleurt-base-512": "https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip",
    "bleurt-large-128": "https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip",
    "bleurt-large-512": "https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip",
    "BLEURT-20-D3": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip",
    "BLEURT-20-D6": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip",
    "BLEURT-20-D12": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip",
    "BLEURT-20": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip",
}






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







	'''simple docstring'''

	def   _UpperCAmelCase       (self	)							->							Optional[Any]:







				'''simple docstring'''





				return datasets.MetricInfo(
				    description=_DESCRIPTION       ,citation=_CITATION       ,homepage='''https://github.com/google-research/bleurt'''       ,inputs_description=_KWARGS_DESCRIPTION       ,features=datasets.Features(
				        {
				            '''predictions''': datasets.Value('''string'''       ,id='''sequence'''	),
				            '''references''': datasets.Value('''string'''       ,id='''sequence'''	),
				        }	)       ,codebase_urls=['''https://github.com/google-research/bleurt''']       ,reference_urls=['''https://github.com/google-research/bleurt''', '''https://arxiv.org/abs/2004.04696''']       ,)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							Optional[int]:







				'''simple docstring'''





				if self.config_name == "default":
							logger.warning(
							    '''Using default BLEURT-Base checkpoint for sequence maximum length 128. '''
							    '''You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').'''	)
							__lowercase			  =							'''bleurt-base-128'''

				if self.config_name.lower() in CHECKPOINT_URLS:
							__lowercase			  =							self.config_name.lower()

				elif self.config_name.upper() in CHECKPOINT_URLS:
							__lowercase			  =							self.config_name.upper()

				else:
							raise KeyError(
							    f"{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}"	)

				# download the model checkpoint specified by self.config_name and set up the scorer
				__lowercase			  =							dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]	)
				__lowercase			  =							score.BleurtScorer(os.path.join(lowerCamelCase__       ,lowerCamelCase__	)	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase	)							->							Optional[Any]:







				'''simple docstring'''





				__lowercase			  =							self.scorer.score(references=lowerCamelCase__       ,candidates=lowerCamelCase__	)
				return {"scores": scores}












'''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


_SCREAMING_SNAKE_CASE   =      {
    '''169M''': 1_2,
    '''430M''': 2_4,
    '''1B5''': 2_4,
    '''3B''': 3_2,
    '''7B''': 3_2,
    '''14B''': 4_0,
}

_SCREAMING_SNAKE_CASE   =      {
    '''169M''': 7_6_8,
    '''430M''': 1_0_2_4,
    '''1B5''': 2_0_4_8,
    '''3B''': 2_5_6_0,
    '''7B''': 4_0_9_6,
    '''14B''': 5_1_2_0,
}




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

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

						__lowercase			  =							weight
			return state_dict




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Optional[int]						,    lowerCamelCase_     :					Union[str, Any]						,    lowerCamelCase_     :					List[str]						,    lowerCamelCase_     :					List[Any]=None						,    lowerCamelCase_     :					List[Any]=None						,    lowerCamelCase_     :					Any=False						,    lowerCamelCase_     :					int=None			):
			# 1. If possible, build the tokenizer.
			if tokenizer_file is None:
						print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''			)
						__lowercase			  =							5_0_2_7_7
						__lowercase			  =							AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''			)
			else:
						__lowercase			  =							PreTrainedTokenizerFast(tokenizer_file=lowerCamelCase_			)
						__lowercase			  =							len(lowerCamelCase_			)
			tokenizer.save_pretrained(lowerCamelCase_			)

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

			__lowercase			  =							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
			__lowercase			  =							hf_hub_download(lowerCamelCase_						,    lowerCamelCase_			)
			__lowercase			  =							torch.load(lowerCamelCase_						,    map_location='''cpu'''			)
			__lowercase			  =							convert_state_dict(lowerCamelCase_			)

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

			if index is not None:
						__lowercase			  =							os.path.join(lowerCamelCase_						,    lowerCamelCase_			)
						# Save the index as well
						with open(lowerCamelCase_						,    '''w'''						,    encoding='''utf-8'''			) as f:
									__lowercase			  =							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.'''			)
						__lowercase			  =							list(shards.keys()			)

						del state_dict
						del shards
						gc.collect()

						for shard_file in shard_files:
									__lowercase			  =							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.'''			)
						__lowercase			  =							AutoModelForCausalLM.from_pretrained(lowerCamelCase_			)
						model.push_to_hub(lowerCamelCase_						,    max_shard_size='''2GB'''			)
						tokenizer.push_to_hub(lowerCamelCase_			)


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

	_SCREAMING_SNAKE_CASE   =      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,
	)





