




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

_SCREAMING_SNAKE_CASE							:   Dict      		= {
    "169M": 768,
    "430M": 1024,
    "1B5": 2048,
    "3B": 2560,
    "7B": 4096,
    "14B": 5120,
}


def      UpperCamelCase_(	snake_case							:       Union[str, Any]     ):

							'''simple docstring'''






							snake_case_						=      list(state_dict.keys()     )
							for name in state_dict_keys:
														snake_case_						=      state_dict.pop(snake_case     )
														# 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"							,				snake_case     )
														# ffn -> feed_forward
														snake_case_						=      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"     ):
																					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      UpperCamelCase_(	snake_case							:       List[Any]							,				snake_case							:       str							,				snake_case							:       Optional[Any]							,				snake_case							:       str=None							,				snake_case							:       Union[str, Any]=None							,				snake_case							:       Any=False							,				snake_case							:       Tuple=None     ):

							'''simple docstring'''






							if tokenizer_file is None:
														print("No `--tokenizer_file` provided, we will use the default tokenizer."     )
														snake_case_						=      5_0_2_7_7
														snake_case_						=      AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"     )
							else:
														snake_case_						=      PreTrainedTokenizerFast(tokenizer_file=snake_case     )
														snake_case_						=      len(snake_case     )
							tokenizer.save_pretrained(snake_case     )

							# 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=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
							snake_case_						=      hf_hub_download(snake_case							,				snake_case     )
							snake_case_						=      torch.load(snake_case							,				map_location="cpu"     )
							snake_case_						=      convert_state_dict(snake_case     )

							# 4. Split in shards and save
							snake_case_					,      snake_case_						=      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:
														snake_case_						=      os.path.join(snake_case							,				snake_case     )
														# Save the index as well
														with open(snake_case							,				"w"							,				encoding="utf-8"     ) as f:
																					snake_case_						=      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."     )
														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(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."     )
														snake_case_						=      AutoModelForCausalLM.from_pretrained(snake_case     )
														model.push_to_hub(snake_case							,				max_shard_size="2GB"     )
														tokenizer.push_to_hub(snake_case     )


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:   Optional[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.",
		)

		_SCREAMING_SNAKE_CASE							:   Dict      		= 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 os

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

import datasets


_lowerCAmelCase      						=	datasets.logging.get_logger(__name__)


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

_lowerCAmelCase      						=	'''\
BLEURT 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)
and 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
it for your specific application (the latter is expected to perform better).

See the project\'s README at https://github.com/google-research/bleurt#readme for more information.
'''

_lowerCAmelCase      						=	'''
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    \'scores\': List of scores.
Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bleurt = datasets.load_metric("bleurt")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results["scores"]])
    [1.03, 1.04]
'''

_lowerCAmelCase      						=	{
    '''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        lowerCAmelCase_(				datasets.Metric     ):
  '''simple docstring'''




  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      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  ,__UpperCAmelCase      )    ->		Tuple:
      # check that config name specifies a valid BLEURT model
      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')."""      )
          lowerCAmelCase__ :  str							      =					"""bleurt-base-128"""

      if self.config_name.lower() in CHECKPOINT_URLS:
          lowerCAmelCase__ :  Union[str, Any]							      =					self.config_name.lower()

      elif self.config_name.upper() in CHECKPOINT_URLS:
          lowerCAmelCase__ :  List[Any]							      =					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
      lowerCAmelCase__ :  int							      =					dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]      )
      lowerCAmelCase__ :  Dict							      =					score.BleurtScorer(os.path.join(__UpperCAmelCase  ,__UpperCAmelCase      )      )






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Union[str, Any]:
      lowerCAmelCase__ :  Union[str, Any]							      =					self.scorer.score(references=__UpperCAmelCase  ,candidates=__UpperCAmelCase      )
      return {"scores": scores}


