


'''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


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

__A	:	Optional[int]     =     {
    '''169M''': 768,
    '''430M''': 1_024,
    '''1B5''': 2_048,
    '''3B''': 2_560,
    '''7B''': 4_096,
    '''14B''': 5_120,
}





def      UpperCAmelCase      (		lowerCamelCase_  :Optional[int] ):







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

				if name != "head.weight":
						snake_case_		:							str			       =						"""rwkv.""" + name

				snake_case_		:							Any			       =						weight
		return state_dict





def      UpperCAmelCase      (		lowerCamelCase_  :List[str]				,      lowerCamelCase_  :str				,      lowerCamelCase_  :int				,      lowerCamelCase_  :Union[str, Any]=None				,      lowerCamelCase_  :Optional[Any]=None				,      lowerCamelCase_  :Union[str, Any]=False				,      lowerCamelCase_  :Union[str, Any]=None ):







		'''simple docstring'''
		# 1. If possible, build the tokenizer.
		if tokenizer_file is None:
				print("""No `--tokenizer_file` provided, we will use the default tokenizer.""" )
				snake_case_		:							Union[str, Any]			       =						5_02_77
				snake_case_		:							Optional[int]			       =						AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b""" )
		else:
				snake_case_		:							Optional[Any]			       =						PreTrainedTokenizerFast(tokenizer_file=_UpperCAmelCase )
				snake_case_		:							Union[str, Any]			       =						len(_UpperCAmelCase )
		tokenizer.save_pretrained(_UpperCAmelCase )

		# 2. Build the config
		snake_case_		:							Dict			       =						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_		:							Tuple			       =						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_		:							int			       =						RwkvConfig(
		    vocab_size=_UpperCAmelCase				,      num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]				,      hidden_size=HIDEN_SIZE_MAPPING[size]				,      )
		config.save_pretrained(_UpperCAmelCase )

		# 3. Download model file then convert state_dict
		snake_case_		:							Optional[Any]			       =						hf_hub_download(_UpperCAmelCase				,      _UpperCAmelCase )
		snake_case_		:							List[Any]			       =						torch.load(_UpperCAmelCase				,      map_location="""cpu""" )
		snake_case_		:							Optional[int]			       =						convert_state_dict(_UpperCAmelCase )

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

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

				# 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_		:							Union[str, Any]			       =						list(shards.keys() )

				del state_dict
				del shards
				gc.collect()

				for shard_file in shard_files:
						snake_case_		:							Optional[int]			       =						torch.load(os.path.join(_UpperCAmelCase				,      _UpperCAmelCase ) )
						torch.save({k: v.cpu().clone() for k, v in state_dict.items()}				,      os.path.join(_UpperCAmelCase				,      _UpperCAmelCase ) )

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


if __name__ == "__main__":
							__A	:	Tuple     =     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.',
							)

							__A	:	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,
							)



'''simple docstring'''






import torch

from transformers import AutoModel
class 						__UpperCamelCase	(						torch.nn.Module							):





							def __init__(  self		:Union[str, Any]  ,_UpperCamelCase		:Tuple="sayef/fsner-bert-base-uncased"			):
									super(_UpperCamelCase  ,self			).__init__()

									snake_case_		:							Dict			       =						AutoModel.from_pretrained(_UpperCamelCase  ,return_dict=_UpperCamelCase			)
									snake_case_		:							Dict			       =						torch.nn.CosineSimilarity(3  ,1E-0_8			)
									snake_case_		:							int			       =						torch.nn.Softmax(dim=1			)





							def 		a__   (  self		:Optional[Any]  ,**_UpperCamelCase		:Optional[Any]			):
									return self.bert(**_UpperCamelCase			).last_hidden_state





							def 		a__   (  self		:List[Any]  ,_UpperCamelCase		:Dict			):
									return token_embeddings.sum(2  ,keepdim=_UpperCamelCase			)





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:List[str]=1			):
									return self.softmax(T * self.cos(_UpperCamelCase  ,_UpperCamelCase			)			)





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:int			):
									snake_case_		:							Union[str, Any]			       =						W_supports["""sizes"""].tolist()
									snake_case_		:							Dict			       =						W_supports["""start_token_id"""].item()
									snake_case_		:							int			       =						W_supports["""end_token_id"""].item()

									del W_supports["sizes"]
									del W_supports["start_token_id"]
									del W_supports["end_token_id"]

									snake_case_		:							int			       =						self.BERT(**_UpperCamelCase			)
									snake_case_		:							int			       =						self.BERT(**_UpperCamelCase			)

									snake_case_		:							Any			       =						None
									snake_case_		:							Any			       =						None

									snake_case_		:							Union[str, Any]			       =						W_supports["""input_ids"""] == start_token_id
									snake_case_		:							Tuple			       =						W_supports["""input_ids"""] == end_token_id

									for i, size in enumerate(_UpperCamelCase			):
											if i == 0:
													snake_case_		:							str			       =						0
											else:
													snake_case_		:							Optional[Any]			       =						support_sizes[i - 1]

											snake_case_		:							Dict			       =						S[s : s + size][start_token_masks[s : s + size]]
											snake_case_		:							Optional[Any]			       =						S[s : s + size][end_token_masks[s : s + size]]

											snake_case_		:							int			       =						torch.matmul(q[i]  ,s_start.T			).sum(1			).softmax(0			)
											snake_case_		:							List[str]			       =						torch.matmul(q[i]  ,s_end.T			).sum(1			).softmax(0			)

											if p_starts is not None:
													snake_case_		:							List[str]			       =						torch.vstack((p_starts, p_start)			)
													snake_case_		:							Tuple			       =						torch.vstack((p_ends, p_end)			)
											else:
													snake_case_		:							Any			       =						p_start
													snake_case_		:							List[str]			       =						p_end

									return p_starts, p_ends