






'''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": 7_68,
    "430M": 10_24,
    "1B5": 20_48,
    "3B": 25_60,
    "7B": 40_96,
    "14B": 51_20,
}


def       __UpperCamelCase					(      lowercase_       :  str   ):
       """simple docstring"""




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

              if name != "head.weight":
                     a_						     =			"rwkv." + name

              a_						     =			weight
       return state_dict


def       __UpperCamelCase					(      lowercase_       :  Optional[int]   ,       lowercase_       :  Dict   ,       lowercase_       :  Optional[int]   ,       lowercase_       :  Optional[int]=None   ,       lowercase_       :  str=None   ,       lowercase_       :  Optional[Any]=False   ,       lowercase_       :  Optional[Any]=None   ):
       """simple docstring"""




       if tokenizer_file is None:
              print('No `--tokenizer_file` provided, we will use the default tokenizer.'   )
              a_						     =			50_277
              a_						     =			AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b'   )
       else:
              a_						     =			PreTrainedTokenizerFast(tokenizer_file=lowerCamelCase_   )
              a_						     =			len(lowerCamelCase_   )
       tokenizer.save_pretrained(lowerCamelCase_   )

       # 2. Build the config
       a_						     =			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_						     =			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_						     =			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
       a_						     =			hf_hub_download(lowerCamelCase_   ,       lowerCamelCase_   )
       a_						     =			torch.load(lowerCamelCase_   ,       map_location='cpu'   )
       a_						     =			convert_state_dict(lowerCamelCase_   )

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

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

              del state_dict
              del shards
              gc.collect()

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


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"""







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
			from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


A		=						logging.get_logger(__name__)

A		=						{
    'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
    'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
    'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
    'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
    'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
    'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}

class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     """bloom"""
			__magic_name__     					=     ["""past_key_values"""]
			__magic_name__     					=     {
			    """num_hidden_layers""": """n_layer""",
			    """num_attention_heads""": """n_head""",
			}



			def __init__(			self							:				Tuple   ,	_lowercase							:				List[Any]=250880   ,	_lowercase							:				int=64   ,	_lowercase							:				Optional[Any]=2   ,	_lowercase							:				Dict=8   ,	_lowercase							:				List[str]=1E-5   ,	_lowercase							:				List[Any]=0.02   ,	_lowercase							:				Optional[Any]=True   ,	_lowercase							:				Dict=1   ,	_lowercase							:				Union[str, Any]=2   ,	_lowercase							:				str=False   ,	_lowercase							:				List[Any]=0.0   ,	_lowercase							:				Tuple=0.0   ,	_lowercase							:				Dict=1   ,	_lowercase							:				int=False   ,	**_lowercase							:				int   ,	)							->					List[str]:
									snake_case   :			Any						    = vocab_size
									# Backward compatibility with n_embed kwarg
									snake_case   :			Any						    = kwargs.pop("n_embed"   ,	_lowercase		)
									snake_case   :			Tuple						    = hidden_size if n_embed is None else n_embed
									snake_case   :			Optional[Any]						    = n_layer
									snake_case   :			Optional[Any]						    = n_head
									snake_case   :			Union[str, Any]						    = layer_norm_epsilon
									snake_case   :			int						    = initializer_range
									snake_case   :			int						    = use_cache
									snake_case   :			int						    = pretraining_tp
									snake_case   :			Tuple						    = apply_residual_connection_post_layernorm
									snake_case   :			Union[str, Any]						    = hidden_dropout
									snake_case   :			Optional[Any]						    = attention_dropout

									snake_case   :			List[Any]						    = bos_token_id
									snake_case   :			Any						    = eos_token_id
									snake_case   :			Optional[Any]						    = slow_but_exact

									super().__init__(bos_token_id=_lowercase   ,	eos_token_id=_lowercase   ,	**_lowercase		)



class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     version.parse("""1.12""")



			def __init__(			self							:				int   ,	_lowercase							:				PretrainedConfig   ,	_lowercase							:				str = "default"   ,	_lowercase							:				List[PatchingSpec] = None   ,	_lowercase							:				bool = False   ,	)							->					Dict:
									super().__init__(_lowercase   ,	task=_lowercase   ,	patching_specs=_lowercase   ,	use_past=_lowercase		)
									if not getattr(self._config   ,	"pad_token_id"   ,	_lowercase		):
															# TODO: how to do that better?
															snake_case   :			int						    = 0



			@property
			def 				__lowercase					(			self							:				str		)							->					Mapping[str, Mapping[int, str]]:
									snake_case   :			int						    = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}		)
									if self.use_past:
															# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
															self.fill_with_past_key_values_(_lowercase   ,	direction="inputs"   ,	inverted_values_shape=_lowercase		)
															snake_case   :			Optional[int]						    = {0: "batch", 1: "past_sequence + sequence"}
									else:
															snake_case   :			Dict						    = {0: "batch", 1: "sequence"}

									return common_inputs



			@property
			def 				__lowercase					(			self							:				int		)							->					int:
									return self._config.n_layer



			@property
			def 				__lowercase					(			self							:				Dict		)							->					int:
									return self._config.n_head



			@property
			def 				__lowercase					(			self							:				Union[str, Any]		)							->					float:
									return 1E-3



			def 				__lowercase					(			self							:				List[str]   ,	_lowercase							:				"PreTrainedTokenizer"   ,	_lowercase							:				int = -1   ,	_lowercase							:				int = -1   ,	_lowercase							:				bool = False   ,	_lowercase							:				Optional["TensorType"] = None   ,	)							->					Mapping[str, Any]:
									snake_case   :			int						    = super(_lowercase   ,	self		).generate_dummy_inputs(
									    _lowercase   ,	batch_size=_lowercase   ,	seq_length=_lowercase   ,	is_pair=_lowercase   ,	framework=_lowercase		)

									# We need to order the input in the way they appears in the forward()
									snake_case   :			Any						    = OrderedDict({"input_ids": common_inputs["input_ids"]}		)

									# Need to add the past_keys
									if self.use_past:
															if not is_torch_available():
																					raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
															else:
																					import torch

																					snake_case    ,			snake_case   :			List[str]						    = common_inputs["input_ids"].shape
																					# Not using the same length for past_key_values
																					snake_case   :			Optional[Any]						    = seqlen + 2
																					snake_case   :			Optional[int]						    = self._config.hidden_size // self.num_attention_heads
																					snake_case   :			List[str]						    = (
																					    batch * self.num_attention_heads,
																					    head_dim,
																					    past_key_values_length,
																					)
																					snake_case   :			Any						    = (
																					    batch * self.num_attention_heads,
																					    past_key_values_length,
																					    head_dim,
																					)
																					snake_case   :			Dict						    = [
																					    (torch.zeros(_lowercase		), torch.zeros(_lowercase		)) for _ in range(self.num_layers		)
																					]

									snake_case   :			Tuple						    = common_inputs["attention_mask"]
									if self.use_past:
															snake_case   :			Any						    = ordered_inputs["attention_mask"].dtype
															snake_case   :			List[str]						    = torch.cat(
															    [ordered_inputs["attention_mask"], torch.ones(_lowercase   ,	_lowercase   ,	dtype=_lowercase		)]   ,	dim=1		)

									return ordered_inputs



			@property
			def 				__lowercase					(			self							:				str		)							->					int:
									return 13



