






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

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





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

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

          __lowerCAmelCase								=					weight
     return state_dict





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case					, __snake_case=None					, __snake_case=None					, __snake_case=False					, __snake_case=None       ):
     # 1. If possible, build the tokenizer.
     if tokenizer_file is None:
          print("No `--tokenizer_file` provided, we will use the default tokenizer."       )
          __lowerCAmelCase								=					5_0277
          __lowerCAmelCase								=					AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"       )
     else:
          __lowerCAmelCase								=					PreTrainedTokenizerFast(tokenizer_file=__snake_case       )
          __lowerCAmelCase								=					len(__snake_case       )
     tokenizer.save_pretrained(__snake_case       )

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

     __lowerCAmelCase								=					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
     __lowerCAmelCase								=					hf_hub_download(__snake_case					, __snake_case       )
     __lowerCAmelCase								=					torch.load(__snake_case					, map_location="cpu"       )
     __lowerCAmelCase								=					convert_state_dict(__snake_case       )

     # 4. Split in shards and save
     __lowerCAmelCase			, __lowerCAmelCase								=					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:
          __lowerCAmelCase								=					os.path.join(__snake_case					, __snake_case       )
          # Save the index as well
          with open(__snake_case					, "w"					, encoding="utf-8"       ) as f:
               __lowerCAmelCase								=					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."       )
          __lowerCAmelCase								=					list(shards.keys()       )

          del state_dict
          del shards
          gc.collect()

          for shard_file in shard_files:
               __lowerCAmelCase								=					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."       )
          __lowerCAmelCase								=					AutoModelForCausalLM.from_pretrained(__snake_case       )
          model.push_to_hub(__snake_case					, max_shard_size="2GB"       )
          tokenizer.push_to_hub(__snake_case       )


if __name__ == "__main__":
 lowerCamelCase			:					str         =	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			:					Union[str, Any]         =	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,
 )














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


lowerCamelCase			:					List[str]         =	logging.get_logger(__name__)

lowerCamelCase			:					Any         =	{
    '''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 							_UpperCamelCase			(a_			):
      snake_case_  =							"""bloom"""
      snake_case_  =							["""past_key_values"""]
      snake_case_  =							{
          """num_hidden_layers""": """n_layer""",
          """num_attention_heads""": """n_head""",
      }


      def __init__(	self					,						__UpperCamelCase=2_5_0_8_8_0					,						__UpperCamelCase=6_4					,						__UpperCamelCase=2					,						__UpperCamelCase=8					,						__UpperCamelCase=1e-5					,						__UpperCamelCase=0.0_2					,						__UpperCamelCase=True					,						__UpperCamelCase=1					,						__UpperCamelCase=2					,						__UpperCamelCase=False					,						__UpperCamelCase=0.0					,						__UpperCamelCase=0.0					,						__UpperCamelCase=1					,						__UpperCamelCase=False					,						**__UpperCamelCase					,						)->	Dict:
           __lowerCAmelCase								=					vocab_size
           # Backward compatibility with n_embed kwarg
           __lowerCAmelCase								=					kwargs.pop("n_embed"					,						__UpperCamelCase	)
           __lowerCAmelCase								=					hidden_size if n_embed is None else n_embed
           __lowerCAmelCase								=					n_layer
           __lowerCAmelCase								=					n_head
           __lowerCAmelCase								=					layer_norm_epsilon
           __lowerCAmelCase								=					initializer_range
           __lowerCAmelCase								=					use_cache
           __lowerCAmelCase								=					pretraining_tp
           __lowerCAmelCase								=					apply_residual_connection_post_layernorm
           __lowerCAmelCase								=					hidden_dropout
           __lowerCAmelCase								=					attention_dropout

           __lowerCAmelCase								=					bos_token_id
           __lowerCAmelCase								=					eos_token_id
           __lowerCAmelCase								=					slow_but_exact

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



class 							_UpperCamelCase			(a_			):
      snake_case_  =							version.parse("""1.12"""			)


      def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase = "default"					,						__UpperCamelCase = None					,						__UpperCamelCase = False					,						)->	str:
           super().__init__(__UpperCamelCase					,						task=__UpperCamelCase					,						patching_specs=__UpperCamelCase					,						use_past=__UpperCamelCase	)
           if not getattr(self._config					,						"pad_token_id"					,						__UpperCamelCase	):
                # TODO: how to do that better?
                __lowerCAmelCase								=					0


      @property
      def  __UpperCAmelCase		(	self	)->	Mapping[str, Mapping[int, str]]:
           __lowerCAmelCase								=					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_(__UpperCamelCase					,						direction="inputs"					,						inverted_values_shape=__UpperCamelCase	)
                __lowerCAmelCase								=					{0: "batch", 1: "past_sequence + sequence"}
           else:
                __lowerCAmelCase								=					{0: "batch", 1: "sequence"}

           return common_inputs


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


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


      @property
      def  __UpperCAmelCase		(	self	)->	float:
           return 1e-3


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase = -1					,						__UpperCamelCase = -1					,						__UpperCamelCase = False					,						__UpperCamelCase = None					,						)->	Mapping[str, Any]:
           __lowerCAmelCase								=					super(__UpperCamelCase					,						self	).generate_dummy_inputs(
               __UpperCamelCase					,						batch_size=__UpperCamelCase					,						seq_length=__UpperCamelCase					,						is_pair=__UpperCamelCase					,						framework=__UpperCamelCase	)

           # We need to order the input in the way they appears in the forward()
           __lowerCAmelCase								=					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

                     __lowerCAmelCase			, __lowerCAmelCase								=					common_inputs["input_ids"].shape
                     # Not using the same length for past_key_values
                     __lowerCAmelCase								=					seqlen + 2
                     __lowerCAmelCase								=					self._config.hidden_size // self.num_attention_heads
                     __lowerCAmelCase								=					(
                         batch * self.num_attention_heads,
                         head_dim,
                         past_key_values_length,
                     )
                     __lowerCAmelCase								=					(
                         batch * self.num_attention_heads,
                         past_key_values_length,
                         head_dim,
                     )
                     __lowerCAmelCase								=					[
                         (torch.zeros(__UpperCamelCase	), torch.zeros(__UpperCamelCase	)) for _ in range(self.num_layers	)
                     ]

           __lowerCAmelCase								=					common_inputs["attention_mask"]
           if self.use_past:
                __lowerCAmelCase								=					ordered_inputs["attention_mask"].dtype
                __lowerCAmelCase								=					torch.cat(
                    [ordered_inputs["attention_mask"], torch.ones(__UpperCamelCase					,						__UpperCamelCase					,						dtype=__UpperCamelCase	)]					,						dim=1	)

           return ordered_inputs


      @property
      def  __UpperCAmelCase		(	self	)->	int:
           return 1_3






