




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							:						List[str]					       =  {
    "169M": 12,
    "430M": 24,
    "1B5": 24,
    "3B": 32,
    "7B": 32,
    "14B": 40,
}

lowerCamelCase							:						Tuple					       =  {
    "169M": 768,
    "430M": 1_024,
    "1B5": 2_048,
    "3B": 2_560,
    "7B": 4_096,
    "14B": 5_120,
}

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Optional[Any]	):


      '''simple docstring'''



      lowerCamelCase_							=       list(state_dict.keys()	)
      for name in state_dict_keys:
            lowerCamelCase_							=       state_dict.pop(lowercase	)
            # 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'      ,				lowercase	)
            # ffn -> feed_forward
            lowerCamelCase_							=       re.sub(r'blocks\.(\d+)\.ffn'      ,				r'blocks.\1.feed_forward'      ,				lowercase	)
            # 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      _SCREAMING_SNAKE_CASE     (				lowercase      :			Dict      ,				lowercase      :			Optional[int]      ,				lowercase      :			Union[str, Any]      ,				lowercase      :			int=None      ,				lowercase      :			str=None      ,				lowercase      :			Any=False      ,				lowercase      :			Tuple=None	):


      '''simple docstring'''



      if tokenizer_file is None:
            print('No `--tokenizer_file` provided, we will use the default tokenizer.'	)
            lowerCamelCase_							=       5_02_77
            lowerCamelCase_							=       AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b'	)
      else:
            lowerCamelCase_							=       PreTrainedTokenizerFast(tokenizer_file=lowercase	)
            lowerCamelCase_							=       len(lowercase	)
      tokenizer.save_pretrained(lowercase	)

      # 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=lowercase      ,				num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]      ,				hidden_size=HIDEN_SIZE_MAPPING[size]      ,				)
      config.save_pretrained(lowercase	)

      # 3. Download model file then convert state_dict
      lowerCamelCase_							=       hf_hub_download(lowercase      ,				lowercase	)
      lowerCamelCase_							=       torch.load(lowercase      ,				map_location='cpu'	)
      lowerCamelCase_							=       convert_state_dict(lowercase	)

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

      if index is not None:
            lowerCamelCase_							=       os.path.join(lowercase      ,				lowercase	)
            # Save the index as well
            with open(lowercase      ,				'w'      ,				encoding='utf-8'	) as f:
                  lowerCamelCase_							=       json.dumps(lowercase      ,				indent=2      ,				sort_keys=lowercase	) + '\n'
                  f.write(lowercase	)

            # 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(lowercase      ,				lowercase	)	)
                  torch.save({k: v.cpu().clone() for k, v in state_dict.items()}      ,				os.path.join(lowercase      ,				lowercase	)	)

      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(lowercase	)
            model.push_to_hub(lowercase      ,				max_shard_size='2GB'	)
            tokenizer.push_to_hub(lowercase	)


if __name__ == "__main__":
 lowerCamelCase							:						List[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							:						Optional[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,
 )











# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput




@dataclass
class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					torch.FloatTensor
       lowerCamelCase				:					torch.FloatTensor




class       snake_case__ (				lowercase_						,     lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					int								=			1



       @register_to_config
       def __init__(				self      ,    a__ = 20_00      ,    a__ = 0.15      ,    a__ = 0.01      ,    a__ = 13_48.0      ,    a__ = 1e-5      ,    a__ = 1      ,    )			->   Any:






            '''simple docstring'''





            __snake_case      :int 							=    sigma_max

            # setable values
            __snake_case      :Any 							=    None

            self.set_sigmas(a__      ,    a__      ,    a__      ,    a__     )



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   torch.FloatTensor:






            '''simple docstring'''





            return sample



       def        __lowercase    (				self      ,    a__      ,    a__ = None      ,    a__ = None     )			->   Union[str, Any]:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    sampling_eps if sampling_eps is not None else self.config.sampling_eps

            __snake_case      :int 							=    torch.linspace(1      ,    a__      ,    a__      ,    device=a__     )



       def        __lowercase    (				self      ,    a__      ,    a__ = None      ,    a__ = None      ,    a__ = None     )			->   Union[str, Any]:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    sigma_min if sigma_min is not None else self.config.sigma_min
            __snake_case      :Any 							=    sigma_max if sigma_max is not None else self.config.sigma_max
            __snake_case      :Any 							=    sampling_eps if sampling_eps is not None else self.config.sampling_eps
            if self.timesteps is None:
                 self.set_timesteps(a__      ,    a__     )

            __snake_case      :int 							=    sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
            __snake_case      :List[str] 							=    torch.exp(torch.linspace(math.log(a__     )      ,    math.log(a__     )      ,    a__     )     )
            __snake_case      :Any 							=    torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]     )



       def        __lowercase    (				self      ,    a__      ,    a__     )			->   Tuple:






            '''simple docstring'''





            return torch.where(
                timesteps == 0      ,    torch.zeros_like(t.to(timesteps.device     )     )      ,    self.discrete_sigmas[timesteps - 1].to(timesteps.device     )      ,    )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__      ,    a__ = None      ,    a__ = True      ,    )			->   Union[SdeVeOutput, Tuple]:






            '''simple docstring'''





            if self.timesteps is None:
                 raise ValueError(
                     """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""     )

            __snake_case      :Optional[int] 							=    timestep * torch.ones(
                sample.shape[0]      ,    device=sample.device     )  # torch.repeat_interleave(timestep, sample.shape[0])
            __snake_case      :Tuple 							=    (timestep * (len(self.timesteps     ) - 1)).long()

            # mps requires indices to be in the same device, so we use cpu as is the default with cuda
            __snake_case      :str 							=    timesteps.to(self.discrete_sigmas.device     )

            __snake_case      :str 							=    self.discrete_sigmas[timesteps].to(sample.device     )
            __snake_case      :Union[str, Any] 							=    self.get_adjacent_sigma(a__      ,    a__     ).to(sample.device     )
            __snake_case      :Dict 							=    torch.zeros_like(a__     )
            __snake_case      :str 							=    (sigma**2 - adjacent_sigma**2) ** 0.5

            # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
            # also equation 47 shows the analog from SDE models to ancestral sampling methods
            __snake_case      :Optional[Any] 							=    diffusion.flatten()
            while len(diffusion.shape     ) < len(sample.shape     ):
                 __snake_case      :int 							=    diffusion.unsqueeze(-1     )
            __snake_case      :Optional[Any] 							=    drift - diffusion**2 * model_output

            #  equation 6: sample noise for the diffusion term of
            __snake_case      :str 							=    randn_tensor(
                sample.shape      ,    layout=sample.layout      ,    generator=a__      ,    device=sample.device      ,    dtype=sample.dtype     )
            __snake_case      :str 							=    sample - drift  # subtract because `dt` is a small negative timestep
            # TODO is the variable diffusion the correct scaling term for the noise?
            __snake_case      :str 							=    prev_sample_mean + diffusion * noise  # add impact of diffusion field g

            if not return_dict:
                 return (prev_sample, prev_sample_mean)

            return SdeVeOutput(prev_sample=a__      ,    prev_sample_mean=a__     )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__ = None      ,    a__ = True      ,    )			->   Union[SchedulerOutput, Tuple]:






            '''simple docstring'''





            if self.timesteps is None:
                 raise ValueError(
                     """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""     )

            # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
            # sample noise for correction
            __snake_case      :Any 							=    randn_tensor(sample.shape      ,    layout=sample.layout      ,    generator=a__     ).to(sample.device     )

            # compute step size from the model_output, the noise, and the snr
            __snake_case      :List[Any] 							=    torch.norm(model_output.reshape(model_output.shape[0]      ,    -1     )      ,    dim=-1     ).mean()
            __snake_case      :Tuple 							=    torch.norm(noise.reshape(noise.shape[0]      ,    -1     )      ,    dim=-1     ).mean()
            __snake_case      :Dict 							=    (self.config.snr * noise_norm / grad_norm) ** 2 * 2
            __snake_case      :int 							=    step_size * torch.ones(sample.shape[0]     ).to(sample.device     )
            # self.repeat_scalar(step_size, sample.shape[0])

            # compute corrected sample: model_output term and noise term
            __snake_case      :Tuple 							=    step_size.flatten()
            while len(step_size.shape     ) < len(sample.shape     ):
                 __snake_case      :Optional[int] 							=    step_size.unsqueeze(-1     )
            __snake_case      :List[str] 							=    sample + step_size * model_output
            __snake_case      :Tuple 							=    prev_sample_mean + ((step_size * 2) ** 0.5) * noise

            if not return_dict:
                 return (prev_sample,)

            return SchedulerOutput(prev_sample=a__     )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__      ,    )			->   torch.FloatTensor:






            '''simple docstring'''





            __snake_case      :Tuple 							=    timesteps.to(original_samples.device     )
            __snake_case      :Optional[Any] 							=    self.discrete_sigmas.to(original_samples.device     )[timesteps]
            __snake_case      :int 							=    (
                noise * sigmas[:, None, None, None]
                if noise is not None
                else torch.randn_like(a__     ) * sigmas[:, None, None, None]
            )
            __snake_case      :str 							=    noise + original_samples
            return noisy_samples



       def __len__(				self     )			->   Optional[int]:






            '''simple docstring'''





            return self.config.num_train_timesteps


