# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class    A_	(       a_ ):
  _SCREAMING_SNAKE_CASE    				=			42
  _SCREAMING_SNAKE_CASE    				=			None



def   __A		(			_A							,				_A=0.999							,				_A="cosine"							,				):



 """simple docstring"""


 if alpha_transform_type == "cosine":

  def alpha_bar_fn(_A					):
   return math.cos((t + 0.008) / 1.008 * math.pi / 2					) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(_A					):
   return math.exp(t * -12.0					)

 else:
  raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""					)

 __a       =  []
 for i in range(_A					):
  __a       =  i / num_diffusion_timesteps
  __a       =  (i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(_A					) / alpha_bar_fn(_A					)							,				_A					)					)
 return torch.tensor(_A							,				dtype=torch.floataa					)






class    A_	(       a_			,       a_ ):
  _SCREAMING_SNAKE_CASE    				=			1

  @register_to_config
  def __init__(		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      int = 10_00     ,							__SCREAMING_SNAKE_CASE :      float = 0.00_01     ,							__SCREAMING_SNAKE_CASE :      float = 0.02     ,							__SCREAMING_SNAKE_CASE :      str = "linear"     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[np.ndarray, List[float]]] = None     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							__SCREAMING_SNAKE_CASE :      int = 0     ,							__SCREAMING_SNAKE_CASE :      str = "epsilon"     ,							__SCREAMING_SNAKE_CASE :      float = 1.0     ,							**__SCREAMING_SNAKE_CASE :      str     ,							):
   if kwargs.get("set_alpha_to_one"     ,							__SCREAMING_SNAKE_CASE       ) is not None:
    __a       =  (
        "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
    )
    deprecate("set_alpha_to_one"     ,							"1.0.0"     ,							__SCREAMING_SNAKE_CASE     ,							standard_warn=__SCREAMING_SNAKE_CASE       )
    __a       =  kwargs["set_alpha_to_one"]
   if trained_betas is not None:
    __a       =  torch.tensor(__SCREAMING_SNAKE_CASE     ,							dtype=torch.floataa       )
   elif beta_schedule == "linear":
    __a       =  torch.linspace(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							dtype=torch.floataa       )
   elif beta_schedule == "scaled_linear":
    # this schedule is very specific to the latent diffusion model.
    __a       =  (
        torch.linspace(beta_start**0.5     ,							beta_end**0.5     ,							__SCREAMING_SNAKE_CASE     ,							dtype=torch.floataa       ) ** 2
    )
   elif beta_schedule == "squaredcos_cap_v2":
    # Glide cosine schedule
    __a       =  betas_for_alpha_bar(__SCREAMING_SNAKE_CASE       )
   else:
    raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""       )

   __a       =  1.0 - self.betas
   __a       =  torch.cumprod(self.alphas     ,							dim=0       )

   # At every step in inverted ddim, we are looking into the next alphas_cumprod
   # For the final step, there is no next alphas_cumprod, and the index is out of bounds
   # `set_alpha_to_zero` decides whether we set this parameter simply to zero
   # in this case, self.step() just output the predicted noise
   # or whether we use the final alpha of the "non-previous" one.
   __a       =  torch.tensor(0.0       ) if set_alpha_to_zero else self.alphas_cumprod[-1]

   # standard deviation of the initial noise distribution
   __a       =  1.0

   # setable values
   __a       =  None
   __a       =  torch.from_numpy(np.arange(0     ,							__SCREAMING_SNAKE_CASE       ).copy().astype(np.intaa       )       )

  def 		_UpperCAmelCase   (		self :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      torch.FloatTensor     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = None       ):
   return sample

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      Union[str, torch.device] = None       ):

   if num_inference_steps > self.config.num_train_timesteps:
    raise ValueError(
        f"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
        f""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
        f""" maximal {self.config.num_train_timesteps} timesteps."""       )

   __a       =  num_inference_steps
   __a       =  self.config.num_train_timesteps // self.num_inference_steps
   # creates integer timesteps by multiplying by ratio
   # casting to int to avoid issues when num_inference_step is power of 3
   __a       =  (np.arange(0     ,							__SCREAMING_SNAKE_CASE       ) * step_ratio).round().copy().astype(np.intaa       )
   __a       =  torch.from_numpy(__SCREAMING_SNAKE_CASE       ).to(__SCREAMING_SNAKE_CASE       )
   self.timesteps += self.config.steps_offset

  def 		_UpperCAmelCase   (		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      torch.FloatTensor     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      torch.FloatTensor     ,							__SCREAMING_SNAKE_CASE :      float = 0.0     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							__SCREAMING_SNAKE_CASE :      Optional[torch.FloatTensor] = None     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							):
   # 1. get previous step value (=t+1)
   __a       =  timestep + self.config.num_train_timesteps // self.num_inference_steps

   # 2. compute alphas, betas
   # change original implementation to exactly match noise levels for analogous forward process
   __a       =  self.alphas_cumprod[timestep]
   __a       =  (
       self.alphas_cumprod[prev_timestep]
       if prev_timestep < self.config.num_train_timesteps
       else self.final_alpha_cumprod
   )

   __a       =  1 - alpha_prod_t

   # 3. compute predicted original sample from predicted noise also called
   # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   if self.config.prediction_type == "epsilon":
    __a       =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
    __a       =  model_output
   elif self.config.prediction_type == "sample":
    __a       =  model_output
    __a       =  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
   elif self.config.prediction_type == "v_prediction":
    __a       =  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
    __a       =  (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
   else:
    raise ValueError(
        f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
        " `v_prediction`"       )

   # 4. Clip or threshold "predicted x_0"
   if self.config.clip_sample:
    __a       =  pred_original_sample.clamp(
        -self.config.clip_sample_range     ,							self.config.clip_sample_range       )

   # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   __a       =  (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

   # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   __a       =  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

   if not return_dict:
    return (prev_sample, pred_original_sample)
   return DDIMSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE     ,							pred_original_sample=__SCREAMING_SNAKE_CASE       )

  def __len__(		self :      str       ):
   return self.config.num_train_timesteps



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 :	str							     =		{
    """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   __A		(			_A					):



 """simple docstring"""


 __a       =  list(state_dict.keys()					)
 for name in state_dict_keys:
  __a       =  state_dict.pop(_A					)
  # 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"							,				_A					)
  # ffn -> feed_forward
  __a       =  re.sub(r"blocks\.(\d+)\.ffn"							,				r"blocks.\1.feed_forward"							,				_A					)
  # 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   __A		(			_A							,				_A							,				_A							,				_A=None							,				_A=None							,				_A=False							,				_A=None					):



 """simple docstring"""


 if tokenizer_file is None:
  print("No `--tokenizer_file` provided, we will use the default tokenizer."					)
  __a       =  5_0277
  __a       =  AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"					)
 else:
  __a       =  PreTrainedTokenizerFast(tokenizer_file=_A					)
  __a       =  len(_A					)
 tokenizer.save_pretrained(_A					)

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

 # 3. Download model file then convert state_dict
 __a       =  hf_hub_download(_A							,				_A					)
 __a       =  torch.load(_A							,				map_location="cpu"					)
 __a       =  convert_state_dict(_A					)

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

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

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

 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(_A					)
  model.push_to_hub(_A							,				max_shard_size="2GB"					)
  tokenizer.push_to_hub(_A					)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	int							     =		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 :	Tuple							     =		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,
  )


