





"""simple docstring"""


import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase=0		)			->     List[Any]:

 '''simple docstring'''






 if name is None:
  snake_case__										= None
 else:
  snake_case__										= "." * max(0		,   spaces - 2		) + "# {:" + str(5_0 - spaces		) + "s}"
  snake_case__										= fmt.format(_UpperCAmelCase		)

 # Print and recurse (if needed).
 if isinstance(_UpperCAmelCase		,   _UpperCAmelCase		):
  if msg is not None:
   print(_UpperCAmelCase		)
  for k in val.keys():
   recursive_print(_UpperCAmelCase		,   val[k]		,   spaces + 2		)
 elif isinstance(_UpperCAmelCase		,   torch.Tensor		):
  print(_UpperCAmelCase		,   ":"		,   val.size()		)
 else:
  print(_UpperCAmelCase		,   ":"		,   _UpperCAmelCase		)





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)			->     List[Any]:

 '''simple docstring'''






 snake_case__										= param.size()
 # other versions store [num_heads * num_splits * hidden_size, :]
 snake_case__										= (num_heads, num_splits, hidden_size) + input_shape[1:]
 snake_case__										= param.view(*_UpperCAmelCase		)
 snake_case__										= param.transpose(0		,   1		).contiguous()
 snake_case__										= param.view(*_UpperCAmelCase		)
 return param





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		)			->     str:

 '''simple docstring'''






 snake_case__										= config.n_positions
 snake_case__										= config.n_layer
 snake_case__										= config.vocab_size
 snake_case__										= config.n_head
 snake_case__										= config.n_embd // config.n_head

 snake_case__										= sd_megatron["model.language_model.embedding.word_embeddings.weight"][:vocab_size, :]
 snake_case__										= {
     "transformer.wte.weight": word_embeddings,
     "transformer.wpe.weight": sd_megatron["model.language_model.embedding.position_embeddings.weight"],
     "transformer.ln_f.weight": sd_megatron["model.language_model.encoder.final_layernorm.weight"],
     "transformer.ln_f.bias": sd_megatron["model.language_model.encoder.final_layernorm.bias"],
 }

 snake_case__										= "model.language_model.encoder.layers."
 for i in range(_UpperCAmelCase		):
  snake_case__										= torch.tril(torch.ones((n_positions, n_positions)		,   dtype=torch.bool		)		)
  snake_case__										= causal_mask.view(1		,   1		,   _UpperCAmelCase		,   _UpperCAmelCase		)
  snake_case__										= causal_mask
  snake_case__										= torch.tensor(-1e4		,   dtype=torch.bfloataa		)

  snake_case__										= sd_megatron[f"{pf}{i}.input_layernorm.weight"]
  snake_case__										= sd_megatron[f"{pf}{i}.input_layernorm.bias"]

  snake_case__										= sd_megatron[f"{pf}{i}.self_attention.query_key_value.weight"]
  snake_case__										= fix_query_key_value_ordering(_UpperCAmelCase		,   3		,   _UpperCAmelCase		,   _UpperCAmelCase		)
  snake_case__										= vala.transpose(0		,   1		).contiguous()

  snake_case__										= sd_megatron[f"{pf}{i}.self_attention.query_key_value.bias"]
  snake_case__										= fix_query_key_value_ordering(_UpperCAmelCase		,   3		,   _UpperCAmelCase		,   _UpperCAmelCase		)
  snake_case__										= vala

  snake_case__										= sd_megatron[f"{pf}{i}.self_attention.dense.weight"].transpose(
      0		,   1		)
  snake_case__										= sd_megatron[f"{pf}{i}.self_attention.dense.bias"]
  snake_case__										= sd_megatron[f"{pf}{i}.post_attention_layernorm.weight"]
  snake_case__										= sd_megatron[f"{pf}{i}.post_attention_layernorm.bias"]
  snake_case__										= sd_megatron[f"{pf}{i}.mlp.dense_h_to_4h.weight"].transpose(0		,   1		)
  snake_case__										= sd_megatron[f"{pf}{i}.mlp.dense_h_to_4h.bias"]
  snake_case__										= sd_megatron[f"{pf}{i}.mlp.dense_4h_to_h.weight"].transpose(
      0		,   1		)
  snake_case__										= sd_megatron[f"{pf}{i}.mlp.dense_4h_to_h.bias"]

 # For LM head, transformers' wants the matrix to weight embeddings.
 snake_case__										= word_embeddings

 return sd_hf





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		)			->     Any:

 '''simple docstring'''






 snake_case__										= 6_4_0_0_0
 snake_case__										= config_megatron["encoder_seq_length"]
 snake_case__										= config_megatron["hidden_size"]
 snake_case__										= config_megatron["num_layers"]
 snake_case__										= config_megatron["num_attention_heads"]
 snake_case__										= config_megatron["ffn_hidden_size"]
 snake_case__										= "gelu"
 snake_case__										= 0.1
 snake_case__										= 0.1
 snake_case__										= 0.1
 snake_case__										= config_megatron["layernorm_epsilon"]  # 1e-5
 snake_case__										= config_megatron["init_method_std"]  # 0.02
 snake_case__										= config_megatron["apply_query_key_layer_scaling"]  # True
 snake_case__										= True
 snake_case__										= True

 # This identifies the 6.7B (7B) model which uses a different tokenizer
 if config_megatron["hidden_size"] == 4_0_9_6:
  snake_case__										= 1  # <|endoftext|>
  snake_case__										= 1  # <|endoftext|>
  snake_case__										= 0  # <unk>
 else:
  snake_case__										= 2  # <s>
  snake_case__										= 3  # <|endoftext|>
  snake_case__										= 0  # <pad>

 return config_hf





def 							a__	(     _UpperCAmelCase		)			->     Dict:

 '''simple docstring'''






 print(_UpperCAmelCase		)

 snake_case__										= args.checkpoint_path
 snake_case__										= args.save_path
 if isfile(_UpperCAmelCase		):
  raise FileNotFoundError(f"ERROR! could not find file {checkpoint_path}"		)

 # Load the model.
 snake_case__										= torch.load(_UpperCAmelCase		,   map_location="cpu"		)

 # Load the config.
 snake_case__										= checkpoint["hyper_parameters"]["cfg"]
 snake_case__										= GPTaConfig()
 snake_case__										= copy_config(config_hf=_UpperCAmelCase		,   config_megatron=_UpperCAmelCase		)
 snake_case__										= ["GPT2LMHeadModel"]

 snake_case__										= checkpoint["state_dict"]

 # Convert.
 print("Converting"		)
 snake_case__										= convert_megatron_checkpoint(_UpperCAmelCase		,   _UpperCAmelCase		)

 # Print the structure of converted state dict.
 if args.print_checkpoint_structure:
  recursive_print(_UpperCAmelCase		,   _UpperCAmelCase		)

 snake_case__										= "GPTSw3Tokenizer"

 # Store the config to file.
 print("Saving config"		)
 config_hf.save_pretrained(_UpperCAmelCase		)

 # Store the state_dict to file.
 snake_case__										= os.path.join(_UpperCAmelCase		,   "pytorch_model.bin"		)
 print(f"Saving checkpoint to \"{output_checkpoint_file}\""		)
 torch.save(_UpperCAmelCase		,   _UpperCAmelCase		)


if __name__ == "__main__":
   a__    :   Dict    						= argparse.ArgumentParser()
   parser.add_argument(
       """--checkpoint_path""",
       type=str,
       required=True,
       help="""e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000""",
   )
   parser.add_argument("""--save_path""", type=str, required=True, help="""e.g. /home/user/gpt-sw3/hf""")
   parser.add_argument("""--print-checkpoint-structure""", action="""store_true""")
   a__    :   Union[str, Any]    						= parser.parse_args()
   main(_args)








"""simple docstring"""


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

import numpy as np
import torch

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



@dataclass
class    lowercase_ (   __lowerCAmelCase							):
       snake_case__ :							torch.FloatTensor
       snake_case__ :							torch.FloatTensor





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase=0.999		,   _UpperCAmelCase="cosine"		,   )			->     str:

 '''simple docstring'''






 if alpha_transform_type == "cosine":

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

 elif alpha_transform_type == "exp":

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

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

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



class    lowercase_ (   __lowerCAmelCase ,__lowerCAmelCase							):
       snake_case__ :							List[Any] 							=   1

       @register_to_config
       def __init__(	self      ,    __SCREAMING_SNAKE_CASE = 1000      ,    __SCREAMING_SNAKE_CASE = 0.0001      ,    __SCREAMING_SNAKE_CASE = 0.02      ,    __SCREAMING_SNAKE_CASE = "linear"      ,    __SCREAMING_SNAKE_CASE = 0.0      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = True      ,    ):






        '''simple docstring'''

        if trained_betas is not None:
         snake_case__										= torch.from_numpy(__SCREAMING_SNAKE_CASE   )
        elif beta_schedule == "linear":
         snake_case__										= 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.
         snake_case__										= (
             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
         snake_case__										= betas_for_alpha_bar(__SCREAMING_SNAKE_CASE   )
        elif beta_schedule == "sigmoid":
         # GeoDiff sigmoid schedule
         snake_case__										= torch.linspace(-6      ,    6      ,    __SCREAMING_SNAKE_CASE   )
         snake_case__										= torch.sigmoid(__SCREAMING_SNAKE_CASE   ) * (beta_end - beta_start) + beta_start
        else:
         raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"   )

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

        snake_case__										= torch.tensor(1.0   )

        # standard deviation of the initial noise distribution
        snake_case__										= 1.0

        # setable values
        snake_case__										= None
        snake_case__										= torch.from_numpy(np.arange(0      ,    __SCREAMING_SNAKE_CASE   )[::-1].copy()   )

        snake_case__										= eta

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None   ):






        '''simple docstring'''

        return sample

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = 10      ,    __SCREAMING_SNAKE_CASE = 10      ,    __SCREAMING_SNAKE_CASE = None      ,    ):






        '''simple docstring'''

        snake_case__										= min(self.config.num_train_timesteps      ,    __SCREAMING_SNAKE_CASE   )
        snake_case__										= num_inference_steps

        snake_case__										= []

        snake_case__										= {}
        for j in range(0      ,    num_inference_steps - jump_length      ,    __SCREAMING_SNAKE_CASE   ):
         snake_case__										= jump_n_sample - 1

        snake_case__										= num_inference_steps
        while t >= 1:
         snake_case__										= t - 1
         timesteps.append(__SCREAMING_SNAKE_CASE   )

         if jumps.get(__SCREAMING_SNAKE_CASE      ,    0   ) > 0:
          snake_case__										= jumps[t] - 1
          for _ in range(__SCREAMING_SNAKE_CASE   ):
           snake_case__										= t + 1
           timesteps.append(__SCREAMING_SNAKE_CASE   )

        snake_case__										= np.array(__SCREAMING_SNAKE_CASE   ) * (self.config.num_train_timesteps // self.num_inference_steps)
        snake_case__										= torch.from_numpy(__SCREAMING_SNAKE_CASE   ).to(__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= t - self.config.num_train_timesteps // self.num_inference_steps

        snake_case__										= self.alphas_cumprod[t]
        snake_case__										= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
        snake_case__										= 1 - alpha_prod_t
        snake_case__										= 1 - alpha_prod_t_prev

        # For t > 0, compute predicted variance βt (see formula (6) and (7) from
        # https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
        # previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
        # variance to pred_sample
        # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
        # without eta.
        # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
        snake_case__										= (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

        return variance

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = True      ,    ):






        '''simple docstring'''

        snake_case__										= timestep
        snake_case__										= timestep - self.config.num_train_timesteps // self.num_inference_steps

        # 1. compute alphas, betas
        snake_case__										= self.alphas_cumprod[t]
        snake_case__										= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
        snake_case__										= 1 - alpha_prod_t

        # 2. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
        snake_case__										= (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

        # 3. Clip "predicted x_0"
        if self.config.clip_sample:
         snake_case__										= torch.clamp(__SCREAMING_SNAKE_CASE      ,    -1      ,    1   )

        # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
        # substitute formula (7) in the algorithm coming from DDPM paper
        # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
        # DDIM schedule gives the same results as DDPM with eta = 1.0
        # Noise is being reused in 7. and 8., but no impact on quality has
        # been observed.

        # 5. Add noise
        snake_case__										= model_output.device
        snake_case__										= randn_tensor(model_output.shape      ,    generator=__SCREAMING_SNAKE_CASE      ,    device=__SCREAMING_SNAKE_CASE      ,    dtype=model_output.dtype   )
        snake_case__										= self.eta * self._get_variance(__SCREAMING_SNAKE_CASE   ) ** 0.5

        snake_case__										= 0
        if t > 0 and self.eta > 0:
         snake_case__										= std_dev_t * noise

        # 6. compute "direction pointing to x_t" of formula (12)
        # from https://arxiv.org/pdf/2010.02502.pdf
        snake_case__										= (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

        # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        snake_case__										= alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

        # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
        snake_case__										= (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

        # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
        snake_case__										= mask * prev_known_part + (1.0 - mask) * prev_unknown_part

        if not return_dict:
         return (
             pred_prev_sample,
             pred_original_sample,
         )

        return RePaintSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE      ,    pred_original_sample=__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE=None   ):






        '''simple docstring'''

        snake_case__										= self.config.num_train_timesteps // self.num_inference_steps

        for i in range(__SCREAMING_SNAKE_CASE   ):
         snake_case__										= self.betas[timestep + i]
         if sample.device.type == "mps":
          # randn does not work reproducibly on mps
          snake_case__										= randn_tensor(sample.shape      ,    dtype=sample.dtype      ,    generator=__SCREAMING_SNAKE_CASE   )
          snake_case__										= noise.to(sample.device   )
         else:
          snake_case__										= randn_tensor(sample.shape      ,    generator=__SCREAMING_SNAKE_CASE      ,    device=sample.device      ,    dtype=sample.dtype   )

         # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
         snake_case__										= (1 - beta) ** 0.5 * sample + beta**0.5 * noise

        return sample

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    ):






        '''simple docstring'''

        raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."   )

       def __len__(	self   ):






        '''simple docstring'''

        return self.config.num_train_timesteps

