





import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig


def _A   (    __magic_name__  ,					__magic_name__  ,					__magic_name__=0       ):
   # Format the message.
   if name is None:
      lowercase__    =  None
   else:
      lowercase__    =  "." * max(0  ,					spaces - 2       ) + "# {:" + str(50 - spaces       ) + "s}"
      lowercase__    =  fmt.format(__magic_name__       )

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


def _A   (    __magic_name__  ,					__magic_name__  ,					__magic_name__  ,					__magic_name__       ):
   # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
   # for compatibility with later versions of NVIDIA Megatron-LM.
   # The inverse operation is performed inside Megatron-LM to read checkpoints:
   # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
   # If param is the weight tensor of the self-attention block, the returned tensor
   # will have to be transposed one more time to be read by HuggingFace GPT2.
   lowercase__    =  param.size()
   # other versions store [num_heads * num_splits * hidden_size, :]
   lowercase__    =  (num_heads, num_splits, hidden_size) + input_shape[1:]
   lowercase__    =  param.view(*__magic_name__       )
   lowercase__    =  param.transpose(0  ,					1       ).contiguous()
   lowercase__    =  param.view(*__magic_name__       )
   return param


def _A   (    __magic_name__  ,					__magic_name__       ):
   lowercase__    =  config.n_positions
   lowercase__    =  config.n_layer
   lowercase__    =  config.vocab_size
   lowercase__    =  config.n_head
   lowercase__    =  config.n_embd // config.n_head

   lowercase__    =  sd_megatron["model.language_model.embedding.word_embeddings.weight"][:vocab_size, :]
   lowercase__    =  {
       "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"],
   }

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

      lowercase__    =  sd_megatron[f'''{pf}{i}.input_layernorm.weight''']
      lowercase__    =  sd_megatron[f'''{pf}{i}.input_layernorm.bias''']

      lowercase__    =  sd_megatron[f'''{pf}{i}.self_attention.query_key_value.weight''']
      lowercase__    =  fix_query_key_value_ordering(__magic_name__  ,					3  ,					__magic_name__  ,					__magic_name__       )
      lowercase__    =  vala.transpose(0  ,					1       ).contiguous()

      lowercase__    =  sd_megatron[f'''{pf}{i}.self_attention.query_key_value.bias''']
      lowercase__    =  fix_query_key_value_ordering(__magic_name__  ,					3  ,					__magic_name__  ,					__magic_name__       )
      lowercase__    =  vala

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

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

   return sd_hf


def _A   (    __magic_name__  ,					__magic_name__       ):
   lowercase__    =  6_4000
   lowercase__    =  config_megatron["encoder_seq_length"]
   lowercase__    =  config_megatron["hidden_size"]
   lowercase__    =  config_megatron["num_layers"]
   lowercase__    =  config_megatron["num_attention_heads"]
   lowercase__    =  config_megatron["ffn_hidden_size"]
   lowercase__    =  "gelu"
   lowercase__    =  0.1
   lowercase__    =  0.1
   lowercase__    =  0.1
   lowercase__    =  config_megatron["layernorm_epsilon"]  # 1e-5
   lowercase__    =  config_megatron["init_method_std"]  # 0.02
   lowercase__    =  config_megatron["apply_query_key_layer_scaling"]  # True
   lowercase__    =  True
   lowercase__    =  True

   # This identifies the 6.7B (7B) model which uses a different tokenizer
   if config_megatron["hidden_size"] == 4096:
      lowercase__    =  1  # <|endoftext|>
      lowercase__    =  1  # <|endoftext|>
      lowercase__    =  0  # <unk>
   else:
      lowercase__    =  2  # <s>
      lowercase__    =  3  # <|endoftext|>
      lowercase__    =  0  # <pad>

   return config_hf


def _A   (    __magic_name__       ):
   print(__magic_name__       )

   lowercase__    =  args.checkpoint_path
   lowercase__    =  args.save_path
   if isfile(__magic_name__       ):
      raise FileNotFoundError(f'''ERROR! could not find file {checkpoint_path}'''       )

   # Load the model.
   lowercase__    =  torch.load(__magic_name__  ,					map_location="cpu"       )

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

   lowercase__    =  checkpoint["state_dict"]

   # Convert.
   print("Converting"       )
   lowercase__    =  convert_megatron_checkpoint(__magic_name__  ,					__magic_name__       )

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

   lowercase__    =  "GPTSw3Tokenizer"

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

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


if __name__ == "__main__":
  _snake_case				= 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""")
  _snake_case				= parser.parse_args()
  main(_args)













from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 					lowerCAmelCase							(   lowercase_					):
  __lowerCamelCase		=			42
  __lowerCamelCase		=			42







  def __init__(			self  :List[Any]    ,							_lowercase  :UNetaDModel    ,							_lowercase  :PNDMScheduler					):






     '''simple docstring'''





     super().__init__()

     lowercase__    =  PNDMScheduler.from_config(scheduler.config					)

     self.register_modules(unet=_lowercase    ,							scheduler=_lowercase					)







  @torch.no_grad()
  def __call__(			self  :Union[str, Any]    ,							_lowercase  :int = 1    ,							_lowercase  :int = 50    ,							_lowercase  :Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,							_lowercase  :Optional[str] = "pil"    ,							_lowercase  :bool = True    ,							**_lowercase  :List[Any]    ,							):






     '''simple docstring'''





     lowercase__    =  randn_tensor(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,							generator=_lowercase    ,							device=self.device    ,							)

     self.scheduler.set_timesteps(_lowercase					)
     for t in self.progress_bar(self.scheduler.timesteps					):
        lowercase__    =  self.unet(_lowercase    ,							_lowercase					).sample

        lowercase__    =  self.scheduler.step(_lowercase    ,							_lowercase    ,							_lowercase					).prev_sample

     lowercase__    =  (image / 2 + 0.5).clamp(0    ,							1					)
     lowercase__    =  image.cpu().permute(0    ,							2    ,							3    ,							1					).numpy()
     if output_type == "pil":
        lowercase__    =  self.numpy_to_pil(_lowercase					)

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=_lowercase					)






