





import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig



def 			_lowerCAmelCase						(							lowerCAmelCase_							:Optional[Any]		,						lowerCAmelCase_							:Dict		,						lowerCAmelCase_							:Any=0							)->List[Any]:
   '''simple docstring'''






   if name is None:
      snake_case_					    =					None
   else:
      snake_case_					    =					"." * max(0		,						spaces - 2							) + "# {:" + str(50 - spaces							) + "s}"
      snake_case_					    =					fmt.format(lowerCAmelCase_							)

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



def 			_lowerCAmelCase						(							lowerCAmelCase_							:str		,						lowerCAmelCase_							:Optional[int]		,						lowerCAmelCase_							:Tuple		,						lowerCAmelCase_							:str							)->int:
   '''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(*lowerCAmelCase_							)
   snake_case_					    =					param.transpose(0		,						1							).contiguous()
   snake_case_					    =					param.view(*lowerCAmelCase_							)
   return param



def 			_lowerCAmelCase						(							lowerCAmelCase_							:Dict		,						lowerCAmelCase_							:Dict							)->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(lowerCAmelCase_							):
      snake_case_					    =					torch.tril(torch.ones((n_positions, n_positions)		,						dtype=torch.bool							)							)
      snake_case_					    =					causal_mask.view(1		,						1		,						lowerCAmelCase_		,						lowerCAmelCase_							)
      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(lowerCAmelCase_		,						3		,						lowerCAmelCase_		,						lowerCAmelCase_							)
      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(lowerCAmelCase_		,						3		,						lowerCAmelCase_		,						lowerCAmelCase_							)
      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 			_lowerCAmelCase						(							lowerCAmelCase_							:Optional[int]		,						lowerCAmelCase_							:Union[str, Any]							)->str:
   '''simple docstring'''






   snake_case_					    =					64_000
   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_096:
      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 			_lowerCAmelCase						(							lowerCAmelCase_							:Union[str, Any]							)->Union[str, Any]:
   '''simple docstring'''






   print(lowerCAmelCase_							)

   snake_case_					    =					args.checkpoint_path
   snake_case_					    =					args.save_path
   if isfile(lowerCAmelCase_							):
      raise FileNotFoundError(F'''ERROR! could not find file {checkpoint_path}'''							)

   # Load the model.
   snake_case_					    =					torch.load(lowerCAmelCase_		,						map_location="cpu"							)

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

   snake_case_					    =					checkpoint["state_dict"]

   # Convert.
   print("Converting"							)
   snake_case_					    =					convert_megatron_checkpoint(lowerCAmelCase_		,						lowerCAmelCase_							)

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

   snake_case_					    =					"GPTSw3Tokenizer"

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

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


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE	:int  		=     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''')
   SCREAMING_SNAKE_CASE	:Optional[Any]  		=     parser.parse_args()
   main(_args)








from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class     __lowerCAmelCase     ( a					):






  """simple docstring"""





  def __init__(     self       :       List[str]    ,				_lowerCAmelCase       :       Optional[Any]    ,				_lowerCAmelCase       :       Tuple  )   -> str:




     """simple docstring"""
     super().__init__()
     self.register_modules(unet=_lowerCAmelCase    ,				scheduler=_lowerCAmelCase  )



  @torch.no_grad()
  def __call__(     self       :       Tuple    ,				_lowerCAmelCase       :       int = 1    ,				_lowerCAmelCase       :       Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,				_lowerCAmelCase       :       int = 1_0_0_0    ,				_lowerCAmelCase       :       Optional[str] = "pil"    ,				_lowerCAmelCase       :       bool = True    ,				)   -> Union[ImagePipelineOutput, Tuple]:




     """simple docstring"""
     # Sample gaussian noise to begin loop
     if isinstance(self.unet.config.sample_size    ,				_lowerCAmelCase  ):
        snake_case_					    =					(
            batch_size,
            self.unet.config.in_channels,
            self.unet.config.sample_size,
            self.unet.config.sample_size,
        )
     else:
        snake_case_					    =					(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

     if self.device.type == "mps":
        # randn does not work reproducibly on mps
        snake_case_					    =					randn_tensor(_lowerCAmelCase    ,				generator=_lowerCAmelCase  )
        snake_case_					    =					image.to(self.device  )
     else:
        snake_case_					    =					randn_tensor(_lowerCAmelCase    ,				generator=_lowerCAmelCase    ,				device=self.device  )

     # set step values
     self.scheduler.set_timesteps(_lowerCAmelCase  )

     for t in self.progress_bar(self.scheduler.timesteps  ):
        # 1. predict noise model_output
        snake_case_					    =					self.unet(_lowerCAmelCase    ,				_lowerCAmelCase  ).sample

        # 2. compute previous image: x_t -> x_t-1
        snake_case_					    =					self.scheduler.step(_lowerCAmelCase    ,				_lowerCAmelCase    ,				_lowerCAmelCase    ,				generator=_lowerCAmelCase  ).prev_sample

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

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=_lowerCAmelCase  )




