


import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Tuple	,    SCREAMING_SNAKE_CASE__      :   Optional[int]	,    SCREAMING_SNAKE_CASE__      :   List[str]=0  ):
       '''simple docstring'''





       if name is None:
              _lowerCamelCase :	List[Any]						=None
       else:
              _lowerCamelCase :	Optional[Any]						='.' * max(0	,    spaces - 2  ) + '# {:' + str(50 - spaces  ) + 's}'
              _lowerCamelCase :	Tuple						=fmt.format(SCREAMING_SNAKE_CASE__  )

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




def 			a_   (					SCREAMING_SNAKE_CASE__      :   int	,    SCREAMING_SNAKE_CASE__      :   str	,    SCREAMING_SNAKE_CASE__      :   str	,    SCREAMING_SNAKE_CASE__      :   Any  ):
       '''simple docstring'''





       _lowerCamelCase :	Union[str, Any]						=param.size()
       # other versions store [num_heads * num_splits * hidden_size, :]
       _lowerCamelCase :	str						=(num_heads, num_splits, hidden_size) + input_shape[1:]
       _lowerCamelCase :	Optional[Any]						=param.view(*SCREAMING_SNAKE_CASE__  )
       _lowerCamelCase :	Dict						=param.transpose(0	,    1  ).contiguous()
       _lowerCamelCase :	Optional[int]						=param.view(*SCREAMING_SNAKE_CASE__  )
       return param




def 			a_   (					SCREAMING_SNAKE_CASE__      :   List[Any]	,    SCREAMING_SNAKE_CASE__      :   Optional[int]  ):
       '''simple docstring'''





       _lowerCamelCase :	Tuple						=config.n_positions
       _lowerCamelCase :	Any						=config.n_layer
       _lowerCamelCase :	Dict						=config.vocab_size
       _lowerCamelCase :	int						=config.n_head
       _lowerCamelCase :	Optional[int]						=config.n_embd // config.n_head

       _lowerCamelCase :	List[Any]						=sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
       _lowerCamelCase :	Any						={
           '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'],
       }

       _lowerCamelCase :	Optional[int]						='model.language_model.encoder.layers.'
       for i in range(SCREAMING_SNAKE_CASE__  ):
              _lowerCamelCase :	List[str]						=torch.tril(torch.ones((n_positions, n_positions)	,    dtype=torch.bool  )  )
              _lowerCamelCase :	Tuple						=causal_mask.view(1	,    1	,    SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  )
              _lowerCamelCase :	Optional[Any]						=causal_mask
              _lowerCamelCase :	Optional[Any]						=torch.tensor(-1e4	,    dtype=torch.bfloataa  )

              _lowerCamelCase :	Dict						=sd_megatron[F'''{pf}{i}.input_layernorm.weight''']
              _lowerCamelCase :	List[Any]						=sd_megatron[F'''{pf}{i}.input_layernorm.bias''']

              _lowerCamelCase :	List[str]						=sd_megatron[F'''{pf}{i}.self_attention.query_key_value.weight''']
              _lowerCamelCase :	str						=fix_query_key_value_ordering(SCREAMING_SNAKE_CASE__	,    3	,    SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  )
              _lowerCamelCase :	List[str]						=vala.transpose(0	,    1  ).contiguous()

              _lowerCamelCase :	Union[str, Any]						=sd_megatron[F'''{pf}{i}.self_attention.query_key_value.bias''']
              _lowerCamelCase :	Optional[Any]						=fix_query_key_value_ordering(SCREAMING_SNAKE_CASE__	,    3	,    SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  )
              _lowerCamelCase :	int						=vala

              _lowerCamelCase :	Optional[Any]						=sd_megatron[F'''{pf}{i}.self_attention.dense.weight'''].transpose(
                  0	,    1  )
              _lowerCamelCase :	List[str]						=sd_megatron[F'''{pf}{i}.self_attention.dense.bias''']
              _lowerCamelCase :	str						=sd_megatron[F'''{pf}{i}.post_attention_layernorm.weight''']
              _lowerCamelCase :	List[Any]						=sd_megatron[F'''{pf}{i}.post_attention_layernorm.bias''']
              _lowerCamelCase :	Any						=sd_megatron[F'''{pf}{i}.mlp.dense_h_to_4h.weight'''].transpose(0	,    1  )
              _lowerCamelCase :	Optional[Any]						=sd_megatron[F'''{pf}{i}.mlp.dense_h_to_4h.bias''']
              _lowerCamelCase :	Dict						=sd_megatron[F'''{pf}{i}.mlp.dense_4h_to_h.weight'''].transpose(
                  0	,    1  )
              _lowerCamelCase :	Dict						=sd_megatron[F'''{pf}{i}.mlp.dense_4h_to_h.bias''']

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

       return sd_hf




def 			a_   (					SCREAMING_SNAKE_CASE__      :   List[str]	,    SCREAMING_SNAKE_CASE__      :   str  ):
       '''simple docstring'''





       _lowerCamelCase :	str						=64_000
       _lowerCamelCase :	Union[str, Any]						=config_megatron['encoder_seq_length']
       _lowerCamelCase :	int						=config_megatron['hidden_size']
       _lowerCamelCase :	List[str]						=config_megatron['num_layers']
       _lowerCamelCase :	Union[str, Any]						=config_megatron['num_attention_heads']
       _lowerCamelCase :	Optional[Any]						=config_megatron['ffn_hidden_size']
       _lowerCamelCase :	Tuple						='gelu'
       _lowerCamelCase :	List[Any]						=0.1
       _lowerCamelCase :	Optional[Any]						=0.1
       _lowerCamelCase :	Tuple						=0.1
       _lowerCamelCase :	int						=config_megatron['layernorm_epsilon']  # 1e-5
       _lowerCamelCase :	Union[str, Any]						=config_megatron['init_method_std']  # 0.02
       _lowerCamelCase :	str						=config_megatron['apply_query_key_layer_scaling']  # True
       _lowerCamelCase :	Optional[Any]						=True
       _lowerCamelCase :	int						=True

       # This identifies the 6.7B (7B) model which uses a different tokenizer
       if config_megatron["hidden_size"] == 4_096:
              _lowerCamelCase :	Tuple						=1  # <|endoftext|>
              _lowerCamelCase :	Any						=1  # <|endoftext|>
              _lowerCamelCase :	Optional[int]						=0  # <unk>
       else:
              _lowerCamelCase :	Any						=2  # <s>
              _lowerCamelCase :	List[str]						=3  # <|endoftext|>
              _lowerCamelCase :	Union[str, Any]						=0  # <pad>

       return config_hf




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Tuple  ):
       '''simple docstring'''





       print(SCREAMING_SNAKE_CASE__  )

       _lowerCamelCase :	Tuple						=args.checkpoint_path
       _lowerCamelCase :	List[Any]						=args.save_path
       if isfile(SCREAMING_SNAKE_CASE__  ):
              raise FileNotFoundError(F'''ERROR! could not find file {checkpoint_path}'''  )

       # Load the model.
       _lowerCamelCase :	Optional[int]						=torch.load(SCREAMING_SNAKE_CASE__	,    map_location='cpu'  )

       # Load the config.
       _lowerCamelCase :	List[Any]						=checkpoint['hyper_parameters']['cfg']
       _lowerCamelCase :	Dict						=GPTaConfig()
       _lowerCamelCase :	List[str]						=copy_config(config_hf=SCREAMING_SNAKE_CASE__	,    config_megatron=SCREAMING_SNAKE_CASE__  )
       _lowerCamelCase :	List[Any]						=['GPT2LMHeadModel']

       _lowerCamelCase :	Optional[Any]						=checkpoint['state_dict']

       # Convert.
       print('Converting'  )
       _lowerCamelCase :	Dict						=convert_megatron_checkpoint(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  )

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

       _lowerCamelCase :	Tuple						='GPTSw3Tokenizer'

       # Store the config to file.
       print('Saving config'  )
       config_hf.save_pretrained(SCREAMING_SNAKE_CASE__  )

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


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






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
 from .image_encoder import PaintByExampleImageEncoder
 from .pipeline_paint_by_example import PaintByExamplePipeline


