

'''simple docstring'''

import torch

from transformers import AutoTokenizer







class lowerCamelCase   (    _lowerCAmelCase				):

				def __init__(       self,						lowercase_ )    ->							str:
					snake_case							=					AutoTokenizer.from_pretrained(A__ )

				def 					_lowerCamelCase    (       self,						lowercase_ )    ->							Dict:

					if isinstance(A__,						A__ ) and all(isinstance(_x,						A__ ) for _x in x ):
						snake_case							=					None
						for l in x:
							snake_case							=					self.tokenizer(
							    A__,						padding='max_length',						max_length=384,						truncation=A__,						return_tensors='pt',						)
							snake_case							=					torch.tensor([len(A__ )] )
							if d is not None:
								for k in d.keys():
									snake_case							=					torch.cat((d[k], t[k]),						0 )
							else:
								snake_case							=					t

						snake_case							=					torch.tensor(self.tokenizer.convert_tokens_to_ids('[E]' ) )
						snake_case							=					torch.tensor(self.tokenizer.convert_tokens_to_ids('[/E]' ) )

					elif isinstance(A__,						A__ ) and all(isinstance(_x,						A__ ) for _x in x ):
						snake_case							=					self.tokenizer(
						    A__,						padding='max_length',						max_length=384,						truncation=A__,						return_tensors='pt',						)

					else:
						raise Exception(
						    'Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'
						    ' strings` for supports are supported.' )

					return d






				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_=0.70 )    ->							Any:
					snake_case							=					[]
					for idx in range(len(W_query['input_ids'] ) ):
						snake_case							=					snake_case							=					range(p_start.shape[1] )

						snake_case							=					[]
						for start_id in start_indexes:
							for end_id in end_indexes:
								if start_id < end_id:
									output.append(
									    (
									        start_id,
									        end_id,
									        p_start[idx][start_id].item(),
									        p_end[idx][end_id].item(),
									    ) )

						output.sort(key=lambda lowercase_ : (tup[2] * tup[3]),						reverse=A__ )
						snake_case							=					[]
						for k in range(len(A__ ) ):
							if output[k][2] * output[k][3] >= thresh:
								snake_case			,			snake_case							=					output[k][0], output[k][1]
								snake_case							=					self.tokenizer.decode(W_query['input_ids'][idx][c_start_pos:c_end_pos] )
								temp.append((decoded, output[k][2] * output[k][3]) )

						final_outputs.append(A__ )

					return final_outputs











import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig

def       lowerCAmelCase_      (	lowerCamelCase:							str  ,					lowerCamelCase:							List[str]  ,					lowerCamelCase:							List[Any]=0    )       ->      Dict:


      '''simple docstring'''




      if name is None:
            lowerCamelCase__   	=					None
      else:
            lowerCamelCase__   	=					'.' * max(0  ,					spaces - 2    ) + '# {:' + str(50 - spaces    ) + 's}'
            lowerCamelCase__   	=					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:							Tuple  ,					lowerCamelCase:							Dict  ,					lowerCamelCase:							List[str]  ,					lowerCamelCase:							Any    )       ->      Optional[int]:


      '''simple docstring'''




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

def       lowerCAmelCase_      (	lowerCamelCase:							List[str]  ,					lowerCamelCase:							int    )       ->      int:


      '''simple docstring'''




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

      lowerCamelCase__   	=					sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
      lowerCamelCase__   	=					{
          '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__   	=					'model.language_model.encoder.layers.'
      for i in range(lowerCamelCase    ):
            lowerCamelCase__   	=					torch.tril(torch.ones((n_positions, n_positions)  ,					dtype=torch.bool    )    )
            lowerCamelCase__   	=					causal_mask.view(1  ,					1  ,					lowerCamelCase  ,					lowerCamelCase    )
            lowerCamelCase__   	=					causal_mask
            lowerCamelCase__   	=					torch.tensor(-1e4  ,					dtype=torch.bfloataa    )

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

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

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

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

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

      return sd_hf

def       lowerCAmelCase_      (	lowerCamelCase:							Optional[int]  ,					lowerCamelCase:							List[Any]    )       ->      Tuple:


      '''simple docstring'''




      lowerCamelCase__   	=					6_40_00
      lowerCamelCase__   	=					config_megatron['encoder_seq_length']
      lowerCamelCase__   	=					config_megatron['hidden_size']
      lowerCamelCase__   	=					config_megatron['num_layers']
      lowerCamelCase__   	=					config_megatron['num_attention_heads']
      lowerCamelCase__   	=					config_megatron['ffn_hidden_size']
      lowerCamelCase__   	=					'gelu'
      lowerCamelCase__   	=					0.1
      lowerCamelCase__   	=					0.1
      lowerCamelCase__   	=					0.1
      lowerCamelCase__   	=					config_megatron['layernorm_epsilon']  # 1e-5
      lowerCamelCase__   	=					config_megatron['init_method_std']  # 0.02
      lowerCamelCase__   	=					config_megatron['apply_query_key_layer_scaling']  # True
      lowerCamelCase__   	=					True
      lowerCamelCase__   	=					True

      # This identifies the 6.7B (7B) model which uses a different tokenizer
      if config_megatron["hidden_size"] == 40_96:
            lowerCamelCase__   	=					1  # <|endoftext|>
            lowerCamelCase__   	=					1  # <|endoftext|>
            lowerCamelCase__   	=					0  # <unk>
      else:
            lowerCamelCase__   	=					2  # <s>
            lowerCamelCase__   	=					3  # <|endoftext|>
            lowerCamelCase__   	=					0  # <pad>

      return config_hf

def       lowerCAmelCase_      (	lowerCamelCase:							Optional[Any]    )       ->      Dict:


      '''simple docstring'''




      print(lowerCamelCase    )

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

      # Load the model.
      lowerCamelCase__   	=					torch.load(lowerCamelCase  ,					map_location='cpu'    )

      # Load the config.
      lowerCamelCase__   	=					checkpoint['hyper_parameters']['cfg']
      lowerCamelCase__   	=					GPTaConfig()
      lowerCamelCase__   	=					copy_config(config_hf=lowerCamelCase  ,					config_megatron=lowerCamelCase    )
      lowerCamelCase__   	=					['GPT2LMHeadModel']

      lowerCamelCase__   	=					checkpoint['state_dict']

      # Convert.
      print('Converting'    )
      lowerCamelCase__   	=					convert_megatron_checkpoint(lowerCamelCase  ,					lowerCamelCase    )

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

      lowerCamelCase__   	=					'GPTSw3Tokenizer'

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

      # Store the state_dict to file.
      lowerCamelCase__   	=					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     : Optional[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     : Tuple      =parser.parse_args()
      main(_args)