



"""simple docstring"""






import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
		from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
		raise ImportError("You need to install the version of fairseq from the Mega repo!")

class 			__SCREAMING_SNAKE_CASE      ( nn.Module  ):


					'''simple docstring'''





					def __init__(			self				:    Any,    lowerCamelCase				:    Any,    lowerCamelCase				:    Tuple,    lowerCamelCase				:    int	)->   str:
								super().__init__()
								lowerCamelCase__						:						Tuple						      =mega_args
								lowerCamelCase__						:						List[Any]						      =nn.Embedding(lowerCamelCase,    self.mega_args.encoder_embed_dim	)
								lowerCamelCase__						:						List[Any]						      =nn.ModuleList([MegaEncoderLayer(self.mega_args	) for _ in range(lowerCamelCase	)]	)
								lowerCamelCase__						:						List[Any]						      =depth




					def 						snake_case			(			self				:    List[Any],    lowerCamelCase				:    int,    lowerCamelCase				:    List[str],    lowerCamelCase				:    Optional[int]=True,    lowerCamelCase				:    List[str]=0	)->   Union[str, Any]:

								# Mega expects embeddings to be (time, batch, embedding size), but
								# Hugging Face returns tokens as (batch, time)
								if batch_first:
											lowerCamelCase__						:						Any						      =input_ids.T

								# to make things more confusing, Mega expects the attention mask to
								# be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
								# which is the opposite of what HF returns
								if ignore_mask_value == 0:
											lowerCamelCase__						:						Union[str, Any]						      =1 - attention_mask

								# get token embeddings from IDs
								lowerCamelCase__						:						Any						      =self.embedding_layer(lowerCamelCase	)

								# pass through the Mega layers
								# input is (time, batch, encoder dim) and output is the same
								for encoder in self.encoders:
											lowerCamelCase__						:						Union[str, Any]						      =encoder(lowerCamelCase,    lowerCamelCase	)

								# return according to the shape specified
								if batch_first:
											# (T, B, H) --> (B, T, H)
											return torch.transpose(lowerCamelCase,    0,    1	)
								else:
											return embeds





class 			__SCREAMING_SNAKE_CASE      ( nn.Module  ):


					'''simple docstring'''





					def __init__(			self				:    Any,    lowerCamelCase				:    List[str],    lowerCamelCase				:    str,    lowerCamelCase				:    Tuple	)->   Dict:
								super().__init__()
								lowerCamelCase__						:						Dict						      =MegaLM(lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								lowerCamelCase__						:						Optional[Any]						      =nn.Linear(mega_args.encoder_embed_dim,    lowerCamelCase	)
								lowerCamelCase__						:						List[Any]						      =nn.Dropout(p=0.1	)




					def 						snake_case			(			self				:    Any,    lowerCamelCase				:    Optional[int],    lowerCamelCase				:    List[str],    lowerCamelCase				:    Optional[int]=True,    lowerCamelCase				:    List[str]=0	)->   int:
								lowerCamelCase__						:						Optional[int]						      =self.mega(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								return self.mlm_head(self.dropout(lowerCamelCase	)	)






def 	snake_case__  (						__lowerCamelCase							:       List[str]				,						__lowerCamelCase							:       List[Any]				,						__lowerCamelCase							:       Tuple      ):
			"""simple docstring"""
			with open(os.path.join(__lowerCamelCase				,						'''model_args.pkl'''      )				,						'''rb'''      ) as f:
						lowerCamelCase__						:						Any						      =pkl.load(__lowerCamelCase      )

			# load the original encoder
			lowerCamelCase__						:						Tuple						      =OriginalMegaForMaskedLM(**__lowerCamelCase      ).eval()

			# load its weights
			print(
			    '''Original Mega encoder:'''				,						original_mlm.mega.load_state_dict(
			        torch.load(os.path.join(__lowerCamelCase				,						'''encoder_weights.pt'''      )				,						map_location='''cpu'''      )      )				,						)
			print(
			    '''Original Mega MLM layer:'''				,						original_mlm.mlm_head.load_state_dict(
			        torch.load(os.path.join(__lowerCamelCase				,						'''mlm_head_weights.pt'''      )				,						map_location='''cpu'''      )      )				,						)

			# create a new config from the old one
			lowerCamelCase__						:						Union[str, Any]						      =MegaConfig(
			    num_hidden_layers=mega_original_args['''depth''']				,						vocab_size=mega_original_args['''vocab_size''']				,						hidden_size=mega_original_args['''mega_args'''].encoder_embed_dim				,						shared_representation_size=mega_original_args['''mega_args'''].encoder_z_dim				,						intermediate_size=mega_original_args['''mega_args'''].encoder_hidden_dim				,						ema_projection_size=mega_original_args['''mega_args'''].encoder_n_dim				,						dropout_prob=mega_original_args['''mega_args'''].dropout				,						attention_probs_dropout_prob=mega_original_args['''mega_args'''].attention_dropout				,						hidden_dropout_prob=mega_original_args['''mega_args'''].hidden_dropout				,						activation=mega_original_args['''mega_args'''].activation_fn				,						attention_activation=mega_original_args['''mega_args'''].attention_activation_fn				,						bidirectional=mega_original_args['''mega_args'''].bidirectional				,						use_chunking=mega_original_args['''mega_args'''].encoder_chunk_size > 0				,						chunk_size=mega_original_args['''mega_args'''].encoder_chunk_size				,						truncation=mega_original_args['''mega_args'''].truncation_length				,						normalization_type=mega_original_args['''mega_args'''].normalization_type				,						normalize_before_mega=__lowerCamelCase				,						norm_affine=__lowerCamelCase				,						use_feature_dropout=mega_original_args['''mega_args'''].feature_dropout				,						relative_positional_bias=mega_original_args['''mega_args'''].rel_pos_bias				,						max_positions=mega_original_args['''mega_args'''].max_source_positions				,						nffn_hidden_size=mega_original_args['''mega_args'''].encoder_ffn_embed_dim				,						normalize_before_ffn=mega_original_args['''mega_args'''].normalize_before				,						nffn_activation_dropout_prob=0.0				,						add_token_type_embeddings=__lowerCamelCase				,						add_lm_hidden_dense_layer=__lowerCamelCase				,						)

			lowerCamelCase__						:						Union[str, Any]						      =MegaForMaskedLM(__lowerCamelCase      ).eval()

			# the originl checkpoint just uses nn.Embedding for the word embeddings
			# we use a wrapper module for embeddings to add support for positional embeddings
			lowerCamelCase__						:						str						      =original_mlm.mega.embedding_layer.weight

			# modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
			# ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
			# also renaming previously confusing parameter names
			lowerCamelCase__						:						int						      =original_mlm.mega.encoders.state_dict()
			lowerCamelCase__						:						List[Any]						      ={}
			for module_name in original_state_dict.keys():
						lowerCamelCase__						:						Union[str, Any]						      =None
						# have to handle gamma, beta, and alpha differently due to their use
						# in multiple modules within the original repository;
						# beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
						# the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
						if "beta" in module_name:
									# EMA sub-layers were always called "move" in the original repo
									if "move.beta" in module_name:
												lowerCamelCase__						:						Any						      =module_name.replace('''move.beta'''				,						'''ema_gate.ema_expansion_matrix'''      )
									elif "mega_layer.beta" in module_name:
												lowerCamelCase__						:						Tuple						      =module_name.replace('''beta'''				,						'''qk_bias'''      )
									else:
												lowerCamelCase__						:						Dict						      =module_name.replace('''beta'''				,						'''b_param'''      )
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
						elif "gamma" in module_name:
									if "move.gamma" in module_name:
												lowerCamelCase__						:						List[str]						      =module_name.replace('''move.gamma'''				,						'''ema_gate.kernel_projection_matrix'''      )
									elif "mega_layer.gamma" in module_name:
												lowerCamelCase__						:						Tuple						      =module_name.replace('''gamma'''				,						'''qk_weight'''      )
									else:
												lowerCamelCase__						:						int						      =module_name.replace('''gamma'''				,						'''g_param'''      )
        # alpha is used in EMA and positional bias; renaming to improve readability
						elif "move.alpha" in module_name:
									lowerCamelCase__						:						int						      =module_name.replace('''move.alpha'''				,						'''ema_gate.decay_factor'''      )
						# delta is only used in EMA; renaming to improve readability
						elif "move.delta" in module_name:
									lowerCamelCase__						:						Union[str, Any]						      =module_name.replace('''move.delta'''				,						'''ema_gate.damping_factor'''      )
						# omega is only used in EMA; renaming to improve readability
						elif "omega" in module_name:
									lowerCamelCase__						:						Any						      =module_name.replace('''move.omega'''				,						'''ema_gate.residual_weight'''      )

						if new_module_name:
									lowerCamelCase__						:						Union[str, Any]						      =new_module_name

			if len(__lowerCamelCase      ) != 0:
						print(f'''Renaming these keys: {updated_keys.keys()}'''      )
			else:
						print('''No need to rename state dict entries'''      )
			for old, new in updated_keys.items():
						lowerCamelCase__						:						Optional[int]						      =original_state_dict.pop(__lowerCamelCase      )

			# now attempt to load the state dictionary with updated names
			# note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
			print('''HF Mega encoder:'''				,						hf_mlm.mega.layers.load_state_dict(__lowerCamelCase      )      )

			# load the MLM head weights directly
			print(
			    '''HF Mega MLM layer:'''				,						hf_mlm.mlm_head.load_state_dict(
			        torch.load(os.path.join(__lowerCamelCase				,						'''mlm_head_weights.pt'''      )				,						map_location='''cpu'''      )      )				,						)

			# test on a randomly generated input sequence
			lowerCamelCase__						:						str						      =torch.randint(0				,						hf_config.vocab_size				,						size=(4, 256)      )
			lowerCamelCase__						:						List[Any]						      =torch.ones_like(__lowerCamelCase      )
			# mask a few tokens to make sure masking is applied appropriately :)
			lowerCamelCase__						:						List[str]						      =0

			# run forward passes
			lowerCamelCase__						:						Optional[int]						      =original_mlm(__lowerCamelCase				,						__lowerCamelCase				,						batch_first=__lowerCamelCase				,						ignore_mask_value=0      )
			lowerCamelCase__						:						Optional[int]						      =hf_mlm(__lowerCamelCase				,						__lowerCamelCase      )[0]

			# print shapes and diff
			print(f'''original output {original_output.shape}'''      )
			print(f'''hf output {hf_output.shape}'''      )
			print(f'''max diff: {(original_output - hf_output).max()}'''      )  # 0.0
			lowerCamelCase__						:						List[Any]						      =torch.allclose(__lowerCamelCase				,						__lowerCamelCase				,						atol=1e-3      )

			if success:
						print('''Yay!'''      )
						hf_mlm.save_pretrained(__lowerCamelCase      )
			else:
						raise RuntimeError(f'''Something\'s broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}'''      )

			if includes_tokenizer:
						print('''Transferring tokenizer'''      )
						lowerCamelCase__						:						Any						      =AutoTokenizer.from_pretrained(__lowerCamelCase      )
						tokenizer.save_pretrained(__lowerCamelCase      )


if __name__ == "__main__":
		_lowercase					:					Dict 		=		argparse.ArgumentParser()

		parser.add_argument(
		    "--pretrained_checkpoint_path",
		    default=None,
		    type=str,
		    required=True,
		    help="Point to the directory containing your model weights using the official Mega repo",
		)

		parser.add_argument(
		    "--output_path", default=None, type=str, required=True, help="Location to save the Hugging Face version"
		)

		parser.add_argument(
		    "--includes_tokenizer",
		    action="store_true",
		    help="Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo",
		)

		_lowercase					:					Dict 		=		parser.parse_args()

		convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)










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)