




'''simple docstring'''







import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_=0  ):



		'''simple docstring'''







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

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






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  ):



		'''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(*UpperCamelCase_  )
		_snake_case	       =       param.transpose(0						,							1  ).contiguous()
		_snake_case	       =       param.view(*UpperCamelCase_  )
		return param






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''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(UpperCamelCase_  ):
				_snake_case	       =       torch.tril(torch.ones((n_positions, n_positions)						,							dtype=torch.bool  )  )
				_snake_case	       =       causal_mask.view(1						,							1						,							UpperCamelCase_						,							UpperCamelCase_  )
				_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(UpperCamelCase_						,							3						,							UpperCamelCase_						,							UpperCamelCase_  )
				_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(UpperCamelCase_						,							3						,							UpperCamelCase_						,							UpperCamelCase_  )
				_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 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case	       =       64000
		_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"] == 4096:
				_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 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		print(UpperCamelCase_  )

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

		# Load the model.
		_snake_case	       =       torch.load(UpperCamelCase_						,							map_location="cpu"  )

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

		_snake_case	       =       checkpoint["""state_dict"""]

		# Convert.
		print("Converting"  )
		_snake_case	       =       convert_megatron_checkpoint(UpperCamelCase_						,							UpperCamelCase_  )

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

		_snake_case	       =       """GPTSw3Tokenizer"""

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

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


if __name__ == "__main__":
						_lowerCamelCase							:						Tuple								=  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							:						Any								=  parser.parse_args()
						main(_args)











from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase					:     Optional[Any]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     str					  =  {
    "google/reformer-crime-and-punishment": (
        "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json"
    ),
    "google/reformer-enwik8": "https://huggingface.co/google/reformer-enwik8/resolve/main/config.json",
}
class 				__snake_case     (_a						):
				lowerCAmelCase__			=   "reformer"
				lowerCAmelCase__			=   ["past_buckets_states"]
				lowerCAmelCase__			=   {}



				def __init__(					self					:						Optional[int]						,		_UpperCAmelCase					:						str=64						,		_UpperCAmelCase					:						Tuple=["local", "lsh", "local", "lsh", "local", "lsh"]						,		_UpperCAmelCase					:						Optional[int]=1.0						,		_UpperCAmelCase					:						str=True						,		_UpperCAmelCase					:						List[Any]=[64, 64]						,		_UpperCAmelCase					:						str=[64, 192]						,		_UpperCAmelCase					:						List[Any]=0						,		_UpperCAmelCase					:						Optional[Any]=2						,		_UpperCAmelCase					:						Dict=512						,		_UpperCAmelCase					:						Optional[int]=None						,		_UpperCAmelCase					:						Tuple="relu"						,		_UpperCAmelCase					:						Tuple=0.05						,		_UpperCAmelCase					:						int=256						,		_UpperCAmelCase					:						List[str]=0.02						,		_UpperCAmelCase					:						Any=False						,		_UpperCAmelCase					:						Any=1E-12						,		_UpperCAmelCase					:						List[Any]=1						,		_UpperCAmelCase					:						Optional[Any]=0						,		_UpperCAmelCase					:						Union[str, Any]=0.05						,		_UpperCAmelCase					:						int=64						,		_UpperCAmelCase					:						List[str]=64						,		_UpperCAmelCase					:						Any=0.0						,		_UpperCAmelCase					:						Any=1						,		_UpperCAmelCase					:						Optional[Any]=0						,		_UpperCAmelCase					:						Union[str, Any]=4096						,		_UpperCAmelCase					:						Dict=12						,		_UpperCAmelCase					:						int=None						,		_UpperCAmelCase					:						List[Any]=1						,		_UpperCAmelCase					:						Union[str, Any]=0						,		_UpperCAmelCase					:						Any=320						,		_UpperCAmelCase					:						Any=False						,		_UpperCAmelCase					:						List[str]=True						,		_UpperCAmelCase					:						Optional[int]=None						,		**_UpperCAmelCase					:						Tuple						,		)     ->			List[str]:





								'''simple docstring'''




								_lowerCAmelCase			:			Union[str, Any]        =					hash_seed
								_lowerCAmelCase			:			int        =					vocab_size
								_lowerCAmelCase			:			Optional[Any]        =					attention_head_size
								_lowerCAmelCase			:			Dict        =					hidden_size
								_lowerCAmelCase			:			Union[str, Any]        =					num_attention_heads
								_lowerCAmelCase			:			int        =					num_hashes
								_lowerCAmelCase			:			Union[str, Any]        =					len(_UpperCAmelCase					)
								_lowerCAmelCase			:			int        =					tuple(_UpperCAmelCase					) if isinstance(_UpperCAmelCase						,		_UpperCAmelCase					) else num_buckets
								_lowerCAmelCase			:			Union[str, Any]        =					lsh_attn_chunk_length
								_lowerCAmelCase			:			List[str]        =					local_attn_chunk_length
								_lowerCAmelCase			:			int        =					lsh_num_chunks_after
								_lowerCAmelCase			:			Union[str, Any]        =					lsh_num_chunks_before
								_lowerCAmelCase			:			str        =					local_num_chunks_after
								_lowerCAmelCase			:			Dict        =					local_num_chunks_before
								_lowerCAmelCase			:			Any        =					hidden_act
								_lowerCAmelCase			:			List[str]        =					feed_forward_size
								_lowerCAmelCase			:			str        =					hidden_dropout_prob
								_lowerCAmelCase			:			Any        =					lsh_attention_probs_dropout_prob
								_lowerCAmelCase			:			Dict        =					local_attention_probs_dropout_prob
								_lowerCAmelCase			:			Tuple        =					max_position_embeddings
								_lowerCAmelCase			:			Any        =					initializer_range
								_lowerCAmelCase			:			List[str]        =					layer_norm_eps
								_lowerCAmelCase			:			Dict        =					axial_pos_embds
								_lowerCAmelCase			:			Union[str, Any]        =					tuple(_UpperCAmelCase					)
								_lowerCAmelCase			:			int        =					tuple(_UpperCAmelCase					)
								_lowerCAmelCase			:			List[str]        =					axial_norm_std
								_lowerCAmelCase			:			List[str]        =					chunk_size_lm_head
								_lowerCAmelCase			:			int        =					attn_layers
								_lowerCAmelCase			:			List[Any]        =					use_cache
								_lowerCAmelCase			:			Optional[int]        =					classifier_dropout
								super().__init__(
								    pad_token_id=_UpperCAmelCase						,		eos_token_id=_UpperCAmelCase						,		is_decoder=_UpperCAmelCase						,		tie_word_embeddings=_UpperCAmelCase						,		**_UpperCAmelCase						,		)

