




"""simple docstring"""






import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__=0	):
				# Format the message.
				if name is None:
								_lowerCamelCase  :    Union[str, Any]					=					None
				else:
								_lowerCamelCase  :    Tuple					=					'.' * max(0							,					spaces - 2	) + '# {:' + str(50 - spaces	) + 's}'
								_lowerCamelCase  :    List[Any]					=					fmt.format(lowercase__	)

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







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__	):
				# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
				# for compatibility with later versions of NVIDIA Megatron-LM.
				# The inverse operation is performed inside Megatron-LM to read checkpoints:
				# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
				# If param is the weight tensor of the self-attention block, the returned tensor
				# will have to be transposed one more time to be read by HuggingFace GPT2.
				_lowerCamelCase  :    Optional[int]					=					param.size()
				if checkpoint_version == 1.0:
								# version 1.0 stores [num_heads * hidden_size * num_splits, :]
								_lowerCamelCase  :    List[Any]					=					(num_heads, hidden_size, num_splits) + input_shape[1:]
								_lowerCamelCase  :    Dict					=					param.view(*lowercase__	)
								_lowerCamelCase  :    Union[str, Any]					=					param.transpose(0							,					2	)
								_lowerCamelCase  :    str					=					param.transpose(1							,					2	).contiguous()
				elif checkpoint_version >= 2.0:
								# other versions store [num_heads * num_splits * hidden_size, :]
								_lowerCamelCase  :    Any					=					(num_heads, num_splits, hidden_size) + input_shape[1:]
								_lowerCamelCase  :    int					=					param.view(*lowercase__	)
								_lowerCamelCase  :    Dict					=					param.transpose(0							,					1	).contiguous()
				_lowerCamelCase  :    List[str]					=					param.view(*lowercase__	)
				return param







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				# The converted output model.
				_lowerCamelCase  :    str					=					{}

				# old versions did not store training args
				_lowerCamelCase  :    Optional[Any]					=					input_state_dict.get('args'							,					lowercase__	)
				if ds_args is not None:
								# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
								# from pprint import pprint
								# pprint(vars(ds_args))

								_lowerCamelCase  :    Tuple					=					ds_args.padded_vocab_size
								_lowerCamelCase  :    Union[str, Any]					=					ds_args.max_position_embeddings
								_lowerCamelCase  :    Tuple					=					ds_args.hidden_size
								_lowerCamelCase  :    List[Any]					=					ds_args.num_layers
								_lowerCamelCase  :    Union[str, Any]					=					ds_args.num_attention_heads
								_lowerCamelCase  :    List[str]					=					ds_args.ffn_hidden_size
								# pprint(config)

				# The number of heads.
				_lowerCamelCase  :    Optional[Any]					=					config.n_head
				# The hidden_size per head.
				_lowerCamelCase  :    Union[str, Any]					=					config.n_embd // config.n_head
				# Megatron-LM checkpoint version
				if "checkpoint_version" in input_state_dict.keys():
								_lowerCamelCase  :    Union[str, Any]					=					input_state_dict['checkpoint_version']
				else:
								_lowerCamelCase  :    int					=					0.0

				# The model.
				_lowerCamelCase  :    Optional[Any]					=					input_state_dict['model']
				# The language model.
				_lowerCamelCase  :    str					=					model['language_model']
				# The embeddings.
				_lowerCamelCase  :    Optional[Any]					=					lm['embedding']

				# The word embeddings.
				_lowerCamelCase  :    Optional[int]					=					embeddings['word_embeddings']['weight']
				# Truncate the embedding table to vocab_size rows.
				_lowerCamelCase  :    Tuple					=					word_embeddings[: config.vocab_size, :]
				_lowerCamelCase  :    Dict					=					word_embeddings

				# The position embeddings.
				_lowerCamelCase  :    Tuple					=					embeddings['position_embeddings']['weight']
				# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
				_lowerCamelCase  :    Any					=					pos_embeddings.size(0	)
				if n_positions != config.n_positions:
								raise ValueError(
								    f'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match'''	)
				# Store the position embeddings.
				_lowerCamelCase  :    Any					=					pos_embeddings

				# The transformer.
				_lowerCamelCase  :    int					=					lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

				# The regex to extract layer names.
				_lowerCamelCase  :    Optional[int]					=					re.compile(r'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'	)

				# The simple map of names for "automated" rules.
				_lowerCamelCase  :    int					=					{
				    'attention.dense': '.attn.c_proj.',
				    'self_attention.dense': '.attn.c_proj.',
				    'mlp.dense_h_to_4h': '.mlp.c_fc.',
				    'mlp.dense_4h_to_h': '.mlp.c_proj.',
				}

				# Extract the layers.
				for key, val in transformer.items():
								# Match the name.
								_lowerCamelCase  :    Any					=					layer_re.match(lowercase__	)

								# Stop if that's not a layer
								if m is None:
												break

								# The index of the layer.
								_lowerCamelCase  :    Tuple					=					int(m.group(1	)	)
								# The name of the operation.
								_lowerCamelCase  :    int					=					m.group(2	)
								# Is it a weight or a bias?
								_lowerCamelCase  :    Optional[Any]					=					m.group(3	)

								# The name of the layer.
								_lowerCamelCase  :    int					=					f'''transformer.h.{layer_idx}'''

								# For layernorm(s), simply store the layer norm.
								if op_name.endswith('layernorm'	):
												_lowerCamelCase  :    List[str]					=					'ln_1' if op_name.startswith('input'	) else 'ln_2'
												_lowerCamelCase  :    Optional[int]					=					val

								# Transpose the QKV matrix.
								elif (
								    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
								) and weight_or_bias == "weight":
												# Insert a tensor of 1x1xDxD bias.
												_lowerCamelCase  :    List[Any]					=					torch.tril(torch.ones((n_positions, n_positions)							,					dtype=torch.floataa	)	).view(
												    1							,					1							,					lowercase__							,					lowercase__	)
												_lowerCamelCase  :    List[Any]					=					causal_mask

												# Insert a "dummy" tensor for masked_bias.
												_lowerCamelCase  :    List[Any]					=					torch.tensor(-1E4							,					dtype=torch.floataa	)
												_lowerCamelCase  :    Union[str, Any]					=					masked_bias

												_lowerCamelCase  :    Any					=					fix_query_key_value_ordering(lowercase__							,					lowercase__							,					3							,					lowercase__							,					lowercase__	)
												# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
												_lowerCamelCase  :    List[Any]					=					out_val.transpose(0							,					1	).contiguous()
												# Store.
												_lowerCamelCase  :    str					=					out_val

								# Transpose the bias.
								elif (
								    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
								) and weight_or_bias == "bias":
												_lowerCamelCase  :    Tuple					=					fix_query_key_value_ordering(lowercase__							,					lowercase__							,					3							,					lowercase__							,					lowercase__	)
												# Store. No change of shape.
												_lowerCamelCase  :    Dict					=					out_val

								# Transpose the weights.
								elif weight_or_bias == "weight":
												_lowerCamelCase  :    Union[str, Any]					=					megatron_to_transformers[op_name]
												_lowerCamelCase  :    List[str]					=					val.transpose(0							,					1	)

								# Copy the bias.
								elif weight_or_bias == "bias":
												_lowerCamelCase  :    List[str]					=					megatron_to_transformers[op_name]
												_lowerCamelCase  :    Union[str, Any]					=					val

    # DEBUG.
				assert config.n_layer == layer_idx + 1

				# The final layernorm.
				_lowerCamelCase  :    List[str]					=					transformer['final_layernorm.weight']
				_lowerCamelCase  :    Optional[int]					=					transformer['final_layernorm.bias']

				# For LM head, transformers' wants the matrix to weight embeddings.
				_lowerCamelCase  :    Optional[int]					=					word_embeddings

				# It should be done!
				return output_state_dict







def   _snake_case    (       ):
				# Create the argument parser.
				_lowerCamelCase  :    Union[str, Any]					=					argparse.ArgumentParser()
				parser.add_argument('--print-checkpoint-structure'							,					action='store_true'	)
				parser.add_argument(
				    'path_to_checkpoint'							,					type=lowercase__							,					help='Path to the checkpoint file (.zip archive or direct .pt file)'							,					)
				parser.add_argument(
				    '--config_file'							,					default=''							,					type=lowercase__							,					help='An optional config json file describing the pre-trained model.'							,					)
				_lowerCamelCase  :    int					=					parser.parse_args()

				# Extract the basename.
				_lowerCamelCase  :    Dict					=					os.path.dirname(args.path_to_checkpoint	)

				# Load the model.
				# the .zip is very optional, let's keep it for backward compatibility
				print(f'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}'''	)
				if args.path_to_checkpoint.endswith('.zip'	):
								with zipfile.ZipFile(args.path_to_checkpoint							,					'r'	) as checkpoint:
												with checkpoint.open('release/mp_rank_00/model_optim_rng.pt'	) as pytorch_dict:
																_lowerCamelCase  :    Union[str, Any]					=					torch.load(lowercase__							,					map_location='cpu'	)
				else:
								_lowerCamelCase  :    Optional[Any]					=					torch.load(args.path_to_checkpoint							,					map_location='cpu'	)

				_lowerCamelCase  :    int					=					input_state_dict.get('args'							,					lowercase__	)

				# Read the config, or default to the model released by NVIDIA.
				if args.config_file == "":
								if ds_args is not None:
												if ds_args.bias_gelu_fusion:
																_lowerCamelCase  :    Optional[Any]					=					'gelu_fast'
												elif ds_args.openai_gelu:
																_lowerCamelCase  :    str					=					'gelu_new'
												else:
																_lowerCamelCase  :    Any					=					'gelu'
								else:
												# in the very early days this used to be "gelu_new"
												_lowerCamelCase  :    str					=					'gelu_new'

								# Spell out all parameters in case the defaults change.
								_lowerCamelCase  :    Optional[int]					=					GPTaConfig(
								    vocab_size=50257							,					n_positions=1024							,					n_embd=1024							,					n_layer=24							,					n_head=16							,					n_inner=4096							,					activation_function=lowercase__							,					resid_pdrop=0.1							,					embd_pdrop=0.1							,					attn_pdrop=0.1							,					layer_norm_epsilon=1E-5							,					initializer_range=0.0_2							,					summary_type='cls_index'							,					summary_use_proj=lowercase__							,					summary_activation=lowercase__							,					summary_proj_to_labels=lowercase__							,					summary_first_dropout=0.1							,					scale_attn_weights=lowercase__							,					use_cache=lowercase__							,					bos_token_id=50256							,					eos_token_id=50256							,					)
				else:
								_lowerCamelCase  :    List[str]					=					GPTaConfig.from_json_file(args.config_file	)

				_lowerCamelCase  :    Optional[Any]					=					['GPT2LMHeadModel']

				# Convert.
				print('Converting'	)
				_lowerCamelCase  :    Optional[int]					=					convert_megatron_checkpoint(lowercase__							,					lowercase__							,					lowercase__	)

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

				# Add tokenizer class info to config
				# see https://github.com/huggingface/transformers/issues/13906)
				if ds_args is not None:
								_lowerCamelCase  :    Tuple					=					ds_args.tokenizer_type
								if tokenizer_type == "GPT2BPETokenizer":
												_lowerCamelCase  :    Dict					=					'gpt2'
								elif tokenizer_type == "PretrainedFromHF":
												_lowerCamelCase  :    Union[str, Any]					=					ds_args.tokenizer_name_or_path
								else:
												raise ValueError(f'''Unrecognized tokenizer_type {tokenizer_type}'''	)
				else:
								_lowerCamelCase  :    Optional[Any]					=					'gpt2'

				_lowerCamelCase  :    Optional[Any]					=					AutoTokenizer.from_pretrained(lowercase__	)
				_lowerCamelCase  :    str					=					type(lowercase__	).__name__
				_lowerCamelCase  :    str					=					tokenizer_class

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

				# Save tokenizer based on args
				print(f'''Adding {tokenizer_class} tokenizer files'''	)
				tokenizer.save_pretrained(lowercase__	)

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


####################################################################################################

if __name__ == "__main__":
			main()

####################################################################################################





"""simple docstring"""






def   _snake_case    (       lowercase__	):
				return "".join(chr(ord(lowercase__	) - 32	) if 'a' <= char <= 'z' else char for char in word	)


if __name__ == "__main__":
			from doctest import testmod

			testmod()