




import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig




def 							_UpperCAmelCase     (UpperCamelCase_       :       List[str]					,			UpperCamelCase_       :       Optional[Any]					,			UpperCamelCase_       :       str=0				):
				'''simple docstring'''
				# Format the message.
				if name is None:
								_lowerCAmelCase			:			Optional[int]        =					None
				else:
								_lowerCAmelCase			:			List[str]        =					""".""" * max(0					,			spaces - 2				) + """# {:""" + str(50 - spaces				) + """s}"""
								_lowerCAmelCase			:			List[Any]        =					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 							_UpperCAmelCase     (UpperCamelCase_       :       int					,			UpperCamelCase_       :       str					,			UpperCamelCase_       :       Optional[Any]					,			UpperCamelCase_       :       Optional[Any]				):
				'''simple docstring'''
				# 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			:			str        =					param.size()
				# other versions store [num_heads * num_splits * hidden_size, :]
				_lowerCAmelCase			:			List[str]        =					(num_heads, num_splits, hidden_size) + input_shape[1:]
				_lowerCAmelCase			:			Union[str, Any]        =					param.view(*UpperCamelCase_				)
				_lowerCAmelCase			:			Optional[Any]        =					param.transpose(0					,			1				).contiguous()
				_lowerCAmelCase			:			Union[str, Any]        =					param.view(*UpperCamelCase_				)
				return param




def 							_UpperCAmelCase     (UpperCamelCase_       :       List[Any]					,			UpperCamelCase_       :       Optional[int]				):
				'''simple docstring'''
				_lowerCAmelCase			:			List[Any]        =					config.n_positions
				_lowerCAmelCase			:			List[str]        =					config.n_layer
				_lowerCAmelCase			:			Dict        =					config.vocab_size
				_lowerCAmelCase			:			Tuple        =					config.n_head
				_lowerCAmelCase			:			List[str]        =					config.n_embd // config.n_head

				_lowerCAmelCase			:			Optional[Any]        =					sd_megatron["""model.language_model.embedding.word_embeddings.weight"""][:vocab_size, :]
				_lowerCAmelCase			:			List[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			:			Dict        =					"""model.language_model.encoder.layers."""
				for i in range(UpperCamelCase_				):
								_lowerCAmelCase			:			str        =					torch.tril(torch.ones((n_positions, n_positions)					,			dtype=torch.bool				)				)
								_lowerCAmelCase			:			Tuple        =					causal_mask.view(1					,			1					,			UpperCamelCase_					,			UpperCamelCase_				)
								_lowerCAmelCase			:			List[str]        =					causal_mask
								_lowerCAmelCase			:			Dict        =					torch.tensor(-1E4					,			dtype=torch.bfloataa				)

								_lowerCAmelCase			:			List[str]        =					sd_megatron[F"{pf}{i}.input_layernorm.weight"]
								_lowerCAmelCase			:			Tuple        =					sd_megatron[F"{pf}{i}.input_layernorm.bias"]

								_lowerCAmelCase			:			List[str]        =					sd_megatron[F"{pf}{i}.self_attention.query_key_value.weight"]
								_lowerCAmelCase			:			List[Any]        =					fix_query_key_value_ordering(UpperCamelCase_					,			3					,			UpperCamelCase_					,			UpperCamelCase_				)
								_lowerCAmelCase			:			Tuple        =					vala.transpose(0					,			1				).contiguous()

								_lowerCAmelCase			:			List[Any]        =					sd_megatron[F"{pf}{i}.self_attention.query_key_value.bias"]
								_lowerCAmelCase			:			Tuple        =					fix_query_key_value_ordering(UpperCamelCase_					,			3					,			UpperCamelCase_					,			UpperCamelCase_				)
								_lowerCAmelCase			:			Tuple        =					vala

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

				# For LM head, transformers' wants the matrix to weight embeddings.
				_lowerCAmelCase			:			List[Any]        =					word_embeddings

				return sd_hf




def 							_UpperCAmelCase     (UpperCamelCase_       :       Tuple					,			UpperCamelCase_       :       Optional[Any]				):
				'''simple docstring'''
				_lowerCAmelCase			:			Optional[Any]        =					64000
				_lowerCAmelCase			:			Tuple        =					config_megatron["""encoder_seq_length"""]
				_lowerCAmelCase			:			Dict        =					config_megatron["""hidden_size"""]
				_lowerCAmelCase			:			Dict        =					config_megatron["""num_layers"""]
				_lowerCAmelCase			:			int        =					config_megatron["""num_attention_heads"""]
				_lowerCAmelCase			:			Any        =					config_megatron["""ffn_hidden_size"""]
				_lowerCAmelCase			:			str        =					"""gelu"""
				_lowerCAmelCase			:			Optional[Any]        =					0.1
				_lowerCAmelCase			:			List[Any]        =					0.1
				_lowerCAmelCase			:			Any        =					0.1
				_lowerCAmelCase			:			Tuple        =					config_megatron["""layernorm_epsilon"""]  # 1e-5
				_lowerCAmelCase			:			List[Any]        =					config_megatron["""init_method_std"""]  # 0.02
				_lowerCAmelCase			:			int        =					config_megatron["""apply_query_key_layer_scaling"""]  # True
				_lowerCAmelCase			:			Union[str, Any]        =					True
				_lowerCAmelCase			:			Any        =					True

				# This identifies the 6.7B (7B) model which uses a different tokenizer
				if config_megatron["hidden_size"] == 4096:
								_lowerCAmelCase			:			Optional[Any]        =					1  # <|endoftext|>
								_lowerCAmelCase			:			List[str]        =					1  # <|endoftext|>
								_lowerCAmelCase			:			Tuple        =					0  # <unk>
				else:
								_lowerCAmelCase			:			List[str]        =					2  # <s>
								_lowerCAmelCase			:			int        =					3  # <|endoftext|>
								_lowerCAmelCase			:			Union[str, Any]        =					0  # <pad>

				return config_hf




def 							_UpperCAmelCase     (UpperCamelCase_       :       Any				):
				'''simple docstring'''
				print(UpperCamelCase_				)

				_lowerCAmelCase			:			Optional[int]        =					args.checkpoint_path
				_lowerCAmelCase			:			Union[str, Any]        =					args.save_path
				if isfile(UpperCamelCase_				):
								raise FileNotFoundError(F"ERROR! could not find file {checkpoint_path}"				)

				# Load the model.
				_lowerCAmelCase			:			List[Any]        =					torch.load(UpperCamelCase_					,			map_location="""cpu"""				)

				# Load the config.
				_lowerCAmelCase			:			Dict        =					checkpoint["""hyper_parameters"""]["""cfg"""]
				_lowerCAmelCase			:			Tuple        =					GPTaConfig()
				_lowerCAmelCase			:			Union[str, Any]        =					copy_config(config_hf=UpperCamelCase_					,			config_megatron=UpperCamelCase_				)
				_lowerCAmelCase			:			Any        =					["""GPT2LMHeadModel"""]

				_lowerCAmelCase			:			Dict        =					checkpoint["""state_dict"""]

				# Convert.
				print("""Converting"""				)
				_lowerCAmelCase			:			Tuple        =					convert_megatron_checkpoint(UpperCamelCase_					,			UpperCamelCase_				)

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

				_lowerCAmelCase			:			Union[str, Any]        =					"""GPTSw3Tokenizer"""

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

				# Store the state_dict to file.
				_lowerCAmelCase			:			Dict        =					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)







import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowerCamelCase					:     List[str]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     Tuple					  =  ["UtteranceLevel"]




@torch.no_grad()
def 							_UpperCAmelCase     (UpperCamelCase_       :       Optional[Any]					,			UpperCamelCase_       :       List[Any]					,			UpperCamelCase_       :       Tuple					,			UpperCamelCase_       :       Dict				):
				'''simple docstring'''
				_lowerCAmelCase			:			List[str]        =					torch.load(UpperCamelCase_					,			map_location="""cpu"""				)
				if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
								raise NotImplementedError(F"The supported s3prl models are {SUPPORTED_MODELS}"				)

				_lowerCAmelCase			:			List[str]        =					checkpoint["""Downstream"""]

				_lowerCAmelCase			:			Optional[int]        =					HubertConfig.from_pretrained(UpperCamelCase_				)
				_lowerCAmelCase			:			str        =					HubertForSequenceClassification.from_pretrained(UpperCamelCase_					,			config=UpperCamelCase_				)
				_lowerCAmelCase			:			Union[str, Any]        =					WavaVecaFeatureExtractor.from_pretrained(
				    UpperCamelCase_					,			return_attention_mask=UpperCamelCase_					,			do_normalize=UpperCamelCase_				)

				if hf_congfig.use_weighted_layer_sum:
								_lowerCAmelCase			:			int        =					checkpoint["""Featurizer"""]["""weights"""]

				_lowerCAmelCase			:			Optional[int]        =					downstream_dict["""projector.weight"""]
				_lowerCAmelCase			:			int        =					downstream_dict["""projector.bias"""]
				_lowerCAmelCase			:			Dict        =					downstream_dict["""model.post_net.linear.weight"""]
				_lowerCAmelCase			:			Dict        =					downstream_dict["""model.post_net.linear.bias"""]

				hf_feature_extractor.save_pretrained(UpperCamelCase_				)
				hf_model.save_pretrained(UpperCamelCase_				)


if __name__ == "__main__":
	_lowerCamelCase					:     Any					  =  argparse.ArgumentParser()
	parser.add_argument(
	    "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
	)
	parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
	parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
	parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
	_lowerCamelCase					:     Dict					  =  parser.parse_args()
	convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

