
import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig




def 				__magic_name__				(     __lowerCAmelCase			:		Any	,						__lowerCAmelCase			:		Any	,						__lowerCAmelCase			:		Union[str, Any]=0							)    ->     Union[str, Any]:
				# Format the message.
				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 				__magic_name__				(     __lowerCAmelCase			:		List[str]	,						__lowerCAmelCase			:		int	,						__lowerCAmelCase			:		Optional[Any]	,						__lowerCAmelCase			:		Tuple							)    ->     List[str]:
				# 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					      =			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 				__magic_name__				(     __lowerCAmelCase			:		List[str]	,						__lowerCAmelCase			:		Optional[int]							)    ->     Union[str, Any]:
				__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 				__magic_name__				(     __lowerCAmelCase			:		Optional[Any]	,						__lowerCAmelCase			:		str							)    ->     Optional[Any]:
				__lowerCamelCase					      =			6_4000
				__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"] == 4096:
								__lowerCamelCase					      =			1  # <|endoftext|>
								__lowerCamelCase					      =			1  # <|endoftext|>
								__lowerCamelCase					      =			0  # <unk>
				else:
								__lowerCamelCase					      =			2  # <s>
								__lowerCamelCase					      =			3  # <|endoftext|>
								__lowerCamelCase					      =			0  # <pad>

				return config_hf




def 				__magic_name__				(     __lowerCAmelCase			:		List[str]							)    ->     Union[str, Any]:
				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__     :    List[str]      							=					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__     :    Optional[Any]      							=					parser.parse_args()
					main(_args)





from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest

@require_tokenizers
class      lowerCAmelCase__  (			__lowercase    ):
		a__ :		Any   						=	SqueezeBertTokenizer
		a__ :		Union[str, Any]   						=	SqueezeBertTokenizerFast
		a__ :		int   						=	True






		def 				__A (	self	:							Optional[int]							,    **SCREAMING_SNAKE_CASE__	:							Union[str, Any]						)  ->							int:
						return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname							,    **SCREAMING_SNAKE_CASE__						)







		@slow
		def 				__A (	self	:							int						)  ->							Optional[int]:
						__lowerCamelCase					      =			SqueezeBertTokenizer.from_pretrained('''squeezebert/squeezebert-mnli-headless'''						)

						__lowerCamelCase					      =			tokenizer.encode('''sequence builders'''							,    add_special_tokens=SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			tokenizer.encode('''multi-sequence build'''							,    add_special_tokens=SCREAMING_SNAKE_CASE__						)

						__lowerCamelCase					      =			tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)

						assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
						assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
						    tokenizer.sep_token_id
						]



