
import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig



def      _a     (   __UpperCamelCase       :  Any	,__UpperCamelCase       :  str	,__UpperCamelCase       :  List[str]=0    ):
			# Format the message.
			if name is None:
						lowerCAmelCase__ :    str				      =			None
			else:
						lowerCAmelCase__ :    str				      =			'''.''' * max(0	,spaces - 2    ) + '''# {:''' + str(50 - spaces    ) + '''s}'''
						lowerCAmelCase__ :    str				      =			fmt.format(a_    )

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



def      _a     (   __UpperCamelCase       :  List[Any]	,__UpperCamelCase       :  str	,__UpperCamelCase       :  Dict	,__UpperCamelCase       :  List[Any]    ):
			# 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__ :    int				      =			param.size()
			# other versions store [num_heads * num_splits * hidden_size, :]
			lowerCAmelCase__ :    Optional[int]				      =			(num_heads, num_splits, hidden_size) + input_shape[1:]
			lowerCAmelCase__ :    int				      =			param.view(*a_    )
			lowerCAmelCase__ :    List[str]				      =			param.transpose(0	,1    ).contiguous()
			lowerCAmelCase__ :    List[str]				      =			param.view(*a_    )
			return param



def      _a     (   __UpperCamelCase       :  List[str]	,__UpperCamelCase       :  List[Any]    ):
			lowerCAmelCase__ :    Any				      =			config.n_positions
			lowerCAmelCase__ :    Dict				      =			config.n_layer
			lowerCAmelCase__ :    Dict				      =			config.vocab_size
			lowerCAmelCase__ :    List[Any]				      =			config.n_head
			lowerCAmelCase__ :    List[Any]				      =			config.n_embd // config.n_head

			lowerCAmelCase__ :    Dict				      =			sd_megatron['''model.language_model.embedding.word_embeddings.weight'''][:vocab_size, :]
			lowerCAmelCase__ :    str				      =			{
			    '''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__ :    str				      =			'''model.language_model.encoder.layers.'''
			for i in range(a_    ):
						lowerCAmelCase__ :    List[str]				      =			torch.tril(torch.ones((n_positions, n_positions)	,dtype=torch.bool    )    )
						lowerCAmelCase__ :    Optional[Any]				      =			causal_mask.view(1	,1	,a_	,a_    )
						lowerCAmelCase__ :    Optional[Any]				      =			causal_mask
						lowerCAmelCase__ :    Optional[int]				      =			torch.tensor(-1e4	,dtype=torch.bfloataa    )

						lowerCAmelCase__ :    Optional[Any]				      =			sd_megatron[f'''{pf}{i}.input_layernorm.weight''']
						lowerCAmelCase__ :    Dict				      =			sd_megatron[f'''{pf}{i}.input_layernorm.bias''']

						lowerCAmelCase__ :    Optional[Any]				      =			sd_megatron[f'''{pf}{i}.self_attention.query_key_value.weight''']
						lowerCAmelCase__ :    Dict				      =			fix_query_key_value_ordering(a_	,3	,a_	,a_    )
						lowerCAmelCase__ :    str				      =			vala.transpose(0	,1    ).contiguous()

						lowerCAmelCase__ :    str				      =			sd_megatron[f'''{pf}{i}.self_attention.query_key_value.bias''']
						lowerCAmelCase__ :    List[Any]				      =			fix_query_key_value_ordering(a_	,3	,a_	,a_    )
						lowerCAmelCase__ :    Tuple				      =			vala

						lowerCAmelCase__ :    str				      =			sd_megatron[f'''{pf}{i}.self_attention.dense.weight'''].transpose(
						    0	,1    )
						lowerCAmelCase__ :    Dict				      =			sd_megatron[f'''{pf}{i}.self_attention.dense.bias''']
						lowerCAmelCase__ :    List[Any]				      =			sd_megatron[f'''{pf}{i}.post_attention_layernorm.weight''']
						lowerCAmelCase__ :    List[Any]				      =			sd_megatron[f'''{pf}{i}.post_attention_layernorm.bias''']
						lowerCAmelCase__ :    int				      =			sd_megatron[f'''{pf}{i}.mlp.dense_h_to_4h.weight'''].transpose(0	,1    )
						lowerCAmelCase__ :    Dict				      =			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__ :    List[Any]				      =			sd_megatron[f'''{pf}{i}.mlp.dense_4h_to_h.bias''']

			# For LM head, transformers' wants the matrix to weight embeddings.
			lowerCAmelCase__ :    Any				      =			word_embeddings

			return sd_hf



def      _a     (   __UpperCamelCase       :  Optional[int]	,__UpperCamelCase       :  Any    ):
			lowerCAmelCase__ :    int				      =			6_4000
			lowerCAmelCase__ :    List[str]				      =			config_megatron['''encoder_seq_length''']
			lowerCAmelCase__ :    Tuple				      =			config_megatron['''hidden_size''']
			lowerCAmelCase__ :    Union[str, Any]				      =			config_megatron['''num_layers''']
			lowerCAmelCase__ :    Dict				      =			config_megatron['''num_attention_heads''']
			lowerCAmelCase__ :    Union[str, Any]				      =			config_megatron['''ffn_hidden_size''']
			lowerCAmelCase__ :    str				      =			'''gelu'''
			lowerCAmelCase__ :    Dict				      =			0.1
			lowerCAmelCase__ :    str				      =			0.1
			lowerCAmelCase__ :    Tuple				      =			0.1
			lowerCAmelCase__ :    Tuple				      =			config_megatron['''layernorm_epsilon''']  # 1e-5
			lowerCAmelCase__ :    Tuple				      =			config_megatron['''init_method_std''']  # 0.02
			lowerCAmelCase__ :    int				      =			config_megatron['''apply_query_key_layer_scaling''']  # True
			lowerCAmelCase__ :    Tuple				      =			True
			lowerCAmelCase__ :    Union[str, 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[Any]				      =			1  # <|endoftext|>
						lowerCAmelCase__ :    Dict				      =			0  # <unk>
			else:
						lowerCAmelCase__ :    int				      =			2  # <s>
						lowerCAmelCase__ :    Optional[int]				      =			3  # <|endoftext|>
						lowerCAmelCase__ :    str				      =			0  # <pad>

			return config_hf



def      _a     (   __UpperCamelCase       :  Optional[Any]    ):
			print(a_    )

			lowerCAmelCase__ :    List[Any]				      =			args.checkpoint_path
			lowerCAmelCase__ :    int				      =			args.save_path
			if isfile(a_    ):
						raise FileNotFoundError(f'''ERROR! could not find file {checkpoint_path}'''    )

			# Load the model.
			lowerCAmelCase__ :    int				      =			torch.load(a_	,map_location='''cpu'''    )

			# Load the config.
			lowerCAmelCase__ :    Union[str, Any]				      =			checkpoint['''hyper_parameters''']['''cfg''']
			lowerCAmelCase__ :    Optional[int]				      =			GPTaConfig()
			lowerCAmelCase__ :    Any				      =			copy_config(config_hf=a_	,config_megatron=a_    )
			lowerCAmelCase__ :    Any				      =			['''GPT2LMHeadModel''']

			lowerCAmelCase__ :    Any				      =			checkpoint['''state_dict''']

			# Convert.
			print('''Converting'''    )
			lowerCAmelCase__ :    List[str]				      =			convert_megatron_checkpoint(a_	,a_    )

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

			lowerCAmelCase__ :    int				      =			'''GPTSw3Tokenizer'''

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

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


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





import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 						lowercase       (  __UpperCamelCase					):
					__a					=    (HeunDiscreteScheduler,)
					__a					=    10
					def lowercase_			(		self ,	**SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Any				      =			{
								    '''num_train_timesteps''': 1100,
								    '''beta_start''': 0.0_001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								}

								config.update(**SCREAMING_SNAKE_CASE__    )
								return config
					def lowercase_			(		self    ):
								"""simple docstring"""







								for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001] ,	[0.0_002, 0.002, 0.02]    ):
											self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__ ,	beta_end=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								for schedule in ["linear", "scaled_linear", "exp"]:
											self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								for clip_sample_range in [1.0, 2.0, 3.0]:
											self.check_over_configs(clip_sample_range=SCREAMING_SNAKE_CASE__ ,	clip_sample=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								for prediction_type in ["epsilon", "v_prediction", "sample"]:
											self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[str]				      =			self.scheduler_classes[0]
								lowerCAmelCase__ :    Optional[Any]				      =			self.get_scheduler_config()
								lowerCAmelCase__ :    Optional[Any]				      =			scheduler_class(**SCREAMING_SNAKE_CASE__    )

								scheduler.set_timesteps(self.num_inference_steps    )

								lowerCAmelCase__ :    str				      =			self.dummy_model()
								lowerCAmelCase__ :    Optional[int]				      =			self.dummy_sample_deter * scheduler.init_noise_sigma
								lowerCAmelCase__ :    Optional[Any]				      =			sample.to(SCREAMING_SNAKE_CASE__    )

								for i, t in enumerate(scheduler.timesteps    ):
											lowerCAmelCase__ :    List[Any]				      =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    str				      =			model(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Tuple				      =			scheduler.step(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    Union[str, Any]				      =			output.prev_sample

								lowerCAmelCase__ :    Any				      =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__    )    )
								lowerCAmelCase__ :    Union[str, Any]				      =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__    )    )

								if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 0.1_233    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
								else:
											# CUDA
											assert abs(result_sum.item() - 0.1_233    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[Any]				      =			self.scheduler_classes[0]
								lowerCAmelCase__ :    str				      =			self.get_scheduler_config(prediction_type='''v_prediction'''    )
								lowerCAmelCase__ :    Any				      =			scheduler_class(**SCREAMING_SNAKE_CASE__    )

								scheduler.set_timesteps(self.num_inference_steps    )

								lowerCAmelCase__ :    int				      =			self.dummy_model()
								lowerCAmelCase__ :    List[str]				      =			self.dummy_sample_deter * scheduler.init_noise_sigma
								lowerCAmelCase__ :    List[Any]				      =			sample.to(SCREAMING_SNAKE_CASE__    )

								for i, t in enumerate(scheduler.timesteps    ):
											lowerCAmelCase__ :    int				      =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Optional[Any]				      =			model(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Optional[int]				      =			scheduler.step(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    Tuple				      =			output.prev_sample

								lowerCAmelCase__ :    List[Any]				      =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__    )    )
								lowerCAmelCase__ :    List[str]				      =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__    )    )

								if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 4.6934E-07    ) < 1E-2
											assert abs(result_mean.item() - 6.1112E-10    ) < 1E-3
								else:
											# CUDA
											assert abs(result_sum.item() - 4.693_4286_5017_0972E-07    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[int]				      =			self.scheduler_classes[0]
								lowerCAmelCase__ :    Optional[Any]				      =			self.get_scheduler_config()
								lowerCAmelCase__ :    Union[str, Any]				      =			scheduler_class(**SCREAMING_SNAKE_CASE__    )

								scheduler.set_timesteps(self.num_inference_steps ,	device=SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    Optional[Any]				      =			self.dummy_model()
								lowerCAmelCase__ :    Dict				      =			self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE__    ) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
											lowerCAmelCase__ :    int				      =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Dict				      =			model(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Any				      =			scheduler.step(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    List[Any]				      =			output.prev_sample

								lowerCAmelCase__ :    Dict				      =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__    )    )
								lowerCAmelCase__ :    List[str]				      =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__    )    )

								if str(SCREAMING_SNAKE_CASE__    ).startswith('''cpu'''    ):
											# The following sum varies between 148 and 156 on mps. Why?
											assert abs(result_sum.item() - 0.1_233    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
								elif str(SCREAMING_SNAKE_CASE__    ).startswith('''mps'''    ):
											# Larger tolerance on mps
											assert abs(result_mean.item() - 0.0_002    ) < 1E-2
								else:
											# CUDA
											assert abs(result_sum.item() - 0.1_233    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Any				      =			self.scheduler_classes[0]
								lowerCAmelCase__ :    Optional[Any]				      =			self.get_scheduler_config()
								lowerCAmelCase__ :    str				      =			scheduler_class(**SCREAMING_SNAKE_CASE__ ,	use_karras_sigmas=SCREAMING_SNAKE_CASE__    )

								scheduler.set_timesteps(self.num_inference_steps ,	device=SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    str				      =			self.dummy_model()
								lowerCAmelCase__ :    Any				      =			self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE__    ) * scheduler.init_noise_sigma
								lowerCAmelCase__ :    List[Any]				      =			sample.to(SCREAMING_SNAKE_CASE__    )

								for t in scheduler.timesteps:
											lowerCAmelCase__ :    Union[str, Any]				      =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Any				      =			model(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    str				      =			scheduler.step(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    List[Any]				      =			output.prev_sample

								lowerCAmelCase__ :    int				      =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__    )    )
								lowerCAmelCase__ :    Dict				      =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__    )    )

								assert abs(result_sum.item() - 0.00_015    ) < 1E-2
								assert abs(result_mean.item() - 1.9869_5545_3503_4695E-07    ) < 1E-2



