
"""simple docstring"""

import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig
def 		UpperCAmelCase					(		a_,						a_,						a_=0			):





		'''simple docstring'''


		if name is None:
				lowerCamelCase     :			List[Any]		=    None
		else:
				lowerCamelCase     :			Dict		=    '.' * max(0,						spaces - 2			) + '# {:' + str(50 - spaces			) + 's}'
				lowerCamelCase     :			Optional[int]		=    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 		UpperCAmelCase					(		a_,						a_,						a_,						a_			):





		'''simple docstring'''


		lowerCamelCase     :			int		=    param.size()
		# other versions store [num_heads * num_splits * hidden_size, :]
		lowerCamelCase     :			Union[str, Any]		=    (num_heads, num_splits, hidden_size) + input_shape[1:]
		lowerCamelCase     :			Optional[int]		=    param.view(*a_			)
		lowerCamelCase     :			str		=    param.transpose(0,						1			).contiguous()
		lowerCamelCase     :			Optional[Any]		=    param.view(*a_			)
		return param
def 		UpperCAmelCase					(		a_,						a_			):





		'''simple docstring'''


		lowerCamelCase     :			Optional[Any]		=    config.n_positions
		lowerCamelCase     :			List[Any]		=    config.n_layer
		lowerCamelCase     :			int		=    config.vocab_size
		lowerCamelCase     :			int		=    config.n_head
		lowerCamelCase     :			Optional[int]		=    config.n_embd // config.n_head

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

				lowerCamelCase     :			str		=    sd_megatron[F"""{pf}{i}.input_layernorm.weight"""]
				lowerCamelCase     :			Optional[int]		=    sd_megatron[F"""{pf}{i}.input_layernorm.bias"""]

				lowerCamelCase     :			int		=    sd_megatron[F"""{pf}{i}.self_attention.query_key_value.weight"""]
				lowerCamelCase     :			Union[str, Any]		=    fix_query_key_value_ordering(a_,						3,						a_,						a_			)
				lowerCamelCase     :			int		=    vala.transpose(0,						1			).contiguous()

				lowerCamelCase     :			str		=    sd_megatron[F"""{pf}{i}.self_attention.query_key_value.bias"""]
				lowerCamelCase     :			Dict		=    fix_query_key_value_ordering(a_,						3,						a_,						a_			)
				lowerCamelCase     :			Union[str, Any]		=    vala

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

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

		return sd_hf
def 		UpperCAmelCase					(		a_,						a_			):





		'''simple docstring'''


		lowerCamelCase     :			int		=    6_4000
		lowerCamelCase     :			Dict		=    config_megatron['encoder_seq_length']
		lowerCamelCase     :			Tuple		=    config_megatron['hidden_size']
		lowerCamelCase     :			Tuple		=    config_megatron['num_layers']
		lowerCamelCase     :			Any		=    config_megatron['num_attention_heads']
		lowerCamelCase     :			List[Any]		=    config_megatron['ffn_hidden_size']
		lowerCamelCase     :			str		=    'gelu'
		lowerCamelCase     :			Union[str, Any]		=    0.1
		lowerCamelCase     :			Tuple		=    0.1
		lowerCamelCase     :			Optional[Any]		=    0.1
		lowerCamelCase     :			Union[str, Any]		=    config_megatron['layernorm_epsilon']  # 1e-5
		lowerCamelCase     :			Optional[int]		=    config_megatron['init_method_std']  # 0.02
		lowerCamelCase     :			Tuple		=    config_megatron['apply_query_key_layer_scaling']  # True
		lowerCamelCase     :			Dict		=    True
		lowerCamelCase     :			Optional[int]		=    True

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

		return config_hf
def 		UpperCAmelCase					(		a_			):





		'''simple docstring'''


		print(a_			)

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

		# Load the model.
		lowerCamelCase     :			Optional[Any]		=    torch.load(a_,						map_location='cpu'			)

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

		lowerCamelCase     :			Optional[int]		=    checkpoint['state_dict']

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

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

		lowerCamelCase     :			Dict		=    'GPTSw3Tokenizer'

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

		# Store the state_dict to file.
		lowerCamelCase     :			List[Any]		=    os.path.join(a_,						'pytorch_model.bin'			)
		print(F"""Saving checkpoint to \"{output_checkpoint_file}\""""			)
		torch.save(a_,						a_			)


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








"""simple docstring"""

import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	(KDPMaAncestralDiscreteScheduler,)
	lowercase_        =	1_0




	def 			_UpperCamelCase    ( self		, **UpperCAmelCase_					)		->							Optional[int]:
			lowerCamelCase     :			Any		=    {
			    'num_train_timesteps': 1100,
			    'beta_start': 0.0001,
			    'beta_end': 0.02,
			    'beta_schedule': 'linear',
			}

			config.update(**UpperCAmelCase_					)
			return config




	def 			_UpperCamelCase    ( self					)		->							Any:
			for timesteps in [10, 50, 100, 1000]:
					self.check_over_configs(num_train_timesteps=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		, [0.0002, 0.002, 0.02]					):
					self.check_over_configs(beta_start=UpperCAmelCase_		, beta_end=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			if torch_device == "mps":
					return
			lowerCamelCase     :			Any		=    self.scheduler_classes[0]
			lowerCamelCase     :			List[str]		=    self.get_scheduler_config()
			lowerCamelCase     :			Union[str, Any]		=    scheduler_class(**UpperCAmelCase_					)

			scheduler.set_timesteps(self.num_inference_steps					)

			lowerCamelCase     :			Optional[Any]		=    torch.manual_seed(0					)

			lowerCamelCase     :			int		=    self.dummy_model()
			lowerCamelCase     :			Tuple		=    self.dummy_sample_deter * scheduler.init_noise_sigma
			lowerCamelCase     :			Union[str, Any]		=    sample.to(UpperCAmelCase_					)

			for i, t in enumerate(scheduler.timesteps					):
					lowerCamelCase     :			Tuple		=    scheduler.scale_model_input(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			List[str]		=    model(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			Tuple		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, generator=UpperCAmelCase_					)
					lowerCamelCase     :			str		=    output.prev_sample

			lowerCamelCase     :			Optional[int]		=    torch.sum(torch.abs(UpperCAmelCase_					)					)
			lowerCamelCase     :			Tuple		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_sum.item() - 13849.3877					) < 1E-2
			assert abs(result_mean.item() - 18.0331					) < 5E-3




	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			if torch_device == "mps":
					return
			lowerCamelCase     :			Dict		=    self.scheduler_classes[0]
			lowerCamelCase     :			Union[str, Any]		=    self.get_scheduler_config(prediction_type='v_prediction'					)
			lowerCamelCase     :			int		=    scheduler_class(**UpperCAmelCase_					)

			scheduler.set_timesteps(self.num_inference_steps					)

			lowerCamelCase     :			int		=    self.dummy_model()
			lowerCamelCase     :			Dict		=    self.dummy_sample_deter * scheduler.init_noise_sigma
			lowerCamelCase     :			Optional[int]		=    sample.to(UpperCAmelCase_					)

			lowerCamelCase     :			Dict		=    torch.manual_seed(0					)

			for i, t in enumerate(scheduler.timesteps					):
					lowerCamelCase     :			Dict		=    scheduler.scale_model_input(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			Optional[Any]		=    model(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			Tuple		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, generator=UpperCAmelCase_					)
					lowerCamelCase     :			Optional[Any]		=    output.prev_sample

			lowerCamelCase     :			Union[str, Any]		=    torch.sum(torch.abs(UpperCAmelCase_					)					)
			lowerCamelCase     :			Optional[Any]		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_sum.item() - 328.9970					) < 1E-2
			assert abs(result_mean.item() - 0.4284					) < 1E-3




	def 			_UpperCamelCase    ( self					)		->							Any:
			if torch_device == "mps":
					return
			lowerCamelCase     :			Union[str, Any]		=    self.scheduler_classes[0]
			lowerCamelCase     :			Dict		=    self.get_scheduler_config()
			lowerCamelCase     :			Dict		=    scheduler_class(**UpperCAmelCase_					)

			scheduler.set_timesteps(self.num_inference_steps		, device=UpperCAmelCase_					)
			lowerCamelCase     :			int		=    torch.manual_seed(0					)

			lowerCamelCase     :			List[Any]		=    self.dummy_model()
			lowerCamelCase     :			Union[str, Any]		=    self.dummy_sample_deter.to(UpperCAmelCase_					) * scheduler.init_noise_sigma

			for t in scheduler.timesteps:
					lowerCamelCase     :			Optional[int]		=    scheduler.scale_model_input(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			str		=    model(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			List[str]		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, generator=UpperCAmelCase_					)
					lowerCamelCase     :			Dict		=    output.prev_sample

			lowerCamelCase     :			List[str]		=    torch.sum(torch.abs(UpperCAmelCase_					)					)
			lowerCamelCase     :			str		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_sum.item() - 13849.3818					) < 1E-1
			assert abs(result_mean.item() - 18.0331					) < 1E-3






