




import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig

def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE=0   ) ->       Dict:


			'''simple docstring'''







			if name is None:
						SCREAMING_SNAKE_CASE_			=	None
			else:
						SCREAMING_SNAKE_CASE_			=	'.' * max(0					,						spaces - 2   ) + '# {:' + str(50 - spaces   ) + 's}'
						SCREAMING_SNAKE_CASE_			=	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 lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       List[Any]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_			=	param.size()
			# other versions store [num_heads * num_splits * hidden_size, :]
			SCREAMING_SNAKE_CASE_			=	(num_heads, num_splits, hidden_size) + input_shape[1:]
			SCREAMING_SNAKE_CASE_			=	param.view(*_UpperCAmelCase   )
			SCREAMING_SNAKE_CASE_			=	param.transpose(0					,						1   ).contiguous()
			SCREAMING_SNAKE_CASE_			=	param.view(*_UpperCAmelCase   )
			return param

def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       str:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_			=	config.n_positions
			SCREAMING_SNAKE_CASE_			=	config.n_layer
			SCREAMING_SNAKE_CASE_			=	config.vocab_size
			SCREAMING_SNAKE_CASE_			=	config.n_head
			SCREAMING_SNAKE_CASE_			=	config.n_embd // config.n_head

			SCREAMING_SNAKE_CASE_			=	sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
			SCREAMING_SNAKE_CASE_			=	{
			    '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'],
			}

			SCREAMING_SNAKE_CASE_			=	'model.language_model.encoder.layers.'
			for i in range(_UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_			=	torch.tril(torch.ones((n_positions, n_positions)					,						dtype=torch.bool   )   )
						SCREAMING_SNAKE_CASE_			=	causal_mask.view(1					,						1					,						_UpperCAmelCase					,						_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_			=	causal_mask
						SCREAMING_SNAKE_CASE_			=	torch.tensor(-1E4					,						dtype=torch.bfloataa   )

						SCREAMING_SNAKE_CASE_			=	sd_megatron[F'{pf}{i}.input_layernorm.weight']
						SCREAMING_SNAKE_CASE_			=	sd_megatron[F'{pf}{i}.input_layernorm.bias']

						SCREAMING_SNAKE_CASE_			=	sd_megatron[F'{pf}{i}.self_attention.query_key_value.weight']
						SCREAMING_SNAKE_CASE_			=	fix_query_key_value_ordering(_UpperCAmelCase					,						3					,						_UpperCAmelCase					,						_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_			=	vala.transpose(0					,						1   ).contiguous()

						SCREAMING_SNAKE_CASE_			=	sd_megatron[F'{pf}{i}.self_attention.query_key_value.bias']
						SCREAMING_SNAKE_CASE_			=	fix_query_key_value_ordering(_UpperCAmelCase					,						3					,						_UpperCAmelCase					,						_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_			=	vala

						SCREAMING_SNAKE_CASE_			=	sd_megatron[F'{pf}{i}.self_attention.dense.weight'].transpose(
						    0					,						1   )
						SCREAMING_SNAKE_CASE_			=	sd_megatron[F'{pf}{i}.self_attention.dense.bias']
						SCREAMING_SNAKE_CASE_			=	sd_megatron[F'{pf}{i}.post_attention_layernorm.weight']
						SCREAMING_SNAKE_CASE_			=	sd_megatron[F'{pf}{i}.post_attention_layernorm.bias']
						SCREAMING_SNAKE_CASE_			=	sd_megatron[F'{pf}{i}.mlp.dense_h_to_4h.weight'].transpose(0					,						1   )
						SCREAMING_SNAKE_CASE_			=	sd_megatron[F'{pf}{i}.mlp.dense_h_to_4h.bias']
						SCREAMING_SNAKE_CASE_			=	sd_megatron[F'{pf}{i}.mlp.dense_4h_to_h.weight'].transpose(
						    0					,						1   )
						SCREAMING_SNAKE_CASE_			=	sd_megatron[F'{pf}{i}.mlp.dense_4h_to_h.bias']

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

			return sd_hf

def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Optional[int]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_			=	6_40_00
			SCREAMING_SNAKE_CASE_			=	config_megatron['encoder_seq_length']
			SCREAMING_SNAKE_CASE_			=	config_megatron['hidden_size']
			SCREAMING_SNAKE_CASE_			=	config_megatron['num_layers']
			SCREAMING_SNAKE_CASE_			=	config_megatron['num_attention_heads']
			SCREAMING_SNAKE_CASE_			=	config_megatron['ffn_hidden_size']
			SCREAMING_SNAKE_CASE_			=	'gelu'
			SCREAMING_SNAKE_CASE_			=	0.1
			SCREAMING_SNAKE_CASE_			=	0.1
			SCREAMING_SNAKE_CASE_			=	0.1
			SCREAMING_SNAKE_CASE_			=	config_megatron['layernorm_epsilon']  # 1e-5
			SCREAMING_SNAKE_CASE_			=	config_megatron['init_method_std']  # 0.02
			SCREAMING_SNAKE_CASE_			=	config_megatron['apply_query_key_layer_scaling']  # True
			SCREAMING_SNAKE_CASE_			=	True
			SCREAMING_SNAKE_CASE_			=	True

			# This identifies the 6.7B (7B) model which uses a different tokenizer
			if config_megatron["hidden_size"] == 40_96:
						SCREAMING_SNAKE_CASE_			=	1  # <|endoftext|>
						SCREAMING_SNAKE_CASE_			=	1  # <|endoftext|>
						SCREAMING_SNAKE_CASE_			=	0  # <unk>
			else:
						SCREAMING_SNAKE_CASE_			=	2  # <s>
						SCREAMING_SNAKE_CASE_			=	3  # <|endoftext|>
						SCREAMING_SNAKE_CASE_			=	0  # <pad>

			return config_hf

def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       Optional[int]:


			'''simple docstring'''







			print(_UpperCAmelCase   )

			SCREAMING_SNAKE_CASE_			=	args.checkpoint_path
			SCREAMING_SNAKE_CASE_			=	args.save_path
			if isfile(_UpperCAmelCase   ):
						raise FileNotFoundError(F'ERROR! could not find file {checkpoint_path}'   )

			# Load the model.
			SCREAMING_SNAKE_CASE_			=	torch.load(_UpperCAmelCase					,						map_location='cpu'   )

			# Load the config.
			SCREAMING_SNAKE_CASE_			=	checkpoint['hyper_parameters']['cfg']
			SCREAMING_SNAKE_CASE_			=	GPTaConfig()
			SCREAMING_SNAKE_CASE_			=	copy_config(config_hf=_UpperCAmelCase					,						config_megatron=_UpperCAmelCase   )
			SCREAMING_SNAKE_CASE_			=	['GPT2LMHeadModel']

			SCREAMING_SNAKE_CASE_			=	checkpoint['state_dict']

			# Convert.
			print('Converting'   )
			SCREAMING_SNAKE_CASE_			=	convert_megatron_checkpoint(_UpperCAmelCase					,						_UpperCAmelCase   )

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

			SCREAMING_SNAKE_CASE_			=	'GPTSw3Tokenizer'

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

			# Store the state_dict to file.
			SCREAMING_SNAKE_CASE_			=	os.path.join(_UpperCAmelCase					,						'pytorch_model.bin'   )
			print(F'Saving checkpoint to \"{output_checkpoint_file}\"'   )
			torch.save(_UpperCAmelCase					,						_UpperCAmelCase   )


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__    :    int								=					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__    :    Union[str, Any]								=					parser.parse_args()
				main(_args)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE__    :    List[Any]								=					{"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				SCREAMING_SNAKE_CASE__    :    int								=					["DetrFeatureExtractor"]
				SCREAMING_SNAKE_CASE__    :    Any								=					["DetrImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				SCREAMING_SNAKE_CASE__    :    List[Any]								=					[
				    "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "DetrForObjectDetection",
				    "DetrForSegmentation",
				    "DetrModel",
				    "DetrPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_detr import DetrFeatureExtractor
								from .image_processing_detr import DetrImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_detr import (
								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DetrForObjectDetection,
								    DetrForSegmentation,
								    DetrModel,
								    DetrPreTrainedModel,
								)

else:
				import sys

				SCREAMING_SNAKE_CASE__    :    Tuple								=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
