



'''simple docstring'''






import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize

class 	_lowerCAmelCase     :






		'''simple docstring'''
		def __init__(						self							:       Union[str, Any]      ,		UpperCamelCase_							:       int      ,		UpperCamelCase_							:       Union[str, Any]=sys.maxsize		)      ->			Optional[int]:







									'''simple docstring'''





									_lowercase							:		Optional[Any]				=   '''bilinear'''
									_lowercase							:		Optional[int]				=   max_size
									_lowercase							:		Tuple				=   short_edge_length





		def __call__(						self							:       Union[str, Any]      ,		UpperCamelCase_							:       int		)      ->			Dict:







									'''simple docstring'''





									_lowercase							:		Optional[Any]				=   []
									for img in imgs:
																_lowercase			, _lowercase							:		Dict				=   img.shape[:2]
																# later: provide list and randomly choose index for resize
																_lowercase							:		Optional[int]				=   np.random.randint(self.short_edge_length[0]      ,		self.short_edge_length[1] + 1		)
																if size == 0:
																							return img
																_lowercase							:		List[str]				=   size * 1.0 / min(UpperCAmelCase_      ,		UpperCAmelCase_		)
																if h < w:
																							_lowercase			, _lowercase							:		List[str]				=   size, scale * w
																else:
																							_lowercase			, _lowercase							:		Dict				=   scale * h, size
																if max(UpperCAmelCase_      ,		UpperCAmelCase_		) > self.max_size:
																							_lowercase							:		Optional[int]				=   self.max_size * 1.0 / max(UpperCAmelCase_      ,		UpperCAmelCase_		)
																							_lowercase							:		Any				=   newh * scale
																							_lowercase							:		List[str]				=   neww * scale
																_lowercase							:		Optional[Any]				=   int(neww + 0.5		)
																_lowercase							:		int				=   int(newh + 0.5		)

																if img.dtype == np.uinta:
																							_lowercase							:		Dict				=   Image.fromarray(UpperCAmelCase_		)
																							_lowercase							:		Optional[int]				=   pil_image.resize((neww, newh)      ,		PILImageResampling.BILINEAR		)
																							_lowercase							:		List[str]				=   np.asarray(UpperCAmelCase_		)
																else:
																							_lowercase							:		List[Any]				=   img.permute(2      ,		0      ,		1		).unsqueeze(0		)  # 3, 0, 1)  # hw(c) -> nchw
																							_lowercase							:		Dict				=   nn.functional.interpolate(
																							    UpperCAmelCase_      ,		(newh, neww)      ,		mode=self.interp_method      ,		align_corners=UpperCAmelCase_		).squeeze(0		)
																img_augs.append(UpperCAmelCase_		)

									return img_augs




class 	_lowerCAmelCase     :






		'''simple docstring'''
		def __init__(						self							:       Union[str, Any]      ,		UpperCamelCase_							:       Tuple		)      ->			Any:







									'''simple docstring'''





									_lowercase							:		Any				=   ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]      ,		cfg.INPUT.MAX_SIZE_TEST		)
									_lowercase							:		Any				=   cfg.INPUT.FORMAT
									_lowercase							:		str				=   cfg.SIZE_DIVISIBILITY
									_lowercase							:		Union[str, Any]				=   cfg.PAD_VALUE
									_lowercase							:		Union[str, Any]				=   cfg.INPUT.MAX_SIZE_TEST
									_lowercase							:		Optional[int]				=   cfg.MODEL.DEVICE
									_lowercase							:		Optional[int]				=   torch.tensor(cfg.MODEL.PIXEL_STD		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		)      ,		1      ,		1		)
									_lowercase							:		str				=   torch.tensor(cfg.MODEL.PIXEL_MEAN		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		)      ,		1      ,		1		)
									_lowercase							:		Optional[int]				=   lambda UpperCamelCase_		: (x - self.pixel_mean) / self.pixel_std
		def 						__lowercase      (						self							:       int      ,		UpperCamelCase_							:       Tuple		)      ->			int:







									'''simple docstring'''





									_lowercase							:		Union[str, Any]				=   tuple(max(UpperCAmelCase_		) for s in zip(*[img.shape for img in images]		)		)
									_lowercase							:		int				=   [im.shape[-2:] for im in images]
									_lowercase							:		Optional[int]				=   [
									    nn.functional.pad(
									        UpperCAmelCase_      ,		[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]      ,		value=self.pad_value      ,		)
									    for size, im in zip(UpperCAmelCase_      ,		UpperCAmelCase_		)
									]

									return torch.stack(UpperCAmelCase_		), torch.tensor(UpperCAmelCase_		)





		def __call__(						self							:       List[Any]      ,		UpperCamelCase_							:       Any      ,		UpperCamelCase_							:       Optional[Any]=False		)      ->			List[str]:







									'''simple docstring'''





									with torch.no_grad():
																if not isinstance(UpperCAmelCase_      ,		UpperCAmelCase_		):
																							_lowercase							:		Optional[Any]				=   [images]
																if single_image:
																							assert len(UpperCAmelCase_		) == 1
																for i in range(len(UpperCAmelCase_		)		):
																							if isinstance(images[i]      ,		torch.Tensor		):
																														images.insert(UpperCAmelCase_      ,		images.pop(UpperCAmelCase_		).to(self.device		).float()		)
																							elif not isinstance(images[i]      ,		torch.Tensor		):
																														images.insert(
																														    UpperCAmelCase_      ,		torch.as_tensor(img_tensorize(images.pop(UpperCAmelCase_		)      ,		input_format=self.input_format		)		)
																														    .to(self.device		)
																														    .float()      ,		)
            # resize smallest edge
																_lowercase							:		int				=   torch.tensor([im.shape[:2] for im in images]		)
																_lowercase							:		int				=   self.aug(UpperCAmelCase_		)
																# transpose images and convert to torch tensors
																# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
																# now normalize before pad to avoid useless arithmetic
																_lowercase							:		List[str]				=   [self.normalizer(UpperCAmelCase_		) for x in images]
																# now pad them to do the following operations
																_lowercase			, _lowercase							:		Union[str, Any]				=   self.pad(UpperCAmelCase_		)
																# Normalize

																if self.size_divisibility > 0:
																							raise NotImplementedError()
																# pad
																_lowercase							:		Any				=   torch.true_divide(UpperCAmelCase_      ,		UpperCAmelCase_		)
																if single_image:
																							return images[0], sizes[0], scales_yx[0]
																else:
																							return images, sizes, scales_yx





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     List[Any] ,       snake_case_    :     Any  )		->Dict:






							'''simple docstring'''
							boxes[:, 0::2] *= scale_yx[:, 1]
							boxes[:, 1::2] *= scale_yx[:, 0]
							return boxes





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     List[Any] ,       snake_case_    :     Tuple[int, int]  )		->Any:






							'''simple docstring'''
							assert torch.isfinite(a__  ).all(), "Box tensor contains infinite or NaN!"
							_lowercase			, _lowercase							:		List[str]				=   box_size
							tensor[:, 0].clamp_(min=0 ,       max=a__  )
							tensor[:, 1].clamp_(min=0 ,       max=a__  )
							tensor[:, 2].clamp_(min=0 ,       max=a__  )
							tensor[:, 3].clamp_(min=0 ,       max=a__  )






"""simple docstring"""

import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig





def 					__UpperCamelCase (     a__:			List[Any]			,						a__:			int			,						a__:			Dict=0      )					->      Any:




					"""simple docstring"""



					if name is None:
										__lowercase        =      None
					else:
										__lowercase        =      '''.''' * max(0			,						spaces - 2      ) + '''# {:''' + str(50 - spaces      ) + '''s}'''
										__lowercase        =      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__:			Optional[int]			,						a__:			Tuple			,						a__:			List[Any]			,						a__:			List[str]      )					->      str:




					"""simple docstring"""



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





def 					__UpperCamelCase (     a__:			Tuple			,						a__:			Optional[int]      )					->      List[str]:




					"""simple docstring"""



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

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

					__lowercase        =      '''model.language_model.encoder.layers.'''
					for i in range(a__      ):
										__lowercase        =      torch.tril(torch.ones((n_positions, n_positions)			,						dtype=torch.bool      )      )
										__lowercase        =      causal_mask.view(1			,						1			,						a__			,						a__      )
										__lowercase        =      causal_mask
										__lowercase        =      torch.tensor(-1e4			,						dtype=torch.bfloataa      )

										__lowercase        =      sd_megatron[f'''{pf}{i}.input_layernorm.weight''']
										__lowercase        =      sd_megatron[f'''{pf}{i}.input_layernorm.bias''']

										__lowercase        =      sd_megatron[f'''{pf}{i}.self_attention.query_key_value.weight''']
										__lowercase        =      fix_query_key_value_ordering(a__			,						3			,						a__			,						a__      )
										__lowercase        =      vala.transpose(0			,						1      ).contiguous()

										__lowercase        =      sd_megatron[f'''{pf}{i}.self_attention.query_key_value.bias''']
										__lowercase        =      fix_query_key_value_ordering(a__			,						3			,						a__			,						a__      )
										__lowercase        =      vala

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

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

					return sd_hf





def 					__UpperCamelCase (     a__:			Any			,						a__:			List[Any]      )					->      List[Any]:




					"""simple docstring"""



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

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

					return config_hf





def 					__UpperCamelCase (     a__:			Dict      )					->      Union[str, Any]:




					"""simple docstring"""



					print(a__      )

					__lowercase        =      args.checkpoint_path
					__lowercase        =      args.save_path
					if isfile(a__      ):
										raise FileNotFoundError(f'''ERROR! could not find file {checkpoint_path}'''      )

					# Load the model.
					__lowercase        =      torch.load(a__			,						map_location='''cpu'''      )

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

					__lowercase        =      checkpoint['''state_dict''']

					# Convert.
					print('''Converting'''      )
					__lowercase        =      convert_megatron_checkpoint(a__			,						a__      )

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

					__lowercase        =      '''GPTSw3Tokenizer'''

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

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


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


