






import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig
def      lowerCamelCase					(						a_ ,						a_ ,						a_=0	)	->			Tuple:



							if name is None:
														lowerCAmelCase_										=     None
							else:
														lowerCAmelCase_										=     "." * max(0 ,						spaces - 2	) + "# {:" + str(50 - spaces	) + "s}"
														lowerCAmelCase_										=     fmt.format(__lowercase	)

							# Print and recurse (if needed).
							if isinstance(__lowercase ,						__lowercase	):
														if msg is not None:
																					print(__lowercase	)
														for k in val.keys():
																					recursive_print(__lowercase ,						val[k] ,						spaces + 2	)
							elif isinstance(__lowercase ,						torch.Tensor	):
														print(__lowercase ,						':' ,						val.size()	)
							else:
														print(__lowercase ,						':' ,						__lowercase	)
def      lowerCamelCase					(						a_ ,						a_ ,						a_ ,						a_	)	->			Union[str, Any]:
							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(*__lowercase	)
							lowerCAmelCase_										=     param.transpose(0 ,						1	).contiguous()
							lowerCAmelCase_										=     param.view(*__lowercase	)
							return param
def      lowerCamelCase					(						a_ ,						a_	)	->			Dict:
							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(__lowercase	):
														lowerCAmelCase_										=     torch.tril(torch.ones((n_positions, n_positions) ,						dtype=torch.bool	)	)
														lowerCAmelCase_										=     causal_mask.view(1 ,						1 ,						__lowercase ,						__lowercase	)
														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(__lowercase ,						3 ,						__lowercase ,						__lowercase	)
														lowerCAmelCase_										=     vala.transpose(0 ,						1	).contiguous()

														lowerCAmelCase_										=     sd_megatron[F'''{pf}{i}.self_attention.query_key_value.bias''']
														lowerCAmelCase_										=     fix_query_key_value_ordering(__lowercase ,						3 ,						__lowercase ,						__lowercase	)
														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      lowerCamelCase					(						a_ ,						a_	)	->			str:
							lowerCAmelCase_										=     64_000
							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"] == 4_096:
														lowerCAmelCase_										=     1  # <|endoftext|>
														lowerCAmelCase_										=     1  # <|endoftext|>
														lowerCAmelCase_										=     0  # <unk>
							else:
														lowerCAmelCase_										=     2  # <s>
														lowerCAmelCase_										=     3  # <|endoftext|>
														lowerCAmelCase_										=     0  # <pad>

							return config_hf
def      lowerCamelCase					(						a_	)	->			Optional[Any]:
							print(__lowercase	)

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

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

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

							lowerCAmelCase_										=     checkpoint["state_dict"]

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

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

							lowerCAmelCase_										=     "GPTSw3Tokenizer"

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

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


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




from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
__SCREAMING_SNAKE_CASE						:    Optional[int]     = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
def 					snake_case						(__lowercase						)       ->       list[int]:

						'''simple docstring'''



						return [int(__lowercase						) for c in F"""{ruleset:08}"""[:8]]
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase						)       ->       list[int]:

						'''simple docstring'''



						_snake_case						:     Any				=    len(cells[0]						)  # 31
						_snake_case						:     str				=    []
						for i in range(__lowercase						):
												# Get the neighbors of each cell
												# Handle neighbours outside bounds by using 0 as their value
												_snake_case						:     Any				=    0 if i == 0 else cells[time][i - 1]
												_snake_case						:     List[str]				=    0 if i == population - 1 else cells[time][i + 1]
												# Define a new cell and add it to the new generation
												_snake_case						:     str				=    7 - int(F"""{left_neighbor}{cells[time][i]}{right_neighbor}"""					,			2						)
												next_generation.append(rule[situation]						)
						return next_generation
def 					snake_case						(__lowercase						)       ->       Image.Image:

						'''simple docstring'''



						_snake_case						:     str				=    Image.new("RGB"					,			(len(cells[0]						), len(__lowercase						))						)
						_snake_case						:     int				=    img.load()
						# Generates image
						for w in range(img.width						):
												for h in range(img.height						):
																		_snake_case						:     List[Any]				=    255 - int(255 * cells[h][w]						)
																		_snake_case						:     Union[str, Any]				=    (color, color, color)
						return img


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:    Any     = bin(int(input('Rule:\n').strip()))[2:]
				__SCREAMING_SNAKE_CASE						:    Tuple     = format_ruleset(int(rule_num))
				for time in range(1_6):
								CELLS.append(new_generation(CELLS, rule, time))
				__SCREAMING_SNAKE_CASE						:    List[Any]     = generate_image(CELLS)
				# Uncomment to save the image
				# img.save(f"rule_{rule_num}.png")
				img.show()