





import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig

def      a    (   _lowercase :  Optional[int]			,    _lowercase :  List[Any]			,    _lowercase :  Any=0							)  ->Dict:






		if name is None:
				UpperCamelCase__								=						None
		else:
				UpperCamelCase__								=						'''.''' * max(0			,    spaces - 2							) + '''# {:''' + str(5_0 - spaces							) + '''s}'''
				UpperCamelCase__								=						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    (   _lowercase :  str			,    _lowercase :  List[str]			,    _lowercase :  Optional[int]			,    _lowercase :  str							)  ->List[Any]:
		UpperCamelCase__								=						param.size()
		# other versions store [num_heads * num_splits * hidden_size, :]
		UpperCamelCase__								=						(num_heads, num_splits, hidden_size) + input_shape[1:]
		UpperCamelCase__								=						param.view(*__A							)
		UpperCamelCase__								=						param.transpose(0			,    1							).contiguous()
		UpperCamelCase__								=						param.view(*__A							)
		return param

def      a    (   _lowercase :  List[str]			,    _lowercase :  Union[str, Any]							)  ->Optional[Any]:
		UpperCamelCase__								=						config.n_positions
		UpperCamelCase__								=						config.n_layer
		UpperCamelCase__								=						config.vocab_size
		UpperCamelCase__								=						config.n_head
		UpperCamelCase__								=						config.n_embd // config.n_head

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

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

				UpperCamelCase__								=						sd_megatron[F"""{pf}{i}.input_layernorm.weight"""]
				UpperCamelCase__								=						sd_megatron[F"""{pf}{i}.input_layernorm.bias"""]

				UpperCamelCase__								=						sd_megatron[F"""{pf}{i}.self_attention.query_key_value.weight"""]
				UpperCamelCase__								=						fix_query_key_value_ordering(__A			,    3			,    __A			,    __A							)
				UpperCamelCase__								=						vala.transpose(0			,    1							).contiguous()

				UpperCamelCase__								=						sd_megatron[F"""{pf}{i}.self_attention.query_key_value.bias"""]
				UpperCamelCase__								=						fix_query_key_value_ordering(__A			,    3			,    __A			,    __A							)
				UpperCamelCase__								=						vala

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

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

		return sd_hf

def      a    (   _lowercase :  Optional[Any]			,    _lowercase :  str							)  ->Union[str, Any]:
		UpperCamelCase__								=						6_4_0_0_0
		UpperCamelCase__								=						config_megatron['''encoder_seq_length''']
		UpperCamelCase__								=						config_megatron['''hidden_size''']
		UpperCamelCase__								=						config_megatron['''num_layers''']
		UpperCamelCase__								=						config_megatron['''num_attention_heads''']
		UpperCamelCase__								=						config_megatron['''ffn_hidden_size''']
		UpperCamelCase__								=						'''gelu'''
		UpperCamelCase__								=						0.1
		UpperCamelCase__								=						0.1
		UpperCamelCase__								=						0.1
		UpperCamelCase__								=						config_megatron['''layernorm_epsilon''']  # 1e-5
		UpperCamelCase__								=						config_megatron['''init_method_std''']  # 0.02
		UpperCamelCase__								=						config_megatron['''apply_query_key_layer_scaling''']  # True
		UpperCamelCase__								=						True
		UpperCamelCase__								=						True

		# This identifies the 6.7B (7B) model which uses a different tokenizer
		if config_megatron["hidden_size"] == 4_0_9_6:
				UpperCamelCase__								=						1  # <|endoftext|>
				UpperCamelCase__								=						1  # <|endoftext|>
				UpperCamelCase__								=						0  # <unk>
		else:
				UpperCamelCase__								=						2  # <s>
				UpperCamelCase__								=						3  # <|endoftext|>
				UpperCamelCase__								=						0  # <pad>

		return config_hf

def      a    (   _lowercase :  Union[str, Any]							)  ->int:
		print(__A							)

		UpperCamelCase__								=						args.checkpoint_path
		UpperCamelCase__								=						args.save_path
		if isfile(__A							):
				raise FileNotFoundError(F"""ERROR! could not find file {checkpoint_path}"""							)

		# Load the model.
		UpperCamelCase__								=						torch.load(__A			,    map_location="""cpu"""							)

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

		UpperCamelCase__								=						checkpoint['''state_dict''']

		# Convert.
		print("""Converting"""							)
		UpperCamelCase__								=						convert_megatron_checkpoint(__A			,    __A							)

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

		UpperCamelCase__								=						'''GPTSw3Tokenizer'''

		# Store the config to file.
		print("""Saving config"""							)
		config_hf.save_pretrained(__A							)

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


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)











def 		lowercase__ (		__A:						list[list[int]]					):





					'''simple docstring'''






					if len(__A					) == 0 or not all(len(__A					) == 2 for pair in coordinates					):
										raise ValueError('''The program cannot work out a fitting polynomial.'''					)

					if len({tuple(__A					) for pair in coordinates}					) != len(__A					):
										raise ValueError('''The program cannot work out a fitting polynomial.'''					)

					__magic_name__				:						Union[str, Any]     				=				{x for x, _ in coordinates}
					if len(__A					) == 1:
										return F'''x={coordinates[0][0]}'''

					if len(__A					) != len(__A					):
										raise ValueError('''The program cannot work out a fitting polynomial.'''					)

					__magic_name__				:						List[Any]     				=				len(__A					)

					__magic_name__				:						int     				=				0
					__magic_name__				:						list[list[float]]     				=				[]
					# put the x and x to the power values in a matrix
					while count_of_line < x:
										__magic_name__				:						List[Any]     				=				0
										__magic_name__				:						List[str]     				=				coordinates[count_of_line][0]
										__magic_name__				:						list[float]     				=				[]
										while count_in_line < x:
															count_line.append(a ** (x - (count_in_line + 1))					)
															count_in_line += 1
										matrix.append(__A					)
										count_of_line += 1

					__magic_name__				:						Union[str, Any]     				=				0
					# put the y values into a vector
					__magic_name__				:						list[float]     				=				[]
					while count_of_line < x:
										vector.append(coordinates[count_of_line][1]					)
										count_of_line += 1

					__magic_name__				:						int     				=				0

					while count < x:
										__magic_name__				:						int     				=				0
										while zahlen < x:
															if count == zahlen:
																				zahlen += 1
															if zahlen == x:
																				break
															__magic_name__				:						Tuple     				=				matrix[zahlen][count] / matrix[count][count]
															for counting_columns, item in enumerate(matrix[count]					):
																				# manipulating all the values in the matrix
																				matrix[zahlen][counting_columns] -= item * bruch
															# manipulating the values in the vector
															vector[zahlen] -= vector[count] * bruch
															zahlen += 1
										count += 1

					__magic_name__				:						str     				=				0
					# make solutions
					__magic_name__				:						list[str]     				=				[]
					while count < x:
										solution.append(str(vector[count] / matrix[count][count]					)					)
										count += 1

					__magic_name__				:						List[Any]     				=				0
					__magic_name__				:						Union[str, Any]     				=				'''f(x)='''

					while count < x:
										__magic_name__				:						list[str]     				=				solution[count].split('''E'''					)
										if len(__A					) > 1:
															__magic_name__				:						Union[str, Any]     				=				F'''{remove_e[0]}*10^{remove_e[1]}'''
										solved += F'''x^{x - (count + 1)}*{solution[count]}'''
										if count + 1 != x:
															solved += "+"
										count += 1

					return solved


if __name__ == "__main__":
	print(points_to_polynomial([]))
	print(points_to_polynomial([[]]))
	print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
	print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
	print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
	print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
	print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
	print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
	print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
	print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))



