
'''simple docstring'''
import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig







def 						A_(			A		:						List[str]						,			A		:						Optional[Any]						,			A		:						str=0):
							# Format the message.
							if name is None:
														UpperCamelCase					       =			None
							else:
														UpperCamelCase					       =			'.' * max(0						,			spaces - 2) + '# {:' + str(50 - 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_(			A		:						Union[str, Any]						,			A		:						Dict						,			A		:						Union[str, Any]						,			A		:						Optional[Any]):
							# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
							# for compatibility with later versions of NVIDIA Megatron-LM.
							# The inverse operation is performed inside Megatron-LM to read checkpoints:
							# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
							# If param is the weight tensor of the self-attention block, the returned tensor
							# will have to be transposed one more time to be read by HuggingFace GPT2.
							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_(			A		:						List[str]						,			A		:						Union[str, 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_(			A		:						Dict						,			A		:						Optional[int]):
							UpperCamelCase					       =			6_4000
							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"] == 4096:
														UpperCamelCase					       =			1  # <|endoftext|>
														UpperCamelCase					       =			1  # <|endoftext|>
														UpperCamelCase					       =			0  # <unk>
							else:
														UpperCamelCase					       =			2  # <s>
														UpperCamelCase					       =			3  # <|endoftext|>
														UpperCamelCase					       =			0  # <pad>

							return config_hf







def 						A_(			A		:						List[Any]):
							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 :  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')
						lowerCAmelCase :  str      =      parser.parse_args()
						main(_args)






'''simple docstring'''
def 						A_(			A		:						list[list[int]]):
							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.')

							UpperCamelCase					       =			{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.')

							UpperCamelCase					       =			len(A)

							UpperCamelCase					       =			0
							UpperCamelCase					       =			[]
							# put the x and x to the power values in a matrix
							while count_of_line < x:
														UpperCamelCase					       =			0
														UpperCamelCase					       =			coordinates[count_of_line][0]
														UpperCamelCase					       =			[]
														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

							UpperCamelCase					       =			0
							# put the y values into a vector
							UpperCamelCase					       =			[]
							while count_of_line < x:
														vector.append(coordinates[count_of_line][1])
														count_of_line += 1

							UpperCamelCase					       =			0

							while count < x:
														UpperCamelCase					       =			0
														while zahlen < x:
																					if count == zahlen:
																												zahlen += 1
																					if zahlen == x:
																												break
																					UpperCamelCase					       =			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

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

							UpperCamelCase					       =			0
							UpperCamelCase					       =			'f(x)='

							while count < x:
														UpperCamelCase					       =			solution[count].split('E')
														if len(A) > 1:
																					UpperCamelCase					       =			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]]))




