
"""simple docstring"""


import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig






def      lowerCAmelCase_     (	snake_case_	:    int							,					snake_case_	:    Optional[Any]							,					snake_case_	:    str=0    )     ->Dict:
			# Format the message.
			if name is None:
						lowerCamelCase__			:  List[Any]  =None
			else:
						lowerCamelCase__			:  int  ='.' * max(0							,					spaces - 2    ) + '# {:' + str(5_0 - spaces    ) + 's}'
						lowerCamelCase__			:  Optional[Any]  =fmt.format(snake_case_    )

			# Print and recurse (if needed).
			if isinstance(snake_case_							,					snake_case_    ):
						if msg is not None:
									print(snake_case_    )
						for k in val.keys():
									recursive_print(snake_case_							,					val[k]							,					spaces + 2    )
			elif isinstance(snake_case_							,					torch.Tensor    ):
						print(snake_case_							,					':'							,					val.size()    )
			else:
						print(snake_case_							,					':'							,					snake_case_    )






def      lowerCAmelCase_     (	snake_case_	:    Union[str, Any]							,					snake_case_	:    str							,					snake_case_	:    Dict							,					snake_case_	:    List[str]    )     ->int:
			# 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.
			lowerCamelCase__			:  int  =param.size()
			# other versions store [num_heads * num_splits * hidden_size, :]
			lowerCamelCase__			:  Tuple  =(num_heads, num_splits, hidden_size) + input_shape[1:]
			lowerCamelCase__			:  Union[str, Any]  =param.view(*snake_case_    )
			lowerCamelCase__			:  List[str]  =param.transpose(0							,					1    ).contiguous()
			lowerCamelCase__			:  Optional[Any]  =param.view(*snake_case_    )
			return param






def      lowerCAmelCase_     (	snake_case_	:    int							,					snake_case_	:    Union[str, Any]    )     ->Union[str, Any]:
			lowerCamelCase__			:  int  =config.n_positions
			lowerCamelCase__			:  List[str]  =config.n_layer
			lowerCamelCase__			:  str  =config.vocab_size
			lowerCamelCase__			:  Tuple  =config.n_head
			lowerCamelCase__			:  List[str]  =config.n_embd // config.n_head

			lowerCamelCase__			:  List[Any]  =sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
			lowerCamelCase__			:  Optional[int]  ={
			    '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__			:  List[str]  ='model.language_model.encoder.layers.'
			for i in range(snake_case_    ):
						lowerCamelCase__			:  Tuple  =torch.tril(torch.ones((n_positions, n_positions)							,					dtype=torch.bool    )    )
						lowerCamelCase__			:  Optional[Any]  =causal_mask.view(1							,					1							,					snake_case_							,					snake_case_    )
						lowerCamelCase__			:  Union[str, Any]  =causal_mask
						lowerCamelCase__			:  int  =torch.tensor(-1E4							,					dtype=torch.bfloataa    )

						lowerCamelCase__			:  Optional[Any]  =sd_megatron[f"""{pf}{i}.input_layernorm.weight"""]
						lowerCamelCase__			:  Optional[Any]  =sd_megatron[f"""{pf}{i}.input_layernorm.bias"""]

						lowerCamelCase__			:  Optional[int]  =sd_megatron[f"""{pf}{i}.self_attention.query_key_value.weight"""]
						lowerCamelCase__			:  Union[str, Any]  =fix_query_key_value_ordering(snake_case_							,					3							,					snake_case_							,					snake_case_    )
						lowerCamelCase__			:  List[Any]  =vala.transpose(0							,					1    ).contiguous()

						lowerCamelCase__			:  Optional[Any]  =sd_megatron[f"""{pf}{i}.self_attention.query_key_value.bias"""]
						lowerCamelCase__			:  str  =fix_query_key_value_ordering(snake_case_							,					3							,					snake_case_							,					snake_case_    )
						lowerCamelCase__			:  Optional[Any]  =vala

						lowerCamelCase__			:  Dict  =sd_megatron[f"""{pf}{i}.self_attention.dense.weight"""].transpose(
						    0							,					1    )
						lowerCamelCase__			:  int  =sd_megatron[f"""{pf}{i}.self_attention.dense.bias"""]
						lowerCamelCase__			:  int  =sd_megatron[f"""{pf}{i}.post_attention_layernorm.weight"""]
						lowerCamelCase__			:  List[Any]  =sd_megatron[f"""{pf}{i}.post_attention_layernorm.bias"""]
						lowerCamelCase__			:  Dict  =sd_megatron[f"""{pf}{i}.mlp.dense_h_to_4h.weight"""].transpose(0							,					1    )
						lowerCamelCase__			:  int  =sd_megatron[f"""{pf}{i}.mlp.dense_h_to_4h.bias"""]
						lowerCamelCase__			:  List[Any]  =sd_megatron[f"""{pf}{i}.mlp.dense_4h_to_h.weight"""].transpose(
						    0							,					1    )
						lowerCamelCase__			:  Dict  =sd_megatron[f"""{pf}{i}.mlp.dense_4h_to_h.bias"""]

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

			return sd_hf






def      lowerCAmelCase_     (	snake_case_	:    Tuple							,					snake_case_	:    List[str]    )     ->str:
			lowerCamelCase__			:  Dict  =6_4_0_0_0
			lowerCamelCase__			:  int  =config_megatron['encoder_seq_length']
			lowerCamelCase__			:  Any  =config_megatron['hidden_size']
			lowerCamelCase__			:  Dict  =config_megatron['num_layers']
			lowerCamelCase__			:  Optional[Any]  =config_megatron['num_attention_heads']
			lowerCamelCase__			:  Optional[int]  =config_megatron['ffn_hidden_size']
			lowerCamelCase__			:  Tuple  ='gelu'
			lowerCamelCase__			:  List[str]  =0.1
			lowerCamelCase__			:  str  =0.1
			lowerCamelCase__			:  Union[str, Any]  =0.1
			lowerCamelCase__			:  Optional[int]  =config_megatron['layernorm_epsilon']  # 1e-5
			lowerCamelCase__			:  Tuple  =config_megatron['init_method_std']  # 0.02
			lowerCamelCase__			:  List[str]  =config_megatron['apply_query_key_layer_scaling']  # True
			lowerCamelCase__			:  Optional[int]  =True
			lowerCamelCase__			:  Union[str, Any]  =True

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

			return config_hf






def      lowerCAmelCase_     (	snake_case_	:    Dict    )     ->Dict:
			print(snake_case_    )

			lowerCamelCase__			:  Union[str, Any]  =args.checkpoint_path
			lowerCamelCase__			:  int  =args.save_path
			if isfile(snake_case_    ):
						raise FileNotFoundError(f"""ERROR! could not find file {checkpoint_path}"""    )

			# Load the model.
			lowerCamelCase__			:  Dict  =torch.load(snake_case_							,					map_location='cpu'    )

			# Load the config.
			lowerCamelCase__			:  List[Any]  =checkpoint['hyper_parameters']['cfg']
			lowerCamelCase__			:  Dict  =GPTaConfig()
			lowerCamelCase__			:  str  =copy_config(config_hf=snake_case_							,					config_megatron=snake_case_    )
			lowerCamelCase__			:  int  =['GPT2LMHeadModel']

			lowerCamelCase__			:  Optional[int]  =checkpoint['state_dict']

			# Convert.
			print('Converting'    )
			lowerCamelCase__			:  Any  =convert_megatron_checkpoint(snake_case_							,					snake_case_    )

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

			lowerCamelCase__			:  Optional[int]  ='GPTSw3Tokenizer'

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

			# Store the state_dict to file.
			lowerCamelCase__			:  Tuple  =os.path.join(snake_case_							,					'pytorch_model.bin'    )
			print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""    )
			torch.save(snake_case_							,					snake_case_    )


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)

"""simple docstring"""


from sklearn.neural_network import MLPClassifier

lowerCAmelCase			=		[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
lowerCAmelCase			=		[0, 1, 0, 0]


lowerCAmelCase			=		MLPClassifier(
    solver="""lbfgs""", alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


lowerCAmelCase			=		[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
lowerCAmelCase			=		clf.predict(test)






def      lowerCAmelCase_     (	snake_case_	:    Tuple    )     ->List[Any]:
			return list(snake_case_    )


if __name__ == "__main__":
							import doctest

							doctest.testmod()