



'''simple docstring'''

import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor







def a_     (    __UpperCAmelCase					)				->					tuple:





			"""simple docstring"""







			return (data["data"], data["target"])







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					np.ndarray:





			"""simple docstring"""







			snake_case:    int      							=XGBRegressor(verbosity=0				,							random_state=42					)
			xgb.fit(__UpperCAmelCase				,							__UpperCAmelCase					)
			# Predict target for test data
			snake_case:    List[str]      							=xgb.predict(__UpperCAmelCase					)
			snake_case:    Tuple      							=predictions.reshape(len(__UpperCAmelCase					)				,							1					)
			return predictions







def a_     (    )				->					None:





			"""simple docstring"""







			snake_case:    List[str]      							=fetch_california_housing()
			snake_case							,  snake_case:    Optional[int]      							=data_handling(__UpperCAmelCase					)
			snake_case							,  snake_case							,  snake_case							,  snake_case:    Dict      							=train_test_split(
			    __UpperCAmelCase				,							__UpperCAmelCase				,							test_size=0.25				,							random_state=1					)
			snake_case:    List[str]      							=xgboost(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)
			# Error printing
			print(f'''Mean Absolute Error : {mean_absolute_error(__UpperCAmelCase				,							__UpperCAmelCase					)}'''					)
			print(f'''Mean Square Error  : {mean_squared_error(__UpperCAmelCase				,							__UpperCAmelCase					)}'''					)


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()









'''simple docstring'''

# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase=0					)				->					Dict:





			"""simple docstring"""







			if name is None:
						snake_case:    Any      							=None
			else:
						snake_case:    Any      							='.' * max(0				,							spaces - 2					) + '# {:' + str(50 - spaces					) + 's}'
						snake_case:    Optional[int]      							=fmt.format(__UpperCAmelCase					)

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







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    Any      							=param.size()
			if checkpoint_version == 1.0:
						# version 1.0 stores [num_heads * hidden_size * num_splits, :]
						snake_case:    Tuple      							=(num_heads, hidden_size, num_splits) + input_shape[1:]
						snake_case:    Tuple      							=param.view(*__UpperCAmelCase					)
						snake_case:    List[Any]      							=param.transpose(0				,							2					)
						snake_case:    Union[str, Any]      							=param.transpose(1				,							2					).contiguous()
			elif checkpoint_version >= 2.0:
						# other versions store [num_heads * num_splits * hidden_size, :]
						snake_case:    Any      							=(num_heads, num_splits, hidden_size) + input_shape[1:]
						snake_case:    str      							=param.view(*__UpperCAmelCase					)
						snake_case:    Optional[Any]      							=param.transpose(0				,							1					).contiguous()
			snake_case:    Any      							=param.view(*__UpperCAmelCase					)
			return param







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    Optional[Any]      							={}

			# old versions did not store training args
			snake_case:    Dict      							=input_state_dict.get('args'				,							__UpperCAmelCase					)
			if ds_args is not None:
						# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
						# from pprint import pprint
						# pprint(vars(ds_args))

						snake_case:    List[Any]      							=ds_args.padded_vocab_size
						snake_case:    List[Any]      							=ds_args.max_position_embeddings
						snake_case:    str      							=ds_args.hidden_size
						snake_case:    Any      							=ds_args.num_layers
						snake_case:    Dict      							=ds_args.num_attention_heads
						snake_case:    Dict      							=ds_args.ffn_hidden_size
						# pprint(config)

			# The number of heads.
			snake_case:    Any      							=config.n_head
			# The hidden_size per head.
			snake_case:    Union[str, Any]      							=config.n_embd // config.n_head
			# Megatron-LM checkpoint version
			if "checkpoint_version" in input_state_dict.keys():
						snake_case:    Any      							=input_state_dict['checkpoint_version']
			else:
						snake_case:    Optional[int]      							=0.0

			# The model.
			snake_case:    List[str]      							=input_state_dict['model']
			# The language model.
			snake_case:    List[Any]      							=model['language_model']
			# The embeddings.
			snake_case:    Union[str, Any]      							=lm['embedding']

			# The word embeddings.
			snake_case:    List[Any]      							=embeddings['word_embeddings']['weight']
			# Truncate the embedding table to vocab_size rows.
			snake_case:    Dict      							=word_embeddings[: config.vocab_size, :]
			snake_case:    List[str]      							=word_embeddings

			# The position embeddings.
			snake_case:    str      							=embeddings['position_embeddings']['weight']
			# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
			snake_case:    Dict      							=pos_embeddings.size(0					)
			if n_positions != config.n_positions:
						raise ValueError(
						    f'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match'''					)
			# Store the position embeddings.
			snake_case:    Any      							=pos_embeddings

			# The transformer.
			snake_case:    Union[str, Any]      							=lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

			# The regex to extract layer names.
			snake_case:    Union[str, Any]      							=re.compile(R'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'					)

			# The simple map of names for "automated" rules.
			snake_case:    List[str]      							={
			    'attention.dense': '.attn.c_proj.',
			    'self_attention.dense': '.attn.c_proj.',
			    'mlp.dense_h_to_4h': '.mlp.c_fc.',
			    'mlp.dense_4h_to_h': '.mlp.c_proj.',
			}

			# Extract the layers.
			for key, val in transformer.items():
						# Match the name.
						snake_case:    Union[str, Any]      							=layer_re.match(__UpperCAmelCase					)

						# Stop if that's not a layer
						if m is None:
									break

						# The index of the layer.
						snake_case:    str      							=int(m.group(1					)					)
						# The name of the operation.
						snake_case:    Optional[Any]      							=m.group(2					)
						# Is it a weight or a bias?
						snake_case:    Any      							=m.group(3					)

						# The name of the layer.
						snake_case:    Tuple      							=f'''transformer.h.{layer_idx}'''

						# For layernorm(s), simply store the layer norm.
						if op_name.endswith('layernorm'					):
									snake_case:    Union[str, Any]      							='ln_1' if op_name.startswith('input'					) else 'ln_2'
									snake_case:    List[str]      							=val

						# Transpose the QKV matrix.
						elif (
						    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
						) and weight_or_bias == "weight":
									# Insert a tensor of 1x1xDxD bias.
									snake_case:    Optional[Any]      							=torch.tril(torch.ones((n_positions, n_positions)				,							dtype=torch.floataa					)					).view(
									    1				,							1				,							__UpperCAmelCase				,							__UpperCAmelCase					)
									snake_case:    int      							=causal_mask

									# Insert a "dummy" tensor for masked_bias.
									snake_case:    Dict      							=torch.tensor(-1e4				,							dtype=torch.floataa					)
									snake_case:    Optional[Any]      							=masked_bias

									snake_case:    Dict      							=fix_query_key_value_ordering(__UpperCAmelCase				,							__UpperCAmelCase				,							3				,							__UpperCAmelCase				,							__UpperCAmelCase					)
									# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
									snake_case:    Dict      							=out_val.transpose(0				,							1					).contiguous()
									# Store.
									snake_case:    Optional[Any]      							=out_val

						# Transpose the bias.
						elif (
						    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
						) and weight_or_bias == "bias":
									snake_case:    Dict      							=fix_query_key_value_ordering(__UpperCAmelCase				,							__UpperCAmelCase				,							3				,							__UpperCAmelCase				,							__UpperCAmelCase					)
									# Store. No change of shape.
									snake_case:    str      							=out_val

						# Transpose the weights.
						elif weight_or_bias == "weight":
									snake_case:    Optional[int]      							=megatron_to_transformers[op_name]
									snake_case:    str      							=val.transpose(0				,							1					)

						# Copy the bias.
						elif weight_or_bias == "bias":
									snake_case:    int      							=megatron_to_transformers[op_name]
									snake_case:    Dict      							=val

    # DEBUG.
			assert config.n_layer == layer_idx + 1

			# The final layernorm.
			snake_case:    Optional[int]      							=transformer['final_layernorm.weight']
			snake_case:    Optional[Any]      							=transformer['final_layernorm.bias']

			# For LM head, transformers' wants the matrix to weight embeddings.
			snake_case:    Union[str, Any]      							=word_embeddings

			# It should be done!
			return output_state_dict







def a_     (    )				->					Tuple:





			"""simple docstring"""







			snake_case:    List[str]      							=argparse.ArgumentParser()
			parser.add_argument('--print-checkpoint-structure'				,							action='store_true'					)
			parser.add_argument(
			    'path_to_checkpoint'				,							type=__UpperCAmelCase				,							help='Path to the checkpoint file (.zip archive or direct .pt file)'				,							)
			parser.add_argument(
			    '--config_file'				,							default=''				,							type=__UpperCAmelCase				,							help='An optional config json file describing the pre-trained model.'				,							)
			snake_case:    List[Any]      							=parser.parse_args()

			# Extract the basename.
			snake_case:    Any      							=os.path.dirname(args.path_to_checkpoint					)

			# Load the model.
			# the .zip is very optional, let's keep it for backward compatibility
			print(f'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}'''					)
			if args.path_to_checkpoint.endswith('.zip'					):
						with zipfile.ZipFile(args.path_to_checkpoint				,							'r'					) as checkpoint:
									with checkpoint.open('release/mp_rank_00/model_optim_rng.pt'					) as pytorch_dict:
												snake_case:    List[Any]      							=torch.load(__UpperCAmelCase				,							map_location='cpu'					)
			else:
						snake_case:    Dict      							=torch.load(args.path_to_checkpoint				,							map_location='cpu'					)

			snake_case:    Optional[Any]      							=input_state_dict.get('args'				,							__UpperCAmelCase					)

			# Read the config, or default to the model released by NVIDIA.
			if args.config_file == "":
						if ds_args is not None:
									if ds_args.bias_gelu_fusion:
												snake_case:    List[Any]      							='gelu_fast'
									elif ds_args.openai_gelu:
												snake_case:    Optional[int]      							='gelu_new'
									else:
												snake_case:    Any      							='gelu'
						else:
									# in the very early days this used to be "gelu_new"
									snake_case:    Dict      							='gelu_new'

						# Spell out all parameters in case the defaults change.
						snake_case:    Union[str, Any]      							=GPTaConfig(
						    vocab_size=5_02_57				,							n_positions=10_24				,							n_embd=10_24				,							n_layer=24				,							n_head=16				,							n_inner=40_96				,							activation_function=__UpperCAmelCase				,							resid_pdrop=0.1				,							embd_pdrop=0.1				,							attn_pdrop=0.1				,							layer_norm_epsilon=1e-5				,							initializer_range=0.02				,							summary_type='cls_index'				,							summary_use_proj=__UpperCAmelCase				,							summary_activation=__UpperCAmelCase				,							summary_proj_to_labels=__UpperCAmelCase				,							summary_first_dropout=0.1				,							scale_attn_weights=__UpperCAmelCase				,							use_cache=__UpperCAmelCase				,							bos_token_id=5_02_56				,							eos_token_id=5_02_56				,							)
			else:
						snake_case:    Optional[Any]      							=GPTaConfig.from_json_file(args.config_file					)

			snake_case:    int      							=['GPT2LMHeadModel']

			# Convert.
			print('Converting'					)
			snake_case:    str      							=convert_megatron_checkpoint(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)

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

			# Add tokenizer class info to config
			# see https://github.com/huggingface/transformers/issues/13906)
			if ds_args is not None:
						snake_case:    Dict      							=ds_args.tokenizer_type
						if tokenizer_type == "GPT2BPETokenizer":
									snake_case:    Tuple      							='gpt2'
						elif tokenizer_type == "PretrainedFromHF":
									snake_case:    int      							=ds_args.tokenizer_name_or_path
						else:
									raise ValueError(f'''Unrecognized tokenizer_type {tokenizer_type}'''					)
			else:
						snake_case:    Optional[Any]      							='gpt2'

			snake_case:    List[Any]      							=AutoTokenizer.from_pretrained(__UpperCAmelCase					)
			snake_case:    Any      							=type(__UpperCAmelCase					).__name__
			snake_case:    Optional[Any]      							=tokenizer_class

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

			# Save tokenizer based on args
			print(f'''Adding {tokenizer_class} tokenizer files'''					)
			tokenizer.save_pretrained(__UpperCAmelCase					)

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


####################################################################################################

if __name__ == "__main__":
	main()

####################################################################################################




