



'''simple docstring'''






import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase:      Dict		 =		logging.get_logger(__name__)
def    lowerCamelCase__ (		_A       ):
  a		:      str       =  torch.load(_A				,    map_location='cpu'       )
  if "model" in sd.keys():
    a		:      Dict       =  torch.load(_A				,    map_location='cpu'       )['model']

  # pop unnecessary weights
  a		:      int       =  [
      'decoder.version',
      'decoder.output_projection.weight',
  ]
  for key in keys_to_delete:
    if key in sd:
      sd.pop(_A       )

  a		:      int       =  {
      'decoder.project_in_dim.weight': 'decoder.project_in.weight',
      'decoder.project_out_dim.weight': 'decoder.project_out.weight',
      'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
      'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
  }
  for old_key, new_key in keys_to_rename.items():
    if old_key in sd:
      a		:      Union[str, Any]       =  sd.pop(_A       )

  a		:      Optional[int]       =  list(sd.keys()       )
  for key in keys:
    if ".qkv_proj." in key:
      a		:      int       =  sd[key]
      # We split QKV in separate Q,K,V

      a		:      Optional[Any]       =  key.replace('.qkv_proj.'				,    '.q_proj.'       )
      a		:      List[str]       =  key.replace('.qkv_proj.'				,    '.k_proj.'       )
      a		:      Dict       =  key.replace('.qkv_proj.'				,    '.v_proj.'       )

      a		:      Dict       =  value.shape[0]
      assert depth % 3 == 0
      # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
      # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
      a     ,			a     ,			a		:      Dict       =  torch.split(_A				,    depth // 3				,    dim=0       )

      a		:      Dict       =  q
      a		:      Optional[Any]       =  k
      a		:      str       =  v
      del sd[key]

  return sd
@torch.no_grad()
def    lowerCamelCase__ (		_A				,    _A				,    _A=None       ):
  a		:      Any       =  load_checkpoint(_A       )

  if config is not None:
    a		:      Union[str, Any]       =  OPTConfig.from_pretrained(_A       )
  else:
    a		:      Dict       =  OPTConfig()

  a		:      int       =  OPTModel(_A       ).half().eval()
  model.load_state_dict(_A       )

  # Check results
  Path(_A       ).mkdir(exist_ok=_A       )
  model.save_pretrained(_A       )


if __name__ == "__main__":
     lowerCAmelCase:      Optional[int]		 =		argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--fairseq_path',
         type=str,
         help=(
             'path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'
             ' https://huggingface.co/models?other=opt_metasq'
         ),
     )
     parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
     parser.add_argument('--hf_config', default=None, type=str, help='Define HF config.')
     lowerCAmelCase:      Optional[int]		 =		parser.parse_args()
     convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)




'''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    lowerCamelCase__ (		_A       ):
  return (data["data"], data["target"])
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Any       =  XGBRegressor(verbosity=0				,    random_state=42       )
  xgb.fit(_A				,    _A       )
  # Predict target for test data
  a		:      Any       =  xgb.predict(_A       )
  a		:      Any       =  predictions.reshape(len(_A       )				,    1       )
  return predictions
def    lowerCamelCase__ (		):
  a		:      Optional[int]       =  fetch_california_housing()
  a     ,			a		:      Optional[Any]       =  data_handling(_A       )
  a     ,			a     ,			a     ,			a		:      Dict       =  train_test_split(
      _A				,    _A				,    test_size=0.25				,    random_state=1       )
  a		:      List[str]       =  xgboost(_A				,    _A				,    _A       )
  # Error printing
  print(f"""Mean Absolute Error : {mean_absolute_error(_A				,    _A       )}"""       )
  print(f"""Mean Square Error  : {mean_squared_error(_A				,    _A       )}"""       )


if __name__ == "__main__":
     import doctest

     doctest.testmod(verbose=True)
     main()