



'''simple docstring'''





import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowercase_									=    data_utils.TransfoXLTokenizer
lowercase_									=    data_utils.TransfoXLCorpus
lowercase_									=    data_utils
lowercase_									=    data_utils




def lowerCAmelCase    (__A    ,      __A    ,      __A    ,      __A):



     """simple docstring"""



     if transfo_xl_dataset_file:
          # Convert a pre-processed corpus (see original TensorFlow repo)
          with open(__A    ,      '''rb''') as fp:
               _a           =    pickle.load(__A    ,      encoding='''latin1''')
          # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
          _a           =    pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
          print(F'''Save vocabulary to {pytorch_vocab_dump_path}''')
          _a           =    corpus.vocab.__dict__
          torch.save(__A    ,      __A)

          _a           =    corpus.__dict__
          corpus_dict_no_vocab.pop('''vocab'''    ,      __A)
          _a           =    pytorch_dump_folder_path + '''/''' + CORPUS_NAME
          print(F'''Save dataset to {pytorch_dataset_dump_path}''')
          torch.save(__A    ,      __A)

     if tf_checkpoint_path:
          # Convert a pre-trained TensorFlow model
          _a           =    os.path.abspath(__A)
          _a           =    os.path.abspath(__A)

          print(F'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.''')
          # Initialise PyTorch model
          if transfo_xl_config_file == "":
               _a           =    TransfoXLConfig()
          else:
               _a           =    TransfoXLConfig.from_json_file(__A)
          print(F'''Building PyTorch model from configuration: {config}''')
          _a           =    TransfoXLLMHeadModel(__A)

          _a           =    load_tf_weights_in_transfo_xl(__A    ,      __A    ,      __A)
          # Save pytorch-model
          _a           =    os.path.join(__A    ,      __A)
          _a           =    os.path.join(__A    ,      __A)
          print(F'''Save PyTorch model to {os.path.abspath(__A)}''')
          torch.save(model.state_dict()    ,      __A)
          print(F'''Save configuration file to {os.path.abspath(__A)}''')
          with open(__A    ,      '''w'''    ,      encoding='''utf-8''') as f:
               f.write(config.to_json_string())


if __name__ == "__main__":
  lowercase_									=    argparse.ArgumentParser()
  parser.add_argument(
      "--pytorch_dump_folder_path",
      default=None,
      type=str,
      required=True,
      help="Path to the folder to store the PyTorch model or dataset/vocab.",
  )
  parser.add_argument(
      "--tf_checkpoint_path",
      default="",
      type=str,
      help="An optional path to a TensorFlow checkpoint path to be converted.",
  )
  parser.add_argument(
      "--transfo_xl_config_file",
      default="",
      type=str,
      help=(
          "An optional config json file corresponding to the pre-trained BERT model. \n"
          "This specifies the model architecture."
      ),
  )
  parser.add_argument(
      "--transfo_xl_dataset_file",
      default="",
      type=str,
      help="An optional dataset file to be converted in a vocabulary.",
  )
  lowercase_									=    parser.parse_args()
  convert_transfo_xl_checkpoint_to_pytorch(
      args.tf_checkpoint_path,
      args.transfo_xl_config_file,
      args.pytorch_dump_folder_path,
      args.transfo_xl_dataset_file,
  )




'''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       (		UpperCamelCase__					: dict							):


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





def    lowerCAmelCase       (		UpperCamelCase__					: np.ndarray     ,       UpperCamelCase__					: np.ndarray     ,       UpperCamelCase__					: np.ndarray							):


	"""simple docstring"""
	__UpperCAmelCase      					=      XGBRegressor(verbosity=0     ,       random_state=4_2							)
	xgb.fit(UpperCamelCase__     ,       UpperCamelCase__							)
	# Predict target for test data
	__UpperCAmelCase      					=      xgb.predict(UpperCamelCase__							)
	__UpperCAmelCase      					=      predictions.reshape(len(UpperCamelCase__							)     ,       1							)
	return predictions





def    lowerCAmelCase       (		):


	"""simple docstring"""
	__UpperCAmelCase      					=      fetch_california_housing()
	__UpperCAmelCase					,      __UpperCAmelCase      					=      data_handling(UpperCamelCase__							)
	__UpperCAmelCase					,      __UpperCAmelCase					,      __UpperCAmelCase					,      __UpperCAmelCase      					=      train_test_split(
	    UpperCamelCase__     ,       UpperCamelCase__     ,       test_size=0.25     ,       random_state=1							)
	__UpperCAmelCase      					=      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()
