
import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowerCamelCase_						     =					50_00_00

lowerCamelCase_     ,					lowerCamelCase_						     =					os.path.split(__file__)
lowerCamelCase_						     =					os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))






@get_duration
def 						__magic_name__			(   __a      :	datasets.Dataset	,		**__a      :	Optional[int]						):



     '''simple docstring'''




     UpperCamelCase__				=    dataset.map(**_lowerCamelCase						)






@get_duration
def 						__magic_name__			(   __a      :	datasets.Dataset	,		**__a      :	Union[str, Any]						):



     '''simple docstring'''




     UpperCamelCase__				=    dataset.filter(**_lowerCamelCase						)






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    {"num examples": SPEED_TEST_N_EXAMPLES}
     with tempfile.TemporaryDirectory() as tmp_dir:
          UpperCamelCase__				=    datasets.Features({"""text""": datasets.Value("""string"""						), """numbers""": datasets.Value("""float32"""						)}						)
          UpperCamelCase__				=    generate_example_dataset(
              os.path.join(_lowerCamelCase	,		"""dataset.arrow"""						)	,		_lowerCamelCase	,		num_examples=_lowerCamelCase						)

          UpperCamelCase__				=    transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""	,		use_fast=_lowerCamelCase						)

          def tokenize(__a      :	Optional[int]						):
               return tokenizer(examples["""text"""]						)

          UpperCamelCase__				=    map(_lowerCamelCase						)

          UpperCamelCase__				=    map(_lowerCamelCase	,		batched=_lowerCamelCase						)

          UpperCamelCase__				=    map(_lowerCamelCase	,		function=lambda __a						: None	,		batched=_lowerCamelCase						)

          with dataset.formatted_as(type="""numpy"""						):
               UpperCamelCase__				=    map(_lowerCamelCase	,		function=lambda __a						: None	,		batched=_lowerCamelCase						)

          with dataset.formatted_as(type="""pandas"""						):
               UpperCamelCase__				=    map(_lowerCamelCase	,		function=lambda __a						: None	,		batched=_lowerCamelCase						)

          with dataset.formatted_as(type="""torch"""	,		columns="""numbers"""						):
               UpperCamelCase__				=    map(_lowerCamelCase	,		function=lambda __a						: None	,		batched=_lowerCamelCase						)

          with dataset.formatted_as(type="""tensorflow"""	,		columns="""numbers"""						):
               UpperCamelCase__				=    map(_lowerCamelCase	,		function=lambda __a						: None	,		batched=_lowerCamelCase						)

          UpperCamelCase__				=    map(_lowerCamelCase	,		function=_lowerCamelCase	,		batched=_lowerCamelCase						)

          UpperCamelCase__				=    filter(_lowerCamelCase						)

          # Activate later when tokenizer support batched inputs
          # with dataset.formatted_as(type='numpy'):
          #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

     with open(_lowerCamelCase	,		"""wb"""						) as f:
          f.write(json.dumps(_lowerCamelCase						).encode("""utf-8"""						)						)


if __name__ == "__main__":  # useful to run the profiler
    benchmark_map_filter()







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 						__magic_name__			(   __a      :	dict						):



     '''simple docstring'''




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






def 						__magic_name__			(   __a      :	np.ndarray	,		__a      :	np.ndarray	,		__a      :	np.ndarray						):



     '''simple docstring'''




     UpperCamelCase__				=    XGBRegressor(verbosity=0	,		random_state=42						)
     xgb.fit(__a	,		__a						)
     # Predict target for test data
     UpperCamelCase__				=    xgb.predict(__a						)
     UpperCamelCase__				=    predictions.reshape(len(__a						)	,		1						)
     return predictions






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    fetch_california_housing()
     UpperCamelCase__		,	UpperCamelCase__				=    data_handling(__a						)
     UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    train_test_split(
         __a	,		__a	,		test_size=0.25	,		random_state=1						)
     UpperCamelCase__				=    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()





