


import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


__snake_case     =		get_logger(__name__)

# Datasets
__snake_case     =		'''https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets'''
__snake_case     =		'''https://cdn-datasets.huggingface.co/datasets/datasets'''
__snake_case     =		'''https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}'''

# Metrics
__snake_case     =		'''https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics'''
__snake_case     =		'''https://cdn-datasets.huggingface.co/datasets/metric'''
__snake_case     =		'''https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}'''

# Hub
__snake_case     =		os.environ.get('''HF_ENDPOINT''', '''https://huggingface.co''')
__snake_case     =		HF_ENDPOINT + '''/datasets/{repo_id}/resolve/{revision}/{path}'''
__snake_case     =		'''main'''

__snake_case     =		version.parse(platform.python_version())

if PY_VERSION < version.parse('''3.8'''):
    import importlib_metadata
else:
    import importlib.metadata as importlib_metadata

# General environment variables accepted values for booleans
__snake_case     =		{'''1''', '''ON''', '''YES''', '''TRUE'''}
__snake_case     =		ENV_VARS_TRUE_VALUES.union({'''AUTO'''})


# Imports
__snake_case     =		version.parse(importlib_metadata.version('''dill'''))
__snake_case     =		version.parse(importlib_metadata.version('''pandas'''))
__snake_case     =		version.parse(importlib_metadata.version('''pyarrow'''))

__snake_case     =		os.environ.get('''USE_TF''', '''AUTO''').upper()
__snake_case     =		os.environ.get('''USE_TORCH''', '''AUTO''').upper()
__snake_case     =		os.environ.get('''USE_JAX''', '''AUTO''').upper()

__snake_case     =		'''N/A'''
__snake_case     =		False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
    __snake_case     =		importlib.util.find_spec('''torch''') is not None
    if TORCH_AVAILABLE:
        try:
            __snake_case     =		version.parse(importlib_metadata.version('''torch'''))
            logger.info(f"""PyTorch version {TORCH_VERSION} available.""")
        except importlib_metadata.PackageNotFoundError:
            pass
else:
    logger.info('''Disabling PyTorch because USE_TF is set''')

__snake_case     =		'''N/A'''
__snake_case     =		False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
    __snake_case     =		importlib.util.find_spec('''tensorflow''') is not None
    if TF_AVAILABLE:
        # For the metadata, we have to look for both tensorflow and tensorflow-cpu
        for package in [
            "tensorflow",
            "tensorflow-cpu",
            "tensorflow-gpu",
            "tf-nightly",
            "tf-nightly-cpu",
            "tf-nightly-gpu",
            "intel-tensorflow",
            "tensorflow-rocm",
            "tensorflow-macos",
        ]:
            try:
                __snake_case     =		version.parse(importlib_metadata.version(package))
            except importlib_metadata.PackageNotFoundError:
                continue
            else:
                break
        else:
            __snake_case     =		False
    if TF_AVAILABLE:
        if TF_VERSION.major < 2:
            logger.info(f"""TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.""")
            __snake_case     =		False
        else:
            logger.info(f"""TensorFlow version {TF_VERSION} available.""")
else:
    logger.info('''Disabling Tensorflow because USE_TORCH is set''')


__snake_case     =		'''N/A'''
__snake_case     =		False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
    __snake_case     =		importlib.util.find_spec('''jax''') is not None and importlib.util.find_spec('''jaxlib''') is not None
    if JAX_AVAILABLE:
        try:
            __snake_case     =		version.parse(importlib_metadata.version('''jax'''))
            logger.info(f"""JAX version {JAX_VERSION} available.""")
        except importlib_metadata.PackageNotFoundError:
            pass
else:
    logger.info('''Disabling JAX because USE_JAX is set to False''')


__snake_case     =		os.environ.get('''USE_BEAM''', '''AUTO''').upper()
__snake_case     =		'''N/A'''
__snake_case     =		False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
    try:
        __snake_case     =		version.parse(importlib_metadata.version('''apache_beam'''))
        __snake_case     =		True
        logger.info(f"""Apache Beam version {BEAM_VERSION} available.""")
    except importlib_metadata.PackageNotFoundError:
        pass
else:
    logger.info('''Disabling Apache Beam because USE_BEAM is set to False''')


# Optional tools for data loading
__snake_case     =		importlib.util.find_spec('''sqlalchemy''') is not None

# Optional tools for feature decoding
__snake_case     =		importlib.util.find_spec('''PIL''') is not None
__snake_case     =		importlib.util.find_spec('''soundfile''') is not None and version.parse(
    importlib.import_module('''soundfile''').__libsndfile_version__
) >= version.parse('''1.0.31''')
__snake_case     =		importlib.util.find_spec('''soundfile''') is not None and version.parse(
    importlib.import_module('''soundfile''').__libsndfile_version__
) >= version.parse('''1.1.0''')

# Optional compression tools
__snake_case     =		importlib.util.find_spec('''rarfile''') is not None
__snake_case     =		importlib.util.find_spec('''zstandard''') is not None
__snake_case     =		importlib.util.find_spec('''lz4''') is not None
__snake_case     =		importlib.util.find_spec('''py7zr''') is not None


# Cache location
__snake_case     =		'''~/.cache'''
__snake_case     =		os.getenv('''XDG_CACHE_HOME''', DEFAULT_XDG_CACHE_HOME)
__snake_case     =		os.path.join(XDG_CACHE_HOME, '''huggingface''')
__snake_case     =		os.path.expanduser(os.getenv('''HF_HOME''', DEFAULT_HF_CACHE_HOME))

__snake_case     =		os.path.join(HF_CACHE_HOME, '''datasets''')
__snake_case     =		Path(os.getenv('''HF_DATASETS_CACHE''', DEFAULT_HF_DATASETS_CACHE))

__snake_case     =		os.path.join(HF_CACHE_HOME, '''metrics''')
__snake_case     =		Path(os.getenv('''HF_METRICS_CACHE''', DEFAULT_HF_METRICS_CACHE))

__snake_case     =		os.path.join(HF_CACHE_HOME, '''modules''')
__snake_case     =		Path(os.getenv('''HF_MODULES_CACHE''', DEFAULT_HF_MODULES_CACHE))

__snake_case     =		'''downloads'''
__snake_case     =		os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
__snake_case     =		Path(os.getenv('''HF_DATASETS_DOWNLOADED_DATASETS_PATH''', DEFAULT_DOWNLOADED_DATASETS_PATH))

__snake_case     =		'''extracted'''
__snake_case     =		os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
__snake_case     =		Path(os.getenv('''HF_DATASETS_EXTRACTED_DATASETS_PATH''', DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
__snake_case     =		(
    os.environ.get('''HF_UPDATE_DOWNLOAD_COUNTS''', '''AUTO''').upper() in ENV_VARS_TRUE_AND_AUTO_VALUES
)

# Batch size constants. For more info, see:
# https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations)
__snake_case     =		1_0_0_0

# Size of the preloaded record batch in `Dataset.__iter__`
__snake_case     =		1_0

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
__snake_case     =		4 << 3_0

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
__snake_case     =		'''500MB'''

# Parquet configuration
__snake_case     =		1_0_0
__snake_case     =		1_0_0
__snake_case     =		1_0_0

# Offline mode
__snake_case     =		os.environ.get('''HF_DATASETS_OFFLINE''', '''AUTO''').upper() in ENV_VARS_TRUE_VALUES

# In-memory
__snake_case     =		0  # Disabled
__snake_case     =		float(os.environ.get('''HF_DATASETS_IN_MEMORY_MAX_SIZE''', DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
__snake_case     =		'''dataset.arrow'''
__snake_case     =		'''indices.arrow'''
__snake_case     =		'''state.json'''
__snake_case     =		'''dataset_info.json'''
__snake_case     =		'''dataset_infos.json'''
__snake_case     =		'''LICENSE'''
__snake_case     =		'''metric_info.json'''
__snake_case     =		'''dataset_dict.json'''

__snake_case     =		'''datasets_modules'''

__snake_case     =		2_5_5

# Streaming
__snake_case     =		2_0
__snake_case     =		5

# Datasets without script
__snake_case     =		2_0_0
__snake_case     =		1_0
__snake_case     =		2_0_0

# Progress bars
__snake_case     =		0.05  # 20 progress updates per sec






import matplotlib.pyplot as plt
import numpy as np






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	len(_lowercase				)  # Number of training samples
     __UpperCamelCase				=	np.eye(_lowercase				)  # Initialize weights as identity matrix
     for j in range(_lowercase				):
          __UpperCamelCase				=	point - x_train[j]
          __UpperCamelCase				=	np.exp(diff @ diff.T / (-2.0 * tau**2)				)

     return weights






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	weight_matrix(_lowercase			,       _lowercase			,       _lowercase				)
     __UpperCamelCase				=	np.linalg.inv(x_train.T @ weight_mat @ x_train				) @ (
         x_train.T @ weight_mat @ y_train.T
     )

     return weight






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	np.zeros(len(_lowercase				)				)  # Initialize array of predictions
     for i, item in enumerate(_lowercase				):
          __UpperCamelCase				=	item @ local_weight(_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)

     return y_pred






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				tuple[np.ndarray, np.ndarray, np.ndarray]:


     """simple docstring"""


     import seaborn as sns

     __UpperCamelCase				=	sns.load_dataset(_lowercase				)
     __UpperCamelCase				=	np.array(data[x_name]				)
     __UpperCamelCase				=	np.array(data[y_name]				)

     __UpperCamelCase				=	np.ones(len(_lowercase				)				)

     # pairing elements of one and x_data
     __UpperCamelCase				=	np.column_stack((one, x_data)				)

     return x_train, x_data, y_data






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       )			->				None:


     """simple docstring"""


     __UpperCamelCase				=	np.sort(_lowercase			,       axis=0				)
     plt.scatter(_lowercase			,       _lowercase			,       color='blue'				)
     plt.plot(
         x_train_sorted[:, 1]			,       preds[x_train[:, 1].argsort(0				)]			,       color='yellow'			,       linewidth=5			,       )
     plt.title('Local Weighted Regression'				)
     plt.xlabel(_lowercase				)
     plt.ylabel(_lowercase				)
     plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Demo with a dataset from the seaborn module
    __snake_case    ,					__snake_case    ,					__snake_case     =		load_data('''tips''', '''total_bill''', '''tip''')
    __snake_case     =		local_weight_regression(training_data_x, tip, 5)
    plot_preds(training_data_x, predictions, total_bill, tip, '''total_bill''', '''tip''')


