




import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


a_										=       get_logger(__name__)

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

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

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

a_										=       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
a_										=       {"1", "ON", "YES", "TRUE"}
a_										=       ENV_VARS_TRUE_VALUES.union({"AUTO"})


# Imports
a_										=       version.parse(importlib_metadata.version("dill"))
a_										=       version.parse(importlib_metadata.version("pandas"))
a_										=       version.parse(importlib_metadata.version("pyarrow"))

a_										=       os.environ.get("USE_TF", "AUTO").upper()
a_										=       os.environ.get("USE_TORCH", "AUTO").upper()
a_										=       os.environ.get("USE_JAX", "AUTO").upper()

a_										=       "N/A"
a_										=       False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
 a_										=       importlib.util.find_spec("torch") is not None
 if TORCH_AVAILABLE:
  try:
   a_										=       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")

a_										=       "N/A"
a_										=       False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
 a_										=       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:
    a_										=       version.parse(importlib_metadata.version(package))
   except importlib_metadata.PackageNotFoundError:
    continue
   else:
    break
  else:
   a_										=       False
 if TF_AVAILABLE:
  if TF_VERSION.major < 2:
   logger.info(f"""TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.""")
   a_										=       False
  else:
   logger.info(f"""TensorFlow version {TF_VERSION} available.""")
else:
 logger.info("Disabling Tensorflow because USE_TORCH is set")


a_										=       "N/A"
a_										=       False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
 a_										=       importlib.util.find_spec("jax") is not None and importlib.util.find_spec("jaxlib") is not None
 if JAX_AVAILABLE:
  try:
   a_										=       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")


a_										=       os.environ.get("USE_BEAM", "AUTO").upper()
a_										=       "N/A"
a_										=       False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
 try:
  a_										=       version.parse(importlib_metadata.version("apache_beam"))
  a_										=       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
a_										=       importlib.util.find_spec("sqlalchemy") is not None

# Optional tools for feature decoding
a_										=       importlib.util.find_spec("PIL") is not None
a_										=       importlib.util.find_spec("soundfile") is not None and version.parse(
    importlib.import_module("soundfile").__libsndfile_version__
) >= version.parse("1.0.31")
a_										=       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
a_										=       importlib.util.find_spec("rarfile") is not None
a_										=       importlib.util.find_spec("zstandard") is not None
a_										=       importlib.util.find_spec("lz4") is not None
a_										=       importlib.util.find_spec("py7zr") is not None


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

a_										=       os.path.join(HF_CACHE_HOME, "datasets")
a_										=       Path(os.getenv("HF_DATASETS_CACHE", DEFAULT_HF_DATASETS_CACHE))

a_										=       os.path.join(HF_CACHE_HOME, "metrics")
a_										=       Path(os.getenv("HF_METRICS_CACHE", DEFAULT_HF_METRICS_CACHE))

a_										=       os.path.join(HF_CACHE_HOME, "modules")
a_										=       Path(os.getenv("HF_MODULES_CACHE", DEFAULT_HF_MODULES_CACHE))

a_										=       "downloads"
a_										=       os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
a_										=       Path(os.getenv("HF_DATASETS_DOWNLOADED_DATASETS_PATH", DEFAULT_DOWNLOADED_DATASETS_PATH))

a_										=       "extracted"
a_										=       os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
a_										=       Path(os.getenv("HF_DATASETS_EXTRACTED_DATASETS_PATH", DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
a_										=       (
    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)
a_										=       1000

# Size of the preloaded record batch in `Dataset.__iter__`
a_										=       10

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
a_										=       4 << 30

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

# Parquet configuration
a_										=       100
a_										=       100
a_										=       100

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

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

# File names
a_										=       "dataset.arrow"
a_										=       "indices.arrow"
a_										=       "state.json"
a_										=       "dataset_info.json"
a_										=       "dataset_infos.json"
a_										=       "LICENSE"
a_										=       "metric_info.json"
a_										=       "dataset_dict.json"

a_										=       "datasets_modules"

a_										=       255

# Streaming
a_										=       20
a_										=       5

# Datasets without script
a_										=       200
a_										=       10
a_										=       200

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












import cva
import numpy as np




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ )    ->		int:

    """simple docstring"""


    try:
        return int(image[x_coordinate][y_coordinate] >= center )
    except (IndexError, TypeError):
        return 0




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		int:

    """simple docstring"""


    __UpperCamelCase											=  image[x_coordinate][y_coordinate]
    __UpperCamelCase											=  [1, 2, 4, 8, 16, 32, 64, 1_28]

    # skip get_neighbors_pixel if center is null
    if center is None:
        return 0

    # Starting from the top right, assigning value to pixels clockwise
    __UpperCamelCase											=  [
        get_neighbors_pixel(lowercase_   ,       x_coordinate - 1   ,       y_coordinate + 1   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       lowercase_   ,       y_coordinate + 1   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       x_coordinate - 1   ,       lowercase_   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       x_coordinate + 1   ,       y_coordinate + 1   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       x_coordinate + 1   ,       lowercase_   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       x_coordinate + 1   ,       y_coordinate - 1   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       lowercase_   ,       y_coordinate - 1   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       x_coordinate - 1   ,       y_coordinate - 1   ,       lowercase_ ),
    ]

    # Converting the binary value to decimal.
    return sum(
        binary_value * power for binary_value, power in zip(lowercase_   ,       lowercase_ ) )


if __name__ == "__main__":
 # Reading the image and converting it to grayscale.
 a_										=       cva.imread(
     "digital_image_processing/image_data/lena.jpg", cva.IMREAD_GRAYSCALE
 )

 # Create a numpy array as the same height and width of read image
 a_										=       np.zeros((image.shape[0], image.shape[1]))

 # Iterating through the image and calculating the
 # local binary pattern value for each pixel.
 for i in range(0, image.shape[0]):
  for j in range(0, image.shape[1]):
   a_										=       local_binary_value(image, i, j)

 cva.imshow("local binary pattern", lbp_image)
 cva.waitKey(0)
 cva.destroyAllWindows()






