




"""simple docstring"""
import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


_SCREAMING_SNAKE_CASE						 =     get_logger(__name__)

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

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

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

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


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

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

_SCREAMING_SNAKE_CASE						 =     """N/A"""
_SCREAMING_SNAKE_CASE						 =     False

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

_SCREAMING_SNAKE_CASE						 =     """N/A"""
_SCREAMING_SNAKE_CASE						 =     False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
    _SCREAMING_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:
                _SCREAMING_SNAKE_CASE						 =     version.parse(importlib_metadata.version(package))
            except importlib_metadata.PackageNotFoundError:
                continue
            else:
                break
        else:
            _SCREAMING_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.')
            _SCREAMING_SNAKE_CASE						 =     False
        else:
            logger.info(f'TensorFlow version {TF_VERSION} available.')
else:
    logger.info("""Disabling Tensorflow because USE_TORCH is set""")


_SCREAMING_SNAKE_CASE						 =     """N/A"""
_SCREAMING_SNAKE_CASE						 =     False

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


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

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


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

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

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

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

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

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

# Download count for the website
_SCREAMING_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)
_SCREAMING_SNAKE_CASE						 =     1_0_0_0

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

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

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

# Parquet configuration
_SCREAMING_SNAKE_CASE						 =     1_0_0
_SCREAMING_SNAKE_CASE						 =     1_0_0
_SCREAMING_SNAKE_CASE						 =     1_0_0

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

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

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

_SCREAMING_SNAKE_CASE						 =     """datasets_modules"""

_SCREAMING_SNAKE_CASE						 =     2_5_5

# Streaming
_SCREAMING_SNAKE_CASE						 =     2_0
_SCREAMING_SNAKE_CASE						 =     5

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

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





from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


__UpperCAmelCase   						=     datasets.utils.logging.get_logger(__name__)





class 				UpperCamelCase__		(	folder_based_builder.FolderBasedBuilderConfig	):






       """simple docstring"""


       UpperCAmelCase_				       =None
       UpperCAmelCase_				       =None







class 				UpperCamelCase__		(	folder_based_builder.FolderBasedBuilder	):






       """simple docstring"""


       UpperCAmelCase_				       =datasets.Image()
       UpperCAmelCase_				       ="image"
       UpperCAmelCase_				       =ImageFolderConfig
       UpperCAmelCase_				       =42  # definition at the bottom of the script
       UpperCAmelCase_				       =ImageClassification(image_column="image"       ,    label_column="label"	)







__UpperCAmelCase   						=     [
    ".blp",
    ".bmp",
    ".dib",
    ".bufr",
    ".cur",
    ".pcx",
    ".dcx",
    ".dds",
    ".ps",
    ".eps",
    ".fit",
    ".fits",
    ".fli",
    ".flc",
    ".ftc",
    ".ftu",
    ".gbr",
    ".gif",
    ".grib",
    ".h5",
    ".hdf",
    ".png",
    ".apng",
    ".jp2",
    ".j2k",
    ".jpc",
    ".jpf",
    ".jpx",
    ".j2c",
    ".icns",
    ".ico",
    ".im",
    ".iim",
    ".tif",
    ".tiff",
    ".jfif",
    ".jpe",
    ".jpg",
    ".jpeg",
    ".mpg",
    ".mpeg",
    ".msp",
    ".pcd",
    ".pxr",
    ".pbm",
    ".pgm",
    ".ppm",
    ".pnm",
    ".psd",
    ".bw",
    ".rgb",
    ".rgba",
    ".sgi",
    ".ras",
    ".tga",
    ".icb",
    ".vda",
    ".vst",
    ".webp",
    ".wmf",
    ".emf",
    ".xbm",
    ".xpm",
]
__UpperCAmelCase   						=     IMAGE_EXTENSIONS


