
import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


lowercase_											=	get_logger(__name__)

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

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

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

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


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

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

lowercase_											=	"""N/A"""
lowercase_											=	False

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

lowercase_											=	"""N/A"""
lowercase_											=	False

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


lowercase_											=	"""N/A"""
lowercase_											=	False

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


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

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


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

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

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

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

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

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

# Download count for the website
lowercase_											=	(
    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)
lowercase_											=	1_000

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

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

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

# Parquet configuration
lowercase_											=	100
lowercase_											=	100
lowercase_											=	100

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

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

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

lowercase_											=	"""datasets_modules"""

lowercase_											=	255

# Streaming
lowercase_											=	20
lowercase_											=	5

# Datasets without script
lowercase_											=	200
lowercase_											=	10
lowercase_											=	200

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


from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


lowercase_											=	[
    """AutomaticSpeechRecognition""",
    """AudioClassification""",
    """ImageClassification""",
    """LanguageModeling""",
    """QuestionAnsweringExtractive""",
    """Summarization""",
    """TaskTemplate""",
    """TextClassification""",
]

lowercase_											=	get_logger(__name__)


lowercase_											=	{
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}


def     a__			(    snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					str			=						task_template_dict.get('''task'''    )
  if task_name is None:
    logger.warning(F'''Couldn\'t find template for task \'{task_name}\'. Available templates: {list(snake_case    )}'''    )
    return None
  __SCREAMING_SNAKE_CASE     :					Any			=						NAMEaTEMPLATE.get(snake_case    )
  return template.from_dict(snake_case    )
