



import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


UpperCAmelCase :				List[Any]							=get_logger(__name__)

# Datasets
UpperCAmelCase :				Dict							="""https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets"""
UpperCAmelCase :				Union[str, Any]							="""https://cdn-datasets.huggingface.co/datasets/datasets"""
UpperCAmelCase :				Optional[Any]							="""https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}"""

# Metrics
UpperCAmelCase :				str							="""https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics"""
UpperCAmelCase :				Optional[int]							="""https://cdn-datasets.huggingface.co/datasets/metric"""
UpperCAmelCase :				Tuple							="""https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}"""

# Hub
UpperCAmelCase :				int							=os.environ.get("""HF_ENDPOINT""", """https://huggingface.co""")
UpperCAmelCase :				Optional[int]							=HF_ENDPOINT + """/datasets/{repo_id}/resolve/{revision}/{path}"""
UpperCAmelCase :				List[Any]							="""main"""

UpperCAmelCase :				List[Any]							=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
UpperCAmelCase :				List[Any]							={"""1""", """ON""", """YES""", """TRUE"""}
UpperCAmelCase :				Optional[Any]							=ENV_VARS_TRUE_VALUES.union({"""AUTO"""})


# Imports
UpperCAmelCase :				Dict							=version.parse(importlib_metadata.version("""dill"""))
UpperCAmelCase :				int							=version.parse(importlib_metadata.version("""pandas"""))
UpperCAmelCase :				Optional[Any]							=version.parse(importlib_metadata.version("""pyarrow"""))

UpperCAmelCase :				Optional[int]							=os.environ.get("""USE_TF""", """AUTO""").upper()
UpperCAmelCase :				List[str]							=os.environ.get("""USE_TORCH""", """AUTO""").upper()
UpperCAmelCase :				Dict							=os.environ.get("""USE_JAX""", """AUTO""").upper()

UpperCAmelCase :				Any							="""N/A"""
UpperCAmelCase :				Tuple							=False

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

UpperCAmelCase :				Tuple							="""N/A"""
UpperCAmelCase :				str							=False

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


UpperCAmelCase :				Optional[Any]							="""N/A"""
UpperCAmelCase :				Any							=False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
	UpperCAmelCase :				Optional[Any]							=importlib.util.find_spec("""jax""") is not None and importlib.util.find_spec("""jaxlib""") is not None
	if JAX_AVAILABLE:
		try:
			UpperCAmelCase :				List[str]							=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""")


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

# Optional tools for feature decoding
UpperCAmelCase :				Union[str, Any]							=importlib.util.find_spec("""PIL""") is not None
UpperCAmelCase :				Tuple							=importlib.util.find_spec("""soundfile""") is not None and version.parse(
    importlib.import_module("""soundfile""").__libsndfile_version__
) >= version.parse("""1.0.31""")
UpperCAmelCase :				Dict							=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
UpperCAmelCase :				Optional[int]							=importlib.util.find_spec("""rarfile""") is not None
UpperCAmelCase :				int							=importlib.util.find_spec("""zstandard""") is not None
UpperCAmelCase :				Optional[int]							=importlib.util.find_spec("""lz4""") is not None
UpperCAmelCase :				Dict							=importlib.util.find_spec("""py7zr""") is not None


# Cache location
UpperCAmelCase :				Tuple							="""~/.cache"""
UpperCAmelCase :				int							=os.getenv("""XDG_CACHE_HOME""", DEFAULT_XDG_CACHE_HOME)
UpperCAmelCase :				Tuple							=os.path.join(XDG_CACHE_HOME, """huggingface""")
UpperCAmelCase :				Optional[int]							=os.path.expanduser(os.getenv("""HF_HOME""", DEFAULT_HF_CACHE_HOME))

UpperCAmelCase :				Optional[int]							=os.path.join(HF_CACHE_HOME, """datasets""")
UpperCAmelCase :				Tuple							=Path(os.getenv("""HF_DATASETS_CACHE""", DEFAULT_HF_DATASETS_CACHE))

UpperCAmelCase :				str							=os.path.join(HF_CACHE_HOME, """metrics""")
UpperCAmelCase :				Tuple							=Path(os.getenv("""HF_METRICS_CACHE""", DEFAULT_HF_METRICS_CACHE))

UpperCAmelCase :				List[str]							=os.path.join(HF_CACHE_HOME, """modules""")
UpperCAmelCase :				Optional[int]							=Path(os.getenv("""HF_MODULES_CACHE""", DEFAULT_HF_MODULES_CACHE))

UpperCAmelCase :				List[Any]							="""downloads"""
UpperCAmelCase :				Tuple							=os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
UpperCAmelCase :				int							=Path(os.getenv("""HF_DATASETS_DOWNLOADED_DATASETS_PATH""", DEFAULT_DOWNLOADED_DATASETS_PATH))

UpperCAmelCase :				int							="""extracted"""
UpperCAmelCase :				Any							=os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
UpperCAmelCase :				Union[str, Any]							=Path(os.getenv("""HF_DATASETS_EXTRACTED_DATASETS_PATH""", DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
UpperCAmelCase :				str							=(
    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)
UpperCAmelCase :				List[str]							=1000

# Size of the preloaded record batch in `Dataset.__iter__`
UpperCAmelCase :				Any							=10

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
UpperCAmelCase :				List[Any]							=4 << 30

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
UpperCAmelCase :				Union[str, Any]							="""500MB"""

# Parquet configuration
UpperCAmelCase :				Union[str, Any]							=100
UpperCAmelCase :				Optional[int]							=100
UpperCAmelCase :				Tuple							=100

# Offline mode
UpperCAmelCase :				Union[str, Any]							=os.environ.get("""HF_DATASETS_OFFLINE""", """AUTO""").upper() in ENV_VARS_TRUE_VALUES

# In-memory
UpperCAmelCase :				int							=0  # Disabled
UpperCAmelCase :				Optional[Any]							=float(os.environ.get("""HF_DATASETS_IN_MEMORY_MAX_SIZE""", DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
UpperCAmelCase :				str							="""dataset.arrow"""
UpperCAmelCase :				Dict							="""indices.arrow"""
UpperCAmelCase :				Tuple							="""state.json"""
UpperCAmelCase :				str							="""dataset_info.json"""
UpperCAmelCase :				Tuple							="""dataset_infos.json"""
UpperCAmelCase :				Tuple							="""LICENSE"""
UpperCAmelCase :				Union[str, Any]							="""metric_info.json"""
UpperCAmelCase :				List[str]							="""dataset_dict.json"""

UpperCAmelCase :				Optional[int]							="""datasets_modules"""

UpperCAmelCase :				List[str]							=255

# Streaming
UpperCAmelCase :				Optional[Any]							=20
UpperCAmelCase :				Any							=5

# Datasets without script
UpperCAmelCase :				int							=200
UpperCAmelCase :				Tuple							=10
UpperCAmelCase :				str							=200

# Progress bars
UpperCAmelCase :				Dict							=0.05  # 20 progress updates per sec





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase :				List[Any]							={"""configuration_qdqbert""": ["""QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """QDQBertConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase :				Dict							=[
	    """QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """QDQBertForMaskedLM""",
	    """QDQBertForMultipleChoice""",
	    """QDQBertForNextSentencePrediction""",
	    """QDQBertForQuestionAnswering""",
	    """QDQBertForSequenceClassification""",
	    """QDQBertForTokenClassification""",
	    """QDQBertLayer""",
	    """QDQBertLMHeadModel""",
	    """QDQBertModel""",
	    """QDQBertPreTrainedModel""",
	    """load_tf_weights_in_qdqbert""",
	]


if TYPE_CHECKING:
	from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_qdqbert import (
		    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    QDQBertForMaskedLM,
		    QDQBertForMultipleChoice,
		    QDQBertForNextSentencePrediction,
		    QDQBertForQuestionAnswering,
		    QDQBertForSequenceClassification,
		    QDQBertForTokenClassification,
		    QDQBertLayer,
		    QDQBertLMHeadModel,
		    QDQBertModel,
		    QDQBertPreTrainedModel,
		    load_tf_weights_in_qdqbert,
		)


else:
	import sys

	UpperCAmelCase :				Optional[Any]							=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
