





import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


UpperCAmelCase			:			Dict	  = get_logger(__name__)

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

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

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

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


# Imports
UpperCAmelCase			:			Union[str, Any]	  = version.parse(importlib_metadata.version("""dill"""))
UpperCAmelCase			:			List[str]	  = version.parse(importlib_metadata.version("""pandas"""))
UpperCAmelCase			:			str	  = version.parse(importlib_metadata.version("""pyarrow"""))

UpperCAmelCase			:			Tuple	  = os.environ.get("""USE_TF""", """AUTO""").upper()
UpperCAmelCase			:			Optional[Any]	  = os.environ.get("""USE_TORCH""", """AUTO""").upper()
UpperCAmelCase			:			Any	  = os.environ.get("""USE_JAX""", """AUTO""").upper()

UpperCAmelCase			:			int	  = "N/A"
UpperCAmelCase			:			Union[str, Any]	  = False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
       UpperCAmelCase			:			str	  = importlib.util.find_spec("""torch""") is not None
       if TORCH_AVAILABLE:
              try:
                     UpperCAmelCase			:			Dict	  = 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			:			Optional[int]	  = "N/A"
UpperCAmelCase			:			Any	  = 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			:			Optional[Any]	  = version.parse(importlib_metadata.version(package))
                     except importlib_metadata.PackageNotFoundError:
                            continue
                     else:
                            break
              else:
                     UpperCAmelCase			:			int	  = 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			:			Any	  = False
              else:
                     logger.info(F"""TensorFlow version {TF_VERSION} available.""")
else:
       logger.info("""Disabling Tensorflow because USE_TORCH is set""")


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

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

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


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

UpperCAmelCase			:			str	  = os.path.join(HF_CACHE_HOME, """datasets""")
UpperCAmelCase			:			str	  = Path(os.getenv("""HF_DATASETS_CACHE""", DEFAULT_HF_DATASETS_CACHE))

UpperCAmelCase			:			Union[str, Any]	  = os.path.join(HF_CACHE_HOME, """metrics""")
UpperCAmelCase			:			Optional[int]	  = Path(os.getenv("""HF_METRICS_CACHE""", DEFAULT_HF_METRICS_CACHE))

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

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

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

# Download count for the website
UpperCAmelCase			:			Optional[Any]	  = (
    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[Any]	  = 1000

# Size of the preloaded record batch in `Dataset.__iter__`
UpperCAmelCase			:			List[str]	  = 10

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
UpperCAmelCase			:			int	  = 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			:			Optional[Any]	  = 100
UpperCAmelCase			:			int	  = 100
UpperCAmelCase			:			List[Any]	  = 100

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

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

# File names
UpperCAmelCase			:			List[Any]	  = "dataset.arrow"
UpperCAmelCase			:			str	  = "indices.arrow"
UpperCAmelCase			:			str	  = "state.json"
UpperCAmelCase			:			str	  = "dataset_info.json"
UpperCAmelCase			:			Any	  = "dataset_infos.json"
UpperCAmelCase			:			Any	  = "LICENSE"
UpperCAmelCase			:			Optional[Any]	  = "metric_info.json"
UpperCAmelCase			:			int	  = "dataset_dict.json"

UpperCAmelCase			:			Optional[int]	  = "datasets_modules"

UpperCAmelCase			:			Tuple	  = 255

# Streaming
UpperCAmelCase			:			List[Any]	  = 20
UpperCAmelCase			:			Dict	  = 5

# Datasets without script
UpperCAmelCase			:			str	  = 200
UpperCAmelCase			:			Optional[Any]	  = 10
UpperCAmelCase			:			Optional[int]	  = 200

# Progress bars
UpperCAmelCase			:			Union[str, Any]	  = 0.0_5  # 20 progress updates per sec













import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp
def   _A (	):





  """simple docstring"""



  a__			:      Optional[int]            =ArgumentParser(
      description=(
          "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
      )		)

  # Optional arguments for the launch helper
  parser.add_argument("--num_cores" , type=SCREAMING_SNAKE_CASE , default=1 , help="Number of TPU cores to use (1 or 8)."		)

  # positional
  parser.add_argument(
      "training_script" , type=SCREAMING_SNAKE_CASE , help=(
          "The full path to the single TPU training "
          "program/script to be launched in parallel, "
          "followed by all the arguments for the "
          "training script"
      ) , )

  # rest from the training program
  parser.add_argument("training_script_args" , nargs=SCREAMING_SNAKE_CASE		)

  return parser.parse_args()
def   _A (	):





  """simple docstring"""



  a__			:      Optional[Any]            =parse_args()

  # Import training_script as a module.
  a__			:      Tuple            =Path(args.training_script		)
  sys.path.append(str(script_fpath.parent.resolve()		)		)
  a__			:      Tuple            =script_fpath.stem
  a__			:      Any            =importlib.import_module(SCREAMING_SNAKE_CASE		)

  # Patch sys.argv
  a__			:      List[str]            =[args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores		)]

  xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores		)


if __name__ == "__main__":
     main()






