






"""simple docstring"""





import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


UpperCamelCase					=     get_logger(__name__)

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

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

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

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


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

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

UpperCamelCase					=     """N/A"""
UpperCamelCase					=     False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
  UpperCamelCase					=     importlib.util.find_spec("""torch""") is not None
  if TORCH_AVAILABLE:
    try:
      UpperCamelCase					=     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					=     """N/A"""
UpperCamelCase					=     False

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


UpperCamelCase					=     """N/A"""
UpperCamelCase					=     False

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

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


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

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

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

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

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

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

# Download count for the website
UpperCamelCase					=     (
    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					=     10_00

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

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

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

# Parquet configuration
UpperCamelCase					=     1_00
UpperCamelCase					=     1_00
UpperCamelCase					=     1_00

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

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

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

UpperCamelCase					=     """datasets_modules"""

UpperCamelCase					=     2_55

# Streaming
UpperCamelCase					=     20
UpperCamelCase					=     5

# Datasets without script
UpperCamelCase					=     2_00
UpperCamelCase					=     10
UpperCamelCase					=     2_00

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














"""simple docstring"""





import doctest

import projectq
from projectq.ops import H, Measure






def 							A(    snake_case_						):
       """simple docstring"""





       lowercase__:							Dict         =     quantum_engine.allocate_qubit()
       H | qubit
       Measure | qubit
       return int(snake_case_						)


if __name__ == "__main__":
  doctest.testmod()

  # initialises a new quantum backend
  UpperCamelCase					=     projectq.MainEngine()

  # Generate a list of 10 random numbers
  UpperCamelCase					=     [get_random_number(quantum_engine) for _ in range(10)]

  # Flushes the quantum engine from memory
  quantum_engine.flush()

  print("""Random numbers""", random_numbers_list)






