



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







from __future__ import annotations

import math
import random

def    UpperCamelCase_ (	a_  ,				a_  ,				a_		)     ->list[int]:
		A				=d * e - 1
		A				=0
		A				=0
		while p == 0:
				A				=random.randint(2  ,				n - 1		)
				A				=k
				while True:
						if t % 2 == 0:
								A				=t // 2
								A				=(g**t) % n
								A				=math.gcd(x - 1  ,				a_		)
								if x > 1 and y > 1:
										A				=y
										A				=n // y
										break  # find the correct factors
						else:
								break  # t is not divisible by 2, break and choose another g
		return sorted([p, q]		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()




