


'''simple docstring'''

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





'''simple docstring'''

from __future__ import annotations






def __UpperCamelCase (      __lowerCamelCase		:			float | int   ,	__lowerCamelCase		:			float | int   ,	__lowerCamelCase		:			float | int   ,	)   ->  list[float | int]:







     '''simple docstring'''






     if not all((nth_term, start_term_a, common_ratio_r)					):
          return []
     _a            =		[]
     _a            =		1
     _a            =		common_ratio_r
     for _ in range(int(__lowerCamelCase					)					):
          if not series:
               series.append(__lowerCamelCase					)
          else:
               power += 1
               series.append(float(start_term_a * multiple					)					)
               _a            =		pow(float(__lowerCamelCase					)   ,	__lowerCamelCase					)
     return series


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   lowercase__					  =		float(input("Enter the last number (n term) of the Geometric Series"))
   lowercase__					  =		float(input("Enter the starting term (a) of the Geometric Series"))
   lowercase__					  =		float(
       input("Enter the common ratio between two terms (r) of the Geometric Series")
   )
   print("Formula of Geometric Series => a + ar + ar^2 ... +ar^n")
   print(geometric_series(nth_term, start_term_a, common_ratio_r))

