






'''simple docstring'''

from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

A:   Union[str, Any]						=					TypeVar("T")
A:   int						=					TypeVar("U")






class 				__magic_name__  (						Generic[T, U]					):







	"""simple docstring"""



	def __init__(				self	,   _lowercase	,   _lowercase							)    ->	List[str]:
							lowercase_   : Optional[int]   	=      key
							lowercase_   : str   	=      val
							lowercase_   : int   	=      0
							lowercase_   : DoubleLinkedListNode[T, U] | None   	=      None
							lowercase_   : DoubleLinkedListNode[T, U] | None   	=      None







	def __repr__(				self							)    ->	str:
							return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
							    self.key	,   self.val	,   self.freq	,   self.next is not None	,   self.prev is not None							)






class 				__magic_name__  (						Generic[T, U]					):







	"""simple docstring"""



	def __init__(				self							)    ->	None:
							lowercase_   : DoubleLinkedListNode[T, U]   	=      DoubleLinkedListNode(_lowercase	,   _lowercase							)
							lowercase_   : DoubleLinkedListNode[T, U]   	=      DoubleLinkedListNode(_lowercase	,   _lowercase							)
							lowercase_	,   lowercase_   : Dict   	=      self.rear, self.head
	def __repr__(				self							)    ->	str:
							lowercase_   : Optional[Any]   	=      ['DoubleLinkedList']
							lowercase_   : Dict   	=      self.head
							while node.next is not None:
													rep.append(str(_lowercase							)							)
													lowercase_   : Optional[int]   	=      node.next
							rep.append(str(self.rear							)							)
							return ",\n    ".join(_lowercase							)
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	None:
							lowercase_   : List[Any]   	=      self.rear.prev

							# All nodes other than self.head are guaranteed to have non-None previous
							assert previous is not None

							lowercase_   : Tuple   	=      node
							lowercase_   : Any   	=      previous
							lowercase_   : List[str]   	=      node
							lowercase_   : Optional[int]   	=      self.rear
							node.freq += 1
							self._position_node(_lowercase							)
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	None:

							while node.prev is not None and node.prev.freq > node.freq:
													# swap node with previous node
													lowercase_   : Dict   	=      node.prev

													lowercase_   : List[Any]   	=      previous_node.prev
													lowercase_   : Dict   	=      node.prev
													lowercase_   : Optional[Any]   	=      previous_node
													lowercase_   : Tuple   	=      node







	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	DoubleLinkedListNode[T, U] | None:

							if node.prev is None or node.next is None:
													return None

							lowercase_   : int   	=      node.next
							lowercase_   : Dict   	=      node.prev
							lowercase_   : List[str]   	=      None
							lowercase_   : Optional[Any]   	=      None
							return node






class 				__magic_name__  (						Generic[T, U]					):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_       :   dict[Callable[[T], U], LFUCache[T, U]]   =	{}
	def __init__(				self	,   _lowercase							)    ->	Union[str, Any]:
							lowercase_   : DoubleLinkedList[T, U]   	=      DoubleLinkedList()
							lowercase_   : Optional[int]   	=      capacity
							lowercase_   : int   	=      0
							lowercase_   : Dict   	=      0
							lowercase_   : List[str]   	=      0
							lowercase_   : dict[T, DoubleLinkedListNode[T, U]]   	=      {}
	def __repr__(				self							)    ->	str:
							return (
							    f"CacheInfo(hits={self.hits}, misses={self.miss}, "
							    f"capacity={self.capacity}, current_size={self.num_keys})"
							)
	def __contains__(				self	,   _lowercase							)    ->	bool:
							return key in self.cache
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	U | None:

							if key in self.cache:
													self.hits += 1
													lowercase_   : DoubleLinkedListNode[T, U]   	=      self.cache[key]
													lowercase_   : Any   	=      self.list.remove(self.cache[key]							)
													assert node == value_node

													# node is guaranteed not None because it is in self.cache
													assert node is not None
													self.list.add(_lowercase							)
													return node.val
							self.miss += 1
							return None
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase							)    ->	None:

							if key not in self.cache:
													if self.num_keys >= self.capacity:
																			# delete first node when over capacity
																			lowercase_   : List[str]   	=      self.list.head.next

																			# guaranteed to have a non-None first node when num_keys > 0
																			# explain to type checker via assertions
																			assert first_node is not None
																			assert first_node.key is not None
																			assert self.list.remove(_lowercase							) is not None
																			# first_node guaranteed to be in list

																			del self.cache[first_node.key]
																			self.num_keys -= 1
													lowercase_   : Union[str, Any]   	=      DoubleLinkedListNode(_lowercase	,   _lowercase							)
													self.list.add(self.cache[key]							)
													self.num_keys += 1

							else:
													lowercase_   : Optional[int]   	=      self.list.remove(self.cache[key]							)
													assert node is not None  # node guaranteed to be in list
													lowercase_   : Optional[int]   	=      value
													self.list.add(_lowercase							)







	@classmethod
	def 							lowerCamelCase__		(				cls	,   _lowercase = 128							)    ->	Callable[[Callable[[T], U]], Callable[..., U]]:

							def cache_decorator_inner(_lowercase							) -> Callable[..., U]:
													def cache_decorator_wrapper(*_lowercase							) -> U:
																			if func not in cls.decorator_function_to_instance_map:
																									lowercase_   : Union[str, Any]   	=      LFUCache(_lowercase							)

																			lowercase_   : Any   	=      cls.decorator_function_to_instance_map[func].get(args[0]							)
																			if result is None:
																									lowercase_   : Dict   	=      func(*_lowercase							)
																									cls.decorator_function_to_instance_map[func].put(args[0]	,   _lowercase							)
																			return result

													def cache_info() -> LFUCache[T, U]:
																			return cls.decorator_function_to_instance_map[func]

													setattr(_lowercase	,   'cache_info'	,   _lowercase							)  # noqa: B010

													return cache_decorator_wrapper

							return cache_decorator_inner


if __name__ == "__main__":
						import doctest

						doctest.testmod()








'''simple docstring'''

import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


A:   str						=					get_logger(__name__)

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

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

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

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


# Imports
A:   Any						=					version.parse(importlib_metadata.version("dill"))
A:   str						=					version.parse(importlib_metadata.version("pandas"))
A:   Tuple						=					version.parse(importlib_metadata.version("pyarrow"))

A:   str						=					os.environ.get("USE_TF", "AUTO").upper()
A:   List[Any]						=					os.environ.get("USE_TORCH", "AUTO").upper()
A:   Union[str, Any]						=					os.environ.get("USE_JAX", "AUTO").upper()

A:   Dict						=					"N/A"
A:   Optional[int]						=					False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
						A:   List[str]						=					importlib.util.find_spec("torch") is not None
						if TORCH_AVAILABLE:
												try:
																		A:   List[str]						=					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")

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

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


A:   Any						=					"N/A"
A:   int						=					False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
						A:   Dict						=					importlib.util.find_spec("jax") is not None and importlib.util.find_spec("jaxlib") is not None
						if JAX_AVAILABLE:
												try:
																		A:   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")


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

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


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

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

A:   int						=					os.path.join(HF_CACHE_HOME, "metrics")
A:   int						=					Path(os.getenv("HF_METRICS_CACHE", DEFAULT_HF_METRICS_CACHE))

A:   Any						=					os.path.join(HF_CACHE_HOME, "modules")
A:   Tuple						=					Path(os.getenv("HF_MODULES_CACHE", DEFAULT_HF_MODULES_CACHE))

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

A:   Tuple						=					"extracted"
A:   Optional[int]						=					os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
A:   Optional[int]						=					Path(os.getenv("HF_DATASETS_EXTRACTED_DATASETS_PATH", DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
A:   Union[str, 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)
A:   List[str]						=					1_0_0_0

# Size of the preloaded record batch in `Dataset.__iter__`
A:   Tuple						=					1_0

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
A:   List[Any]						=					4 << 3_0

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

# Parquet configuration
A:   Union[str, Any]						=					1_0_0
A:   Optional[int]						=					1_0_0
A:   Any						=					1_0_0

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

# In-memory
A:   Any						=					0  # Disabled
A:   List[str]						=					float(os.environ.get("HF_DATASETS_IN_MEMORY_MAX_SIZE", DEFAULT_IN_MEMORY_MAX_SIZE))

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

A:   Dict						=					"datasets_modules"

A:   List[str]						=					2_5_5

# Streaming
A:   Any						=					2_0
A:   Dict						=					5

# Datasets without script
A:   Union[str, Any]						=					2_0_0
A:   Union[str, Any]						=					1_0
A:   List[Any]						=					2_0_0

# Progress bars
A:   Tuple						=					0.05  # 20 progress updates per sec
