


import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


a_  :    Optional[int]      =			get_logger(__name__)

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

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

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

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


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

a_  :    Optional[Any]      =			os.environ.get("""USE_TF""", """AUTO""").upper()
a_  :    Dict      =			os.environ.get("""USE_TORCH""", """AUTO""").upper()
a_  :    int      =			os.environ.get("""USE_JAX""", """AUTO""").upper()

a_  :    Optional[Any]      =			"""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_  :    Any      =			importlib.util.find_spec("""torch""") is not None
     if TORCH_AVAILABLE:
          try:
               a_  :    List[Any]      =			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_  :    Any      =			"""N/A"""
a_  :    int      =			False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
     a_  :    List[str]      =			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_  :    List[Any]      =			version.parse(importlib_metadata.version(package))
               except importlib_metadata.PackageNotFoundError:
                    continue
               else:
                    break
          else:
               a_  :    List[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_  :    List[str]      =			"""N/A"""
a_  :    Optional[int]      =			False

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

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


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

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

a_  :    List[str]      =			os.path.join(HF_CACHE_HOME, """metrics""")
a_  :    Dict      =			Path(os.getenv("""HF_METRICS_CACHE""", DEFAULT_HF_METRICS_CACHE))

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

a_  :    Tuple      =			"""downloads"""
a_  :    Any      =			os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
a_  :    Tuple      =			Path(os.getenv("""HF_DATASETS_DOWNLOADED_DATASETS_PATH""", DEFAULT_DOWNLOADED_DATASETS_PATH))

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

# Download count for the website
a_  :    List[str]      =			(
    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_  :    Tuple      =			1000

# Size of the preloaded record batch in `Dataset.__iter__`
a_  :    str      =			10

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

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

# Parquet configuration
a_  :    List[str]      =			100
a_  :    Optional[int]      =			100
a_  :    List[str]      =			100

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

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

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

a_  :    List[str]      =			"""datasets_modules"""

a_  :    str      =			255

# Streaming
a_  :    Optional[int]      =			20
a_  :    Union[str, Any]      =			5

# Datasets without script
a_  :    Optional[int]      =			200
a_  :    str      =			10
a_  :    Dict      =			200

# Progress bars
a_  :    Tuple      =			0.0_5  # 20 progress updates per sec







'''simple docstring'''

import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
a_  :    Any      =			"""\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
a_  :    Optional[Any]      =			"""\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
a_  :    List[str]      =			""""""

# TODO: Add the licence for the dataset here if you can find it
a_  :    Dict      =			""""""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
a_  :    Optional[int]      =			{
    """first_domain""": """https://huggingface.co/great-new-dataset-first_domain.zip""",
    """second_domain""": """https://huggingface.co/great-new-dataset-second_domain.zip""",
}





class 					snake_case   (   datasets.GeneratorBasedBuilder      ):





  """simple docstring"""




  _lowerCamelCase		   =  datasets.Version("1.1.0"      )

  # This is an example of a dataset with multiple configurations.
  # If you don't want/need to define several sub-sets in your dataset,
  # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

  # If you need to make complex sub-parts in the datasets with configurable options
  # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
  # BUILDER_CONFIG_CLASS = MyBuilderConfig

  # You will be able to load one or the other configurations in the following list with
  # data = datasets.load_dataset('my_dataset', 'first_domain')
  # data = datasets.load_dataset('my_dataset', 'second_domain')
  _lowerCamelCase		   =  [
      datasets.BuilderConfig(name="first_domain"      ,						version=lowercase      ,						description="This part of my dataset covers a first domain"      ),
      datasets.BuilderConfig(name="second_domain"      ,						version=lowercase      ,						description="This part of my dataset covers a second domain"      ),
  ]

  _lowerCamelCase		   =  "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.






  def   snake_case		(   self					):







   """simple docstring"""
   # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
   if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
    lowerCamelCase_								=  datasets.Features(
        {
            "sentence": datasets.Value("string"					),
            "option1": datasets.Value("string"					),
            "answer": datasets.Value("string"					)
            # These are the features of your dataset like images, labels ...
        }					)
   else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
    lowerCamelCase_								=  datasets.Features(
        {
            "sentence": datasets.Value("string"					),
            "option2": datasets.Value("string"					),
            "second_domain_answer": datasets.Value("string"					)
            # These are the features of your dataset like images, labels ...
        }					)
   return datasets.DatasetInfo(
       # This is the description that will appear on the datasets page.
       description=_DESCRIPTION      ,  features=UpperCamelCase      ,  homepage=_HOMEPAGE      ,  license=_LICENSE      ,  citation=_CITATION      ,  )






  def   snake_case		(   self      ,  UpperCamelCase					):







   """simple docstring"""
   # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
   # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

   # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
   # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
   # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
   lowerCamelCase_								=  _URLS[self.config.name]
   lowerCamelCase_								=  dl_manager.download_and_extract(UpperCamelCase					)
   return [
       datasets.SplitGenerator(
           name=datasets.Split.TRAIN      ,  gen_kwargs={
               "filepath": os.path.join(UpperCamelCase      ,  "train.jsonl"					),
               "split": "train",
           }      ,  ),
       datasets.SplitGenerator(
           name=datasets.Split.VALIDATION      ,  gen_kwargs={
               "filepath": os.path.join(UpperCamelCase      ,  "dev.jsonl"					),
               "split": "dev",
           }      ,  ),
       datasets.SplitGenerator(
           name=datasets.Split.TEST      ,  gen_kwargs={
               "filepath": os.path.join(UpperCamelCase      ,  "test.jsonl"					),
               "split": "test"
           }      ,  ),
   ]







  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
   # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
   with open(UpperCamelCase      ,  encoding="utf-8"					) as f:
    for key, row in enumerate(UpperCamelCase					):
     lowerCamelCase_								=  json.loads(UpperCamelCase					)
     if self.config.name == "first_domain":
      # Yields examples as (key, example) tuples
      yield key, {
          "sentence": data["sentence"],
          "option1": data["option1"],
          "answer": "" if split == "test" else data["answer"],
      }
     else:
      yield key, {
          "sentence": data["sentence"],
          "option2": data["option2"],
          "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
      }



