

import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


SCREAMING_SNAKE_CASE_            = [
    {'dataset': 'wikipedia', 'config_name': '20220301.de'},
    {'dataset': 'wikipedia', 'config_name': '20220301.en'},
    {'dataset': 'wikipedia', 'config_name': '20220301.fr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.frr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.it'},
    {'dataset': 'wikipedia', 'config_name': '20220301.simple'},
    {'dataset': 'snli', 'config_name': 'plain_text'},
    {'dataset': 'eli5', 'config_name': 'LFQA_reddit'},
    {'dataset': 'wiki40b', 'config_name': 'en'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.compressed'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.no_index'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.multiset.no_index'},
    {'dataset': 'natural_questions', 'config_name': 'default'},
]

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int=True	)				->    Dict:
				if with_config:
								return [
								    {
								        "testcase_name": d["dataset"] + "/" + d["config_name"],
								        "dataset": d["dataset"],
								        "config_name": d["config_name"],
								    }
								    for d in DATASETS_ON_HF_GCP
								]
				else:
								return [
								    {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
								]

@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=UpperCAmelCase			)			)
class 			a					(		UpperCAmelCase			):
						_lowercase        =     None
						_lowercase        =     None


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										with TemporaryDirectory() as tmp_dir:
														_UpperCAmelCase    :						Tuple							=      dataset_module_factory(A_  ,   cache_dir=A_		)

														_UpperCAmelCase    :						str							=      import_main_class(dataset_module.module_path  ,   dataset=A_		)

														_UpperCAmelCase    :						DatasetBuilder							=      builder_cls(
														    cache_dir=A_  ,   config_name=A_  ,   hash=dataset_module.hash  ,   )

														_UpperCAmelCase    :						int							=      "/".join(
														    [
														        HF_GCP_BASE_URL,
														        builder_instance._relative_data_dir(with_hash=A_		).replace(os.sep  ,   "/"		),
														        config.DATASET_INFO_FILENAME,
														    ]		)
														_UpperCAmelCase    :						int							=      cached_path(A_  ,   cache_dir=A_		)
														self.assertTrue(os.path.exists(A_		)		)







@pytest.mark.integration
def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Tuple	)				->    Optional[Any]:
				_UpperCAmelCase    :						List[str]							=      tmp_path_factory.mktemp("test_hf_gcp"	) / "test_wikipedia_simple"
				_UpperCAmelCase    :						Any							=      dataset_module_factory("wikipedia"					, cache_dir=lowerCAmelCase	)
				_UpperCAmelCase    :						Tuple							=      import_main_class(dataset_module.module_path	)
				_UpperCAmelCase    :						DatasetBuilder							=      builder_cls(
				    cache_dir=lowerCAmelCase					, config_name="20220301.frr"					, hash=dataset_module.hash					, )
				# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
				_UpperCAmelCase    :						Optional[Any]							=      None
				builder_instance.download_and_prepare()
				_UpperCAmelCase    :						Any							=      builder_instance.as_dataset()
				assert ds





@pytest.mark.integration
def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[Any]	)				->    List[Any]:
				_UpperCAmelCase    :						List[str]							=      dataset_module_factory("wikipedia"					, cache_dir=lowerCAmelCase	)
				_UpperCAmelCase    :						str							=      import_main_class(dataset_module.module_path					, dataset=lowerCAmelCase	)
				_UpperCAmelCase    :						DatasetBuilder							=      builder_cls(
				    cache_dir=lowerCAmelCase					, config_name="20220301.frr"					, hash=dataset_module.hash					, )
				_UpperCAmelCase    :						Any							=      builder_instance.as_streaming_dataset()
				assert ds
				assert isinstance(lowerCAmelCase					, lowerCAmelCase	)
				assert "train" in ds
				assert isinstance(ds["train"]					, lowerCAmelCase	)
				assert next(iter(ds["train"]	)	)





from __future__ import annotations

class 			a					:


						def __init__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      order

										# a_{0} ... a_{k}
										_UpperCAmelCase    :						Tuple							=      [1.0] + [0.0] * order
										# b_{0} ... b_{k}
										_UpperCAmelCase    :						int							=      [1.0] + [0.0] * order

										# x[n-1] ... x[n-k]
										_UpperCAmelCase    :						Optional[Any]							=      [0.0] * self.order
										# y[n-1] ... y[n-k]
										_UpperCAmelCase    :						Dict							=      [0.0] * self.order


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										if len(A_		) < self.order:
														_UpperCAmelCase    :						List[str]							=      [1.0, *a_coeffs]

										if len(A_		) != self.order + 1:
														_UpperCAmelCase    :						List[Any]							=      (
														    f'Expected a_coeffs to have {self.order + 1} elements '
														    f'for {self.order}-order filter, got {len(A_		)}'
														)
														raise ValueError(A_		)

										if len(A_		) != self.order + 1:
														_UpperCAmelCase    :						int							=      (
														    f'Expected b_coeffs to have {self.order + 1} elements '
														    f'for {self.order}-order filter, got {len(A_		)}'
														)
														raise ValueError(A_		)

										_UpperCAmelCase    :						Optional[Any]							=      a_coeffs
										_UpperCAmelCase    :						Union[str, Any]							=      b_coeffs





						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      0.0

										# Start at index 1 and do index 0 at the end.
										for i in range(1  ,   self.order + 1		):
														result += (
														    self.b_coeffs[i] * self.input_history[i - 1]
														    - self.a_coeffs[i] * self.output_history[i - 1]
														)

										_UpperCAmelCase    :						Dict							=      (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

										_UpperCAmelCase    :						Optional[Any]							=      self.input_history[:-1]
										_UpperCAmelCase    :						Optional[int]							=      self.output_history[:-1]

										_UpperCAmelCase    :						Optional[Any]							=      sample
										_UpperCAmelCase    :						str							=      result

										return result


