




"""simple docstring"""







import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


__A   :  Dict					=      'http://www.mocksite.com/file1.txt'
__A   :  Any					=      '"text": ["foo", "foo"]'
__A   :  List[Any]					=      '6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'




class       lowerCAmelCase__       :


				"""simple docstring"""





				__UpperCAmelCase :     Dict       =      200
				__UpperCAmelCase :     Optional[int]       =      {"Content-Length": "100"}
				__UpperCAmelCase :     Optional[Any]       =      {}



				def       snake_case    (     self					:	List[str]    ,   **lowercase__					:	Union[str, Any]						):
						return [bytes(lowercase__    ,   "utf-8"						)]





def 						snake_case__       (					*_lowerCamelCase,  **_lowerCamelCase				)			->int:
		return MockResponse()

@pytest.mark.parametrize("urls_type",  [str, list, dict]				)
def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->Any:
		import requests

		monkeypatch.setattr(_lowerCamelCase,  "request",  _lowerCamelCase				)

		__lowercase				:		Dict												=				URL
		if issubclass(_lowerCamelCase,  _lowerCamelCase				):
				__lowercase				:		Optional[Any]												=				url
		elif issubclass(_lowerCamelCase,  _lowerCamelCase				):
				__lowercase				:		str												=				[url]
		elif issubclass(_lowerCamelCase,  _lowerCamelCase				):
				__lowercase				:		List[str]												=				{"train": url}
		__lowercase				:		str												=				"dummy"
		__lowercase				:		str												=				"downloads"
		__lowercase				:		Any												=				tmp_path
		__lowercase				:		int												=				DownloadConfig(
		    cache_dir=os.path.join(_lowerCamelCase,  _lowerCamelCase				),  use_etag=_lowerCamelCase,  )
		__lowercase				:		Union[str, Any]												=				DownloadManager(dataset_name=_lowerCamelCase,  download_config=_lowerCamelCase				)
		__lowercase				:		List[str]												=				dl_manager.download(_lowerCamelCase				)
		__lowercase				:		Tuple												=				urls
		for downloaded_paths in [downloaded_paths]:
				if isinstance(_lowerCamelCase,  _lowerCamelCase				):
						__lowercase				:		Dict												=				[downloaded_paths]
						__lowercase				:		Any												=				[urls]
				elif isinstance(_lowerCamelCase,  _lowerCamelCase				):
						assert "train" in downloaded_paths.keys()
						__lowercase				:		int												=				downloaded_paths.values()
						__lowercase				:		Any												=				urls.values()
				assert downloaded_paths
				for downloaded_path, input_url in zip(_lowerCamelCase,  _lowerCamelCase				):
						assert downloaded_path == dl_manager.downloaded_paths[input_url]
						__lowercase				:		Any												=				Path(_lowerCamelCase				)
						__lowercase				:		Optional[int]												=				downloaded_path.parts
						assert parts[-1] == HASH
						assert parts[-2] == cache_subdir
						assert downloaded_path.exists()
						__lowercase				:		List[str]												=				downloaded_path.read_text()
						assert content == CONTENT
						__lowercase				:		Union[str, Any]												=				downloaded_path.with_suffix(".json"				)
						assert metadata_downloaded_path.exists()
						__lowercase				:		Union[str, Any]												=				json.loads(metadata_downloaded_path.read_text()				)
						assert metadata_content == {"url": URL, "etag": None}

@pytest.mark.parametrize("paths_type",  [str, list, dict]				)
def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->int:
		__lowercase				:		Dict												=				str(_lowerCamelCase				)
		if issubclass(_lowerCamelCase,  _lowerCamelCase				):
				__lowercase				:		Optional[Any]												=				filename
		elif issubclass(_lowerCamelCase,  _lowerCamelCase				):
				__lowercase				:		List[str]												=				[filename]
		elif issubclass(_lowerCamelCase,  _lowerCamelCase				):
				__lowercase				:		str												=				{"train": filename}
		__lowercase				:		str												=				"dummy"
		__lowercase				:		str												=				xz_file.parent
		__lowercase				:		int												=				"extracted"
		__lowercase				:		Union[str, Any]												=				DownloadConfig(
		    cache_dir=_lowerCamelCase,  use_etag=_lowerCamelCase,  )
		__lowercase				:		Union[str, Any]												=				DownloadManager(dataset_name=_lowerCamelCase,  download_config=_lowerCamelCase				)
		__lowercase				:		Optional[int]												=				dl_manager.extract(_lowerCamelCase				)
		__lowercase				:		Any												=				paths
		for extracted_paths in [extracted_paths]:
				if isinstance(_lowerCamelCase,  _lowerCamelCase				):
						__lowercase				:		int												=				[extracted_paths]
						__lowercase				:		List[str]												=				[paths]
				elif isinstance(_lowerCamelCase,  _lowerCamelCase				):
						assert "train" in extracted_paths.keys()
						__lowercase				:		Tuple												=				extracted_paths.values()
						__lowercase				:		List[str]												=				paths.values()
				assert extracted_paths
				for extracted_path, input_path in zip(_lowerCamelCase,  _lowerCamelCase				):
						assert extracted_path == dl_manager.extracted_paths[input_path]
						__lowercase				:		Union[str, Any]												=				Path(_lowerCamelCase				)
						__lowercase				:		Optional[int]												=				extracted_path.parts
						assert parts[-1] == hash_url_to_filename(_lowerCamelCase,  etag=_lowerCamelCase				)
						assert parts[-2] == extracted_subdir
						assert extracted_path.exists()
						__lowercase				:		List[Any]												=				extracted_path.read_text()
						__lowercase				:		Any												=				text_file.read_text()
						assert extracted_file_content == expected_file_content

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->Tuple:
		assert path.endswith(".jsonl"				)
		for num_items, line in enumerate(_lowerCamelCase,  start=1				):
				__lowercase				:		Tuple												=				json.loads(line.decode("utf-8"				)				)
				assert item.keys() == {"col_1", "col_2", "col_3"}
		assert num_items == 4

@pytest.mark.parametrize("archive_jsonl",  ["tar_jsonl_path", "zip_jsonl_path"]				)
def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->List[Any]:
		__lowercase				:		List[Any]												=				request.getfixturevalue(_lowerCamelCase				)
		__lowercase				:		Union[str, Any]												=				DownloadManager()
		for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(_lowerCamelCase				),  start=1				):
				_test_jsonl(_lowerCamelCase,  _lowerCamelCase				)
		assert num_jsonl == 2

@pytest.mark.parametrize("archive_nested_jsonl",  ["tar_nested_jsonl_path", "zip_nested_jsonl_path"]				)
def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->Optional[Any]:
		__lowercase				:		int												=				request.getfixturevalue(_lowerCamelCase				)
		__lowercase				:		str												=				DownloadManager()
		for num_tar, (path, file) in enumerate(dl_manager.iter_archive(_lowerCamelCase				),  start=1				):
				for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(_lowerCamelCase				),  start=1				):
						_test_jsonl(_lowerCamelCase,  _lowerCamelCase				)
		assert num_tar == 1
		assert num_jsonl == 2





def 						snake_case__       (					_lowerCamelCase				)			->Tuple:
		__lowercase				:		Optional[Any]												=				DownloadManager()
		for num_file, file in enumerate(dl_manager.iter_files(_lowerCamelCase				),  start=1				):
				assert os.path.basename(_lowerCamelCase				) == ("test.txt" if num_file == 1 else "train.txt")
		assert num_file == 2






"""simple docstring"""







from scipy.stats import spearmanr

import datasets


__A   :  Optional[int]					=      '\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n'

__A   :  List[Any]					=      '\nArgs:\n    predictions (`List[float]`): Predicted labels, as returned by a model.\n    references (`List[float]`): Ground truth labels.\n    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n            only the spearmanr score. Defaults to `False`.\nReturns:\n    spearmanr (`float`): Spearman correlation coefficient.\n    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n    Example 1:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n        >>> print(results)\n        {\'spearmanr\': -0.7}\n\n    Example 2:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n        ...                                     predictions=[10, 9, 2.5, 6, 4],\n        ...                                     return_pvalue=True)\n        >>> print(results[\'spearmanr\'])\n        -0.7\n        >>> print(round(results[\'spearmanr_pvalue\'], 2))\n        0.19\n'

__A   :  Optional[int]					=      R'\\n@book{kokoska2000crc,\n  title={CRC standard probability and statistics tables and formulae},\n  author={Kokoska, Stephen and Zwillinger, Daniel},\n  year={2000},\n  publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n            Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n            Kern, Robert and Larson, Eric and Carey, C J and\n            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and\n            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n            Harris, Charles R. and Archibald, Anne M. and\n            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and\n            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n            Computing in Python}},\n  journal = {Nature Methods},\n  year    = {2020},\n  volume  = {17},\n  pages   = {261--272},\n  adsurl  = {https://rdcu.be/b08Wh},\n  doi     = {10.1038/s41592-019-0686-2},\n}\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,			_KWARGS_DESCRIPTION    )
class       lowerCAmelCase__       (			datasets.Metric    ):


				"""simple docstring"""





				def       snake_case    (     self					:	List[str]						):
						return datasets.MetricInfo(
						    description=_DESCRIPTION    ,   citation=_CITATION    ,   inputs_description=_KWARGS_DESCRIPTION    ,   features=datasets.Features(
						        {
						            "predictions": datasets.Value("float"						),
						            "references": datasets.Value("float"						),
						        }						)    ,   reference_urls=["https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"]    ,   )



				def       snake_case    (     self					:	Optional[int]    ,   lowercase__					:	str    ,   lowercase__					:	Any    ,   lowercase__					:	int=False						):
						__lowercase				:		Optional[int]												=				spearmanr(lowercase__    ,   lowercase__						)
						if return_pvalue:
								return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
						else:
								return {"spearmanr": results[0]}
