

import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


SCREAMING_SNAKE_CASE_          =   namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)

def 				__SCREAMING_SNAKE_CASE				(					lowerCAmelCase:	Union[str, Any]	,   lowerCAmelCase:	Dict			) ->							Dict:
				return (abs(source - target			) / target) < 0.01





@pytest.mark.integration
def 				__SCREAMING_SNAKE_CASE				(					lowerCAmelCase:	List[str]			) ->							Union[str, Any]:
				_UpperCAmelCase							:		List[str]					=     _TestCommandArgs(dataset=__a	,   all_configs=__a	,   save_infos=__a			)
				_UpperCAmelCase							:		Any					=     TestCommand(*__a			)
				test_command.run()
				_UpperCAmelCase							:		Tuple					=     os.path.join(__a	,   "README.md"			)
				assert os.path.exists(__a			)
				_UpperCAmelCase							:		Tuple					=     DatasetInfosDict.from_directory(__a			)
				_UpperCAmelCase							:		Dict					=     DatasetInfosDict(
				    {
				        "default": DatasetInfo(
				            features=Features(
				                {
				                    "tokens": Sequence(Value("string"			)			),
				                    "ner_tags": Sequence(
				                        ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]			)			),
				                    "langs": Sequence(Value("string"			)			),
				                    "spans": Sequence(Value("string"			)			),
				                }			)	,   splits=[
				                {
				                    "name": "train",
				                    "num_bytes": 235_1563,
				                    "num_examples": 1_0000,
				                },
				                {
				                    "name": "validation",
				                    "num_bytes": 23_8418,
				                    "num_examples": 1000,
				                },
				            ]	,   download_size=394_0680	,   dataset_size=258_9981	,   )
				    }			)
				assert dataset_infos.keys() == expected_dataset_infos.keys()
				for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
								_UpperCAmelCase		,				_UpperCAmelCase							:		Optional[Any]					=     getattr(dataset_infos["default"]	,   __a			), getattr(expected_dataset_infos["default"]	,   __a			)
								if key == "num_bytes":
												assert is_apercent_close(__a	,   __a			)
								elif key == "splits":
												assert list(__a			) == list(__a			)
												for split in result:
																assert result[split].name == expected[split].name
																assert result[split].num_examples == expected[split].num_examples
																assert is_apercent_close(result[split].num_bytes	,   expected[split].num_bytes			)
								else:
												result == expected




"""simple docstring"""




import doctest
from collections import deque

import numpy as np





class 			snake_case  :

							def __init__(self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			[2, 1, 2, -1]
									SCREAMING_SNAKE_CASE_    			=			[1, 2, 3, 4]







							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			len(self.first_signal     )
									SCREAMING_SNAKE_CASE_    			=			len(self.second_signal     )

									SCREAMING_SNAKE_CASE_    			=			max(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

									# create a zero matrix of max_length x max_length
									SCREAMING_SNAKE_CASE_    			=			[[0] * max_length for i in range(SCREAMING_SNAKE_CASE_     )]

									# fills the smaller signal with zeros to make both signals of same length
									if length_first_signal < length_second_signal:
											self.first_signal += [0] * (max_length - length_first_signal)
									elif length_first_signal > length_second_signal:
											self.second_signal += [0] * (max_length - length_second_signal)
									for i in range(SCREAMING_SNAKE_CASE_     ):
											SCREAMING_SNAKE_CASE_    			=			deque(self.second_signal     )
											rotated_signal.rotate(SCREAMING_SNAKE_CASE_     )
											for j, item in enumerate(SCREAMING_SNAKE_CASE_     ):
													matrix[i][j] += item

        # multiply the matrix with the first signal
									SCREAMING_SNAKE_CASE_    			=			np.matmul(np.transpose(SCREAMING_SNAKE_CASE_     )							,							np.transpose(self.first_signal     )     )

									# rounding-off to two decimal places
									return [round(SCREAMING_SNAKE_CASE_							,							2     ) for i in final_signal]


if __name__ == "__main__":
						doctest.testmod()