import os

from ...utils import logging
from .utils import DataProcessor, InputExample


_lowerCamelCase    :      Any	 =						logging.get_logger(__name__)


class   lowercase			(   a   ):




							def __init__(  self      :    List[Any]		,							_UpperCamelCase      :    List[str]		,							_UpperCamelCase      :    Dict=None					)    -> str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				language
												SCREAMING_SNAKE_CASE				=				train_language




							def 		__snake_case(  self      :    Any		,							_UpperCamelCase      :    Union[str, Any]					)    -> Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.language if self.train_language is None else self.train_language
												SCREAMING_SNAKE_CASE				=				self._read_tsv(os.path.join(_UpperCamelCase		,							F"XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv"					)					)
												SCREAMING_SNAKE_CASE				=				[]
												for i, line in enumerate(_UpperCamelCase					):
																	if i == 0:
																						continue
																	SCREAMING_SNAKE_CASE				=				F"train-{i}"
																	SCREAMING_SNAKE_CASE				=				line[0]
																	SCREAMING_SNAKE_CASE				=				line[1]
																	SCREAMING_SNAKE_CASE				=				"contradiction" if line[2] == "contradictory" else line[2]
																	if not isinstance(_UpperCamelCase		,							_UpperCamelCase					):
																						raise ValueError(F"Training input {text_a} is not a string"					)
																	if not isinstance(_UpperCamelCase		,							_UpperCamelCase					):
																						raise ValueError(F"Training input {text_b} is not a string"					)
																	if not isinstance(_UpperCamelCase		,							_UpperCamelCase					):
																						raise ValueError(F"Training label {label} is not a string"					)
																	examples.append(InputExample(guid=_UpperCamelCase		,							text_a=_UpperCamelCase		,							text_b=_UpperCamelCase		,							label=_UpperCamelCase					)					)
												return examples




							def 		__snake_case(  self      :    Any		,							_UpperCamelCase      :    Any					)    -> str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self._read_tsv(os.path.join(_UpperCamelCase		,							"XNLI-1.0/xnli.test.tsv"					)					)
												SCREAMING_SNAKE_CASE				=				[]
												for i, line in enumerate(_UpperCamelCase					):
																	if i == 0:
																						continue
																	SCREAMING_SNAKE_CASE				=				line[0]
																	if language != self.language:
																						continue
																	SCREAMING_SNAKE_CASE				=				F"test-{i}"
																	SCREAMING_SNAKE_CASE				=				line[6]
																	SCREAMING_SNAKE_CASE				=				line[7]
																	SCREAMING_SNAKE_CASE				=				line[1]
																	if not isinstance(_UpperCamelCase		,							_UpperCamelCase					):
																						raise ValueError(F"Training input {text_a} is not a string"					)
																	if not isinstance(_UpperCamelCase		,							_UpperCamelCase					):
																						raise ValueError(F"Training input {text_b} is not a string"					)
																	if not isinstance(_UpperCamelCase		,							_UpperCamelCase					):
																						raise ValueError(F"Training label {label} is not a string"					)
																	examples.append(InputExample(guid=_UpperCamelCase		,							text_a=_UpperCamelCase		,							text_b=_UpperCamelCase		,							label=_UpperCamelCase					)					)
												return examples





							def 		__snake_case(  self      :    int					)    -> List[str]:



												'''simple docstring'''

												return ["contradiction", "entailment", "neutral"]



_lowerCamelCase    :      List[str]	 =						{
    '''xnli''': XnliProcessor,
}

_lowerCamelCase    :      Optional[int]	 =						{
    '''xnli''': '''classification''',
}

_lowerCamelCase    :      Union[str, Any]	 =						{
    '''xnli''': 3,
}





import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


_lowerCamelCase    :      int	 =						10_00_00_00_00_00
_lowerCamelCase    :      Union[str, Any]	 =						1_00_00

_lowerCamelCase ,  _lowerCamelCase    :      List[str]	 =						os.path.split(__file__)
_lowerCamelCase    :      Any	 =						os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))





def 		__lowerCamelCase							(UpperCAmelCase__   :							int					,       UpperCAmelCase__   :							int     ):
					SCREAMING_SNAKE_CASE				=				pa.Table.from_pydict({"col": [0] * chunk_size}     )
					SCREAMING_SNAKE_CASE				=				pa.concat_tables([table] * (num_examples // chunk_size)     )
					return datasets.Dataset(UpperCAmelCase__					,       fingerprint="table_100B"     )


@dataclass
class   lowercase			:
							lowercase__  :				int
							lowercase__  :				int
							lowercase__  :				int
							lowercase__  :				int




							def 		__snake_case(  self      :    Tuple					)    -> Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				np.random.default_rng(self.seed					)
												SCREAMING_SNAKE_CASE				=				rng.integers(low=self.low		,							high=self.high		,							size=self.size					).tolist()




							def __iter__(  self      :    Union[str, Any]					)    -> Dict:



												'''simple docstring'''

												return iter(self._sampled_values					)





							def __len__(  self      :    Any					)    -> int:



												'''simple docstring'''

												return self.size





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							datasets.Dataset     ):
					SCREAMING_SNAKE_CASE				=				dataset[0]





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							datasets.Dataset     ):
					SCREAMING_SNAKE_CASE				=				dataset[-1]





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							datasets.Dataset     ):
					SCREAMING_SNAKE_CASE				=				dataset[range(len(UpperCAmelCase__     ) // 2					,       len(UpperCAmelCase__     ) // 2 + 1_0_2_4     )]





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							datasets.Dataset     ):
					SCREAMING_SNAKE_CASE				=				dataset[RandIter(0					,       len(UpperCAmelCase__     )					,       1_0_2_4					,       seed=4_2     )]





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE				=				{"num examples": SPEED_TEST_N_EXAMPLES}
					SCREAMING_SNAKE_CASE				=				(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
					print("generating dataset"     )
					SCREAMING_SNAKE_CASE				=				generate_100B_dataset(num_examples=UpperCAmelCase__					,       chunk_size=UpperCAmelCase__     )
					print("Functions"     )
					for func in functions:
										print(func.__name__     )
										SCREAMING_SNAKE_CASE				=				func(UpperCAmelCase__     )

					with open(UpperCAmelCase__					,       "wb"     ) as f:
										f.write(json.dumps(UpperCAmelCase__     ).encode("utf-8"     )     )


if __name__ == "__main__":  # useful to run the profiler
							benchmark_table_100B()




