


'''simple docstring'''



import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases



def       UpperCAmelCase__ (     UpperCAmelCase_   :    Union[str, Any]				,   UpperCAmelCase_   :    Tuple							)					->		Union[str, Any]:
     assert isinstance(UpperCAmelCase_				,   UpperCAmelCase_							)
     assert dataset.num_rows == 4
     assert dataset.num_columns == 3
     assert dataset.column_names == ["col_1", "col_2", "col_3"]
     for feature, expected_dtype in expected_features.items():
          assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize('keep_in_memory'				,   [False, True]							)
def       UpperCAmelCase__ (     UpperCAmelCase_   :    str				,   UpperCAmelCase_   :    List[str]				,   UpperCAmelCase_   :    Dict							)					->		Union[str, Any]:
     __lowerCamelCase       :     Optional[int]        =		tmp_path / 'cache'
     __lowerCamelCase       :     Tuple        =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          __lowerCamelCase       :     List[Any]        =		CsvDatasetReader(UpperCAmelCase_				,   cache_dir=UpperCAmelCase_				,   keep_in_memory=UpperCAmelCase_							).read()
     _check_csv_dataset(UpperCAmelCase_				,   UpperCAmelCase_							)



@pytest.mark.parametrize(
    'features'				,   [
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]				,   )
def       UpperCAmelCase__ (     UpperCAmelCase_   :    str				,   UpperCAmelCase_   :    Union[str, Any]				,   UpperCAmelCase_   :    Any							)					->		Optional[int]:
     __lowerCamelCase       :     List[str]        =		tmp_path / 'cache'
     # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
     __lowerCamelCase       :     Optional[int]        =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     __lowerCamelCase       :     List[str]        =		features.copy() if features else default_expected_features
     __lowerCamelCase       :     Dict        =		(
         Features({feature: Value(UpperCAmelCase_							) for feature, dtype in features.items()}							) if features is not None else None
     )
     __lowerCamelCase       :     str        =		CsvDatasetReader(UpperCAmelCase_				,   features=UpperCAmelCase_				,   cache_dir=UpperCAmelCase_							).read()
     _check_csv_dataset(UpperCAmelCase_				,   UpperCAmelCase_							)



@pytest.mark.parametrize('split'				,   [None, NamedSplit('train'							), 'train', 'test']							)
def       UpperCAmelCase__ (     UpperCAmelCase_   :    Tuple				,   UpperCAmelCase_   :    int				,   UpperCAmelCase_   :    Dict							)					->		Optional[Any]:
     __lowerCamelCase       :     Union[str, Any]        =		tmp_path / 'cache'
     __lowerCamelCase       :     str        =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     __lowerCamelCase       :     int        =		CsvDatasetReader(UpperCAmelCase_				,   cache_dir=UpperCAmelCase_				,   split=UpperCAmelCase_							).read()
     _check_csv_dataset(UpperCAmelCase_				,   UpperCAmelCase_							)
     assert dataset.split == split if split else "train"



@pytest.mark.parametrize('path_type'				,   [str, list]							)
def       UpperCAmelCase__ (     UpperCAmelCase_   :    List[str]				,   UpperCAmelCase_   :    Tuple				,   UpperCAmelCase_   :    Dict							)					->		Optional[Any]:
     if issubclass(UpperCAmelCase_				,   UpperCAmelCase_							):
          __lowerCamelCase       :     Tuple        =		csv_path
     elif issubclass(UpperCAmelCase_				,   UpperCAmelCase_							):
          __lowerCamelCase       :     List[str]        =		[csv_path]
     __lowerCamelCase       :     int        =		tmp_path / 'cache'
     __lowerCamelCase       :     str        =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     __lowerCamelCase       :     int        =		CsvDatasetReader(UpperCAmelCase_				,   cache_dir=UpperCAmelCase_							).read()
     _check_csv_dataset(UpperCAmelCase_				,   UpperCAmelCase_							)



def       UpperCAmelCase__ (     UpperCAmelCase_   :    Tuple				,   UpperCAmelCase_   :    Dict				,   UpperCAmelCase_   :    Dict=("train",)							)					->		Dict:
     assert isinstance(UpperCAmelCase_				,   UpperCAmelCase_							)
     for split in splits:
          __lowerCamelCase       :     List[Any]        =		dataset_dict[split]
          assert dataset.num_rows == 4
          assert dataset.num_columns == 3
          assert dataset.column_names == ["col_1", "col_2", "col_3"]
          for feature, expected_dtype in expected_features.items():
               assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize('keep_in_memory'				,   [False, True]							)
def       UpperCAmelCase__ (     UpperCAmelCase_   :    int				,   UpperCAmelCase_   :    List[Any]				,   UpperCAmelCase_   :    Any							)					->		str:
     __lowerCamelCase       :     Dict        =		tmp_path / 'cache'
     __lowerCamelCase       :     Optional[int]        =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          __lowerCamelCase       :     Any        =		CsvDatasetReader({'train': csv_path}				,   cache_dir=UpperCAmelCase_				,   keep_in_memory=UpperCAmelCase_							).read()
     _check_csv_datasetdict(UpperCAmelCase_				,   UpperCAmelCase_							)



@pytest.mark.parametrize(
    'features'				,   [
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]				,   )
def       UpperCAmelCase__ (     UpperCAmelCase_   :    List[str]				,   UpperCAmelCase_   :    str				,   UpperCAmelCase_   :    List[str]							)					->		List[str]:
     __lowerCamelCase       :     int        =		tmp_path / 'cache'
     # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
     __lowerCamelCase       :     Any        =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     __lowerCamelCase       :     Optional[Any]        =		features.copy() if features else default_expected_features
     __lowerCamelCase       :     Dict        =		(
         Features({feature: Value(UpperCAmelCase_							) for feature, dtype in features.items()}							) if features is not None else None
     )
     __lowerCamelCase       :     Tuple        =		CsvDatasetReader({'train': csv_path}				,   features=UpperCAmelCase_				,   cache_dir=UpperCAmelCase_							).read()
     _check_csv_datasetdict(UpperCAmelCase_				,   UpperCAmelCase_							)



@pytest.mark.parametrize('split'				,   [None, NamedSplit('train'							), 'train', 'test']							)
def       UpperCAmelCase__ (     UpperCAmelCase_   :    Union[str, Any]				,   UpperCAmelCase_   :    Optional[int]				,   UpperCAmelCase_   :    List[str]							)					->		List[str]:
     if split:
          __lowerCamelCase       :     Optional[int]        =		{split: csv_path}
     else:
          __lowerCamelCase       :     Dict        =		{'train': csv_path, 'test': csv_path}
     __lowerCamelCase       :     Optional[int]        =		tmp_path / 'cache'
     __lowerCamelCase       :     Tuple        =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     __lowerCamelCase       :     Optional[int]        =		CsvDatasetReader(UpperCAmelCase_				,   cache_dir=UpperCAmelCase_							).read()
     _check_csv_datasetdict(UpperCAmelCase_				,   UpperCAmelCase_				,   splits=list(path.keys()							)							)
     assert all(dataset[split].split == split for split in path.keys()							)



def       UpperCAmelCase__ (     UpperCAmelCase_   :    Optional[Any]							)					->		Tuple:
     with open(UpperCAmelCase_				,   encoding='utf-8'							) as csvfile:
          yield from csv.reader(UpperCAmelCase_							)



def       UpperCAmelCase__ (     UpperCAmelCase_   :    int				,   UpperCAmelCase_   :    Dict							)					->		int:
     __lowerCamelCase       :     Tuple        =		tmp_path / 'cache'
     __lowerCamelCase       :     int        =		os.path.join(UpperCAmelCase_				,   'tmp.csv'							)
     __lowerCamelCase       :     List[Any]        =		CsvDatasetReader({'train': csv_path}				,   cache_dir=UpperCAmelCase_							).read()
     CsvDatasetWriter(dataset['train']				,   UpperCAmelCase_				,   num_proc=1							).write()

     __lowerCamelCase       :     Any        =		iter_csv_file(UpperCAmelCase_							)
     __lowerCamelCase       :     Union[str, Any]        =		iter_csv_file(UpperCAmelCase_							)

     for rowa, rowa in zip(UpperCAmelCase_				,   UpperCAmelCase_							):
          assert rowa == rowa



def       UpperCAmelCase__ (     UpperCAmelCase_   :    Union[str, Any]				,   UpperCAmelCase_   :    str							)					->		str:
     __lowerCamelCase       :     Any        =		tmp_path / 'cache'
     __lowerCamelCase       :     Tuple        =		os.path.join(UpperCAmelCase_				,   'tmp.csv'							)
     __lowerCamelCase       :     Dict        =		CsvDatasetReader({'train': csv_path}				,   cache_dir=UpperCAmelCase_							).read()
     CsvDatasetWriter(dataset['train']				,   UpperCAmelCase_				,   num_proc=2							).write()

     __lowerCamelCase       :     Tuple        =		iter_csv_file(UpperCAmelCase_							)
     __lowerCamelCase       :     int        =		iter_csv_file(UpperCAmelCase_							)

     for rowa, rowa in zip(UpperCAmelCase_				,   UpperCAmelCase_							):
          assert rowa == rowa



def       UpperCAmelCase__ (     UpperCAmelCase_   :    str				,   UpperCAmelCase_   :    Tuple							)					->		Dict:
     __lowerCamelCase       :     List[Any]        =		tmp_path / 'cache'
     __lowerCamelCase       :     Optional[int]        =		os.path.join(UpperCAmelCase_				,   'tmp.csv'							)
     __lowerCamelCase       :     int        =		CsvDatasetReader({'train': csv_path}				,   cache_dir=UpperCAmelCase_							).read()
     with pytest.raises(UpperCAmelCase_							):
          CsvDatasetWriter(dataset['train']				,   UpperCAmelCase_				,   num_proc=0							)










'''simple docstring'''



import torch

from transformers import AutoTokenizer





class        UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    def __init__(							self      ,					SCREAMING_SNAKE_CASE_     )    ->					int:
         __lowerCamelCase       :     Dict        =		AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_     )







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_     )    ->					int:

         if isinstance(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     ) and all(isinstance(_x      ,					SCREAMING_SNAKE_CASE_     ) for _x in x     ):
              __lowerCamelCase       :     Union[str, Any]        =		None
              for l in x:
                   __lowerCamelCase       :     Tuple        =		self.tokenizer(
                       SCREAMING_SNAKE_CASE_      ,					padding='max_length'      ,					max_length=3_84      ,					truncation=SCREAMING_SNAKE_CASE_      ,					return_tensors='pt'      ,					)
                   __lowerCamelCase       :     str        =		torch.tensor([len(SCREAMING_SNAKE_CASE_     )]     )
                   if d is not None:
                        for k in d.keys():
                             __lowerCamelCase       :     List[Any]        =		torch.cat((d[k], t[k])      ,					0     )
                   else:
                        __lowerCamelCase       :     str        =		t

              __lowerCamelCase       :     List[Any]        =		torch.tensor(self.tokenizer.convert_tokens_to_ids('[E]'     )     )
              __lowerCamelCase       :     Tuple        =		torch.tensor(self.tokenizer.convert_tokens_to_ids('[/E]'     )     )

         elif isinstance(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     ) and all(isinstance(_x      ,					SCREAMING_SNAKE_CASE_     ) for _x in x     ):
              __lowerCamelCase       :     Optional[Any]        =		self.tokenizer(
                  SCREAMING_SNAKE_CASE_      ,					padding='max_length'      ,					max_length=3_84      ,					truncation=SCREAMING_SNAKE_CASE_      ,					return_tensors='pt'      ,					)

         else:
              raise Exception(
                  'Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'
                  ' strings` for supports are supported.'     )

         return d







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_=0.7_0     )    ->					str:
         __lowerCamelCase       :     str        =		[]
         for idx in range(len(W_query['input_ids']     )     ):
              __lowerCamelCase       :     Optional[Any]        =		range(p_start.shape[1]     )

              __lowerCamelCase       :     List[Any]        =		[]
              for start_id in start_indexes:
                   for end_id in end_indexes:
                        if start_id < end_id:
                             output.append(
                                 (
                                     start_id,
                                     end_id,
                                     p_start[idx][start_id].item(),
                                     p_end[idx][end_id].item(),
                                 )     )

              output.sort(key=lambda SCREAMING_SNAKE_CASE_     : (tup[2] * tup[3])      ,					reverse=SCREAMING_SNAKE_CASE_     )
              __lowerCamelCase       :     str        =		[]
              for k in range(len(SCREAMING_SNAKE_CASE_     )     ):
                   if output[k][2] * output[k][3] >= thresh:
                        __lowerCamelCase    ,	__lowerCamelCase       :     Union[str, Any]        =		output[k][0], output[k][1]
                        __lowerCamelCase       :     List[str]        =		self.tokenizer.decode(W_query['input_ids'][idx][c_start_pos:c_end_pos]     )
                        temp.append((decoded, output[k][2] * output[k][3])     )

              final_outputs.append(SCREAMING_SNAKE_CASE_     )

         return final_outputs






