






import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		pytest.mark.integration

@pytest.mark.parametrize('''path''',	['''paws''', '''csv''']			)
def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				inspect_dataset(snake_case,	snake_case			)
				__magic_name__		:Optional[Any]   =   path + '''.py'''
				assert script_name in os.listdir(snake_case			)
				assert "__pycache__" not in os.listdir(snake_case			)

@pytest.mark.filterwarnings('''ignore:inspect_metric is deprecated:FutureWarning'''			)
@pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning'''			)
@pytest.mark.parametrize('''path''',	['''accuracy''']			)
def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				inspect_metric(snake_case,	snake_case			)
				__magic_name__		:Dict   =   path + '''.py'''
				assert script_name in os.listdir(snake_case			)
				assert "__pycache__" not in os.listdir(snake_case			)

@pytest.mark.parametrize(
    '''path, config_name, expected_splits''',	[
        ('''squad''', '''plain_text''', ['''train''', '''validation''']),
        ('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
        ('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
    ],	)
def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:int   =   get_dataset_config_info(snake_case,	config_name=snake_case			)
				assert info.config_name == config_name
				assert list(info.splits.keys()			) == expected_splits

@pytest.mark.parametrize(
    '''path, config_name, expected_exception''',	[
        ('''paws''', None, ValueError),
    ],	)
def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				with pytest.raises(snake_case			):
								get_dataset_config_info(snake_case,	config_name=snake_case			)

@pytest.mark.parametrize(
    '''path, expected''',	[
        ('''squad''', '''plain_text'''),
        ('''acronym_identification''', '''default'''),
        ('''lhoestq/squad''', '''plain_text'''),
        ('''lhoestq/test''', '''default'''),
        ('''lhoestq/demo1''', '''lhoestq--demo1'''),
        ('''dalle-mini/wit''', '''dalle-mini--wit'''),
    ],	)
def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Tuple   =   get_dataset_config_names(snake_case			)
				assert expected in config_names

@pytest.mark.parametrize(
    '''path, expected_configs, expected_splits_in_first_config''',	[
        ('''squad''', ['''plain_text'''], ['''train''', '''validation''']),
        ('''dalle-mini/wit''', ['''dalle-mini--wit'''], ['''train''']),
        ('''paws''', ['''labeled_final''', '''labeled_swap''', '''unlabeled_final'''], ['''train''', '''test''', '''validation''']),
    ],	)
def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:int   =   get_dataset_infos(snake_case			)
				assert list(infos.keys()			) == expected_configs
				__magic_name__		:List[Any]   =   expected_configs[0]
				assert expected_config in infos
				__magic_name__		:int   =   infos[expected_config]
				assert info.config_name == expected_config
				assert list(info.splits.keys()			) == expected_splits_in_first_config

@pytest.mark.parametrize(
    '''path, expected_config, expected_splits''',	[
        ('''squad''', '''plain_text''', ['''train''', '''validation''']),
        ('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
        ('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
    ],	)
def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Optional[int]   =   get_dataset_infos(snake_case			)
				assert expected_config in infos
				__magic_name__		:Optional[int]   =   infos[expected_config]
				assert info.config_name == expected_config
				assert list(info.splits.keys()			) == expected_splits

@pytest.mark.parametrize(
    '''path, config_name, expected_exception''',	[
        ('''paws''', None, ValueError),
    ],	)
def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				with pytest.raises(snake_case			):
								get_dataset_split_names(snake_case,	config_name=snake_case			)














import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger
class     lowerCamelCase_					(   lowerCamelCase			):







				def __init__(    self							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   params

								__magic_name__		:Any   =   np.array(__lowerCAmelCase  )
								__magic_name__		:Optional[Any]   =   np.array([len(__lowerCAmelCase  ) for t in data]  )

								self.check()
								self.remove_long_sequences()
								self.remove_empty_sequences()
								self.remove_unknown_sequences()
								self.check()
								self.print_statistics()







				def __getitem__(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								return (self.token_ids[index], self.lengths[index])







				def __len__(    self  ):
								"""simple docstring"""
								return len(self.lengths  )







				def       A						(    self  ):
								"""simple docstring"""
								assert len(self.token_ids  ) == len(self.lengths  )
								assert all(self.lengths[i] == len(self.token_ids[i]  ) for i in range(len(self.lengths  )  )  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Any   =   self.params.max_model_input_size
								__magic_name__		:int   =   self.lengths > max_len
								logger.info(F'''Splitting {sum(__lowerCAmelCase  )} too long sequences.'''  )

								def divide_chunks(__lowerCAmelCase							,       __lowerCAmelCase  ):
												return [l[i : i + n] for i in range(0							,       len(__lowerCAmelCase  )							,       __lowerCAmelCase  )]

								__magic_name__		:Optional[int]   =   []
								__magic_name__		:List[Any]   =   []
								if self.params.mlm:
												__magic_name__  ,					__magic_name__		:Optional[Any]   =   self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
								else:
												__magic_name__  ,					__magic_name__		:Tuple   =   self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']

								for seq_, len_ in zip(self.token_ids							,       self.lengths  ):
												assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
												if len_ <= max_len:
																new_tok_ids.append(seq_  )
																new_lengths.append(len_  )
												else:
																__magic_name__		:int   =   []
																for sub_s in divide_chunks(seq_							,       max_len - 2  ):
																				if sub_s[0] != cls_id:
																								__magic_name__		:List[Any]   =   np.insert(__lowerCAmelCase							,       0							,       __lowerCAmelCase  )
																				if sub_s[-1] != sep_id:
																								__magic_name__		:Union[str, Any]   =   np.insert(__lowerCAmelCase							,       len(__lowerCAmelCase  )							,       __lowerCAmelCase  )
																				assert len(__lowerCAmelCase  ) <= max_len
																				assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																				sub_seqs.append(__lowerCAmelCase  )

																new_tok_ids.extend(__lowerCAmelCase  )
																new_lengths.extend([len(__lowerCAmelCase  ) for l in sub_seqs]  )

								__magic_name__		:Tuple   =   np.array(__lowerCAmelCase  )
								__magic_name__		:Optional[int]   =   np.array(__lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   len(self  )
								__magic_name__		:int   =   self.lengths > 1_1
								__magic_name__		:List[str]   =   self.token_ids[indices]
								__magic_name__		:Union[str, Any]   =   self.lengths[indices]
								__magic_name__		:List[str]   =   len(self  )
								logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''  )







				def       A						(    self  ):
								"""simple docstring"""
								if "unk_token" not in self.params.special_tok_ids:
												return
								else:
												__magic_name__		:Tuple   =   self.params.special_tok_ids['''unk_token''']
								__magic_name__		:Dict   =   len(self  )
								__magic_name__		:Tuple   =   np.array([np.count_nonzero(a == unk_token_id  ) for a in self.token_ids]  )
								__magic_name__		:int   =   (unk_occs / self.lengths) < 0.5
								__magic_name__		:str   =   self.token_ids[indices]
								__magic_name__		:str   =   self.lengths[indices]
								__magic_name__		:Any   =   len(self  )
								logger.info(F'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'''  )







				def       A						(    self  ):
								"""simple docstring"""
								if not self.params.is_master:
												return
								logger.info(F'''{len(self  )} sequences'''  )
								# data_len = sum(self.lengths)
								# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
								# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

								# unk_idx = self.params.special_tok_ids['unk_token']
								# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
								# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   [t[0] for t in batch]
								__magic_name__		:List[Any]   =   [t[1] for t in batch]
								assert len(__lowerCAmelCase  ) == len(__lowerCAmelCase  )

								# Max for paddings
								__magic_name__		:Tuple   =   max(__lowerCAmelCase  )

								# Pad token ids
								if self.params.mlm:
												__magic_name__		:Any   =   self.params.special_tok_ids['''pad_token''']
								else:
												__magic_name__		:str   =   self.params.special_tok_ids['''unk_token''']
								__magic_name__		:Any   =   [list(t.astype(__lowerCAmelCase  )  ) + [pad_idx] * (max_seq_len_ - len(__lowerCAmelCase  )) for t in token_ids]
								assert len(tk_  ) == len(__lowerCAmelCase  )
								assert all(len(__lowerCAmelCase  ) == max_seq_len_ for t in tk_  )

								__magic_name__		:Optional[int]   =   torch.tensor(tk_  )  # (bs, max_seq_len_)
								__magic_name__		:Optional[int]   =   torch.tensor(__lowerCAmelCase  )  # (bs)
								return tk_t, lg_t






