






import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class     lowerCamelCase_					(   lowerCamelCase			):







				def __init__(    self							,       __lowerCAmelCase							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = False							,       __lowerCAmelCase = False							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								super().__init__(
								    __lowerCAmelCase							,       split=__lowerCAmelCase							,       features=__lowerCAmelCase							,       cache_dir=__lowerCAmelCase							,       keep_in_memory=__lowerCAmelCase							,       streaming=__lowerCAmelCase							,       num_proc=__lowerCAmelCase							,       **__lowerCAmelCase							,       )
								__magic_name__		:Optional[int]   =   field
								__magic_name__		:List[Any]   =   path_or_paths if isinstance(__lowerCAmelCase							,       __lowerCAmelCase  ) else {self.split: path_or_paths}
								__magic_name__		:Optional[int]   =   Json(
								    cache_dir=__lowerCAmelCase							,       data_files=__lowerCAmelCase							,       features=__lowerCAmelCase							,       field=__lowerCAmelCase							,       **__lowerCAmelCase							,       )







				def       A						(    self  ):
								"""simple docstring"""
								# Build iterable dataset
								if self.streaming:
												__magic_name__		:Dict   =   self.builder.as_streaming_dataset(split=self.split  )
								# Build regular (map-style) dataset
								else:
												__magic_name__		:int   =   None
												__magic_name__		:Optional[Any]   =   None
												__magic_name__		:int   =   None
												__magic_name__		:str   =   None

												self.builder.download_and_prepare(
												    download_config=__lowerCAmelCase							,       download_mode=__lowerCAmelCase							,       verification_mode=__lowerCAmelCase							,       base_path=__lowerCAmelCase							,       num_proc=self.num_proc							,       )
												__magic_name__		:Union[str, Any]   =   self.builder.as_dataset(
												    split=self.split							,       verification_mode=__lowerCAmelCase							,       in_memory=self.keep_in_memory  )
								return dataset







class     lowerCamelCase_					:







				def __init__(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								if num_proc is not None and num_proc <= 0:
												raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''  )

								__magic_name__		:Tuple   =   dataset
								__magic_name__		:Tuple   =   path_or_buf
								__magic_name__		:Tuple   =   batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
								__magic_name__		:Dict   =   num_proc
								__magic_name__		:Dict   =   '''utf-8'''
								__magic_name__		:List[Any]   =   to_json_kwargs







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.to_json_kwargs.pop('''path_or_buf'''							,       __lowerCAmelCase  )
								__magic_name__		:Any   =   self.to_json_kwargs.pop('''orient'''							,       '''records'''  )
								__magic_name__		:Dict   =   self.to_json_kwargs.pop('''lines'''							,       True if orient == '''records''' else False  )
								__magic_name__		:Union[str, Any]   =   self.to_json_kwargs.pop('''index'''							,       False if orient in ['''split''', '''table'''] else True  )
								__magic_name__		:Union[str, Any]   =   self.to_json_kwargs.pop('''compression'''							,       __lowerCAmelCase  )

								if compression not in [None, "infer", "gzip", "bz2", "xz"]:
												raise NotImplementedError(F'''`datasets` currently does not support {compression} compression'''  )

								if isinstance(self.path_or_buf							,       (str, bytes, os.PathLike)  ):
												with fsspec.open(self.path_or_buf							,       '''wb'''							,       compression=__lowerCAmelCase  ) as buffer:
																__magic_name__		:Optional[Any]   =   self._write(file_obj=__lowerCAmelCase							,       orient=__lowerCAmelCase							,       lines=__lowerCAmelCase							,       index=__lowerCAmelCase							,       **self.to_json_kwargs  )
								else:
												if compression:
																raise NotImplementedError(
																    F'''The compression parameter is not supported when writing to a buffer, but compression={compression}'''
																    ''' was passed. Please provide a local path instead.'''  )
												__magic_name__		:int   =   self._write(
												    file_obj=self.path_or_buf							,       orient=__lowerCAmelCase							,       lines=__lowerCAmelCase							,       index=__lowerCAmelCase							,       **self.to_json_kwargs  )
								return written







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__  ,					__magic_name__  ,					__magic_name__  ,					__magic_name__  ,					__magic_name__		:Tuple   =   args

								__magic_name__		:int   =   query_table(
								    table=self.dataset.data							,       key=slice(__lowerCAmelCase							,       offset + self.batch_size  )							,       indices=self.dataset._indices							,       )
								__magic_name__		:List[Any]   =   batch.to_pandas().to_json(
								    path_or_buf=__lowerCAmelCase							,       orient=__lowerCAmelCase							,       lines=__lowerCAmelCase							,       index=__lowerCAmelCase							,       **__lowerCAmelCase  )
								if not json_str.endswith('''\n'''  ):
												json_str += "\n"
								return json_str.encode(self.encoding  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   0

								if self.num_proc is None or self.num_proc == 1:
												for offset in logging.tqdm(
												    range(0							,       len(self.dataset  )							,       self.batch_size  )							,       unit='''ba'''							,       disable=not logging.is_progress_bar_enabled()							,       desc='''Creating json from Arrow format'''							,       ):
																__magic_name__		:Union[str, Any]   =   self._batch_json((offset, orient, lines, index, to_json_kwargs)  )
																written += file_obj.write(__lowerCAmelCase  )
								else:
												__magic_name__  ,					__magic_name__		:List[Any]   =   len(self.dataset  ), self.batch_size
												with multiprocessing.Pool(self.num_proc  ) as pool:
																for json_str in logging.tqdm(
																    pool.imap(
																        self._batch_json							,       [(offset, orient, lines, index, to_json_kwargs) for offset in range(0							,       __lowerCAmelCase							,       __lowerCAmelCase  )]							,       )							,       total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size							,       unit='''ba'''							,       disable=not logging.is_progress_bar_enabled()							,       desc='''Creating json from Arrow format'''							,       ):
																				written += file_obj.write(__lowerCAmelCase  )

								return written














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






