
'''simple docstring'''



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    _a				(_lowerCamelCase):





       """simple docstring"""




       def __init__(							self       ,							A__       ,							A__ = None       ,							A__ = None       ,							A__ = None       ,							A__ = False       ,							A__ = False       ,							A__ = None       ,							A__ = None       ,							**A__       ,							)    ->			int:
          super().__init__(
              A__       ,							split=A__       ,							features=A__       ,							cache_dir=A__       ,							keep_in_memory=A__       ,							streaming=A__       ,							num_proc=A__       ,							**A__       ,							)
          _SCREAMING_SNAKE_CASE      =				field
          _SCREAMING_SNAKE_CASE      =				path_or_paths if isinstance(A__       ,							A__      ) else {self.split: path_or_paths}
          _SCREAMING_SNAKE_CASE      =				Json(
              cache_dir=A__       ,							data_files=A__       ,							features=A__       ,							field=A__       ,							**A__       ,							)






       def        UpperCamelCase				(							self      )    ->			int:
          # Build iterable dataset
          if self.streaming:
             _SCREAMING_SNAKE_CASE      =				self.builder.as_streaming_dataset(split=self.split      )
          # Build regular (map-style) dataset
          else:
             _SCREAMING_SNAKE_CASE      =				None
             _SCREAMING_SNAKE_CASE      =				None
             _SCREAMING_SNAKE_CASE      =				None
             _SCREAMING_SNAKE_CASE      =				None

             self.builder.download_and_prepare(
                 download_config=A__       ,							download_mode=A__       ,							verification_mode=A__       ,							base_path=A__       ,							num_proc=self.num_proc       ,							)
             _SCREAMING_SNAKE_CASE      =				self.builder.as_dataset(
                 split=self.split       ,							verification_mode=A__       ,							in_memory=self.keep_in_memory      )
          return dataset






class    _a				:





       """simple docstring"""




       def __init__(							self       ,							A__       ,							A__       ,							A__ = None       ,							A__ = None       ,							**A__       ,							)    ->			List[str]:
          if num_proc is not None and num_proc <= 0:
             raise ValueError(F"num_proc {num_proc} must be an integer > 0."      )

          _SCREAMING_SNAKE_CASE      =				dataset
          _SCREAMING_SNAKE_CASE      =				path_or_buf
          _SCREAMING_SNAKE_CASE      =				batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
          _SCREAMING_SNAKE_CASE      =				num_proc
          _SCREAMING_SNAKE_CASE      =				"""utf-8"""
          _SCREAMING_SNAKE_CASE      =				to_json_kwargs






       def        UpperCamelCase				(							self      )    ->			int:
          _SCREAMING_SNAKE_CASE      =				self.to_json_kwargs.pop("""path_or_buf"""       ,							A__      )
          _SCREAMING_SNAKE_CASE      =				self.to_json_kwargs.pop("""orient"""       ,							"""records"""      )
          _SCREAMING_SNAKE_CASE      =				self.to_json_kwargs.pop("""lines"""       ,							True if orient == """records""" else False      )
          _SCREAMING_SNAKE_CASE      =				self.to_json_kwargs.pop("""index"""       ,							False if orient in ["""split""", """table"""] else True      )
          _SCREAMING_SNAKE_CASE      =				self.to_json_kwargs.pop("""compression"""       ,							A__      )

          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=A__      ) as buffer:
                _SCREAMING_SNAKE_CASE      =				self._write(file_obj=A__       ,							orient=A__       ,							lines=A__       ,							index=A__       ,							**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."""      )
             _SCREAMING_SNAKE_CASE      =				self._write(
                 file_obj=self.path_or_buf       ,							orient=A__       ,							lines=A__       ,							index=A__       ,							**self.to_json_kwargs      )
          return written






       def        UpperCamelCase				(							self       ,							A__      )    ->			List[Any]:
          _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				args

          _SCREAMING_SNAKE_CASE      =				query_table(
              table=self.dataset.data       ,							key=slice(A__       ,							offset + self.batch_size      )       ,							indices=self.dataset._indices       ,							)
          _SCREAMING_SNAKE_CASE      =				batch.to_pandas().to_json(
              path_or_buf=A__       ,							orient=A__       ,							lines=A__       ,							index=A__       ,							**A__      )
          if not json_str.endswith("""\n"""      ):
             json_str += "\n"
          return json_str.encode(self.encoding      )






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__       ,							A__       ,							**A__       ,							)    ->			int:
          _SCREAMING_SNAKE_CASE      =				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"""       ,							):
                _SCREAMING_SNAKE_CASE      =				self._batch_json((offset, orient, lines, index, to_json_kwargs)      )
                written += file_obj.write(A__      )
          else:
             _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				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       ,							A__       ,							A__      )]       ,							)       ,							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(A__      )

          return written








'''simple docstring'''



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

from utils import logger

class    _a				(_lowerCamelCase):





       """simple docstring"""




       def __init__(							self       ,							A__       ,							A__      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				params

          _SCREAMING_SNAKE_CASE      =				np.array(A__      )
          _SCREAMING_SNAKE_CASE      =				np.array([len(A__      ) 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       ,							A__      )    ->			Dict:
          return (self.token_ids[index], self.lengths[index])






       def __len__(							self      )    ->			Tuple:
          return len(self.lengths      )






       def        UpperCamelCase				(							self      )    ->			Dict:
          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        UpperCamelCase				(							self      )    ->			List[str]:
          _SCREAMING_SNAKE_CASE      =				self.params.max_model_input_size
          _SCREAMING_SNAKE_CASE      =				self.lengths > max_len
          logger.info(F"Splitting {sum(A__      )} too long sequences."      )

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

          _SCREAMING_SNAKE_CASE      =				[]
          _SCREAMING_SNAKE_CASE      =				[]
          if self.params.mlm:
             _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
          else:
             _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				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:
                _SCREAMING_SNAKE_CASE      =				[]
                for sub_s in divide_chunks(seq_       ,							max_len - 2      ):
                   if sub_s[0] != cls_id:
                      _SCREAMING_SNAKE_CASE      =				np.insert(A__       ,							0       ,							A__      )
                   if sub_s[-1] != sep_id:
                      _SCREAMING_SNAKE_CASE      =				np.insert(A__       ,							len(A__      )       ,							A__      )
                   assert len(A__      ) <= max_len
                   assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                   sub_seqs.append(A__      )

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

          _SCREAMING_SNAKE_CASE      =				np.array(A__      )
          _SCREAMING_SNAKE_CASE      =				np.array(A__      )






       def        UpperCamelCase				(							self      )    ->			List[str]:
          _SCREAMING_SNAKE_CASE      =				len(self      )
          _SCREAMING_SNAKE_CASE      =				self.lengths > 11
          _SCREAMING_SNAKE_CASE      =				self.token_ids[indices]
          _SCREAMING_SNAKE_CASE      =				self.lengths[indices]
          _SCREAMING_SNAKE_CASE      =				len(self      )
          logger.info(F"Remove {init_size - new_size} too short (<=11 tokens) sequences."      )






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






       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          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        UpperCamelCase				(							self       ,							A__      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				[t[0] for t in batch]
          _SCREAMING_SNAKE_CASE      =				[t[1] for t in batch]
          assert len(A__      ) == len(A__      )

          # Max for paddings
          _SCREAMING_SNAKE_CASE      =				max(A__      )

          # Pad token ids
          if self.params.mlm:
             _SCREAMING_SNAKE_CASE      =				self.params.special_tok_ids["""pad_token"""]
          else:
             _SCREAMING_SNAKE_CASE      =				self.params.special_tok_ids["""unk_token"""]
          _SCREAMING_SNAKE_CASE      =				[list(t.astype(A__      )      ) + [pad_idx] * (max_seq_len_ - len(A__      )) for t in token_ids]
          assert len(tk_      ) == len(A__      )
          assert all(len(A__      ) == max_seq_len_ for t in tk_      )

          _SCREAMING_SNAKE_CASE      =				torch.tensor(tk_      )  # (bs, max_seq_len_)
          _SCREAMING_SNAKE_CASE      =				torch.tensor(A__      )  # (bs)
          return tk_t, lg_t






