





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

from utils import logger







class   __lowerCAmelCase			(					_UpperCamelCase  ):





   def __init__( self					,			snake_case					,			snake_case      )				->  List[Any]:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  params

      a__	:						List[str]      		=  np.array(snake_case      )
      a__	:						Dict      		=  np.array([len(snake_case      ) 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					,			snake_case      )				->  int:
      """simple docstring"""





      return (self.token_ids[index], self.lengths[index])





   def __len__( self      )				->  Optional[int]:
      """simple docstring"""





      return len(self.lengths      )





   def 	_snake_case ( self      )				->  Dict:
      """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 	_snake_case ( self      )				->  Union[str, Any]:
      """simple docstring"""





      a__	:						Optional[Any]      		=  self.params.max_model_input_size
      a__	:						Tuple      		=  self.lengths > max_len
      logger.info(F"""Splitting {sum(snake_case      )} too long sequences."""      )

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

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

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

      a__	:						int      		=  np.array(snake_case      )
      a__	:						List[Any]      		=  np.array(snake_case      )





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





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





   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





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





   def 	_snake_case ( self      )				->  str:
      """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 	_snake_case ( self					,			snake_case      )				->  Any:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  [t[0] for t in batch]
      a__	:						List[str]      		=  [t[1] for t in batch]
      assert len(snake_case      ) == len(snake_case      )

      # Max for paddings
      a__	:						Any      		=  max(snake_case      )

      # Pad token ids
      if self.params.mlm:
         a__	:						Optional[int]      		=  self.params.special_tok_ids["pad_token"]
      else:
         a__	:						List[str]      		=  self.params.special_tok_ids["unk_token"]
      a__	:						Any      		=  [list(t.astype(snake_case      )      ) + [pad_idx] * (max_seq_len_ - len(snake_case      )) for t in token_ids]
      assert len(tk_      ) == len(snake_case      )
      assert all(len(snake_case      ) == max_seq_len_ for t in tk_      )

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








# Lint as: python3
import itertools
import os
import re


SCREAMING_SNAKE_CASE__       :			Tuple			      =						re.compile(R"""([A-Z]+)([A-Z][a-z])""")
SCREAMING_SNAKE_CASE__       :			List[str]			      =						re.compile(R"""([a-z\d])([A-Z])""")

SCREAMING_SNAKE_CASE__       :			Tuple			      =						re.compile(R"""(?<!_)_(?!_)""")
SCREAMING_SNAKE_CASE__       :			Dict			      =						re.compile(R"""(_{2,})""")

SCREAMING_SNAKE_CASE__       :			List[Any]			      =						R"""^\w+(\.\w+)*$"""

SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						R"""<>:/\|?*"""






def       _A  (      lowerCamelCase   ):
   a__	:						List[str]      		=  _uppercase_uppercase_re.sub(r"\1_\2"							, lowerCamelCase   )
   a__	:						Dict      		=  _lowercase_uppercase_re.sub(r"\1_\2"							, lowerCamelCase   )
   return name.lower()






def       _A  (      lowerCamelCase   ):
   a__	:						Tuple      		=  _single_underscore_re.split(lowerCamelCase   )
   a__	:						Any      		=  [_multiple_underscores_re.split(lowerCamelCase   ) for n in name]
   return "".join(n.capitalize() for n in itertools.chain.from_iterable(lowerCamelCase   ) if n != ""   )






def       _A  (      lowerCamelCase   ):
   if os.path.basename(lowerCamelCase   ) != name:
      raise ValueError(F"""Should be a dataset name, not a path: {name}"""   )
   return camelcase_to_snakecase(lowerCamelCase   )






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   if os.path.basename(lowerCamelCase   ) != name:
      raise ValueError(F"""Should be a dataset name, not a path: {name}"""   )
   if not re.match(_split_re							, lowerCamelCase   ):
      raise ValueError(F"""Split name should match '{_split_re}'' but got '{split}'."""   )
   return F"""{filename_prefix_for_name(lowerCamelCase   )}-{split}"""






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase=None   ):
   a__	:						Union[str, Any]      		=  filename_prefix_for_split(lowerCamelCase							, lowerCamelCase   )
   if filetype_suffix:
      prefix += F""".{filetype_suffix}"""
   a__	:						Any      		=  os.path.join(lowerCamelCase							, lowerCamelCase   )
   return F"""{filepath}*"""






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase=None							, lowerCamelCase=None   ):
   a__	:						List[str]      		=  filename_prefix_for_split(lowerCamelCase							, lowerCamelCase   )
   a__	:						Tuple      		=  os.path.join(lowerCamelCase							, lowerCamelCase   )

   if shard_lengths:
      a__	:						List[str]      		=  len(lowerCamelCase   )
      a__	:						str      		=  [F"""{prefix}-{shard_id:05d}-of-{num_shards:05d}""" for shard_id in range(lowerCamelCase   )]
      if filetype_suffix:
         a__	:						Optional[Any]      		=  [filename + F""".{filetype_suffix}""" for filename in filenames]
      return filenames
   else:
      a__	:						Optional[int]      		=  prefix
      if filetype_suffix:
         filename += F""".{filetype_suffix}"""
      return [filename]

