

"""simple docstring"""





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

from utils import logger







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					def __init__(			self     :							Dict  ,lowercase_     :							int  ,lowercase_     :							Dict						):
						lowerCAmelCase__      :		str    							=      params

						lowerCAmelCase__      :		str    							=      np.array(lowercase_						)
						lowerCAmelCase__      :		Optional[Any]    							=      np.array([len(lowercase_						) 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     :							str  ,lowercase_     :							Union[str, Any]						):
						return (self.token_ids[index], self.lengths[index])







					def __len__(			self     :							List[Any]						):
						return len(self.lengths						)







					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						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 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		Optional[int]    							=      self.params.max_model_input_size
						lowerCAmelCase__      :		Union[str, Any]    							=      self.lengths > max_len
						logger.info(F'Splitting {sum(lowercase_						)} too long sequences.'						)

						def divide_chunks(lowercase_     :							List[Any]  ,lowercase_     :							List[str]						):
							return [l[i : i + n] for i in range(0  ,len(lowercase_						)  ,lowercase_						)]

						lowerCAmelCase__      :		Any    							=      []
						lowerCAmelCase__      :		Dict    							=      []
						if self.params.mlm:
							lowerCAmelCase__							,lowerCAmelCase__      :		Any    							=      self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
						else:
							lowerCAmelCase__							,lowerCAmelCase__      :		Optional[int]    							=      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:
								lowerCAmelCase__      :		Any    							=      []
								for sub_s in divide_chunks(seq_  ,max_len - 2						):
									if sub_s[0] != cls_id:
										lowerCAmelCase__      :		Union[str, Any]    							=      np.insert(lowercase_  ,0  ,lowercase_						)
									if sub_s[-1] != sep_id:
										lowerCAmelCase__      :		Union[str, Any]    							=      np.insert(lowercase_  ,len(lowercase_						)  ,lowercase_						)
									assert len(lowercase_						) <= max_len
									assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
									sub_seqs.append(lowercase_						)

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

						lowerCAmelCase__      :		List[str]    							=      np.array(lowercase_						)
						lowerCAmelCase__      :		Dict    							=      np.array(lowercase_						)







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		List[str]    							=      len(self						)
						lowerCAmelCase__      :		List[Any]    							=      self.lengths > 1_1
						lowerCAmelCase__      :		str    							=      self.token_ids[indices]
						lowerCAmelCase__      :		str    							=      self.lengths[indices]
						lowerCAmelCase__      :		str    							=      len(self						)
						logger.info(F'Remove {init_size - new_size} too short (<=11 tokens) sequences.'						)







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







					def 			__lowerCAmelCase					(			self     :							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 			__lowerCAmelCase					(			self     :							List[str]  ,lowercase_     :							Union[str, Any]						):
						lowerCAmelCase__      :		List[str]    							=      [t[0] for t in batch]
						lowerCAmelCase__      :		int    							=      [t[1] for t in batch]
						assert len(lowercase_						) == len(lowercase_						)

						# Max for paddings
						lowerCAmelCase__      :		Dict    							=      max(lowercase_						)

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

						lowerCAmelCase__      :		Optional[Any]    							=      torch.tensor(tk_						)  # (bs, max_seq_len_)
						lowerCAmelCase__      :		List[Any]    							=      torch.tensor(lowercase_						)  # (bs)
						return tk_t, lg_t







"""simple docstring"""





import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__UpperCamelCase    : str				 =						4
__UpperCamelCase    : List[str]				 =						3







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					pass

def     __SCREAMING_SNAKE_CASE    ( A_					):
	for shard in shards:
		for i in range(A_					):
			yield {"i": i, "shard": shard}

def     __SCREAMING_SNAKE_CASE    ( ):
	lowerCAmelCase__      :		List[str]    							=      int(os.environ['''RANK''']					)
	lowerCAmelCase__      :		Union[str, Any]    							=      int(os.environ['''WORLD_SIZE''']					)

	lowerCAmelCase__      :		Tuple    							=      ArgumentParser()
	parser.add_argument('''--streaming'''		,	type=A_					)
	parser.add_argument('''--local_rank'''		,	type=A_					)
	parser.add_argument('''--num_workers'''		,	type=A_		,	default=0					)
	lowerCAmelCase__      :		Tuple    							=      parser.parse_args()
	lowerCAmelCase__      :		Tuple    							=      args.streaming
	lowerCAmelCase__      :		Any    							=      args.num_workers

	lowerCAmelCase__      :		Dict    							=      {'''shards''': [f'shard_{shard_idx}' for shard_idx in range(A_					)]}
	lowerCAmelCase__      :		Optional[int]    							=      IterableDataset.from_generator(A_		,	gen_kwargs=A_					)
	if not streaming:
		lowerCAmelCase__      :		Dict    							=      Dataset.from_list(list(A_					)					)

	lowerCAmelCase__      :		Optional[Any]    							=      split_dataset_by_node(A_		,	rank=A_		,	world_size=A_					)
	lowerCAmelCase__      :		List[Any]    							=      torch.utils.data.DataLoader(A_		,	num_workers=A_					)

	lowerCAmelCase__      :		Optional[int]    							=      NUM_SHARDS * NUM_ITEMS_PER_SHARD
	lowerCAmelCase__      :		List[str]    							=      full_size // world_size
	expected_local_size += int(rank < (full_size % world_size)					)

	lowerCAmelCase__      :		int    							=      sum(1 for _ in dataloader					)
	if local_size != expected_local_size:
		raise FailedTestError(f'local_size {local_size} != expected_local_size {expected_local_size}'					)


if __name__ == "__main__":
							main()




