



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

from utils import logger





class  __magic_name__   (							UpperCAmelCase_						):






		def __init__(						self      :     str				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     List[Any]      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     params

								UpperCAmelCase         =     np.array(_snake_case      )
								UpperCAmelCase         =     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      :     List[Any]				,		UpperCamelCase__      :     Tuple      )				->					Optional[Any]:
								'''simple docstring'''







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






		def __len__(						self      :     Optional[Any]      )				->					Tuple:
								'''simple docstring'''







								return len(self.lengths      )






		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					str:
								'''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 SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     self.params.max_model_input_size
								UpperCAmelCase         =     self.lengths > max_len
								logger.info(F'Splitting {sum(_snake_case      )} too long sequences.'      )

								def divide_chunks(UpperCamelCase__      :     int				,		UpperCamelCase__      :     Optional[int]      ):
														return [l[i : i + n] for i in range(0				,		len(_snake_case      )				,		_snake_case      )]

								UpperCAmelCase         =     []
								UpperCAmelCase         =     []
								if self.params.mlm:
														UpperCAmelCase       ,		UpperCAmelCase         =     self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
								else:
														UpperCAmelCase       ,		UpperCAmelCase         =     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:
																				UpperCAmelCase         =     []
																				for sub_s in divide_chunks(seq_				,		max_len - 2      ):
																										if sub_s[0] != cls_id:
																																UpperCAmelCase         =     np.insert(_snake_case				,		0				,		_snake_case      )
																										if sub_s[-1] != sep_id:
																																UpperCAmelCase         =     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]      )

								UpperCAmelCase         =     np.array(_snake_case      )
								UpperCAmelCase         =     np.array(_snake_case      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     len(self      )
								UpperCAmelCase         =     self.lengths > 11
								UpperCAmelCase         =     self.token_ids[indices]
								UpperCAmelCase         =     self.lengths[indices]
								UpperCAmelCase         =     len(self      )
								logger.info(F'Remove {init_size - new_size} too short (<=11 tokens) sequences.'      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					Any:
								'''simple docstring'''







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






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					Optional[Any]:
								'''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 SCREAMING_SNAKE_CASE_			(						self      :     int				,		UpperCamelCase__      :     Optional[Any]      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     [t[0] for t in batch]
								UpperCAmelCase         =     [t[1] for t in batch]
								assert len(_snake_case      ) == len(_snake_case      )

								# Max for paddings
								UpperCAmelCase         =     max(_snake_case      )

								# Pad token ids
								if self.params.mlm:
														UpperCAmelCase         =     self.params.special_tok_ids["pad_token"]
								else:
														UpperCAmelCase         =     self.params.special_tok_ids["unk_token"]
								UpperCAmelCase         =     [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_      )

								UpperCAmelCase         =     torch.tensor(tk_      )  # (bs, max_seq_len_)
								UpperCAmelCase         =     torch.tensor(_snake_case      )  # (bs)
								return tk_t, lg_t








import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin





@require_jieba
class  __magic_name__   (							A__,  unittest.TestCase						):
		lowercase      :       Optional[Any]          =CpmAntTokenizer
		lowercase      :       Dict          =False






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Dict:
								'''simple docstring'''







								super().setUp()

								UpperCAmelCase         =     [
								    "<d>",
								    "</d>",
								    "<s>",
								    "</s>",
								    "</_>",
								    "<unk>",
								    "<pad>",
								    "</n>",
								    "我",
								    "是",
								    "C",
								    "P",
								    "M",
								    "A",
								    "n",
								    "t",
								]
								UpperCAmelCase         =     os.path.join(self.tmpdirname				,		VOCAB_FILES_NAMES["vocab_file"]      )
								with open(self.vocab_file				,		"w"				,		encoding="utf-8"      ) as vocab_writer:
														vocab_writer.write("".join([x + "\n" for x in vocab_tokens]      )      )







		@tooslow
		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase         =     CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b"      )
								UpperCAmelCase         =     "今天天气真好！"
								UpperCAmelCase         =     ["今天", "天气", "真", "好", "！"]
								UpperCAmelCase         =     tokenizer.tokenize(UpperCamelCase__      )
								self.assertListEqual(UpperCamelCase__				,		UpperCamelCase__      )
								UpperCAmelCase         =     "今天天气真好！"
								UpperCAmelCase         =     [tokenizer.bos_token] + tokens

								UpperCAmelCase         =     [6, 98_02, 1_49_62, 20_82, 8_31, 2_44]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__      )				,		UpperCamelCase__      )

								UpperCAmelCase         =     tokenizer.decode(UpperCamelCase__      )
								self.assertEqual(UpperCamelCase__				,		UpperCamelCase__      )



