



"""simple docstring"""

import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__    :  Tuple							    =	get_tests_dir('''fixtures/test_sentencepiece.model''')




@require_sentencepiece
class        _UpperCAmelCase       (			lowerCAmelCase__    ,     unittest.TestCase):
 _lowerCAmelCase   :			str							=		XLMProphetNetTokenizer
 _lowerCAmelCase   :			Optional[int]							=		False
 _lowerCAmelCase   :			Dict							=		True

 def 						_snake_case					(     self		:       int				):
     super().setUp()

     # We have a SentencePiece fixture for testing
     snake_case_ :     Any				=	XLMProphetNetTokenizer(lowercase_  ,     keep_accents=lowercase_				)
     tokenizer.save_pretrained(self.tmpdirname				)

 def 						_snake_case					(     self		:       Tuple				):
     snake_case_ :     int				=	'''[PAD]'''
     snake_case_ :     int				=	0

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_				)  ,     lowercase_				)
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_				)  ,     lowercase_				)

 def 						_snake_case					(     self		:       int				):
     snake_case_ :     Optional[int]				=	list(self.get_tokenizer().get_vocab().keys()				)

     self.assertEqual(vocab_keys[0]  ,     '''[PAD]'''				)
     self.assertEqual(vocab_keys[1]  ,     '''[CLS]'''				)
     self.assertEqual(vocab_keys[-1]  ,     '''j'''				)
     self.assertEqual(len(lowercase_				)  ,     1012				)

 def 						_snake_case					(     self		:       Dict				):
     self.assertEqual(self.get_tokenizer().vocab_size  ,     1012				)

 def 						_snake_case					(     self		:       List[str]				):
     snake_case_ :     Any				=	XLMProphetNetTokenizer(lowercase_  ,     keep_accents=lowercase_				)

     snake_case_ :     str				=	tokenizer.tokenize('''This is a test'''				)
     self.assertListEqual(lowercase_  ,     ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(lowercase_				)  ,     [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]  ,     )

     snake_case_ :     List[str]				=	tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
     self.assertListEqual(
         lowercase_  ,     [
             SPIECE_UNDERLINE + '''I''',
             SPIECE_UNDERLINE + '''was''',
             SPIECE_UNDERLINE + '''b''',
             '''or''',
             '''n''',
             SPIECE_UNDERLINE + '''in''',
             SPIECE_UNDERLINE + '''''',
             '''9''',
             '''2''',
             '''0''',
             '''0''',
             '''0''',
             ''',''',
             SPIECE_UNDERLINE + '''and''',
             SPIECE_UNDERLINE + '''this''',
             SPIECE_UNDERLINE + '''is''',
             SPIECE_UNDERLINE + '''f''',
             '''al''',
             '''s''',
             '''é''',
             '''.''',
         ]  ,     )
     snake_case_ :     Any				=	tokenizer.convert_tokens_to_ids(lowercase_				)
     self.assertListEqual(
         lowercase_  ,     [
             value + tokenizer.fairseq_offset
             for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
         ]  ,     )

     snake_case_ :     Optional[int]				=	tokenizer.convert_ids_to_tokens(lowercase_				)
     self.assertListEqual(
         lowercase_  ,     [
             SPIECE_UNDERLINE + '''I''',
             SPIECE_UNDERLINE + '''was''',
             SPIECE_UNDERLINE + '''b''',
             '''or''',
             '''n''',
             SPIECE_UNDERLINE + '''in''',
             SPIECE_UNDERLINE + '''''',
             '''[UNK]''',
             '''2''',
             '''0''',
             '''0''',
             '''0''',
             ''',''',
             SPIECE_UNDERLINE + '''and''',
             SPIECE_UNDERLINE + '''this''',
             SPIECE_UNDERLINE + '''is''',
             SPIECE_UNDERLINE + '''f''',
             '''al''',
             '''s''',
             '''[UNK]''',
             '''.''',
         ]  ,     )

 @cached_property
 def 						_snake_case					(     self		:       List[str]				):
     return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased'''				)

 @slow
 def 						_snake_case					(     self		:       int				):
     snake_case_ :     Any				=	'''Hello World!'''
     snake_case_ :     Any				=	[35389, 6672, 49, 2]
     self.assertListEqual(lowercase_  ,     self.big_tokenizer.encode(lowercase_				)				)






 @slow
 def 						_snake_case					(     self		:       Dict				):
     # fmt: off
     snake_case_ :     List[Any]				=	{'''input_ids''': [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=lowercase_  ,     model_name='''microsoft/xprophetnet-large-wiki100-cased'''  ,     revision='''1acad1643ddd54a44df6a1b797ada8373685d90e'''  ,     )










"""simple docstring"""

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

from utils import logger




class        _UpperCAmelCase       (			lowerCAmelCase__):

 def __init__(     self		:       Optional[int]  ,     lowercase_		:       str  ,     lowercase_		:       int				):
     snake_case_ :     Dict				=	params

     snake_case_ :     Union[str, Any]				=	np.array(lowercase_				)
     snake_case_ :     str				=	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		:       Dict  ,     lowercase_		:       Union[str, Any]				):
     return (self.token_ids[index], self.lengths[index])

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

 def 						_snake_case					(     self		:       Tuple				):
     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		:       Tuple				):
     snake_case_ :     str				=	self.params.max_model_input_size
     snake_case_ :     Dict				=	self.lengths > max_len
     logger.info(f"Splitting {sum(lowercase_				)} too long sequences."				)

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

     snake_case_ :     Tuple				=	[]
     snake_case_ :     Any				=	[]
     if self.params.mlm:
         snake_case_,	snake_case_ :     Union[str, Any]				=	self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
     else:
         snake_case_,	snake_case_ :     Dict				=	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:
             snake_case_ :     Any				=	[]
             for sub_s in divide_chunks(seq_  ,     max_len - 2				):
                 if sub_s[0] != cls_id:
                     snake_case_ :     Dict				=	np.insert(lowercase_  ,     0  ,     lowercase_				)
                 if sub_s[-1] != sep_id:
                     snake_case_ :     Tuple				=	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]				)

     snake_case_ :     List[str]				=	np.array(lowercase_				)
     snake_case_ :     Optional[Any]				=	np.array(lowercase_				)

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

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

 def 						_snake_case					(     self		:       Dict				):
     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		:       List[str]  ,     lowercase_		:       Dict				):
     snake_case_ :     Optional[int]				=	[t[0] for t in batch]
     snake_case_ :     str				=	[t[1] for t in batch]
     assert len(lowercase_				) == len(lowercase_				)

     # Max for paddings
     snake_case_ :     str				=	max(lowercase_				)

     # Pad token ids
     if self.params.mlm:
         snake_case_ :     Tuple				=	self.params.special_tok_ids['''pad_token''']
     else:
         snake_case_ :     Dict				=	self.params.special_tok_ids['''unk_token''']
     snake_case_ :     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_				)

     snake_case_ :     str				=	torch.tensor(tk_				)  # (bs, max_seq_len_)
     snake_case_ :     Optional[int]				=	torch.tensor(lowercase_				)  # (bs)
     return tk_t, lg_t





