



import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class        snake_case__    (lowerCamelCase_ ,     unittest.TestCase		):







						"""simple docstring"""




						__lowerCAmelCase					:int      =					MvpTokenizer
						__lowerCAmelCase					:List[Any]      =					MvpTokenizerFast
						__lowerCAmelCase					:Optional[int]      =					True
						__lowerCAmelCase					:Tuple      =					filter_roberta_detectors

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




									super().setUp()
									a__       :						Optional[Any]							=  [
									    """l""",
									    """o""",
									    """w""",
									    """e""",
									    """r""",
									    """s""",
									    """t""",
									    """i""",
									    """d""",
									    """n""",
									    """\u0120""",
									    """\u0120l""",
									    """\u0120n""",
									    """\u0120lo""",
									    """\u0120low""",
									    """er""",
									    """\u0120lowest""",
									    """\u0120newer""",
									    """\u0120wider""",
									    """<unk>""",
									]
									a__       :						Tuple							=  dict(zip(__snake_case ,       range(len(__snake_case  )  )  )  )
									a__       :						int							=  ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
									a__       :						Optional[Any]							=  {"""unk_token""": """<unk>"""}

									a__       :						int							=  os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES["""vocab_file"""]  )
									a__       :						Union[str, Any]							=  os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES["""merges_file"""]  )
									with open(self.vocab_file ,       """w""" ,       encoding="""utf-8"""  ) as fp:
												fp.write(json.dumps(__snake_case  ) + """\n"""  )
									with open(self.merges_file ,       """w""" ,       encoding="""utf-8"""  ) as fp:
												fp.write("""\n""".join(__snake_case  )  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       **__lowercase  ) ->				int:
									"""simple docstring"""




									kwargs.update(self.special_tokens_map  )
									return self.tokenizer_class.from_pretrained(self.tmpdirname ,       **__snake_case  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       **__lowercase  ) ->				Dict:
									"""simple docstring"""




									kwargs.update(self.special_tokens_map  )
									return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,       **__snake_case  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				List[Any]:
									"""simple docstring"""




									return "lower newer", "lower newer"

						@cached_property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									return MvpTokenizer.from_pretrained("""RUCAIBox/mvp"""  )

						@cached_property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp"""  )

						@require_torch
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									a__       :						Optional[int]							=  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
									a__       :						List[Any]							=  [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												a__       :						List[Any]							=  tokenizer(__snake_case ,       max_length=len(__snake_case  ) ,       padding=__snake_case ,       return_tensors="""pt"""  )
												self.assertIsInstance(__snake_case ,       __snake_case  )

												self.assertEqual((2, 9) ,       batch.input_ids.shape  )
												self.assertEqual((2, 9) ,       batch.attention_mask.shape  )
												a__       :						Tuple							=  batch.input_ids.tolist()[0]
												self.assertListEqual(__snake_case ,       __snake_case  )
												# Test that special tokens are reset

						@require_torch
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""simple docstring"""




									a__       :						Optional[Any]							=  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												a__       :						str							=  tokenizer(__snake_case ,       padding=__snake_case ,       return_tensors="""pt"""  )
												# check if input_ids are returned and no labels
												self.assertIn("""input_ids""" ,       __snake_case  )
												self.assertIn("""attention_mask""" ,       __snake_case  )
												self.assertNotIn("""labels""" ,       __snake_case  )
												self.assertNotIn("""decoder_attention_mask""" ,       __snake_case  )

						@require_torch
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				int:
									"""simple docstring"""




									a__       :						Optional[Any]							=  [
									    """Summary of the text.""",
									    """Another summary.""",
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												a__       :						Union[str, Any]							=  tokenizer(text_target=__snake_case ,       max_length=3_2 ,       padding="""max_length""" ,       return_tensors="""pt"""  )
												self.assertEqual(3_2 ,       targets["""input_ids"""].shape[1]  )

						@require_torch
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												a__       :						Any							=  tokenizer(
												    ["""I am a small frog""" * 1_0_2_4, """I am a small frog"""] ,       padding=__snake_case ,       truncation=__snake_case ,       return_tensors="""pt"""  )
												self.assertIsInstance(__snake_case ,       __snake_case  )
												self.assertEqual(batch.input_ids.shape ,       (2, 1_0_2_4)  )

						@require_torch
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Any:
									"""simple docstring"""




									a__       :						List[Any]							=  ["""A long paragraph for summarization."""]
									a__       :						List[str]							=  [
									    """Summary of the text.""",
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												a__       :						Union[str, Any]							=  tokenizer(__snake_case ,       text_target=__snake_case ,       return_tensors="""pt"""  )
												a__       :						Optional[Any]							=  inputs["""input_ids"""]
												a__       :						Tuple							=  inputs["""labels"""]
												self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
												self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
												self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
												self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Tuple:
									"""simple docstring"""




									pass

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
												with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
															a__       :						Optional[int]							=  self.rust_tokenizer_class.from_pretrained(__snake_case ,       **__snake_case  )
															a__       :						Dict							=  self.tokenizer_class.from_pretrained(__snake_case ,       **__snake_case  )
															a__       :						Union[str, Any]							=  """A, <mask> AllenNLP sentence."""
															a__       :						Optional[Any]							=  tokenizer_r.encode_plus(__snake_case ,       add_special_tokens=__snake_case ,       return_token_type_ids=__snake_case  )
															a__       :						int							=  tokenizer_p.encode_plus(__snake_case ,       add_special_tokens=__snake_case ,       return_token_type_ids=__snake_case  )

															# token_type_ids should put 0 everywhere
															self.assertEqual(sum(tokens_r["""token_type_ids"""]  ) ,       sum(tokens_p["""token_type_ids"""]  )  )

															# attention_mask should put 1 everywhere, so sum over length should be 1
															self.assertEqual(
															    sum(tokens_r["""attention_mask"""]  ) / len(tokens_r["""attention_mask"""]  ) ,       sum(tokens_p["""attention_mask"""]  ) / len(tokens_p["""attention_mask"""]  ) ,       )

															a__       :						Optional[int]							=  tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]  )
															a__       :						str							=  tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]  )

															# Rust correctly handles the space before the mask while python doesnt
															self.assertSequenceEqual(tokens_p["""input_ids"""] ,       [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]  )
															self.assertSequenceEqual(tokens_r["""input_ids"""] ,       [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]  )

															self.assertSequenceEqual(
															    __snake_case ,       ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]  )
															self.assertSequenceEqual(
															    __snake_case ,       ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]  )








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

from utils import logger

class        snake_case__    (A__		):







						"""simple docstring"""




						def __init__(       self ,       __lowercase ,       __lowercase  ) ->				int:
									"""simple docstring"""




									a__       :						Tuple							=  params

									a__       :						str							=  np.array(__lowercase  )
									a__       :						List[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 ,       __lowercase  ) ->				Any:
									"""simple docstring"""




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

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




									return len(self.lengths  )

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




									a__       :						int							=  self.params.max_model_input_size
									a__       :						int							=  self.lengths > max_len
									logger.info(F'''Splitting {sum(__lowercase  )} too long sequences.'''  )

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

									a__       :						Any							=  []
									a__       :						Optional[int]							=  []
									if self.params.mlm:
												a__	,						a__       :						Any							=  self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
									else:
												a__	,						a__       :						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:
															a__       :						int							=  []
															for sub_s in divide_chunks(seq_ ,       max_len - 2  ):
																		if sub_s[0] != cls_id:
																					a__       :						str							=  np.insert(__lowercase ,       0 ,       __lowercase  )
																		if sub_s[-1] != sep_id:
																					a__       :						List[str]							=  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]  )

									a__       :						Optional[int]							=  np.array(__lowercase  )
									a__       :						Any							=  np.array(__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""simple docstring"""




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

						def 							SCREAMING_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 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Optional[int]:
									"""simple docstring"""




									a__       :						Optional[int]							=  [t[0] for t in batch]
									a__       :						Any							=  [t[1] for t in batch]
									assert len(__lowercase  ) == len(__lowercase  )

									# Max for paddings
									a__       :						List[Any]							=  max(__lowercase  )

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

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




