
'''simple docstring'''



import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
     UpperCamelCase__      :		int       							=  "pt"
elif is_tf_available():
     UpperCamelCase__      :		Dict       							=  "tf"
else:
     UpperCamelCase__      :		Optional[int]       							=  "jax"

class    _a				(_lowerCamelCase  ,					unittest.TestCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  PerceiverTokenizer
       SCREAMING_SNAKE_CASE      =  False






       def        UpperCamelCase				(							self      )    ->			List[Any]:
          super().setUp()
          _SCREAMING_SNAKE_CASE      =				PerceiverTokenizer()
          tokenizer.save_pretrained(self.tmpdirname      )






       @cached_property
       def        UpperCamelCase				(							self      )    ->			str:
          return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver"""      )






       def        UpperCamelCase				(							self       ,							**A__      )    ->			PerceiverTokenizer:
          return self.tokenizer_class.from_pretrained(self.tmpdirname       ,							**A__      )






       def        UpperCamelCase				(							self       ,							A__       ,							A__=False       ,							A__=20       ,							A__=5      )    ->			Tuple[str, list]:
          # XXX The default common tokenizer tests assume that every ID is decodable on its own.
          # This assumption is invalid for Perceiver because single bytes might not be
          # valid utf-8 (byte 128 for instance).
          # Here we're overriding the smallest possible method to provide
          # a clean sequence without making the same assumption.

          _SCREAMING_SNAKE_CASE      =				[]
          for i in range(len(A__      )      ):
             try:
                _SCREAMING_SNAKE_CASE      =				tokenizer.decode([i]       ,							clean_up_tokenization_spaces=A__      )
             except UnicodeDecodeError:
                pass
             toks.append((i, tok)      )

          _SCREAMING_SNAKE_CASE      =				list(filter(lambda A__      : re.match(R"""^[ a-zA-Z]+$"""       ,							t[1]      )       ,							A__      )      )
          _SCREAMING_SNAKE_CASE      =				list(filter(lambda A__      : [t[0]] == tokenizer.encode(t[1]       ,							add_special_tokens=A__      )       ,							A__      )      )
          if max_length is not None and len(A__      ) > max_length:
             _SCREAMING_SNAKE_CASE      =				toks[:max_length]
          if min_length is not None and len(A__      ) < min_length and len(A__      ) > 0:
             while len(A__      ) < min_length:
                _SCREAMING_SNAKE_CASE      =				toks + toks
        # toks_str = [t[1] for t in toks]
          _SCREAMING_SNAKE_CASE      =				[t[0] for t in toks]

          # Ensure consistency
          _SCREAMING_SNAKE_CASE      =				tokenizer.decode(A__       ,							clean_up_tokenization_spaces=A__      )
          if " " not in output_txt and len(A__      ) > 1:
             _SCREAMING_SNAKE_CASE      =				(
                 tokenizer.decode([toks_ids[0]]       ,							clean_up_tokenization_spaces=A__      )
                 + """ """
                 + tokenizer.decode(toks_ids[1:]       ,							clean_up_tokenization_spaces=A__      )
             )
          if with_prefix_space:
             _SCREAMING_SNAKE_CASE      =				""" """ + output_txt
          _SCREAMING_SNAKE_CASE      =				tokenizer.encode(A__       ,							add_special_tokens=A__      )
          return output_txt, output_ids






       def        UpperCamelCase				(							self      )    ->			int:
          _SCREAMING_SNAKE_CASE      =				self.perceiver_tokenizer
          _SCREAMING_SNAKE_CASE      =				"""Unicode €."""
          _SCREAMING_SNAKE_CASE      =				tokenizer(A__      )
          _SCREAMING_SNAKE_CASE      =				[4, 91, 1_16, 1_11, 1_05, 1_17, 1_06, 1_07, 38, 2_32, 1_36, 1_78, 52, 5]
          self.assertEqual(encoded["""input_ids"""]       ,							A__      )

          # decoding
          _SCREAMING_SNAKE_CASE      =				tokenizer.decode(A__      )
          self.assertEqual(A__       ,							"""[CLS]Unicode €.[SEP]"""      )

          _SCREAMING_SNAKE_CASE      =				tokenizer("""e è é ê ë"""      )
          _SCREAMING_SNAKE_CASE      =				[4, 1_07, 38, 2_01, 1_74, 38, 2_01, 1_75, 38, 2_01, 1_76, 38, 2_01, 1_77, 5]
          self.assertEqual(encoded["""input_ids"""]       ,							A__      )
          # decoding
          _SCREAMING_SNAKE_CASE      =				tokenizer.decode(A__      )
          self.assertEqual(A__       ,							"""[CLS]e è é ê ë[SEP]"""      )

          # encode/decode, but with `encode` instead of `__call__`
          self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""      )      )       ,							"""[CLS]e è é ê ë[SEP]"""      )






       def        UpperCamelCase				(							self      )    ->			Dict:
          _SCREAMING_SNAKE_CASE      =				self.perceiver_tokenizer
          _SCREAMING_SNAKE_CASE      =				["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
          # fmt: off
          _SCREAMING_SNAKE_CASE      =				[4, 71, 38, 1_14, 1_17, 1_16, 1_09, 38, 1_18, 1_03, 1_20, 1_03, 1_09, 1_20, 1_03, 1_18, 1_10, 38, 1_08, 1_17, 1_20, 38, 1_21, 1_23, 1_15, 1_15, 1_03, 1_20, 1_11, 1_28, 1_03, 1_22, 1_11, 1_17, 1_16, 52, 5, 0]
          # fmt: on
          _SCREAMING_SNAKE_CASE      =				tokenizer(A__       ,							padding=A__       ,							return_tensors=A__      )
          self.assertIsInstance(A__       ,							A__      )

          if FRAMEWORK != "jax":
             _SCREAMING_SNAKE_CASE      =				list(batch.input_ids.numpy()[0]      )
          else:
             _SCREAMING_SNAKE_CASE      =				list(batch.input_ids.tolist()[0]      )

          self.assertListEqual(A__       ,							A__      )

          self.assertEqual((2, 38)       ,							batch.input_ids.shape      )
          self.assertEqual((2, 38)       ,							batch.attention_mask.shape      )






       def        UpperCamelCase				(							self      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				self.perceiver_tokenizer
          _SCREAMING_SNAKE_CASE      =				["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
          _SCREAMING_SNAKE_CASE      =				tokenizer(A__       ,							padding=A__       ,							return_tensors=A__      )
          # check if input_ids are returned and no decoder_input_ids
          self.assertIn("""input_ids"""       ,							A__      )
          self.assertIn("""attention_mask"""       ,							A__      )
          self.assertNotIn("""decoder_input_ids"""       ,							A__      )
          self.assertNotIn("""decoder_attention_mask"""       ,							A__      )






       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				self.perceiver_tokenizer
          _SCREAMING_SNAKE_CASE      =				[
              """Summary of the text.""",
              """Another summary.""",
          ]
          _SCREAMING_SNAKE_CASE      =				tokenizer(
              text_target=A__       ,							max_length=32       ,							padding="""max_length"""       ,							truncation=A__       ,							return_tensors=A__      )
          self.assertEqual(32       ,							targets["""input_ids"""].shape[1]      )






       def        UpperCamelCase				(							self      )    ->			Dict:
          # safety check on max_len default value so we are sure the test works
          _SCREAMING_SNAKE_CASE      =				self.get_tokenizers()
          for tokenizer in tokenizers:
             with self.subTest(F"{tokenizer.__class__.__name__}"      ):
                self.assertNotEqual(tokenizer.model_max_length       ,							42      )

        # Now let's start the test
          _SCREAMING_SNAKE_CASE      =				self.get_tokenizers()
          for tokenizer in tokenizers:
             with self.subTest(F"{tokenizer.__class__.__name__}"      ):
                # Isolate this from the other tests because we save additional tokens/etc
                _SCREAMING_SNAKE_CASE      =				tempfile.mkdtemp()

                _SCREAMING_SNAKE_CASE      =				""" He is very happy, UNwant\u00E9d,running"""
                _SCREAMING_SNAKE_CASE      =				tokenizer.encode(A__       ,							add_special_tokens=A__      )
                tokenizer.save_pretrained(A__      )

                _SCREAMING_SNAKE_CASE      =				tokenizer.__class__.from_pretrained(A__      )
                _SCREAMING_SNAKE_CASE      =				after_tokenizer.encode(A__       ,							add_special_tokens=A__      )
                self.assertListEqual(A__       ,							A__      )

                shutil.rmtree(A__      )

          _SCREAMING_SNAKE_CASE      =				self.get_tokenizers(model_max_length=42      )
          for tokenizer in tokenizers:
             with self.subTest(F"{tokenizer.__class__.__name__}"      ):
                # Isolate this from the other tests because we save additional tokens/etc
                _SCREAMING_SNAKE_CASE      =				tempfile.mkdtemp()

                _SCREAMING_SNAKE_CASE      =				""" He is very happy, UNwant\u00E9d,running"""
                tokenizer.add_tokens(["""bim""", """bambam"""]      )
                _SCREAMING_SNAKE_CASE      =				tokenizer.additional_special_tokens
                additional_special_tokens.append("""new_additional_special_token"""      )
                tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}      )
                _SCREAMING_SNAKE_CASE      =				tokenizer.encode(A__       ,							add_special_tokens=A__      )
                tokenizer.save_pretrained(A__      )

                _SCREAMING_SNAKE_CASE      =				tokenizer.__class__.from_pretrained(A__      )
                _SCREAMING_SNAKE_CASE      =				after_tokenizer.encode(A__       ,							add_special_tokens=A__      )
                self.assertListEqual(A__       ,							A__      )
                self.assertIn("""new_additional_special_token"""       ,							after_tokenizer.additional_special_tokens      )
                self.assertEqual(after_tokenizer.model_max_length       ,							42      )

                _SCREAMING_SNAKE_CASE      =				tokenizer.__class__.from_pretrained(A__       ,							model_max_length=43      )
                self.assertEqual(tokenizer.model_max_length       ,							43      )

                shutil.rmtree(A__      )






       def        UpperCamelCase				(							self      )    ->			Union[str, Any]:
          _SCREAMING_SNAKE_CASE      =				[]
          if self.test_slow_tokenizer:
             tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())      )

          if self.test_rust_tokenizer:
             tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())      )

          for tokenizer_class, tokenizer_utils in tokenizer_list:
             with tempfile.TemporaryDirectory() as tmp_dir:
                tokenizer_utils.save_pretrained(A__      )

                with open(os.path.join(A__       ,							"""special_tokens_map.json"""      )       ,							encoding="""utf-8"""      ) as json_file:
                   _SCREAMING_SNAKE_CASE      =				json.load(A__      )

                with open(os.path.join(A__       ,							"""tokenizer_config.json"""      )       ,							encoding="""utf-8"""      ) as json_file:
                   _SCREAMING_SNAKE_CASE      =				json.load(A__      )

                _SCREAMING_SNAKE_CASE      =				[F"<extra_id_{i}>" for i in range(1_25      )]

                _SCREAMING_SNAKE_CASE      =				added_tokens_extra_ids + [
                    """an_additional_special_token"""
                ]
                _SCREAMING_SNAKE_CASE      =				added_tokens_extra_ids + [
                    """an_additional_special_token"""
                ]

                with open(os.path.join(A__       ,							"""special_tokens_map.json"""      )       ,							"""w"""       ,							encoding="""utf-8"""      ) as outfile:
                   json.dump(A__       ,							A__      )
                with open(os.path.join(A__       ,							"""tokenizer_config.json"""      )       ,							"""w"""       ,							encoding="""utf-8"""      ) as outfile:
                   json.dump(A__       ,							A__      )

                # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                # "special_tokens_map.json" files
                _SCREAMING_SNAKE_CASE      =				tokenizer_class.from_pretrained(
                    A__       ,							)
                self.assertIn(
                    """an_additional_special_token"""       ,							tokenizer_without_change_in_init.additional_special_tokens      )
                self.assertEqual(
                    ["""an_additional_special_token"""]       ,							tokenizer_without_change_in_init.convert_ids_to_tokens(
                        tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""]      )      )       ,							)

                # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                _SCREAMING_SNAKE_CASE      =				added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""       ,							lstrip=A__      )]
                _SCREAMING_SNAKE_CASE      =				tokenizer_class.from_pretrained(
                    A__       ,							additional_special_tokens=A__       ,							)

                self.assertIn("""a_new_additional_special_token"""       ,							tokenizer.additional_special_tokens      )
                self.assertEqual(
                    ["""a_new_additional_special_token"""]       ,							tokenizer.convert_ids_to_tokens(
                        tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""]      )      )       ,							)






       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          _SCREAMING_SNAKE_CASE      =				self.perceiver_tokenizer
          self.assertEqual(tokenizer.decode([1_78]      )       ,							"""�"""      )






       def        UpperCamelCase				(							self      )    ->			Tuple:
          pass






       def        UpperCamelCase				(							self      )    ->			Dict:
          pass






       def        UpperCamelCase				(							self      )    ->			Dict:
          pass






       def        UpperCamelCase				(							self      )    ->			Tuple:
          pass






       def        UpperCamelCase				(							self      )    ->			Union[str, Any]:
          # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
          # strings and special added tokens as tokens
          _SCREAMING_SNAKE_CASE      =				self.get_tokenizers(fast=A__       ,							do_lower_case=A__      )
          for tokenizer in tokenizers:
             with self.subTest(F"{tokenizer.__class__.__name__}"      ):
                _SCREAMING_SNAKE_CASE      =				["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
                _SCREAMING_SNAKE_CASE      =				tokenizer.convert_tokens_to_string(A__      )

                self.assertIsInstance(A__       ,							A__      )








'''simple docstring'''



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

from utils import logger

class    _a				(_lowerCamelCase):





       """simple docstring"""




       def __init__(							self       ,							A__       ,							A__      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				params

          _SCREAMING_SNAKE_CASE      =				np.array(A__      )
          _SCREAMING_SNAKE_CASE      =				np.array([len(A__      ) 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       ,							A__      )    ->			Dict:
          return (self.token_ids[index], self.lengths[index])






       def __len__(							self      )    ->			Tuple:
          return len(self.lengths      )






       def        UpperCamelCase				(							self      )    ->			Dict:
          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        UpperCamelCase				(							self      )    ->			List[str]:
          _SCREAMING_SNAKE_CASE      =				self.params.max_model_input_size
          _SCREAMING_SNAKE_CASE      =				self.lengths > max_len
          logger.info(F"Splitting {sum(A__      )} too long sequences."      )

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

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

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

          _SCREAMING_SNAKE_CASE      =				np.array(A__      )
          _SCREAMING_SNAKE_CASE      =				np.array(A__      )






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






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






       def        UpperCamelCase				(							self      )    ->			Optional[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        UpperCamelCase				(							self       ,							A__      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				[t[0] for t in batch]
          _SCREAMING_SNAKE_CASE      =				[t[1] for t in batch]
          assert len(A__      ) == len(A__      )

          # Max for paddings
          _SCREAMING_SNAKE_CASE      =				max(A__      )

          # Pad token ids
          if self.params.mlm:
             _SCREAMING_SNAKE_CASE      =				self.params.special_tok_ids["""pad_token"""]
          else:
             _SCREAMING_SNAKE_CASE      =				self.params.special_tok_ids["""unk_token"""]
          _SCREAMING_SNAKE_CASE      =				[list(t.astype(A__      )      ) + [pad_idx] * (max_seq_len_ - len(A__      )) for t in token_ids]
          assert len(tk_      ) == len(A__      )
          assert all(len(A__      ) == max_seq_len_ for t in tk_      )

          _SCREAMING_SNAKE_CASE      =				torch.tensor(tk_      )  # (bs, max_seq_len_)
          _SCREAMING_SNAKE_CASE      =				torch.tensor(A__      )  # (bs)
          return tk_t, lg_t






