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

from utils import logger




class        __snake_case	(					lowerCamelCase_    ):



      def __init__(						self   ,      lowercase   ,      lowercase)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					List[Any]      							=     params

        a__:					Dict      							=     np.array(_UpperCAmelCase)
        a__:					Optional[int]      							=     np.array([len(_UpperCAmelCase) 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)     ->      Union[str, Any]:





        '''simple docstring'''





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



      def __len__(						self)     ->      Tuple:





        '''simple docstring'''





        return len(self.lengths)



      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''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   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        a__:					List[Any]      							=     self.params.max_model_input_size
        a__:					int      							=     self.lengths > max_len
        logger.info(f'Splitting {sum(_UpperCAmelCase)} too long sequences.')

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

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

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

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



      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        a__:					Tuple      							=     len(self)
        a__:					Dict      							=     self.lengths > 11
        a__:					Optional[Any]      							=     self.token_ids[indices]
        a__:					Optional[int]      							=     self.lengths[indices]
        a__:					Tuple      							=     len(self)
        logger.info(f'Remove {init_size - new_size} too short (<=11 tokens) sequences.')



      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self)     ->      int:





        '''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   lowerCamelCase_       (						self   ,      lowercase)     ->      Tuple:





        '''simple docstring'''





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

        # Max for paddings
        a__:					List[str]      							=     max(_UpperCAmelCase)

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

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



"""simple docstring"""
import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class        __snake_case	(					__lowerCAmelCase	,				unittest.TestCase    ):
      a__				=		DebertaTokenizer
      a__				=		True
      a__				=		DebertaTokenizerFast



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        a__:					List[str]      							=     [
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            '\u0120',
            '\u0120l',
            '\u0120n',
            '\u0120lo',
            '\u0120low',
            'er',
            '\u0120lowest',
            '\u0120newer',
            '\u0120wider',
            '[UNK]',
        ]
        a__:					List[str]      							=     dict(zip(lowercase   ,      range(len(lowercase))))
        a__:					Any      							=     ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
        a__:					Optional[Any]      							=     {'unk_token': '[UNK]'}

        a__:					Any      							=     os.path.join(self.tmpdirname   ,      VOCAB_FILES_NAMES['vocab_file'])
        a__:					Dict      							=     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(lowercase) + '\n')
        with open(self.merges_file   ,      'w'   ,      encoding='utf-8') as fp:
          fp.write('\n'.join(lowercase))



      def   lowerCamelCase_       (						self   ,      **lowercase)     ->      int:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      Dict:





        '''simple docstring'''





        a__:					Tuple      							=     'lower newer'
        a__:					int      							=     'lower newer'
        return input_text, output_text



      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        a__:					Optional[int]      							=     self.get_tokenizer()
        a__:					List[Any]      							=     'lower newer'
        a__:					Optional[int]      							=     ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
        a__:					Optional[Any]      							=     tokenizer.tokenize(lowercase)
        self.assertListEqual(lowercase   ,      lowercase)

        a__:					int      							=     tokens + [tokenizer.unk_token]
        a__:					Optional[int]      							=     [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase)   ,      lowercase)



      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        a__:					int      							=     self.get_tokenizer()
        a__:					Any      							=     tokenizer('Hello'   ,      'World')
        a__:					Optional[Any]      							=     [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
        self.assertListEqual(tokd['token_type_ids']   ,      lowercase)



      @slow
      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        a__:					Dict      							=     self.tokenizer_class.from_pretrained('microsoft/deberta-base')

        a__:					Union[str, Any]      							=     tokenizer.encode('sequence builders'   ,      add_special_tokens=lowercase)
        a__:					Optional[Any]      							=     tokenizer.encode('multi-sequence build'   ,      add_special_tokens=lowercase)

        a__:					List[str]      							=     tokenizer.encode(
            'sequence builders'   ,      add_special_tokens=lowercase   ,      add_prefix_space=lowercase)
        a__:					Any      							=     tokenizer.encode(
            'sequence builders'   ,      'multi-sequence build'   ,      add_special_tokens=lowercase   ,      add_prefix_space=lowercase)

        a__:					Optional[int]      							=     tokenizer.build_inputs_with_special_tokens(lowercase)
        a__:					Union[str, Any]      							=     tokenizer.build_inputs_with_special_tokens(lowercase   ,      lowercase)

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode



      @slow
      def   lowerCamelCase_       (						self)     ->      Any:





        '''simple docstring'''





        a__:					List[Any]      							=     [self.tokenizer_class]
        if self.test_rust_tokenizer:
          tokenizer_classes.append(self.rust_tokenizer_class)

        for tokenizer_class in tokenizer_classes:
          a__:					int      							=     tokenizer_class.from_pretrained('microsoft/deberta-base')

          a__:					List[str]      							=     [
              'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
              'ALBERT incorporates two parameter reduction techniques',
              'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
              ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
              ' vocabulary embedding.',
          ]

          a__:					Dict      							=     tokenizer(lowercase   ,      padding=lowercase)
          a__:					Union[str, Any]      							=     [tokenizer.decode(lowercase   ,      skip_special_tokens=lowercase) for seq in encoding['input_ids']]

          # fmt: off
          a__:					Any      							=     {
              'input_ids': [
                  [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 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],
                  [1, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 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],
                  [1, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
              ],
              'token_type_ids': [
                  [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, 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, 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, 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, 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]
              ]
          }
          # fmt: on

          a__:					str      							=     [
              'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
              'ALBERT incorporates two parameter reduction techniques',
              'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
              ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
              ' vocabulary embedding.',
          ]

          self.assertDictEqual(encoding.data   ,      lowercase)

          for expected, decoded in zip(lowercase   ,      lowercase):
            self.assertEqual(lowercase   ,      lowercase)


