





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

from utils import logger







class 				__lowerCAmelCase       (    lowerCAmelCase_):







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						Tuple:



        '''simple docstring'''




        a__	:					Union[str, Any]										=params

        a__	:					Optional[Any]										=np.array(_snake_case							)
        a__	:					Tuple										=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		,      lowerCAmelCase__							)   ->						str:



        '''simple docstring'''




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







      def __len__(    self							)   ->						str:



        '''simple docstring'''




        return len(self.lengths							)







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



        '''simple docstring'''




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

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

        a__	:					Dict										=[]
        a__	:					Any										=[]
        if self.params.mlm:
          a__	:					str										=self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
        else:
          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__	:					List[Any]										=np.insert(_snake_case		,      0		,      _snake_case							)
              if sub_s[-1] != sep_id:
                a__	:					Optional[Any]										=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]							)

        a__	:					int										=np.array(_snake_case							)
        a__	:					Union[str, Any]										=np.array(_snake_case							)







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



        '''simple docstring'''




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







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




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







      def    _lowercase  (    self							)   ->						Optional[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    _lowercase  (    self		,      lowerCAmelCase__							)   ->						List[Any]:



        '''simple docstring'''




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

        # Max for paddings
        a__	:					Tuple										=max(_snake_case							)

        # Pad token ids
        if self.params.mlm:
          a__	:					Dict										=self.params.special_tok_ids['pad_token']
        else:
          a__	:					List[Any]										=self.params.special_tok_ids['unk_token']
        a__	:					Union[str, Any]										=[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_							)

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











import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin




class 					snake_case__						(       lowerCAmelCase_				,     unittest.TestCase     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	TransfoXLTokenizer
     _SCREAMING_SNAKE_CASE						=	False
     _SCREAMING_SNAKE_CASE						=	False






     def    lowercase_   (       self  :  Optional[int]					)   ->Any:
            super().setUp()

            snake_case__							:	Tuple   	=	[
                '<unk>',
                '[CLS]',
                '[SEP]',
                'want',
                'unwanted',
                'wa',
                'un',
                'running',
                ',',
                'low',
                'l',
            ]
            snake_case__							:	Any   	=	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]					)					)






     def    lowercase_   (       self  :  Union[str, Any], **_snake_case  :  List[Any]					)   ->Dict:
            snake_case__							:	str   	=	True
            return TransfoXLTokenizer.from_pretrained(self.tmpdirname, **_snake_case					)






     def    lowercase_   (       self  :  Optional[Any], _snake_case  :  str					)   ->Dict:
            snake_case__							:	List[Any]   	=	'<unk> UNwanted , running'
            snake_case__							:	List[Any]   	=	'<unk> unwanted, running'
            return input_text, output_text






     def    lowercase_   (       self  :  List[Any]					)   ->Tuple:
            snake_case__							:	Dict   	=	TransfoXLTokenizer(vocab_file=self.vocab_file, lower_case=_snake_case					)

            snake_case__							:	str   	=	tokenizer.tokenize('<unk> UNwanted , running'					)
            self.assertListEqual(_snake_case, ['<unk>', 'unwanted', ',', 'running']					)

            self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case					), [0, 4, 8, 7]					)






     def    lowercase_   (       self  :  List[str]					)   ->List[Any]:
            snake_case__							:	str   	=	TransfoXLTokenizer(lower_case=_snake_case					)

            self.assertListEqual(
                tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '					), ['hello', '!', 'how', 'are', 'you', '?']					)






     def    lowercase_   (       self  :  Optional[int]					)   ->Optional[Any]:
            snake_case__							:	Optional[int]   	=	TransfoXLTokenizer(lower_case=_snake_case					)

            self.assertListEqual(
                tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '					), ['HeLLo', '!', 'how', 'Are', 'yoU', '?']					)






     def    lowercase_   (       self  :  Optional[int]					)   ->Union[str, Any]:
            snake_case__							:	List[Any]   	=	TransfoXLTokenizer(lower_case=_snake_case					)
            snake_case__							:	Dict   	=	'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
            snake_case__							:	List[Any]   	=	[
                'Hello',
                '(',
                'bracket',
                ')',
                'and',
                'side',
                '@-@',
                'scrolled',
                '[',
                'and',
                ']',
                'Henry',
                '\'s',
                '$',
                '5',
                '@,@',
                '000',
                'with',
                '3',
                '@.@',
                '34',
                'm',
                '.',
                'What',
                '\'s',
                'up',
                '!',
                '?',
            ]

            self.assertListEqual(tokenizer.tokenize(_snake_case					), _snake_case					)

            self.assertEqual(tokenizer.convert_tokens_to_string(_snake_case					), _snake_case					)






     def    lowercase_   (       self  :  Dict					)   ->Any:
            snake_case__							:	Dict   	=	self.get_tokenizer()
            snake_case__							:	Optional[Any]   	=	len(_snake_case					)

            tokenizer.add_tokens(['new1', 'new2']					)
            tokenizer.move_added_token('new1', 1					)

            # Check that moved token is not copied (duplicate)
            self.assertEqual(len(_snake_case					), original_len + 2					)
            # Check that token is moved to specified id
            self.assertEqual(tokenizer.encode('new1'					), [1]					)
            self.assertEqual(tokenizer.decode([1]					), 'new1'					)






