



import os
import unittest

from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english





@require_tokenizers
class 		UpperCAmelCase						(						snake_case_       ,unittest.TestCase				):
   SCREAMING_SNAKE_CASE__							=     MobileBertTokenizer
   SCREAMING_SNAKE_CASE__							=     MobileBertTokenizerFast
   SCREAMING_SNAKE_CASE__							=     True
   SCREAMING_SNAKE_CASE__							=     True
   SCREAMING_SNAKE_CASE__							=     filter_non_english
   SCREAMING_SNAKE_CASE__							=     '''google/mobilebert-uncased'''





   def 	__lowerCAmelCase							(	self			):
         super().setUp()

         _lowerCAmelCase 			=  [
             '''[UNK]''',
             '''[CLS]''',
             '''[SEP]''',
             '''[PAD]''',
             '''[MASK]''',
             '''want''',
             '''##want''',
             '''##ed''',
             '''wa''',
             '''un''',
             '''runn''',
             '''##ing''',
             ''',''',
             '''low''',
             '''lowest''',
         ]
         _lowerCAmelCase 			=  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]			)			)

         _lowerCAmelCase 			=  [
             (tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2])  # else the 'google/' prefix is stripped
             for tokenizer_def in self.tokenizers_list
         ]





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  '''UNwant\u00E9d,running'''
         _lowerCAmelCase 			=  '''unwanted, running'''
         return input_text, output_text





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.tokenizer_class(self.vocab_file			)

         _lowerCAmelCase 			=  tokenizer.tokenize('''UNwant\u00E9d,running'''			)
         self.assertListEqual(_lowerCAmelCase       ,     ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']			)
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase			)       ,     [9, 6, 7, 12, 10, 11]			)





   def 	__lowerCAmelCase							(	self			):
         if not self.test_rust_tokenizer:
               return

         _lowerCAmelCase 			=  self.get_tokenizer()
         _lowerCAmelCase 			=  self.get_rust_tokenizer()

         _lowerCAmelCase 			=  '''UNwant\u00E9d,running'''

         _lowerCAmelCase 			=  tokenizer.tokenize(_lowerCAmelCase			)
         _lowerCAmelCase 			=  rust_tokenizer.tokenize(_lowerCAmelCase			)
         self.assertListEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)

         _lowerCAmelCase 			=  tokenizer.encode(_lowerCAmelCase       ,     add_special_tokens=_lowerCAmelCase			)
         _lowerCAmelCase 			=  rust_tokenizer.encode(_lowerCAmelCase       ,     add_special_tokens=_lowerCAmelCase			)
         self.assertListEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)

         _lowerCAmelCase 			=  self.get_rust_tokenizer()
         _lowerCAmelCase 			=  tokenizer.encode(_lowerCAmelCase			)
         _lowerCAmelCase 			=  rust_tokenizer.encode(_lowerCAmelCase			)
         self.assertListEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)

         # With lower casing
         _lowerCAmelCase 			=  self.get_tokenizer(do_lower_case=_lowerCAmelCase			)
         _lowerCAmelCase 			=  self.get_rust_tokenizer(do_lower_case=_lowerCAmelCase			)

         _lowerCAmelCase 			=  '''UNwant\u00E9d,running'''

         _lowerCAmelCase 			=  tokenizer.tokenize(_lowerCAmelCase			)
         _lowerCAmelCase 			=  rust_tokenizer.tokenize(_lowerCAmelCase			)
         self.assertListEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)

         _lowerCAmelCase 			=  tokenizer.encode(_lowerCAmelCase       ,     add_special_tokens=_lowerCAmelCase			)
         _lowerCAmelCase 			=  rust_tokenizer.encode(_lowerCAmelCase       ,     add_special_tokens=_lowerCAmelCase			)
         self.assertListEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)

         _lowerCAmelCase 			=  self.get_rust_tokenizer()
         _lowerCAmelCase 			=  tokenizer.encode(_lowerCAmelCase			)
         _lowerCAmelCase 			=  rust_tokenizer.encode(_lowerCAmelCase			)
         self.assertListEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  BasicTokenizer()

         self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz'''			)       ,     ['''ah''', '''\u535A''', '''\u63A8''', '''zz''']			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  BasicTokenizer(do_lower_case=_lowerCAmelCase			)

         self.assertListEqual(
             tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  '''			)       ,     ['''hello''', '''!''', '''how''', '''are''', '''you''', '''?''']			)
         self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''			)       ,     ['''hello''']			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  BasicTokenizer(do_lower_case=_lowerCAmelCase       ,     strip_accents=_lowerCAmelCase			)

         self.assertListEqual(
             tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''			)       ,     ['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?''']			)
         self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''			)       ,     ['''h\u00E9llo''']			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  BasicTokenizer(do_lower_case=_lowerCAmelCase       ,     strip_accents=_lowerCAmelCase			)

         self.assertListEqual(
             tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''			)       ,     ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?''']			)
         self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''			)       ,     ['''hello''']			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  BasicTokenizer(do_lower_case=_lowerCAmelCase			)

         self.assertListEqual(
             tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''			)       ,     ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?''']			)
         self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''			)       ,     ['''hello''']			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  BasicTokenizer(do_lower_case=_lowerCAmelCase			)

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





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  BasicTokenizer(do_lower_case=_lowerCAmelCase       ,     strip_accents=_lowerCAmelCase			)

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





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  BasicTokenizer(do_lower_case=_lowerCAmelCase       ,     strip_accents=_lowerCAmelCase			)

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





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  BasicTokenizer(do_lower_case=_lowerCAmelCase       ,     never_split=['''[UNK]''']			)

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





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

         _lowerCAmelCase 			=  {}
         for i, token in enumerate(_lowerCAmelCase			):
               _lowerCAmelCase 			=  i
         _lowerCAmelCase 			=  WordpieceTokenizer(vocab=_lowerCAmelCase       ,     unk_token='''[UNK]'''			)

         self.assertListEqual(tokenizer.tokenize(''''''			)       ,     []			)

         self.assertListEqual(tokenizer.tokenize('''unwanted running'''			)       ,     ['''un''', '''##want''', '''##ed''', '''runn''', '''##ing''']			)

         self.assertListEqual(tokenizer.tokenize('''unwantedX running'''			)       ,     ['''[UNK]''', '''runn''', '''##ing''']			)





   def 	__lowerCAmelCase							(	self			):
         self.assertTrue(_is_whitespace(''' '''			)			)
         self.assertTrue(_is_whitespace('''\t'''			)			)
         self.assertTrue(_is_whitespace('''\r'''			)			)
         self.assertTrue(_is_whitespace('''\n'''			)			)
         self.assertTrue(_is_whitespace('''\u00A0'''			)			)

         self.assertFalse(_is_whitespace('''A'''			)			)
         self.assertFalse(_is_whitespace('''-'''			)			)





   def 	__lowerCAmelCase							(	self			):
         self.assertTrue(_is_control('''\u0005'''			)			)

         self.assertFalse(_is_control('''A'''			)			)
         self.assertFalse(_is_control(''' '''			)			)
         self.assertFalse(_is_control('''\t'''			)			)
         self.assertFalse(_is_control('''\r'''			)			)





   def 	__lowerCAmelCase							(	self			):
         self.assertTrue(_is_punctuation('''-'''			)			)
         self.assertTrue(_is_punctuation('''$'''			)			)
         self.assertTrue(_is_punctuation('''`'''			)			)
         self.assertTrue(_is_punctuation('''.'''			)			)

         self.assertFalse(_is_punctuation('''A'''			)			)
         self.assertFalse(_is_punctuation(''' '''			)			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.get_tokenizer()
         _lowerCAmelCase 			=  self.get_rust_tokenizer()

         # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
         self.assertListEqual([tokenizer.tokenize(_lowerCAmelCase			) for t in ['''Test''', '''\xad''', '''test''']]       ,     [['''[UNK]'''], [], ['''[UNK]''']]			)

         self.assertListEqual(
             [rust_tokenizer.tokenize(_lowerCAmelCase			) for t in ['''Test''', '''\xad''', '''test''']]       ,     [['''[UNK]'''], [], ['''[UNK]''']]			)





   @slow
   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.tokenizer_class.from_pretrained('''google/mobilebert-uncased'''			)

         _lowerCAmelCase 			=  tokenizer.encode('''sequence builders'''       ,     add_special_tokens=_lowerCAmelCase			)
         _lowerCAmelCase 			=  tokenizer.encode('''multi-sequence build'''       ,     add_special_tokens=_lowerCAmelCase			)

         _lowerCAmelCase 			=  tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase			)
         _lowerCAmelCase 			=  tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase       ,     _lowerCAmelCase			)

         assert encoded_sentence == [101] + text + [102]
         assert encoded_pair == [101] + text + [102] + text_a + [102]





   def 	__lowerCAmelCase							(	self			):
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''			):
                     _lowerCAmelCase 			=  self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase       ,     **_lowerCAmelCase			)

                     _lowerCAmelCase 			=  F'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
                     _lowerCAmelCase 			=  tokenizer_r.encode_plus(
                         _lowerCAmelCase       ,     return_attention_mask=_lowerCAmelCase       ,     return_token_type_ids=_lowerCAmelCase       ,     return_offsets_mapping=_lowerCAmelCase       ,     add_special_tokens=_lowerCAmelCase       ,     )

                     _lowerCAmelCase 			=  tokenizer_r.do_lower_case if hasattr(_lowerCAmelCase       ,     '''do_lower_case'''			) else False
                     _lowerCAmelCase 			=  (
                         [
                             ((0, 0), tokenizer_r.cls_token),
                             ((0, 1), '''A'''),
                             ((1, 2), ''','''),
                             ((3, 5), '''na'''),
                             ((5, 6), '''##ï'''),
                             ((6, 8), '''##ve'''),
                             ((9, 15), tokenizer_r.mask_token),
                             ((16, 21), '''Allen'''),
                             ((21, 23), '''##NL'''),
                             ((23, 24), '''##P'''),
                             ((25, 33), '''sentence'''),
                             ((33, 34), '''.'''),
                             ((0, 0), tokenizer_r.sep_token),
                         ]
                         if not do_lower_case
                         else [
                             ((0, 0), tokenizer_r.cls_token),
                             ((0, 1), '''a'''),
                             ((1, 2), ''','''),
                             ((3, 8), '''naive'''),
                             ((9, 15), tokenizer_r.mask_token),
                             ((16, 21), '''allen'''),
                             ((21, 23), '''##nl'''),
                             ((23, 24), '''##p'''),
                             ((25, 33), '''sentence'''),
                             ((33, 34), '''.'''),
                             ((0, 0), tokenizer_r.sep_token),
                         ]
                     )

                     self.assertEqual(
                         [e[1] for e in expected_results]       ,     tokenizer_r.convert_ids_to_tokens(tokens['''input_ids''']			)			)
                     self.assertEqual([e[0] for e in expected_results]       ,     tokens['''offset_mapping''']			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  ['''的''', '''人''', '''有''']
         _lowerCAmelCase 			=  ''''''.join(_lowerCAmelCase			)
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''			):
                     _lowerCAmelCase 			=  True
                     _lowerCAmelCase 			=  self.tokenizer_class.from_pretrained(_lowerCAmelCase       ,     **_lowerCAmelCase			)
                     _lowerCAmelCase 			=  self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase       ,     **_lowerCAmelCase			)

                     _lowerCAmelCase 			=  tokenizer_p.encode(_lowerCAmelCase       ,     add_special_tokens=_lowerCAmelCase			)
                     _lowerCAmelCase 			=  tokenizer_r.encode(_lowerCAmelCase       ,     add_special_tokens=_lowerCAmelCase			)

                     _lowerCAmelCase 			=  tokenizer_r.convert_ids_to_tokens(_lowerCAmelCase			)
                     _lowerCAmelCase 			=  tokenizer_p.convert_ids_to_tokens(_lowerCAmelCase			)

                     # it is expected that each Chinese character is not preceded by "##"
                     self.assertListEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)
                     self.assertListEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)

                     _lowerCAmelCase 			=  False
                     _lowerCAmelCase 			=  self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase       ,     **_lowerCAmelCase			)
                     _lowerCAmelCase 			=  self.tokenizer_class.from_pretrained(_lowerCAmelCase       ,     **_lowerCAmelCase			)

                     _lowerCAmelCase 			=  tokenizer_r.encode(_lowerCAmelCase       ,     add_special_tokens=_lowerCAmelCase			)
                     _lowerCAmelCase 			=  tokenizer_p.encode(_lowerCAmelCase       ,     add_special_tokens=_lowerCAmelCase			)

                     _lowerCAmelCase 			=  tokenizer_r.convert_ids_to_tokens(_lowerCAmelCase			)
                     _lowerCAmelCase 			=  tokenizer_p.convert_ids_to_tokens(_lowerCAmelCase			)

                     # it is expected that only the first Chinese character is not preceded by "##".
                     _lowerCAmelCase 			=  [
                         F'''##{token}''' if idx != 0 else token for idx, token in enumerate(_lowerCAmelCase			)
                     ]
                     self.assertListEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)
                     self.assertListEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)




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

from ..utils.generic import ModelOutput





class 		UpperCAmelCase						(						snake_case_				):





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  dataset
         _lowerCAmelCase 			=  process
         _lowerCAmelCase 			=  params





   def __len__(	self			):
         return len(self.dataset			)





   def __getitem__(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  self.dataset[i]
         _lowerCAmelCase 			=  self.process(_lowerCAmelCase       ,     **self.params			)
         return processed






class 		UpperCAmelCase						(						snake_case_				):





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase=None			):
         _lowerCAmelCase 			=  loader
         _lowerCAmelCase 			=  infer
         _lowerCAmelCase 			=  params
         if loader_batch_size == 1:
               # Let's spare some time by deactivating altogether
               _lowerCAmelCase 			=  None
         _lowerCAmelCase 			=  loader_batch_size

         # Internal bookkeeping
         _lowerCAmelCase 			=  None
         _lowerCAmelCase 			=  None





   def __len__(	self			):
         return len(self.loader			)





   def __iter__(	self			):
         _lowerCAmelCase 			=  iter(self.loader			)
         return self





   def 	__lowerCAmelCase							(	self			):
         if isinstance(self._loader_batch_data       ,     torch.Tensor			):
               # Batch data is simple tensor, just fetch the slice
               _lowerCAmelCase 			=  self._loader_batch_data[self._loader_batch_index]
         else:
               # Batch data is assumed to be BaseModelOutput (or dict)
               _lowerCAmelCase 			=  {}
               for k, element in self._loader_batch_data.items():
                     if isinstance(_lowerCAmelCase       ,     _lowerCAmelCase			):
                           # Convert ModelOutput to tuple first
                           _lowerCAmelCase 			=  element.to_tuple()
                           if isinstance(element[0]       ,     torch.Tensor			):
                                 _lowerCAmelCase 			=  tuple(el[self._loader_batch_index].unsqueeze(0			) for el in element			)
                           elif isinstance(element[0]       ,     np.ndarray			):
                                 _lowerCAmelCase 			=  tuple(np.expand_dims(el[self._loader_batch_index]       ,     0			) for el in element			)
                           continue
                     if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(_lowerCAmelCase       ,     _lowerCAmelCase			):
                           # Those are stored as lists of tensors so need specific unbatching.
                           if isinstance(element[0]       ,     torch.Tensor			):
                                 _lowerCAmelCase 			=  tuple(el[self._loader_batch_index].unsqueeze(0			) for el in element			)
                           elif isinstance(element[0]       ,     np.ndarray			):
                                 _lowerCAmelCase 			=  tuple(np.expand_dims(el[self._loader_batch_index]       ,     0			) for el in element			)
                           continue
                     if element is None:
                           # This can happen for optional data that get passed around
                           _lowerCAmelCase 			=  None
                     elif isinstance(element[self._loader_batch_index]       ,     torch.Tensor			):
                           # Take correct batch data, but make it looked like batch_size=1
                           # For compatibility with other methods within transformers

                           _lowerCAmelCase 			=  element[self._loader_batch_index].unsqueeze(0			)
                     elif isinstance(element[self._loader_batch_index]       ,     np.ndarray			):
                           # Take correct batch data, but make it looked like batch_size=1
                           # For compatibility with other methods within transformers
                           _lowerCAmelCase 			=  np.expand_dims(element[self._loader_batch_index]       ,     0			)
                     else:
                           # This is typically a list, so no need to `unsqueeze`.
                           _lowerCAmelCase 			=  element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
               _lowerCAmelCase 			=  self._loader_batch_data.__class__(_lowerCAmelCase			)
         self._loader_batch_index += 1
         return result





   def 	__lowerCAmelCase							(	self			):
         if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
               # We are currently unrolling a batch so we just need to return
               # the current item within a batch
               return self.loader_batch_item()

         # We're out of items within a batch
         _lowerCAmelCase 			=  next(self.iterator			)
         _lowerCAmelCase 			=  self.infer(_lowerCAmelCase       ,     **self.params			)
         # We now have a batch of "inferred things".
         if self.loader_batch_size is not None:
               # Try to infer the size of the batch
               if isinstance(_lowerCAmelCase       ,     torch.Tensor			):
                     _lowerCAmelCase 			=  processed
               else:
                     _lowerCAmelCase 			=  list(processed.keys()			)[0]
                     _lowerCAmelCase 			=  processed[key]
               if isinstance(_lowerCAmelCase       ,     _lowerCAmelCase			):
                     _lowerCAmelCase 			=  len(_lowerCAmelCase			)
               else:
                     _lowerCAmelCase 			=  first_tensor.shape[0]
               if 0 < observed_batch_size < self.loader_batch_size:
                     # could be last batch so we can't unroll as many
                     # elements.
                     _lowerCAmelCase 			=  observed_batch_size
               # Setting internal index to unwrap the batch
               _lowerCAmelCase 			=  processed
               _lowerCAmelCase 			=  0
               return self.loader_batch_item()
         else:
               # We're not unrolling batches
               return processed






class 		UpperCAmelCase						(						snake_case_				):





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase=None			):
         super().__init__(_lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			)





   def __iter__(	self			):
         _lowerCAmelCase 			=  iter(self.loader			)
         _lowerCAmelCase 			=  None
         return self





   def 	__lowerCAmelCase							(	self			):
         if self.subiterator is None:
               _lowerCAmelCase 			=  self.infer(next(self.iterator			)       ,     **self.params			)
         try:
               # Try to return next item
               _lowerCAmelCase 			=  next(self.subiterator			)
         except StopIteration:
               # When a preprocess iterator ends, we can start lookig at the next item
               # ChunkIterator will keep feeding until ALL elements of iterator
               # all have created their subiterator and have been iterating against.
               #
               # Another way to look at it, is we're basically flattening lists of lists
               # into a single list, but with generators
               _lowerCAmelCase 			=  self.infer(next(self.iterator			)       ,     **self.params			)
               _lowerCAmelCase 			=  next(self.subiterator			)
         return processed






class 		UpperCAmelCase						(						snake_case_				):





   def __iter__(	self			):
         _lowerCAmelCase 			=  iter(self.loader			)
         return self





   def 	__lowerCAmelCase							(	self			):
         # Extremely similar to PipelineIterator in its unpacking mechanism
         # BUT, we have an extra required item which is the presence of `is_last`
         # That is because everything is flattened by `PipelineChunkIterator` we
         # need to keep track of how to regroup here in the original `process`
         # boundaries so that `process` and `postprocess` see the same data.

         # This iterator accumulates items (possibly while unbatching) until it
         # its a `is_last` and then just passes it on to the caller.
         _lowerCAmelCase 			=  False
         _lowerCAmelCase 			=  []
         if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
               while self._loader_batch_index < self.loader_batch_size:
                     _lowerCAmelCase 			=  self.loader_batch_item()
                     _lowerCAmelCase 			=  item.pop('''is_last'''			)
                     accumulator.append(_lowerCAmelCase			)
                     if is_last:
                           return accumulator

         while not is_last:
               _lowerCAmelCase 			=  self.infer(next(self.iterator			)       ,     **self.params			)
               if self.loader_batch_size is not None:
                     if isinstance(_lowerCAmelCase       ,     torch.Tensor			):
                           _lowerCAmelCase 			=  processed
                     else:
                           _lowerCAmelCase 			=  list(processed.keys()			)[0]
                           _lowerCAmelCase 			=  processed[key]
                     if isinstance(_lowerCAmelCase       ,     _lowerCAmelCase			):
                           _lowerCAmelCase 			=  len(_lowerCAmelCase			)
                     else:
                           _lowerCAmelCase 			=  first_tensor.shape[0]
                     if 0 < observed_batch_size < self.loader_batch_size:
                           # could be last batch so we can't unroll as many
                           # elements.
                           _lowerCAmelCase 			=  observed_batch_size
                     _lowerCAmelCase 			=  processed
                     _lowerCAmelCase 			=  0
                     while self._loader_batch_index < self.loader_batch_size:
                           _lowerCAmelCase 			=  self.loader_batch_item()
                           _lowerCAmelCase 			=  item.pop('''is_last'''			)
                           accumulator.append(_lowerCAmelCase			)
                           if is_last:
                                 return accumulator
               else:
                     _lowerCAmelCase 			=  processed
                     _lowerCAmelCase 			=  item.pop('''is_last'''			)
                     accumulator.append(_lowerCAmelCase			)
         return accumulator






class 		UpperCAmelCase						(						snake_case_				):





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  dataset
         _lowerCAmelCase 			=  key





   def __len__(	self			):
         return len(self.dataset			)





   def __getitem__(	self       ,     _lowerCAmelCase			):
         return self.dataset[i][self.key]






class 		UpperCAmelCase						(						snake_case_				):





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  dataset
         _lowerCAmelCase 			=  keya
         _lowerCAmelCase 			=  keya





   def __len__(	self			):
         return len(self.dataset			)





   def __getitem__(	self       ,     _lowerCAmelCase			):
         return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}