



from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        TF_MODEL_FOR_PRETRAINING_MAPPING,
        TFMobileBertForMaskedLM,
        TFMobileBertForMultipleChoice,
        TFMobileBertForNextSentencePrediction,
        TFMobileBertForPreTraining,
        TFMobileBertForQuestionAnswering,
        TFMobileBertForSequenceClassification,
        TFMobileBertForTokenClassification,
        TFMobileBertModel,
    )





@require_tf
class 		UpperCAmelCase						(						_lowerCAmelCase       ,_lowerCAmelCase       ,unittest.TestCase				):
   SCREAMING_SNAKE_CASE__							=     (
       (
           TFMobileBertModel,
           TFMobileBertForMaskedLM,
           TFMobileBertForNextSentencePrediction,
           TFMobileBertForPreTraining,
           TFMobileBertForQuestionAnswering,
           TFMobileBertForSequenceClassification,
           TFMobileBertForTokenClassification,
           TFMobileBertForMultipleChoice,
       )
       if is_tf_available()
       else ()
   )
   SCREAMING_SNAKE_CASE__							=     (
       {
           'feature-extraction': TFMobileBertModel,
           'fill-mask': TFMobileBertForMaskedLM,
           'question-answering': TFMobileBertForQuestionAnswering,
           'text-classification': TFMobileBertForSequenceClassification,
           'token-classification': TFMobileBertForTokenClassification,
           'zero-shot': TFMobileBertForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   SCREAMING_SNAKE_CASE__							=     False
   SCREAMING_SNAKE_CASE__							=     False





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase=False			):
         _lowerCAmelCase 			=  super()._prepare_for_class(_lowerCAmelCase       ,     _lowerCAmelCase       ,     return_labels=_lowerCAmelCase			)

         if return_labels:
               if model_class in get_values(_lowerCAmelCase			):
                     _lowerCAmelCase 			=  tf.zeros(self.model_tester.batch_size       ,     dtype=tf.intaa			)

         return inputs_dict





   class 		UpperCAmelCase						(						_lowerCAmelCase				):





      def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase=13       ,     _lowerCAmelCase=7       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase=99       ,     _lowerCAmelCase=32       ,     _lowerCAmelCase=32       ,     _lowerCAmelCase=2       ,     _lowerCAmelCase=4       ,     _lowerCAmelCase=37       ,     _lowerCAmelCase="gelu"       ,     _lowerCAmelCase=0.1       ,     _lowerCAmelCase=0.1       ,     _lowerCAmelCase=512       ,     _lowerCAmelCase=16       ,     _lowerCAmelCase=2       ,     _lowerCAmelCase=0.02       ,     _lowerCAmelCase=3       ,     _lowerCAmelCase=4       ,     _lowerCAmelCase=None       ,     ):
            _lowerCAmelCase 			=  parent
            _lowerCAmelCase 			=  batch_size
            _lowerCAmelCase 			=  seq_length
            _lowerCAmelCase 			=  is_training
            _lowerCAmelCase 			=  use_input_mask
            _lowerCAmelCase 			=  use_token_type_ids
            _lowerCAmelCase 			=  use_labels
            _lowerCAmelCase 			=  vocab_size
            _lowerCAmelCase 			=  hidden_size
            _lowerCAmelCase 			=  num_hidden_layers
            _lowerCAmelCase 			=  num_attention_heads
            _lowerCAmelCase 			=  intermediate_size
            _lowerCAmelCase 			=  hidden_act
            _lowerCAmelCase 			=  hidden_dropout_prob
            _lowerCAmelCase 			=  attention_probs_dropout_prob
            _lowerCAmelCase 			=  max_position_embeddings
            _lowerCAmelCase 			=  type_vocab_size
            _lowerCAmelCase 			=  type_sequence_label_size
            _lowerCAmelCase 			=  initializer_range
            _lowerCAmelCase 			=  num_labels
            _lowerCAmelCase 			=  num_choices
            _lowerCAmelCase 			=  scope
            _lowerCAmelCase 			=  embedding_size





      def 	__lowerCAmelCase							(	self			):
            _lowerCAmelCase 			=  ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size			)

            _lowerCAmelCase 			=  None
            if self.use_input_mask:
                  _lowerCAmelCase 			=  random_attention_mask([self.batch_size, self.seq_length]			)

            _lowerCAmelCase 			=  None
            if self.use_token_type_ids:
                  _lowerCAmelCase 			=  ids_tensor([self.batch_size, self.seq_length]       ,     self.type_vocab_size			)

            _lowerCAmelCase 			=  None
            _lowerCAmelCase 			=  None
            _lowerCAmelCase 			=  None
            if self.use_labels:
                  _lowerCAmelCase 			=  ids_tensor([self.batch_size]       ,     self.type_sequence_label_size			)
                  _lowerCAmelCase 			=  ids_tensor([self.batch_size, self.seq_length]       ,     self.num_labels			)
                  _lowerCAmelCase 			=  ids_tensor([self.batch_size]       ,     self.num_choices			)

            _lowerCAmelCase 			=  MobileBertConfig(
                vocab_size=self.vocab_size       ,     hidden_size=self.hidden_size       ,     num_hidden_layers=self.num_hidden_layers       ,     num_attention_heads=self.num_attention_heads       ,     intermediate_size=self.intermediate_size       ,     hidden_act=self.hidden_act       ,     hidden_dropout_prob=self.hidden_dropout_prob       ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,     max_position_embeddings=self.max_position_embeddings       ,     type_vocab_size=self.type_vocab_size       ,     initializer_range=self.initializer_range       ,     embedding_size=self.embedding_size       ,     )

            return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





      def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
            _lowerCAmelCase 			=  TFMobileBertModel(config=_lowerCAmelCase			)
            _lowerCAmelCase 			=  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
            _lowerCAmelCase 			=  model(_lowerCAmelCase			)

            _lowerCAmelCase 			=  [input_ids, input_mask]
            _lowerCAmelCase 			=  model(_lowerCAmelCase			)

            _lowerCAmelCase 			=  model(_lowerCAmelCase			)

            self.parent.assertEqual(
                result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.hidden_size)			)
            self.parent.assertEqual(result.pooler_output.shape       ,     (self.batch_size, self.hidden_size)			)





      def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
            _lowerCAmelCase 			=  TFMobileBertForMaskedLM(config=_lowerCAmelCase			)
            _lowerCAmelCase 			=  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
            _lowerCAmelCase 			=  model(_lowerCAmelCase			)
            self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.vocab_size)			)





      def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
            _lowerCAmelCase 			=  TFMobileBertForNextSentencePrediction(config=_lowerCAmelCase			)
            _lowerCAmelCase 			=  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
            _lowerCAmelCase 			=  model(_lowerCAmelCase			)
            self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, 2)			)





      def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
            _lowerCAmelCase 			=  TFMobileBertForPreTraining(config=_lowerCAmelCase			)
            _lowerCAmelCase 			=  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
            _lowerCAmelCase 			=  model(_lowerCAmelCase			)
            self.parent.assertEqual(
                result.prediction_logits.shape       ,     (self.batch_size, self.seq_length, self.vocab_size)			)
            self.parent.assertEqual(result.seq_relationship_logits.shape       ,     (self.batch_size, 2)			)





      def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
            _lowerCAmelCase 			=  self.num_labels
            _lowerCAmelCase 			=  TFMobileBertForSequenceClassification(config=_lowerCAmelCase			)
            _lowerCAmelCase 			=  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
            _lowerCAmelCase 			=  model(_lowerCAmelCase			)
            self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_labels)			)





      def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
            _lowerCAmelCase 			=  self.num_choices
            _lowerCAmelCase 			=  TFMobileBertForMultipleChoice(config=_lowerCAmelCase			)
            _lowerCAmelCase 			=  tf.tile(tf.expand_dims(_lowerCAmelCase       ,     1			)       ,     (1, self.num_choices, 1)			)
            _lowerCAmelCase 			=  tf.tile(tf.expand_dims(_lowerCAmelCase       ,     1			)       ,     (1, self.num_choices, 1)			)
            _lowerCAmelCase 			=  tf.tile(tf.expand_dims(_lowerCAmelCase       ,     1			)       ,     (1, self.num_choices, 1)			)
            _lowerCAmelCase 			=  {
                '''input_ids''': multiple_choice_inputs_ids,
                '''attention_mask''': multiple_choice_input_mask,
                '''token_type_ids''': multiple_choice_token_type_ids,
            }
            _lowerCAmelCase 			=  model(_lowerCAmelCase			)
            self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_choices)			)





      def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
            _lowerCAmelCase 			=  self.num_labels
            _lowerCAmelCase 			=  TFMobileBertForTokenClassification(config=_lowerCAmelCase			)
            _lowerCAmelCase 			=  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
            _lowerCAmelCase 			=  model(_lowerCAmelCase			)
            self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.num_labels)			)





      def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
            _lowerCAmelCase 			=  TFMobileBertForQuestionAnswering(config=_lowerCAmelCase			)
            _lowerCAmelCase 			=  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
            _lowerCAmelCase 			=  model(_lowerCAmelCase			)
            self.parent.assertEqual(result.start_logits.shape       ,     (self.batch_size, self.seq_length)			)
            self.parent.assertEqual(result.end_logits.shape       ,     (self.batch_size, self.seq_length)			)





      def 	__lowerCAmelCase							(	self			):
            _lowerCAmelCase 			=  self.prepare_config_and_inputs()
            (
                (
                _lowerCAmelCase						
            )			,   (
                _lowerCAmelCase						
            )			,   (
                _lowerCAmelCase						
            )			,   (
                _lowerCAmelCase						
            )			,   (
                _lowerCAmelCase						
            )			,   (
                _lowerCAmelCase						
            )			,   (
                _lowerCAmelCase						
            )			,   						
            ) 			=  config_and_inputs
            _lowerCAmelCase 			=  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
            return config, inputs_dict






   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  TFMobileBertModelTest.TFMobileBertModelTester(self			)
         _lowerCAmelCase 			=  ConfigTester(self       ,     config_class=_lowerCAmelCase       ,     hidden_size=37			)





   def 	__lowerCAmelCase							(	self			):
         self.config_tester.run_common_tests()





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_model(*_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_masked_lm(*_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_multiple_choice(*_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_pretraining(*_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_question_answering(*_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_sequence_classification(*_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_token_classification(*_lowerCAmelCase			)





   @slow
   def 	__lowerCAmelCase							(	self			):







         for model_name in ["google/mobilebert-uncased"]:
               _lowerCAmelCase 			=  TFMobileBertModel.from_pretrained(_lowerCAmelCase			)
               self.assertIsNotNone(_lowerCAmelCase			)






@require_tf
class 		UpperCAmelCase						(						unittest.TestCase				):





   @slow
   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  TFMobileBertForPreTraining.from_pretrained('''google/mobilebert-uncased'''			)
         _lowerCAmelCase 			=  tf.constant([[0, 1, 2, 3, 4, 5]]			)
         _lowerCAmelCase 			=  model(_lowerCAmelCase			)[0]

         _lowerCAmelCase 			=  [1, 6, 30_522]
         self.assertEqual(output.shape       ,     _lowerCAmelCase			)

         _lowerCAmelCase 			=  tf.constant(
             [
                 [
                     [-4.5_919_547, -9.248_295, -9.645_256],
                     [-6.7_306_175, -6.440_284, -6.6_052_837],
                     [-7.2_743_506, -6.7_847_915, -6.024_673],
                 ]
             ]			)
         tf.debugging.assert_near(output[:, :3, :3]       ,     _lowerCAmelCase       ,     atol=1E-4			)




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]}