from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, 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 numpy as np
					import tensorflow as tf

					from transformers import (
					    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
					    FlaubertConfig,
					    TFFlaubertForMultipleChoice,
					    TFFlaubertForQuestionAnsweringSimple,
					    TFFlaubertForSequenceClassification,
					    TFFlaubertForTokenClassification,
					    TFFlaubertModel,
					    TFFlaubertWithLMHeadModel,
					)





class       lowercase__		:


						def __init__( self			:       List[str]					,	UpperCamelCase__			:       List[str]					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							parent
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							13
												SCREAMING_SNAKE_CASE							:					Any           =							7
												SCREAMING_SNAKE_CASE							:					int           =							True
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							True
												SCREAMING_SNAKE_CASE							:					List[Any]           =							True
												SCREAMING_SNAKE_CASE							:					Any           =							True
												SCREAMING_SNAKE_CASE							:					int           =							True
												SCREAMING_SNAKE_CASE							:					str           =							False
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							False
												SCREAMING_SNAKE_CASE							:					Tuple           =							False
												SCREAMING_SNAKE_CASE							:					int           =							2
												SCREAMING_SNAKE_CASE							:					Any           =							99
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							0
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							32
												SCREAMING_SNAKE_CASE							:					List[Any]           =							2
												SCREAMING_SNAKE_CASE							:					Any           =							4
												SCREAMING_SNAKE_CASE							:					Dict           =							0.1
												SCREAMING_SNAKE_CASE							:					Tuple           =							0.1
												SCREAMING_SNAKE_CASE							:					Dict           =							512
												SCREAMING_SNAKE_CASE							:					Tuple           =							16
												SCREAMING_SNAKE_CASE							:					Any           =							2
												SCREAMING_SNAKE_CASE							:					int           =							0.02
												SCREAMING_SNAKE_CASE							:					Tuple           =							3
												SCREAMING_SNAKE_CASE							:					List[Any]           =							4
												SCREAMING_SNAKE_CASE							:					int           =							'''last'''
												SCREAMING_SNAKE_CASE							:					int           =							True
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							None
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							0


						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size		)
												SCREAMING_SNAKE_CASE							:					int           =							random_attention_mask([self.batch_size, self.seq_length]					,	dtype=tf.floataa		)

												SCREAMING_SNAKE_CASE							:					int           =							None
												if self.use_input_lengths:
																		SCREAMING_SNAKE_CASE							:					Dict           =							(
																		    ids_tensor([self.batch_size]					,	vocab_size=2		) + self.seq_length - 2
																		)  # small variation of seq_length

												SCREAMING_SNAKE_CASE							:					int           =							None
												if self.use_token_type_ids:
																		SCREAMING_SNAKE_CASE							:					str           =							ids_tensor([self.batch_size, self.seq_length]					,	self.n_langs		)

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							None
												SCREAMING_SNAKE_CASE							:					List[str]           =							None
												SCREAMING_SNAKE_CASE							:					Tuple           =							None
												if self.use_labels:
																		SCREAMING_SNAKE_CASE							:					str           =							ids_tensor([self.batch_size]					,	self.type_sequence_label_size		)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							ids_tensor([self.batch_size, self.seq_length]					,	self.num_labels		)
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							ids_tensor([self.batch_size]					,	2					,	dtype=tf.floataa		)
																		SCREAMING_SNAKE_CASE							:					Dict           =							ids_tensor([self.batch_size]					,	self.num_choices		)

												SCREAMING_SNAKE_CASE							:					List[str]           =							FlaubertConfig(
												    vocab_size=self.vocab_size					,	n_special=self.n_special					,	emb_dim=self.hidden_size					,	n_layers=self.num_hidden_layers					,	n_heads=self.num_attention_heads					,	dropout=self.hidden_dropout_prob					,	attention_dropout=self.attention_probs_dropout_prob					,	gelu_activation=self.gelu_activation					,	sinusoidal_embeddings=self.sinusoidal_embeddings					,	asm=self.asm					,	causal=self.causal					,	n_langs=self.n_langs					,	max_position_embeddings=self.max_position_embeddings					,	initializer_range=self.initializer_range					,	summary_type=self.summary_type					,	use_proj=self.use_proj					,	bos_token_id=self.bos_token_id					,	)

												return (
												    config,
												    input_ids,
												    token_type_ids,
												    input_lengths,
												    sequence_labels,
												    token_labels,
												    is_impossible_labels,
												    choice_labels,
												    input_mask,
												)


						def  __A ( self			:       int					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       List[str]					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       int					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							TFFlaubertModel(config=UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							model(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					List[str]           =							[input_ids, input_mask]
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							model(UpperCamelCase__		)
												self.parent.assertEqual(result.last_hidden_state.shape					,	(self.batch_size, self.seq_length, self.hidden_size)		)


						def  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       Optional[Any]					,	UpperCamelCase__			:       Tuple					,	UpperCamelCase__			:       Any					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       List[str]					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       int					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							TFFlaubertWithLMHeadModel(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
												SCREAMING_SNAKE_CASE							:					Any           =							model(UpperCamelCase__		)

												self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.seq_length, self.vocab_size)		)


						def  __A ( self			:       List[str]					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       Tuple					,	UpperCamelCase__			:       Optional[Any]					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       Dict					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							TFFlaubertForQuestionAnsweringSimple(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Dict           =							{'''input_ids''': input_ids, '''lengths''': input_lengths}

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							model(UpperCamelCase__		)

												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  __A ( self			:       str					,	UpperCamelCase__			:       Any					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       Any					,	UpperCamelCase__			:       List[str]					,	UpperCamelCase__			:       Tuple					,	UpperCamelCase__			:       Tuple					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Tuple					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							TFFlaubertForSequenceClassification(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					List[str]           =							{'''input_ids''': input_ids, '''lengths''': input_lengths}

												SCREAMING_SNAKE_CASE							:					Any           =							model(UpperCamelCase__		)

												self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.type_sequence_label_size)		)


						def  __A ( self			:       List[str]					,	UpperCamelCase__			:       Optional[Any]					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       Optional[Any]					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       List[str]					,	UpperCamelCase__			:       int					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							self.num_labels
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							TFFlaubertForTokenClassification(config=UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							model(UpperCamelCase__		)
												self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.seq_length, self.num_labels)		)


						def  __A ( self			:       str					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Optional[Any]					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       List[str]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       Any					,	UpperCamelCase__			:       Tuple					,	UpperCamelCase__			:       Any					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.num_choices
												SCREAMING_SNAKE_CASE							:					int           =							TFFlaubertForMultipleChoice(config=UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							tf.tile(tf.expand_dims(UpperCamelCase__					,	1		)					,	(1, self.num_choices, 1)		)
												SCREAMING_SNAKE_CASE							:					Dict           =							tf.tile(tf.expand_dims(UpperCamelCase__					,	1		)					,	(1, self.num_choices, 1)		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							tf.tile(tf.expand_dims(UpperCamelCase__					,	1		)					,	(1, self.num_choices, 1)		)
												SCREAMING_SNAKE_CASE							:					int           =							{
												    '''input_ids''': multiple_choice_inputs_ids,
												    '''attention_mask''': multiple_choice_input_mask,
												    '''token_type_ids''': multiple_choice_token_type_ids,
												}
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							model(UpperCamelCase__		)
												self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.num_choices)		)



						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.prepare_config_and_inputs()
												(
												    (
												    SCREAMING_SNAKE_CASE
												)       ,						(
												    SCREAMING_SNAKE_CASE
												)       ,						(
												    SCREAMING_SNAKE_CASE
												)       ,						(
												    SCREAMING_SNAKE_CASE
												)       ,						(
												    SCREAMING_SNAKE_CASE
												)       ,						(
												    SCREAMING_SNAKE_CASE
												)       ,						(
												    SCREAMING_SNAKE_CASE
												)       ,						(
												    SCREAMING_SNAKE_CASE
												)       ,						(
												    SCREAMING_SNAKE_CASE
												)       ,						
												)							:					Optional[int]           =							config_and_inputs
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							{
												    '''input_ids''': input_ids,
												    '''token_type_ids''': token_type_ids,
												    '''langs''': token_type_ids,
												    '''lengths''': input_lengths,
												}
												return config, inputs_dict






@require_tf
class       lowercase__		(     UpperCamelCase_			,							UpperCamelCase_			,							unittest.TestCase):
						UpperCamelCase_  =	(
						    (
						        TFFlaubertModel,
						        TFFlaubertWithLMHeadModel,
						        TFFlaubertForSequenceClassification,
						        TFFlaubertForQuestionAnsweringSimple,
						        TFFlaubertForTokenClassification,
						        TFFlaubertForMultipleChoice,
						    )
						    if is_tf_available()
						    else ()
						)
						UpperCamelCase_  =	(
						    (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
						)  # TODO (PVP): Check other models whether language generation is also applicable
						UpperCamelCase_  =	(
						    {
						        """feature-extraction""": TFFlaubertModel,
						        """fill-mask""": TFFlaubertWithLMHeadModel,
						        """question-answering""": TFFlaubertForQuestionAnsweringSimple,
						        """text-classification""": TFFlaubertForSequenceClassification,
						        """token-classification""": TFFlaubertForTokenClassification,
						        """zero-shot""": TFFlaubertForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						UpperCamelCase_  =	False
						UpperCamelCase_  =	False


						def  __A ( self			:       int					,	UpperCamelCase__			:       Optional[Any]					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												if (
												    pipeline_test_casse_name == "QAPipelineTests"
												    and tokenizer_name is not None
												    and not tokenizer_name.endswith('''Fast'''		)
												):
																		# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
																		# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
																		# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
																		return True

												return False


						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							TFFlaubertModelTester(self		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							ConfigTester(self					,	config_class=UpperCamelCase__					,	emb_dim=37		)


						def  __A ( self			:       int		):





												'''simple docstring'''





												self.config_tester.run_common_tests()


						def  __A ( self			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_flaubert_model(*UpperCamelCase__		)


						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_flaubert_lm_head(*UpperCamelCase__		)


						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_flaubert_qa(*UpperCamelCase__		)


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_flaubert_sequence_classif(*UpperCamelCase__		)


						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_flaubert_for_token_classification(*UpperCamelCase__		)


						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_flaubert_for_multiple_choice(*UpperCamelCase__		)



						@slow
						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							TFFlaubertModel.from_pretrained(UpperCamelCase__		)
																		self.assertIsNotNone(UpperCamelCase__		)






@require_tf
@require_sentencepiece
@require_tokenizers
class       lowercase__		(     unittest.TestCase):


						@slow
						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							TFFlaubertModel.from_pretrained('''jplu/tf-flaubert-small-cased'''		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							tf.convert_to_tensor(
												    [[0, 158, 735, 2592, 1424, 6727, 82, 1]]					,	dtype=tf.intaa					,	)  # "J'aime flaubert !"

												SCREAMING_SNAKE_CASE							:					Tuple           =							model(UpperCamelCase__		)[0]
												SCREAMING_SNAKE_CASE							:					str           =							tf.TensorShape((1, 8, 512)		)
												self.assertEqual(output.shape					,	UpperCamelCase__		)
												# compare the actual values for a slice.
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							tf.convert_to_tensor(
												    [
												        [
												            [-1.876_8773, -1.56_6555, 0.2707_2418],
												            [-1.692_0038, -0.587_3505, 1.932_9599],
												            [-2.956_3985, -1.699_3835, 1.797_2052],
												        ]
												    ]					,	dtype=tf.floataa					,	)

												self.assertTrue(np.allclose(output[:, :3, :3].numpy()					,	expected_slice.numpy()					,	atol=1E-4		)		)






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

from ..utils.generic import ModelOutput





class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       str					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							dataset
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							process
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							params


						def __len__( self			:       Tuple		):





												'''simple docstring'''





												return len(self.dataset		)



						def __getitem__( self			:       List[str]					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.dataset[i]
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							self.process(UpperCamelCase__					,	**self.params		)
												return processed






class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       Dict					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       List[Any]=None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							loader
												SCREAMING_SNAKE_CASE							:					List[Any]           =							infer
												SCREAMING_SNAKE_CASE							:					int           =							params
												if loader_batch_size == 1:
																		# Let's spare some time by deactivating altogether
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							None
												SCREAMING_SNAKE_CASE							:					List[str]           =							loader_batch_size

												# Internal bookkeeping
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							None
												SCREAMING_SNAKE_CASE							:					int           =							None


						def __len__( self			:       int		):





												'''simple docstring'''





												return len(self.loader		)


						def __iter__( self			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							iter(self.loader		)
												return self


						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												if isinstance(self._loader_batch_data					,	torch.Tensor		):
																		# Batch data is simple tensor, just fetch the slice
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self._loader_batch_data[self._loader_batch_index]
												else:
																		# Batch data is assumed to be BaseModelOutput (or dict)
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							{}
																		for k, element in self._loader_batch_data.items():
																								if isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																														# Convert ModelOutput to tuple first
																														SCREAMING_SNAKE_CASE							:					Dict           =							element.to_tuple()
																														if isinstance(element[0]					,	torch.Tensor		):
																																				SCREAMING_SNAKE_CASE							:					Dict           =							tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
																														elif isinstance(element[0]					,	np.ndarray		):
																																				SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							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(UpperCamelCase__					,	UpperCamelCase__		):
																														# Those are stored as lists of tensors so need specific unbatching.
																														if isinstance(element[0]					,	torch.Tensor		):
																																				SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
																														elif isinstance(element[0]					,	np.ndarray		):
																																				SCREAMING_SNAKE_CASE							:					List[str]           =							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
																														SCREAMING_SNAKE_CASE							:					int           =							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

																														SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							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
																														SCREAMING_SNAKE_CASE							:					Tuple           =							np.expand_dims(element[self._loader_batch_index]					,	0		)
																								else:
																														# This is typically a list, so no need to `unsqueeze`.
																														SCREAMING_SNAKE_CASE							:					Tuple           =							element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
																		SCREAMING_SNAKE_CASE							:					Any           =							self._loader_batch_data.__class__(UpperCamelCase__		)
												self._loader_batch_index += 1
												return result



						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												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
												SCREAMING_SNAKE_CASE							:					Tuple           =							next(self.iterator		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.infer(UpperCamelCase__					,	**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(UpperCamelCase__					,	torch.Tensor		):
																								SCREAMING_SNAKE_CASE							:					Optional[int]           =							processed
																		else:
																								SCREAMING_SNAKE_CASE							:					int           =							list(processed.keys()		)[0]
																								SCREAMING_SNAKE_CASE							:					int           =							processed[key]
																		if isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							len(UpperCamelCase__		)
																		else:
																								SCREAMING_SNAKE_CASE							:					Dict           =							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.
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							observed_batch_size
																		# Setting internal index to unwrap the batch
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							processed
																		SCREAMING_SNAKE_CASE							:					int           =							0
																		return self.loader_batch_item()
												else:
																		# We're not unrolling batches
																		return processed






class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       str					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Union[str, Any]=None		):





												'''simple docstring'''





												super().__init__(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)


						def __iter__( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							iter(self.loader		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							None
												return self



						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												if self.subiterator is None:
																		SCREAMING_SNAKE_CASE							:					Dict           =							self.infer(next(self.iterator		)					,	**self.params		)
												try:
																		# Try to return next item
																		SCREAMING_SNAKE_CASE							:					Any           =							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
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.infer(next(self.iterator		)					,	**self.params		)
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							next(self.subiterator		)
												return processed






class       lowercase__		(     UpperCamelCase_):


						def __iter__( self			:       Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							iter(self.loader		)
												return self



						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							False
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							[]
												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:
																								SCREAMING_SNAKE_CASE							:					Tuple           =							self.loader_batch_item()
																								SCREAMING_SNAKE_CASE							:					Any           =							item.pop('''is_last'''		)
																								accumulator.append(UpperCamelCase__		)
																								if is_last:
																														return accumulator

												while not is_last:
																		SCREAMING_SNAKE_CASE							:					Any           =							self.infer(next(self.iterator		)					,	**self.params		)
																		if self.loader_batch_size is not None:
																								if isinstance(UpperCamelCase__					,	torch.Tensor		):
																														SCREAMING_SNAKE_CASE							:					Tuple           =							processed
																								else:
																														SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							list(processed.keys()		)[0]
																														SCREAMING_SNAKE_CASE							:					List[str]           =							processed[key]
																								if isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																														SCREAMING_SNAKE_CASE							:					List[Any]           =							len(UpperCamelCase__		)
																								else:
																														SCREAMING_SNAKE_CASE							:					int           =							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.
																														SCREAMING_SNAKE_CASE							:					List[str]           =							observed_batch_size
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							processed
																								SCREAMING_SNAKE_CASE							:					str           =							0
																								while self._loader_batch_index < self.loader_batch_size:
																														SCREAMING_SNAKE_CASE							:					Any           =							self.loader_batch_item()
																														SCREAMING_SNAKE_CASE							:					List[Any]           =							item.pop('''is_last'''		)
																														accumulator.append(UpperCamelCase__		)
																														if is_last:
																																				return accumulator
																		else:
																								SCREAMING_SNAKE_CASE							:					int           =							processed
																								SCREAMING_SNAKE_CASE							:					List[str]           =							item.pop('''is_last'''		)
																								accumulator.append(UpperCamelCase__		)
												return accumulator






class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       Optional[Any]					,	UpperCamelCase__			:       Dataset					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							dataset
												SCREAMING_SNAKE_CASE							:					Dict           =							key


						def __len__( self			:       Optional[int]		):





												'''simple docstring'''





												return len(self.dataset		)



						def __getitem__( self			:       Dict					,	UpperCamelCase__			:       Tuple		):





												'''simple docstring'''





												return self.dataset[i][self.key]






class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       List[Any]					,	UpperCamelCase__			:       Dataset					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							dataset
												SCREAMING_SNAKE_CASE							:					List[str]           =							keya
												SCREAMING_SNAKE_CASE							:					Tuple           =							keya


						def __len__( self			:       List[str]		):





												'''simple docstring'''





												return len(self.dataset		)



						def __getitem__( self			:       Union[str, Any]					,	UpperCamelCase__			:       Any		):





												'''simple docstring'''





												return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}





