import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402


__UpperCamelCase	: Any    		=  {
    'return_dict': False,
    'output_hidden_states': True,
    'output_attentions': True,
    'torchscript': True,
    'torch_dtype': 'float16',
    'use_bfloat16': True,
    'tf_legacy_loss': True,
    'pruned_heads': {'a': 1},
    'tie_word_embeddings': False,
    'is_decoder': True,
    'cross_attention_hidden_size': 128,
    'add_cross_attention': True,
    'tie_encoder_decoder': True,
    'max_length': 50,
    'min_length': 3,
    'do_sample': True,
    'early_stopping': True,
    'num_beams': 3,
    'num_beam_groups': 3,
    'diversity_penalty': 0.5,
    'temperature': 2.0,
    'top_k': 10,
    'top_p': 0.7,
    'typical_p': 0.2,
    'repetition_penalty': 0.8,
    'length_penalty': 0.8,
    'no_repeat_ngram_size': 5,
    'encoder_no_repeat_ngram_size': 5,
    'bad_words_ids': [1, 2, 3],
    'num_return_sequences': 3,
    'chunk_size_feed_forward': 5,
    'output_scores': True,
    'return_dict_in_generate': True,
    'forced_bos_token_id': 2,
    'forced_eos_token_id': 3,
    'remove_invalid_values': True,
    'architectures': ['BertModel'],
    'finetuning_task': 'translation',
    'id2label': {0: 'label'},
    'label2id': {'label': '0'},
    'tokenizer_class': 'BertTokenizerFast',
    'prefix': 'prefix',
    'bos_token_id': 6,
    'pad_token_id': 7,
    'eos_token_id': 8,
    'sep_token_id': 9,
    'decoder_start_token_id': 10,
    'exponential_decay_length_penalty': (5, 1.01),
    'suppress_tokens': [0, 1],
    'begin_suppress_tokens': 2,
    'task_specific_params': {'translation': 'some_params'},
    'problem_type': 'regression',
}





@is_staging_test
class       lowercase__		(     unittest.TestCase):


						@classmethod
						def  __A ( cls			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							TOKEN
												HfFolder.save_token(UpperCamelCase__		)


						@classmethod
						def  __A ( cls			:       Optional[Any]		):





												'''simple docstring'''





												try:
																		delete_repo(token=cls._token					,	repo_id='''test-config'''		)
												except HTTPError:
																		pass

												try:
																		delete_repo(token=cls._token					,	repo_id='''valid_org/test-config-org'''		)
												except HTTPError:
																		pass

												try:
																		delete_repo(token=cls._token					,	repo_id='''test-dynamic-config'''		)
												except HTTPError:
																		pass


						def  __A ( self			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							BertConfig(
												    vocab_size=99					,	hidden_size=32					,	num_hidden_layers=5					,	num_attention_heads=4					,	intermediate_size=37		)
												config.push_to_hub('''test-config'''					,	use_auth_token=self._token		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							BertConfig.from_pretrained(f"""{USER}/test-config"""		)
												for k, v in config.to_dict().items():
																		if k != "transformers_version":
																								self.assertEqual(UpperCamelCase__					,	getattr(UpperCamelCase__					,	UpperCamelCase__		)		)

        # Reset repo
												delete_repo(token=self._token					,	repo_id='''test-config'''		)

												# Push to hub via save_pretrained
												with tempfile.TemporaryDirectory() as tmp_dir:
																		config.save_pretrained(UpperCamelCase__					,	repo_id='''test-config'''					,	push_to_hub=UpperCamelCase__					,	use_auth_token=self._token		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							BertConfig.from_pretrained(f"""{USER}/test-config"""		)
												for k, v in config.to_dict().items():
																		if k != "transformers_version":
																								self.assertEqual(UpperCamelCase__					,	getattr(UpperCamelCase__					,	UpperCamelCase__		)		)


						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							BertConfig(
												    vocab_size=99					,	hidden_size=32					,	num_hidden_layers=5					,	num_attention_heads=4					,	intermediate_size=37		)
												config.push_to_hub('''valid_org/test-config-org'''					,	use_auth_token=self._token		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							BertConfig.from_pretrained('''valid_org/test-config-org'''		)
												for k, v in config.to_dict().items():
																		if k != "transformers_version":
																								self.assertEqual(UpperCamelCase__					,	getattr(UpperCamelCase__					,	UpperCamelCase__		)		)

        # Reset repo
												delete_repo(token=self._token					,	repo_id='''valid_org/test-config-org'''		)

												# Push to hub via save_pretrained
												with tempfile.TemporaryDirectory() as tmp_dir:
																		config.save_pretrained(
																		    UpperCamelCase__					,	repo_id='''valid_org/test-config-org'''					,	push_to_hub=UpperCamelCase__					,	use_auth_token=self._token		)

												SCREAMING_SNAKE_CASE							:					Any           =							BertConfig.from_pretrained('''valid_org/test-config-org'''		)
												for k, v in config.to_dict().items():
																		if k != "transformers_version":
																								self.assertEqual(UpperCamelCase__					,	getattr(UpperCamelCase__					,	UpperCamelCase__		)		)



						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												CustomConfig.register_for_auto_class()
												SCREAMING_SNAKE_CASE							:					List[Any]           =							CustomConfig(attribute=42		)

												config.push_to_hub('''test-dynamic-config'''					,	use_auth_token=self._token		)

												# This has added the proper auto_map field to the config
												self.assertDictEqual(config.auto_map					,	{'''AutoConfig''': '''custom_configuration.CustomConfig'''}		)

												SCREAMING_SNAKE_CASE							:					int           =							AutoConfig.from_pretrained(f"""{USER}/test-dynamic-config"""					,	trust_remote_code=UpperCamelCase__		)
												# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
												self.assertEqual(new_config.__class__.__name__					,	'''CustomConfig'''		)
												self.assertEqual(new_config.attribute					,	42		)






class       lowercase__		(     unittest.TestCase):


						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							GPTaConfig()

												# attempt to modify each of int/float/bool/str config records and verify they were updated
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							c.n_embd + 1  # int
												SCREAMING_SNAKE_CASE							:					Dict           =							c.resid_pdrop + 1.0  # float
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							not c.scale_attn_weights  # bool
												SCREAMING_SNAKE_CASE							:					int           =							c.summary_type + '''foo'''  # str
												c.update_from_string(
												    f"""n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}"""		)
												self.assertEqual(UpperCamelCase__					,	c.n_embd					,	'''mismatch for key: n_embd'''		)
												self.assertEqual(UpperCamelCase__					,	c.resid_pdrop					,	'''mismatch for key: resid_pdrop'''		)
												self.assertEqual(UpperCamelCase__					,	c.scale_attn_weights					,	'''mismatch for key: scale_attn_weights'''		)
												self.assertEqual(UpperCamelCase__					,	c.summary_type					,	'''mismatch for key: summary_type'''		)


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							PretrainedConfig()
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							[key for key in base_config.__dict__ if key not in config_common_kwargs]
												# If this part of the test fails, you have arguments to addin config_common_kwargs above.
												self.assertListEqual(
												    UpperCamelCase__					,	['''is_encoder_decoder''', '''_name_or_path''', '''_commit_hash''', '''transformers_version''']		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							[key for key, value in config_common_kwargs.items() if value == getattr(UpperCamelCase__					,	UpperCamelCase__		)]
												if len(UpperCamelCase__		) > 0:
																		raise ValueError(
																		    '''The following keys are set with the default values in'''
																		    ''' `test_configuration_common.config_common_kwargs` pick another value for them:'''
																		    f""" {', '.join(UpperCamelCase__		)}."""		)


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





												'''simple docstring'''





												with self.assertRaises(UpperCamelCase__		):
																		# config is in subfolder, the following should not work without specifying the subfolder
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							BertConfig.from_pretrained('''hf-internal-testing/tiny-random-bert-subfolder'''		)

												SCREAMING_SNAKE_CASE							:					Tuple           =							BertConfig.from_pretrained('''hf-internal-testing/tiny-random-bert-subfolder'''					,	subfolder='''bert'''		)

												self.assertIsNotNone(UpperCamelCase__		)


						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							mock.Mock()
												SCREAMING_SNAKE_CASE							:					int           =							500
												SCREAMING_SNAKE_CASE							:					str           =							{}
												SCREAMING_SNAKE_CASE							:					Any           =							HTTPError
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							{}

												# Download this model to make sure it's in the cache.
												SCREAMING_SNAKE_CASE							:					Dict           =							BertConfig.from_pretrained('''hf-internal-testing/tiny-random-bert'''		)

												# Under the mock environment we get a 500 error when trying to reach the model.
												with mock.patch('''requests.Session.request'''					,	return_value=UpperCamelCase__		) as mock_head:
																		SCREAMING_SNAKE_CASE							:					str           =							BertConfig.from_pretrained('''hf-internal-testing/tiny-random-bert'''		)
																		# This check we did call the fake head request
																		mock_head.assert_called()


						def  __A ( self			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							BertConfig.from_pretrained(
												    '''https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json'''		)


						def  __A ( self			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							AutoConfig.from_pretrained('''bert-base-cased'''		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							['''config.4.0.0.json''']

												with tempfile.TemporaryDirectory() as tmp_dir:
																		configuration.save_pretrained(UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							2
																		json.dump(configuration.to_dict()					,	open(os.path.join(UpperCamelCase__					,	'''config.4.0.0.json'''		)					,	'''w'''		)		)

																		# This should pick the new configuration file as the version of Transformers is > 4.0.0
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							AutoConfig.from_pretrained(UpperCamelCase__		)
																		self.assertEqual(new_configuration.hidden_size					,	2		)

																		# Will need to be adjusted if we reach v42 and this test is still here.
																		# Should pick the old configuration file as the version of Transformers is < 4.42.0
																		SCREAMING_SNAKE_CASE							:					Any           =							['''config.42.0.0.json''']
																		SCREAMING_SNAKE_CASE							:					Dict           =							768
																		configuration.save_pretrained(UpperCamelCase__		)
																		shutil.move(os.path.join(UpperCamelCase__					,	'''config.4.0.0.json'''		)					,	os.path.join(UpperCamelCase__					,	'''config.42.0.0.json'''		)		)
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							AutoConfig.from_pretrained(UpperCamelCase__		)
																		self.assertEqual(new_configuration.hidden_size					,	768		)



						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							'''hf-internal-testing/test-two-configs'''

												import transformers as new_transformers

												SCREAMING_SNAKE_CASE							:					int           =							'''v4.0.0'''
												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Dict           =							new_transformers.models.auto.AutoConfig.from_pretrained(
												    UpperCamelCase__					,	return_unused_kwargs=UpperCamelCase__		)
												self.assertEqual(new_configuration.hidden_size					,	2		)
												# This checks `_configuration_file` ia not kept in the kwargs by mistake.
												self.assertDictEqual(UpperCamelCase__					,	{}		)

												# Testing an older version by monkey-patching the version in the module it's used.
												import transformers as old_transformers

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							'''v3.0.0'''
												SCREAMING_SNAKE_CASE							:					Dict           =							old_transformers.models.auto.AutoConfig.from_pretrained(UpperCamelCase__		)
												self.assertEqual(old_configuration.hidden_size					,	768		)






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]}





