
"""simple docstring"""



import argparse
import os

# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_snake_case          =						1_6
_snake_case          =						3_2






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Accelerator     ,  SCREAMING_SNAKE_CASE:					int = 16						):






							"""simple docstring"""



							_lowerCAmelCase  	=			AutoTokenizer.from_pretrained('bert-base-cased'						)
							_lowerCAmelCase  	=			load_dataset('glue'     ,  'mrpc'						)

							def tokenize_function(SCREAMING_SNAKE_CASE:					Optional[int]						):
														# max_length=None => use the model max length (it's actually the default)
														_lowerCAmelCase  	=			tokenizer(examples['sentence1']     ,  examples['sentence2']     ,  truncation=SCREAMING_SNAKE_CASE     ,  max_length=SCREAMING_SNAKE_CASE						)
														return outputs

							# Apply the method we just defined to all the examples in all the splits of the dataset
							# starting with the main process first:
							with accelerator.main_process_first():
														_lowerCAmelCase  	=			datasets.map(
														    SCREAMING_SNAKE_CASE     ,  batched=SCREAMING_SNAKE_CASE     ,  remove_columns=['idx', 'sentence1', 'sentence2']     ,  )

							# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
							# transformers library
							_lowerCAmelCase  	=			tokenized_datasets.rename_column('label'     ,  'labels'						)

							def collate_fn(SCREAMING_SNAKE_CASE:					Tuple						):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														_lowerCAmelCase  	=			128 if accelerator.distributed_type == DistributedType.TPU else None
														# When using mixed precision we want round multiples of 8/16
														if accelerator.mixed_precision == "fp8":
																					_lowerCAmelCase  	=			16
														elif accelerator.mixed_precision != "no":
																					_lowerCAmelCase  	=			8
														else:
																					_lowerCAmelCase  	=			None

														return tokenizer.pad(
														    SCREAMING_SNAKE_CASE     ,  padding='longest'     ,  max_length=SCREAMING_SNAKE_CASE     ,  pad_to_multiple_of=SCREAMING_SNAKE_CASE     ,  return_tensors='pt'     ,  )

							# Instantiate dataloaders.
							_lowerCAmelCase  	=			DataLoader(
							    tokenized_datasets['train']     ,  shuffle=SCREAMING_SNAKE_CASE     ,  collate_fn=SCREAMING_SNAKE_CASE     ,  batch_size=SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			DataLoader(
							    tokenized_datasets['validation']     ,  shuffle=SCREAMING_SNAKE_CASE     ,  collate_fn=SCREAMING_SNAKE_CASE     ,  batch_size=SCREAMING_SNAKE_CASE						)

							return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
	from accelerate.test_utils.training import mocked_dataloaders

	_snake_case          =						mocked_dataloaders  # noqa: F811






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[Any]     ,  SCREAMING_SNAKE_CASE:					List[str]						):






							"""simple docstring"""



							if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,  SCREAMING_SNAKE_CASE						) == "1":
														_lowerCAmelCase  	=			2
							# Initialize accelerator
							_lowerCAmelCase  	=			Accelerator(cpu=args.cpu     ,  mixed_precision=args.mixed_precision						)
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							_lowerCAmelCase  	=			config['lr']
							_lowerCAmelCase  	=			int(config['num_epochs']						)
							_lowerCAmelCase  	=			int(config['seed']						)
							_lowerCAmelCase  	=			int(config['batch_size']						)

							_lowerCAmelCase  	=			evaluate.load('glue'     ,  'mrpc'						)

							# New Code #
							# We now can define an inner training loop function. It should take a batch size as the only parameter,
							# and build the dataloaders in there.
							# It also gets our decorator
							@find_executable_batch_size(starting_batch_size=SCREAMING_SNAKE_CASE						)
							def inner_training_loop(SCREAMING_SNAKE_CASE:					Dict						):
														# And now just move everything below under this function
														# We need to bring in the Accelerator object from earlier
														nonlocal accelerator
														# And reset all of its attributes that could hold onto any memory:
														accelerator.free_memory()

														# Then we can declare the model, optimizer, and everything else:
														set_seed(SCREAMING_SNAKE_CASE						)

														# Instantiate the model (we build the model here so that the seed also control new weights initialization)
														_lowerCAmelCase  	=			AutoModelForSequenceClassification.from_pretrained('bert-base-cased'     ,  return_dict=SCREAMING_SNAKE_CASE						)

														# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
														# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
														# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
														_lowerCAmelCase  	=			model.to(accelerator.device						)

														# Instantiate optimizer
														_lowerCAmelCase  	=			AdamW(params=model.parameters()     ,  lr=SCREAMING_SNAKE_CASE						)
														_lowerCAmelCase	,		_lowerCAmelCase  	=			get_dataloaders(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)

														# Instantiate scheduler
														_lowerCAmelCase  	=			get_linear_schedule_with_warmup(
														    optimizer=SCREAMING_SNAKE_CASE     ,  num_warmup_steps=100     ,  num_training_steps=(len(SCREAMING_SNAKE_CASE						) * num_epochs)     ,  )

														# Prepare everything
														# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
														# prepare method.
														_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase  	=			accelerator.prepare(
														    SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)

														# Now we train the model
														for epoch in range(SCREAMING_SNAKE_CASE						):
																					model.train()
																					for step, batch in enumerate(SCREAMING_SNAKE_CASE						):
																												# We could avoid this line since we set the accelerator with `device_placement=True`.
																												batch.to(accelerator.device						)
																												_lowerCAmelCase  	=			model(**SCREAMING_SNAKE_CASE						)
																												_lowerCAmelCase  	=			outputs.loss
																												accelerator.backward(SCREAMING_SNAKE_CASE						)
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

																					model.eval()
																					for step, batch in enumerate(SCREAMING_SNAKE_CASE						):
																												# We could avoid this line since we set the accelerator with `device_placement=True`.
																												batch.to(accelerator.device						)
																												with torch.no_grad():
																																			_lowerCAmelCase  	=			model(**SCREAMING_SNAKE_CASE						)
																												_lowerCAmelCase  	=			outputs.logits.argmax(dim=-1						)
																												_lowerCAmelCase	,		_lowerCAmelCase  	=			accelerator.gather_for_metrics((predictions, batch['labels'])						)
																												metric.add_batch(
																												    predictions=SCREAMING_SNAKE_CASE     ,  references=SCREAMING_SNAKE_CASE     ,  )

																					_lowerCAmelCase  	=			metric.compute()
																					# Use accelerator.print to print only on the main process.
																					accelerator.print(f"""epoch {epoch}:"""     ,  SCREAMING_SNAKE_CASE						)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
							inner_training_loop()






def 	__snake_case  (    ):






							"""simple docstring"""



							_lowerCAmelCase  	=			argparse.ArgumentParser(description='Simple example of training script.'						)
							parser.add_argument(
							    '--mixed_precision'     ,  type=SCREAMING_SNAKE_CASE     ,  default=SCREAMING_SNAKE_CASE     ,  choices=['no', 'fp16', 'bf16', 'fp8']     ,  help='Whether to use mixed precision. Choose'
							    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
							    'and an Nvidia Ampere GPU.'     ,  )
							parser.add_argument('--cpu'     ,  action='store_true'     ,  help='If passed, will train on the CPU.'						)
							_lowerCAmelCase  	=			parser.parse_args()
							_lowerCAmelCase  	=			{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
	main()








"""simple docstring"""



import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


_snake_case          =						logging.getLogger(__name__)
@dataclass
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							str
				SCREAMING_SNAKE_CASE_:							List[str]
				SCREAMING_SNAKE_CASE_:							Optional[List[str]]






@dataclass
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							List[int]
				SCREAMING_SNAKE_CASE_:							List[int]
				SCREAMING_SNAKE_CASE_:							Optional[List[int]]							=					None
				SCREAMING_SNAKE_CASE_:							Optional[List[int]]							=					None






class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Union[str, Any]							=					"train"
				SCREAMING_SNAKE_CASE_:							Tuple							=					"dev"
				SCREAMING_SNAKE_CASE_:							List[str]							=					"test"






class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				@staticmethod
				def 				__lowerCamelCase	(					UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			Union[Split, str]  )		->  List[InputExample]:







											"""simple docstring"""




											raise NotImplementedError


				@staticmethod
				def 				__lowerCamelCase	(					UpperCAmelCase_	:			str  )		->  List[str]:







											"""simple docstring"""




											raise NotImplementedError







				@staticmethod
				def 				__lowerCamelCase	(					UpperCAmelCase_	:			List[InputExample]				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			PreTrainedTokenizer				,     UpperCAmelCase_	:			Union[str, Any]=False				,     UpperCAmelCase_	:			Optional[int]="[CLS]"				,     UpperCAmelCase_	:			Tuple=1				,     UpperCAmelCase_	:			str="[SEP]"				,     UpperCAmelCase_	:			Optional[Any]=False				,     UpperCAmelCase_	:			Union[str, Any]=False				,     UpperCAmelCase_	:			Union[str, Any]=0				,     UpperCAmelCase_	:			Dict=0				,     UpperCAmelCase_	:			List[Any]=-100				,     UpperCAmelCase_	:			Dict=0				,     UpperCAmelCase_	:			List[Any]=True				,     )		->  List[InputFeatures]:







											"""simple docstring"""




											_lowerCAmelCase  	=			{label: i for i, label in enumerate(UpperCAmelCase_  )}

											_lowerCAmelCase  	=			[]
											for ex_index, example in enumerate(UpperCAmelCase_  ):
																		if ex_index % 10_000 == 0:
																									logger.info('Writing example %d of %d'				,     UpperCAmelCase_				,     len(UpperCAmelCase_  )  )

																		_lowerCAmelCase  	=			[]
																		_lowerCAmelCase  	=			[]
																		for word, label in zip(example.words				,     example.labels  ):
																									_lowerCAmelCase  	=			tokenizer.tokenize(UpperCAmelCase_  )

																									# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																									if len(UpperCAmelCase_  ) > 0:
																																tokens.extend(UpperCAmelCase_  )
																																# Use the real label id for the first token of the word, and padding ids for the remaining tokens
																																label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(UpperCAmelCase_  ) - 1)  )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
																		_lowerCAmelCase  	=			tokenizer.num_special_tokens_to_add()
																		if len(UpperCAmelCase_  ) > max_seq_length - special_tokens_count:
																									_lowerCAmelCase  	=			tokens[: (max_seq_length - special_tokens_count)]
																									_lowerCAmelCase  	=			label_ids[: (max_seq_length - special_tokens_count)]

																		# The convention in BERT is:
																		# (a) For sequence pairs:
																		#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
																		#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
																		# (b) For single sequences:
																		#  tokens:   [CLS] the dog is hairy . [SEP]
																		#  type_ids:   0   0   0   0  0     0   0
																		#
																		# Where "type_ids" are used to indicate whether this is the first
																		# sequence or the second sequence. The embedding vectors for `type=0` and
																		# `type=1` were learned during pre-training and are added to the wordpiece
																		# embedding vector (and position vector). This is not *strictly* necessary
																		# since the [SEP] token unambiguously separates the sequences, but it makes
																		# it easier for the model to learn the concept of sequences.
																		#
																		# For classification tasks, the first vector (corresponding to [CLS]) is
																		# used as the "sentence vector". Note that this only makes sense because
																		# the entire model is fine-tuned.
																		tokens += [sep_token]
																		label_ids += [pad_token_label_id]
																		if sep_token_extra:
																									# roberta uses an extra separator b/w pairs of sentences
																									tokens += [sep_token]
																									label_ids += [pad_token_label_id]
																		_lowerCAmelCase  	=			[sequence_a_segment_id] * len(UpperCAmelCase_  )

																		if cls_token_at_end:
																									tokens += [cls_token]
																									label_ids += [pad_token_label_id]
																									segment_ids += [cls_token_segment_id]
																		else:
																									_lowerCAmelCase  	=			[cls_token] + tokens
																									_lowerCAmelCase  	=			[pad_token_label_id] + label_ids
																									_lowerCAmelCase  	=			[cls_token_segment_id] + segment_ids

																		_lowerCAmelCase  	=			tokenizer.convert_tokens_to_ids(UpperCAmelCase_  )

																		# The mask has 1 for real tokens and 0 for padding tokens. Only real
																		# tokens are attended to.
																		_lowerCAmelCase  	=			[1 if mask_padding_with_zero else 0] * len(UpperCAmelCase_  )

																		# Zero-pad up to the sequence length.
																		_lowerCAmelCase  	=			max_seq_length - len(UpperCAmelCase_  )
																		if pad_on_left:
																									_lowerCAmelCase  	=			([pad_token] * padding_length) + input_ids
																									_lowerCAmelCase  	=			([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																									_lowerCAmelCase  	=			([pad_token_segment_id] * padding_length) + segment_ids
																									_lowerCAmelCase  	=			([pad_token_label_id] * padding_length) + label_ids
																		else:
																									input_ids += [pad_token] * padding_length
																									input_mask += [0 if mask_padding_with_zero else 1] * padding_length
																									segment_ids += [pad_token_segment_id] * padding_length
																									label_ids += [pad_token_label_id] * padding_length

																		assert len(UpperCAmelCase_  ) == max_seq_length
																		assert len(UpperCAmelCase_  ) == max_seq_length
																		assert len(UpperCAmelCase_  ) == max_seq_length
																		assert len(UpperCAmelCase_  ) == max_seq_length

																		if ex_index < 5:
																									logger.info('*** Example ***'  )
																									logger.info('guid: %s'				,     example.guid  )
																									logger.info('tokens: %s'				,     ' '.join([str(UpperCAmelCase_  ) for x in tokens]  )  )
																									logger.info('input_ids: %s'				,     ' '.join([str(UpperCAmelCase_  ) for x in input_ids]  )  )
																									logger.info('input_mask: %s'				,     ' '.join([str(UpperCAmelCase_  ) for x in input_mask]  )  )
																									logger.info('segment_ids: %s'				,     ' '.join([str(UpperCAmelCase_  ) for x in segment_ids]  )  )
																									logger.info('label_ids: %s'				,     ' '.join([str(UpperCAmelCase_  ) for x in label_ids]  )  )

																		if "token_type_ids" not in tokenizer.model_input_names:
																									_lowerCAmelCase  	=			None

																		features.append(
																		    InputFeatures(
																		        input_ids=UpperCAmelCase_				,     attention_mask=UpperCAmelCase_				,     token_type_ids=UpperCAmelCase_				,     label_ids=UpperCAmelCase_  )  )
											return features


if is_torch_available():
	import torch
	from torch import nn
	from torch.utils.data import Dataset
	class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






					'''simple docstring'''



					SCREAMING_SNAKE_CASE_:							List[InputFeatures]
					SCREAMING_SNAKE_CASE_:							int							=					nn.CrossEntropyLoss().ignore_index


					def __init__(					self	:			List[Any]				,     UpperCAmelCase_	:			TokenClassificationTask				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			PreTrainedTokenizer				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			Optional[int] = None				,     UpperCAmelCase_	:			List[str]=False				,     UpperCAmelCase_	:			Split = Split.train				,     )		->  List[str]:







												"""simple docstring"""




												_lowerCAmelCase  	=			os.path.join(
												    UpperCAmelCase_				,     'cached_{}_{}_{}'.format(mode.value				,     tokenizer.__class__.__name__				,     str(UpperCAmelCase_  )  )				,     )

												# Make sure only the first process in distributed training processes the dataset,
												# and the others will use the cache.
												_lowerCAmelCase  	=			cached_features_file + '.lock'
												with FileLock(UpperCAmelCase_  ):
																			if os.path.exists(UpperCAmelCase_  ) and not overwrite_cache:
																										logger.info(F"""Loading features from cached file {cached_features_file}"""  )
																										_lowerCAmelCase  	=			torch.load(UpperCAmelCase_  )
																			else:
																										logger.info(F"""Creating features from dataset file at {data_dir}"""  )
																										_lowerCAmelCase  	=			token_classification_task.read_examples_from_file(UpperCAmelCase_				,     UpperCAmelCase_  )
																										# TODO clean up all this to leverage built-in features of tokenizers
																										_lowerCAmelCase  	=			token_classification_task.convert_examples_to_features(
																										    UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     cls_token_at_end=bool(model_type in ['xlnet']  )				,     cls_token=tokenizer.cls_token				,     cls_token_segment_id=2 if model_type in ['xlnet'] else 0				,     sep_token=tokenizer.sep_token				,     sep_token_extra=UpperCAmelCase_				,     pad_on_left=bool(tokenizer.padding_side == 'left'  )				,     pad_token=tokenizer.pad_token_id				,     pad_token_segment_id=tokenizer.pad_token_type_id				,     pad_token_label_id=self.pad_token_label_id				,     )
																										logger.info(F"""Saving features into cached file {cached_features_file}"""  )
																										torch.save(self.features				,     UpperCAmelCase_  )


					def __len__(					self	:			Dict  )		->  int:







												"""simple docstring"""




												return len(self.features  )







					def __getitem__(					self	:			List[str]				,     UpperCAmelCase_	:			List[Any]  )		->  InputFeatures:







												"""simple docstring"""




												return self.features[i]


if is_tf_available():
	import tensorflow as tf
	class        _SCREAMING_SNAKE_CASE  :






					'''simple docstring'''



					SCREAMING_SNAKE_CASE_:							List[InputFeatures]
					SCREAMING_SNAKE_CASE_:							int							=					-1_0_0


					def __init__(					self	:			Tuple				,     UpperCAmelCase_	:			TokenClassificationTask				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			PreTrainedTokenizer				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			Optional[int] = None				,     UpperCAmelCase_	:			Optional[Any]=False				,     UpperCAmelCase_	:			Split = Split.train				,     )		->  Dict:







												"""simple docstring"""




												_lowerCAmelCase  	=			token_classification_task.read_examples_from_file(UpperCAmelCase_				,     UpperCAmelCase_  )
												# TODO clean up all this to leverage built-in features of tokenizers
												_lowerCAmelCase  	=			token_classification_task.convert_examples_to_features(
												    UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     cls_token_at_end=bool(model_type in ['xlnet']  )				,     cls_token=tokenizer.cls_token				,     cls_token_segment_id=2 if model_type in ['xlnet'] else 0				,     sep_token=tokenizer.sep_token				,     sep_token_extra=UpperCAmelCase_				,     pad_on_left=bool(tokenizer.padding_side == 'left'  )				,     pad_token=tokenizer.pad_token_id				,     pad_token_segment_id=tokenizer.pad_token_type_id				,     pad_token_label_id=self.pad_token_label_id				,     )

												def gen():
																			for ex in self.features:
																										if ex.token_type_ids is None:
																																	yield (
																																	    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																																	    ex.label_ids,
																																	)
																										else:
																																	yield (
																																	    {
																																	        "input_ids": ex.input_ids,
																																	        "attention_mask": ex.attention_mask,
																																	        "token_type_ids": ex.token_type_ids,
																																	    },
																																	    ex.label_ids,
																																	)

												if "token_type_ids" not in tokenizer.model_input_names:
																			_lowerCAmelCase  	=			tf.data.Dataset.from_generator(
																			    UpperCAmelCase_				,     ({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)				,     (
																			        {'input_ids': tf.TensorShape([None]  ), 'attention_mask': tf.TensorShape([None]  )},
																			        tf.TensorShape([None]  ),
																			    )				,     )
												else:
																			_lowerCAmelCase  	=			tf.data.Dataset.from_generator(
																			    UpperCAmelCase_				,     ({'input_ids': tf.intaa, 'attention_mask': tf.intaa, 'token_type_ids': tf.intaa}, tf.intaa)				,     (
																			        {
																			            'input_ids': tf.TensorShape([None]  ),
																			            'attention_mask': tf.TensorShape([None]  ),
																			            'token_type_ids': tf.TensorShape([None]  ),
																			        },
																			        tf.TensorShape([None]  ),
																			    )				,     )


					def 				__lowerCamelCase	(					self	:			str  )		->  Tuple:







												"""simple docstring"""




												_lowerCAmelCase  	=			self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features  )  )  )

												return self.dataset


					def __len__(					self	:			List[Any]  )		->  Optional[Any]:







												"""simple docstring"""




												return len(self.features  )







					def __getitem__(					self	:			int				,     UpperCAmelCase_	:			List[Any]  )		->  InputFeatures:







												"""simple docstring"""




												return self.features[i]






