






import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import DonutImageProcessor

class 		_snake_case  (  unittest.TestCase					):






			def __init__(       self		,					a		,					a=7		,					a=3		,					a=18		,					a=30		,					a=400		,					a=True		,					a=None		,					a=True		,					a=False		,					a=True		,					a=True		,					a=[0.5, 0.5, 0.5]		,					a=[0.5, 0.5, 0.5]		,					)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= parent
						SCREAMING_SNAKE_CASE						= batch_size
						SCREAMING_SNAKE_CASE						= num_channels
						SCREAMING_SNAKE_CASE						= image_size
						SCREAMING_SNAKE_CASE						= min_resolution
						SCREAMING_SNAKE_CASE						= max_resolution
						SCREAMING_SNAKE_CASE						= do_resize
						SCREAMING_SNAKE_CASE						= size if size is not None else {'height': 18, 'width': 20}
						SCREAMING_SNAKE_CASE						= do_thumbnail
						SCREAMING_SNAKE_CASE						= do_align_axis
						SCREAMING_SNAKE_CASE						= do_pad
						SCREAMING_SNAKE_CASE						= do_normalize
						SCREAMING_SNAKE_CASE						= image_mean
						SCREAMING_SNAKE_CASE						= image_std







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_thumbnail": self.do_thumbnail,
						    "do_align_long_axis": self.do_align_axis,
						    "do_pad": self.do_pad,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						}



@require_torch
@require_vision
class 		_snake_case  (  A__   ,    unittest.TestCase					):
			_lowercase		:    Any			     =							DonutImageProcessor if is_vision_available() else None






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= DonutImageProcessingTester(self)






			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						return self.image_processor_tester.prepare_image_processor_dict()






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(a		,					'do_resize'))
						self.assertTrue(hasattr(a		,					'size'))
						self.assertTrue(hasattr(a		,					'do_thumbnail'))
						self.assertTrue(hasattr(a		,					'do_align_long_axis'))
						self.assertTrue(hasattr(a		,					'do_pad'))
						self.assertTrue(hasattr(a		,					'do_normalize'))
						self.assertTrue(hasattr(a		,					'image_mean'))
						self.assertTrue(hasattr(a		,					'image_std'))






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE						= self.image_processing_class.from_dict(self.image_processor_dict)
						self.assertEqual(image_processor.size		,					{'height': 18, 'width': 20})

						SCREAMING_SNAKE_CASE						= self.image_processing_class.from_dict(self.image_processor_dict		,					size=42)
						self.assertEqual(image_processor.size		,					{'height': 42, 'width': 42})

						# Previous config had dimensions in (width, height) order
						SCREAMING_SNAKE_CASE						= self.image_processing_class.from_dict(self.image_processor_dict		,					size=(42, 84))
						self.assertEqual(image_processor.size		,					{'height': 84, 'width': 42})






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						pass






			@is_flaky()
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a)
						for image in image_inputs:
									self.assertIsInstance(a		,					Image.Image)

						# Test not batched input
						SCREAMING_SNAKE_CASE						= image_processing(image_inputs[0]		,					return_tensors='pt').pixel_values
						self.assertEqual(
						    encoded_images.shape		,					(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size['height'],
						        self.image_processor_tester.size['width'],
						    )		,					)

						# Test batched
						SCREAMING_SNAKE_CASE						= image_processing(a		,					return_tensors='pt').pixel_values
						self.assertEqual(
						    encoded_images.shape		,					(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size['height'],
						        self.image_processor_tester.size['width'],
						    )		,					)






			@is_flaky()
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a		,					numpify=a)
						for image in image_inputs:
									self.assertIsInstance(a		,					np.ndarray)

						# Test not batched input
						SCREAMING_SNAKE_CASE						= image_processing(image_inputs[0]		,					return_tensors='pt').pixel_values
						self.assertEqual(
						    encoded_images.shape		,					(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size['height'],
						        self.image_processor_tester.size['width'],
						    )		,					)

						# Test batched
						SCREAMING_SNAKE_CASE						= image_processing(a		,					return_tensors='pt').pixel_values
						self.assertEqual(
						    encoded_images.shape		,					(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size['height'],
						        self.image_processor_tester.size['width'],
						    )		,					)







			@is_flaky()
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a		,					torchify=a)
						for image in image_inputs:
									self.assertIsInstance(a		,					torch.Tensor)

						# Test not batched input
						SCREAMING_SNAKE_CASE						= image_processing(image_inputs[0]		,					return_tensors='pt').pixel_values
						self.assertEqual(
						    encoded_images.shape		,					(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size['height'],
						        self.image_processor_tester.size['width'],
						    )		,					)

						# Test batched
						SCREAMING_SNAKE_CASE						= image_processing(a		,					return_tensors='pt').pixel_values
						self.assertEqual(
						    encoded_images.shape		,					(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size['height'],
						        self.image_processor_tester.size['width'],
						    )		,					)












import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/text-classification/requirements.txt')

a_		:						Union[str, Any]             =    logging.getLogger(__name__)

@dataclass
class 		_snake_case  :
			_lowercase		:    Optional[int]			     =							field(
			    default=1_28   ,    metadata={
			        '''help''': (
			            '''The maximum total input sequence length after tokenization. Sequences longer '''
			            '''than this will be truncated, sequences shorter will be padded.'''
			        )
			    }   ,    )
			_lowercase		:    bool			     =							field(
			    default=A__   ,    metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}					)
			_lowercase		:    bool			     =							field(
			    default=A__   ,    metadata={
			        '''help''': (
			            '''Whether to pad all samples to `max_seq_length`. '''
			            '''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
			        )
			    }   ,    )
			_lowercase		:    Optional[int]			     =							field(
			    default=A__   ,    metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
			            '''value if set.'''
			        )
			    }   ,    )
			_lowercase		:    Optional[int]			     =							field(
			    default=A__   ,    metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
			            '''value if set.'''
			        )
			    }   ,    )
			_lowercase		:    Optional[int]			     =							field(
			    default=A__   ,    metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
			            '''value if set.'''
			        )
			    }   ,    )



@dataclass
class 		_snake_case  :
			_lowercase		:    str			     =							field(
			    default=A__   ,    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}					)
			_lowercase		:    str			     =							field(
			    default=A__   ,    metadata={'''help''': '''Evaluation language. Also train language if `train_language` is set to None.'''}					)
			_lowercase		:    Optional[str]			     =							field(
			    default=A__   ,    metadata={'''help''': '''Train language if it is different from the evaluation language.'''}					)
			_lowercase		:    Optional[str]			     =							field(
			    default=A__   ,    metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}					)
			_lowercase		:    Optional[str]			     =							field(
			    default=A__   ,    metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}					)
			_lowercase		:    Optional[str]			     =							field(
			    default=A__   ,    metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}   ,    )
			_lowercase		:    Optional[bool]			     =							field(
			    default=A__   ,    metadata={'''help''': '''arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'''}   ,    )
			_lowercase		:    bool			     =							field(
			    default=A__   ,    metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}   ,    )
			_lowercase		:    str			     =							field(
			    default='''main'''   ,    metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}   ,    )
			_lowercase		:    bool			     =							field(
			    default=A__   ,    metadata={
			        '''help''': (
			            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
			            '''with private models).'''
			        )
			    }   ,    )
			_lowercase		:    bool			     =							field(
			    default=A__   ,    metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''}   ,    )






def 				lowerCamelCase__				():
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.

			SCREAMING_SNAKE_CASE						= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
			SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= parser.parse_args_into_dataclasses()

			# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
			# information sent is the one passed as arguments along with your Python/PyTorch versions.
			send_example_telemetry('run_xnli'							,       _UpperCAmelCase)

			# Setup logging
			logging.basicConfig(
			    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,       datefmt='%m/%d/%Y %H:%M:%S'							,       handlers=[logging.StreamHandler(sys.stdout)]							,       )

			if training_args.should_log:
						# The default of training_args.log_level is passive, so we set log level at info here to have that default.
						transformers.utils.logging.set_verbosity_info()

			SCREAMING_SNAKE_CASE						= training_args.get_process_log_level()
			logger.setLevel(_UpperCAmelCase)
			datasets.utils.logging.set_verbosity(_UpperCAmelCase)
			transformers.utils.logging.set_verbosity(_UpperCAmelCase)
			transformers.utils.logging.enable_default_handler()
			transformers.utils.logging.enable_explicit_format()

			# Log on each process the small summary:
			logger.warning(
			    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
			    + F'''distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fpaa}''')
			logger.info(F'''Training/evaluation parameters {training_args}''')

			# Detecting last checkpoint.
			SCREAMING_SNAKE_CASE						= None
			if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
						SCREAMING_SNAKE_CASE						= get_last_checkpoint(training_args.output_dir)
						if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
									raise ValueError(
									    F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
									    'Use --overwrite_output_dir to overcome.')
						elif last_checkpoint is not None:
									logger.info(
									    F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
									    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.')

    # Set seed before initializing model.
			set_seed(training_args.seed)

			# In distributed training, the load_dataset function guarantees that only one local process can concurrently
			# download the dataset.
			# Downloading and loading xnli dataset from the hub.
			if training_args.do_train:
						if model_args.train_language is None:
									SCREAMING_SNAKE_CASE						= load_dataset(
									    'xnli'							,       model_args.language							,       split='train'							,       cache_dir=model_args.cache_dir							,       use_auth_token=True if model_args.use_auth_token else None							,       )
						else:
									SCREAMING_SNAKE_CASE						= load_dataset(
									    'xnli'							,       model_args.train_language							,       split='train'							,       cache_dir=model_args.cache_dir							,       use_auth_token=True if model_args.use_auth_token else None							,       )
						SCREAMING_SNAKE_CASE						= train_dataset.features['label'].names

			if training_args.do_eval:
						SCREAMING_SNAKE_CASE						= load_dataset(
						    'xnli'							,       model_args.language							,       split='validation'							,       cache_dir=model_args.cache_dir							,       use_auth_token=True if model_args.use_auth_token else None							,       )
						SCREAMING_SNAKE_CASE						= eval_dataset.features['label'].names

			if training_args.do_predict:
						SCREAMING_SNAKE_CASE						= load_dataset(
						    'xnli'							,       model_args.language							,       split='test'							,       cache_dir=model_args.cache_dir							,       use_auth_token=True if model_args.use_auth_token else None							,       )
						SCREAMING_SNAKE_CASE						= predict_dataset.features['label'].names

			# Labels
			SCREAMING_SNAKE_CASE						= len(_UpperCAmelCase)

			# Load pretrained model and tokenizer
			# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.
			SCREAMING_SNAKE_CASE						= AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path							,       num_labels=_UpperCAmelCase							,       idalabel={str(_UpperCAmelCase): label for i, label in enumerate(_UpperCAmelCase)}							,       labelaid={label: i for i, label in enumerate(_UpperCAmelCase)}							,       finetuning_task='xnli'							,       cache_dir=model_args.cache_dir							,       revision=model_args.model_revision							,       use_auth_token=True if model_args.use_auth_token else None							,       )
			SCREAMING_SNAKE_CASE						= AutoTokenizer.from_pretrained(
			    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path							,       do_lower_case=model_args.do_lower_case							,       cache_dir=model_args.cache_dir							,       use_fast=model_args.use_fast_tokenizer							,       revision=model_args.model_revision							,       use_auth_token=True if model_args.use_auth_token else None							,       )
			SCREAMING_SNAKE_CASE						= AutoModelForSequenceClassification.from_pretrained(
			    model_args.model_name_or_path							,       from_tf=bool('.ckpt' in model_args.model_name_or_path)							,       config=_UpperCAmelCase							,       cache_dir=model_args.cache_dir							,       revision=model_args.model_revision							,       use_auth_token=True if model_args.use_auth_token else None							,       ignore_mismatched_sizes=model_args.ignore_mismatched_sizes							,       )

			# Preprocessing the datasets
			# Padding strategy
			if data_args.pad_to_max_length:
						SCREAMING_SNAKE_CASE						= 'max_length'
			else:
						# We will pad later, dynamically at batch creation, to the max sequence length in each batch
						SCREAMING_SNAKE_CASE						= False

			def preprocess_function(_UpperCAmelCase):
						# Tokenize the texts
						return tokenizer(
						    examples['premise']							,       examples['hypothesis']							,       padding=_UpperCAmelCase							,       max_length=data_args.max_seq_length							,       truncation=_UpperCAmelCase							,       )

			if training_args.do_train:
						if data_args.max_train_samples is not None:
									SCREAMING_SNAKE_CASE						= min(len(_UpperCAmelCase)							,       data_args.max_train_samples)
									SCREAMING_SNAKE_CASE						= train_dataset.select(range(_UpperCAmelCase))
						with training_args.main_process_first(desc='train dataset map pre-processing'):
									SCREAMING_SNAKE_CASE						= train_dataset.map(
									    _UpperCAmelCase							,       batched=_UpperCAmelCase							,       load_from_cache_file=not data_args.overwrite_cache							,       desc='Running tokenizer on train dataset'							,       )
						# Log a few random samples from the training set:
						for index in random.sample(range(len(_UpperCAmelCase))							,       3):
									logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''')

			if training_args.do_eval:
						if data_args.max_eval_samples is not None:
									SCREAMING_SNAKE_CASE						= min(len(_UpperCAmelCase)							,       data_args.max_eval_samples)
									SCREAMING_SNAKE_CASE						= eval_dataset.select(range(_UpperCAmelCase))
						with training_args.main_process_first(desc='validation dataset map pre-processing'):
									SCREAMING_SNAKE_CASE						= eval_dataset.map(
									    _UpperCAmelCase							,       batched=_UpperCAmelCase							,       load_from_cache_file=not data_args.overwrite_cache							,       desc='Running tokenizer on validation dataset'							,       )

			if training_args.do_predict:
						if data_args.max_predict_samples is not None:
									SCREAMING_SNAKE_CASE						= min(len(_UpperCAmelCase)							,       data_args.max_predict_samples)
									SCREAMING_SNAKE_CASE						= predict_dataset.select(range(_UpperCAmelCase))
						with training_args.main_process_first(desc='prediction dataset map pre-processing'):
									SCREAMING_SNAKE_CASE						= predict_dataset.map(
									    _UpperCAmelCase							,       batched=_UpperCAmelCase							,       load_from_cache_file=not data_args.overwrite_cache							,       desc='Running tokenizer on prediction dataset'							,       )

    # Get the metric function
			SCREAMING_SNAKE_CASE						= evaluate.load('xnli')

			# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
			# predictions and label_ids field) and has to return a dictionary string to float.
			def compute_metrics(_UpperCAmelCase):
						SCREAMING_SNAKE_CASE						= p.predictions[0] if isinstance(p.predictions							,       _UpperCAmelCase) else p.predictions
						SCREAMING_SNAKE_CASE						= np.argmax(_UpperCAmelCase							,       axis=1)
						return metric.compute(predictions=_UpperCAmelCase							,       references=p.label_ids)

			# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
			if data_args.pad_to_max_length:
						SCREAMING_SNAKE_CASE						= default_data_collator
			elif training_args.fpaa:
						SCREAMING_SNAKE_CASE						= DataCollatorWithPadding(_UpperCAmelCase							,       pad_to_multiple_of=8)
			else:
						SCREAMING_SNAKE_CASE						= None

			# Initialize our Trainer
			SCREAMING_SNAKE_CASE						= Trainer(
			    model=_UpperCAmelCase							,       args=_UpperCAmelCase							,       train_dataset=train_dataset if training_args.do_train else None							,       eval_dataset=eval_dataset if training_args.do_eval else None							,       compute_metrics=_UpperCAmelCase							,       tokenizer=_UpperCAmelCase							,       data_collator=_UpperCAmelCase							,       )

			# Training
			if training_args.do_train:
						SCREAMING_SNAKE_CASE						= None
						if training_args.resume_from_checkpoint is not None:
									SCREAMING_SNAKE_CASE						= training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									SCREAMING_SNAKE_CASE						= last_checkpoint
						SCREAMING_SNAKE_CASE						= trainer.train(resume_from_checkpoint=_UpperCAmelCase)
						SCREAMING_SNAKE_CASE						= train_result.metrics
						SCREAMING_SNAKE_CASE						= (
						    data_args.max_train_samples if data_args.max_train_samples is not None else len(_UpperCAmelCase)
						)
						SCREAMING_SNAKE_CASE						= min(_UpperCAmelCase							,       len(_UpperCAmelCase))

						trainer.save_model()  # Saves the tokenizer too for easy upload

						trainer.log_metrics('train'							,       _UpperCAmelCase)
						trainer.save_metrics('train'							,       _UpperCAmelCase)
						trainer.save_state()

			# Evaluation
			if training_args.do_eval:
						logger.info('*** Evaluate ***')
						SCREAMING_SNAKE_CASE						= trainer.evaluate(eval_dataset=_UpperCAmelCase)

						SCREAMING_SNAKE_CASE						= data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_UpperCAmelCase)
						SCREAMING_SNAKE_CASE						= min(_UpperCAmelCase							,       len(_UpperCAmelCase))

						trainer.log_metrics('eval'							,       _UpperCAmelCase)
						trainer.save_metrics('eval'							,       _UpperCAmelCase)

			# Prediction
			if training_args.do_predict:
						logger.info('*** Predict ***')
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= trainer.predict(_UpperCAmelCase							,       metric_key_prefix='predict')

						SCREAMING_SNAKE_CASE						= (
						    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(_UpperCAmelCase)
						)
						SCREAMING_SNAKE_CASE						= min(_UpperCAmelCase							,       len(_UpperCAmelCase))

						trainer.log_metrics('predict'							,       _UpperCAmelCase)
						trainer.save_metrics('predict'							,       _UpperCAmelCase)

						SCREAMING_SNAKE_CASE						= np.argmax(_UpperCAmelCase							,       axis=1)
						SCREAMING_SNAKE_CASE						= os.path.join(training_args.output_dir							,       'predictions.txt')
						if trainer.is_world_process_zero():
									with open(_UpperCAmelCase							,       'w') as writer:
												writer.write('index\tprediction\n')
												for index, item in enumerate(_UpperCAmelCase):
															SCREAMING_SNAKE_CASE						= label_list[item]
															writer.write(F'''{index}\t{item}\n''')


if __name__ == "__main__":
				main()




