




"""simple docstring"""
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


if is_apex_available():
					from apex import amp


if version.parse(version.parse(torch.__version__).base_version) >= version.parse('''1.6'''):
					a_				:    List[str]										=     True
					from torch.cuda.amp import autocast

a_				:    int										=     logging.getLogger(__name__)





def 						UpperCAmelCase				(			A__:    Any=None							,				A__:    List[Any]=None     )  ->     List[Any]:
			return field(default_factory=lambda: default							,				metadata=A__     )

@dataclass
class __lowercase:




					'''simple docstring'''


					__a						:						str     		= field(
					    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}				)
					__a						:						Optional[str]     		= field(
					    default=lowercase__	,	metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}	,	)
					__a						:						Optional[bool]     		= field(
					    default=lowercase__	,	metadata={'help': 'Whether to freeze the feature extractor layers of the model.'}				)
					__a						:						Optional[float]     		= field(
					    default=0.1	,	metadata={'help': 'The dropout ratio for the attention probabilities.'}				)
					__a						:						Optional[float]     		= field(
					    default=0.1	,	metadata={'help': 'The dropout ratio for activations inside the fully connected layer.'}				)
					__a						:						Optional[float]     		= field(
					    default=0.1	,	metadata={
					        'help': 'The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.'
					    }	,	)
					__a						:						Optional[float]     		= field(
					    default=0.1	,	metadata={'help': 'The dropout probabilitiy for all 1D convolutional layers in feature extractor.'}	,	)
					__a						:						Optional[float]     		= field(
					    default=0.0_5	,	metadata={
					        'help': (
					            'Propability of each feature vector along the time axis to be chosen as the start of the vector'
					            'span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature'
					            'vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``.'
					        )
					    }	,	)
					__a						:						Optional[float]     		= field(default=0.0	,	metadata={'help': 'The LayerDrop probability.'}				)



@dataclass
class __lowercase:




					'''simple docstring'''


					__a						:						Optional[str]     		= field(
					    default=lowercase__	,	metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}				)
					__a						:						Optional[str]     		= field(
					    default='train+validation'	,	metadata={
					        'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
					    }	,	)
					__a						:						bool     		= field(
					    default=lowercase__	,	metadata={'help': 'Overwrite the cached preprocessed datasets or not.'}				)
					__a						:						Optional[int]     		= field(
					    default=lowercase__	,	metadata={'help': 'The number of processes to use for the preprocessing.'}	,	)
					__a						:						Optional[int]     		= field(
					    default=lowercase__	,	metadata={
					        'help': (
					            'For debugging purposes or quicker training, truncate the number of training examples to this '
					            'value if set.'
					        )
					    }	,	)
					__a						:						Optional[int]     		= field(
					    default=lowercase__	,	metadata={
					        'help': (
					            'For debugging purposes or quicker training, truncate the number of validation examples to this '
					            'value if set.'
					        )
					    }	,	)
					__a						:						List[str]     		= list_field(
					    default=[',', '?', '.', '!', '-', ';', ':', '""', '%', '\'', '"', '�']	,	metadata={'help': 'A list of characters to remove from the transcripts.'}	,	)



@dataclass
class __lowercase:




					'''simple docstring'''


					__a						:						WavaVecaProcessor
					__a						:						Union[bool, str]     		= True
					__a						:						Optional[int]     		= None
					__a						:						Optional[int]     		= None
					__a						:						Optional[int]     		= None
					__a						:						Optional[int]     		= None
					def __call__(    self						,	__a     ):
								# split inputs and labels since they have to be of different lenghts and need
								# different padding methods
								__lowerCamelCase					:	Union[str, Any]	       =				[{'input_values': feature['input_values']} for feature in features]
								__lowerCamelCase					:	Dict	       =				[{'input_ids': feature['labels']} for feature in features]

								__lowerCamelCase					:	Tuple	       =				self.processor.pad(
								    __a						,	padding=self.padding						,	max_length=self.max_length						,	pad_to_multiple_of=self.pad_to_multiple_of						,	return_tensors='pt'						,	)
								__lowerCamelCase					:	Union[str, Any]	       =				self.processor.pad(
								    labels=__a						,	padding=self.padding						,	max_length=self.max_length_labels						,	pad_to_multiple_of=self.pad_to_multiple_of_labels						,	return_tensors='pt'						,	)

								# replace padding with -100 to ignore loss correctly
								__lowerCamelCase					:	List[str]	       =				labels_batch['input_ids'].masked_fill(labels_batch.attention_mask.ne(1     )						,	-100     )

								__lowerCamelCase					:	Any	       =				labels

								return batch



class __lowercase(      lowercase__				):




					'''simple docstring'''


					def     snake_case_				(    self						,	__a						,	__a     ):
								model.train()
								__lowerCamelCase					:	Union[str, Any]	       =				self._prepare_inputs(__a     )

								if self.use_amp:
											with autocast():
														__lowerCamelCase					:	Union[str, Any]	       =				self.compute_loss(__a						,	__a     )
								else:
											__lowerCamelCase					:	int	       =				self.compute_loss(__a						,	__a     )

								if self.args.n_gpu > 1:
											if model.module.config.ctc_loss_reduction == "mean":
														__lowerCamelCase					:	Optional[int]	       =				loss.mean()
											elif model.module.config.ctc_loss_reduction == "sum":
														__lowerCamelCase					:	List[Any]	       =				loss.sum() / (inputs['labels'] >= 0).sum()
											else:
														raise ValueError(f'''{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']'''     )

								if self.args.gradient_accumulation_steps > 1:
											__lowerCamelCase					:	Optional[int]	       =				loss / self.args.gradient_accumulation_steps

								if self.use_amp:
											self.scaler.scale(__a     ).backward()
								elif self.use_apex:
											with amp.scale_loss(__a						,	self.optimizer     ) as scaled_loss:
														scaled_loss.backward()
								elif self.deepspeed:
											self.deepspeed.backward(__a     )
								else:
											loss.backward()

								return loss.detach()





def 						UpperCAmelCase				(			)  ->     Dict:
			# 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.

			__lowerCamelCase					:	List[str]	       =				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
			if len(sys.argv     ) == 2 and sys.argv[1].endswith('.json'     ):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						__lowerCamelCase					:	Tuple	       =				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
			else:
						__lowerCamelCase					:	Optional[int]	       =				parser.parse_args_into_dataclasses()

			# Detecting last checkpoint.
			__lowerCamelCase					:	Optional[int]	       =				None
			if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
						__lowerCamelCase					:	Optional[int]	       =				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.'     )

    # 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     )]							,				)
			logger.setLevel(logging.INFO if is_main_process(training_args.local_rank     ) else logging.WARN     )

			# 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}'''     )
			# Set the verbosity to info of the Transformers logger (on main process only):
			if is_main_process(training_args.local_rank     ):
						transformers.utils.logging.set_verbosity_info()
			logger.info('Training/evaluation parameters %s'							,				A__     )

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

			# Get the datasets:
			__lowerCamelCase					:	List[Any]	       =				datasets.load_dataset(
			    'common_voice'							,				data_args.dataset_config_name							,				split=data_args.train_split_name     )
			__lowerCamelCase					:	Any	       =				datasets.load_dataset('common_voice'							,				data_args.dataset_config_name							,				split='test'     )

			# Create and save tokenizer
			__lowerCamelCase					:	List[Any]	       =				f'''[{"".join(data_args.chars_to_ignore     )}]'''

			def remove_special_characters(A__:    Union[str, Any]     ):
						__lowerCamelCase					:	str	       =				re.sub(A__							,				''							,				batch['sentence']     ).lower() + ' '
						return batch

			__lowerCamelCase					:	Optional[int]	       =				train_dataset.map(A__							,				remove_columns=['sentence']     )
			__lowerCamelCase					:	Tuple	       =				eval_dataset.map(A__							,				remove_columns=['sentence']     )

			def extract_all_chars(A__:    str     ):
						__lowerCamelCase					:	Optional[int]	       =				' '.join(batch['text']     )
						__lowerCamelCase					:	Optional[int]	       =				list(set(A__     )     )
						return {"vocab": [vocab], "all_text": [all_text]}

			__lowerCamelCase					:	Optional[int]	       =				train_dataset.map(
			    A__							,				batched=A__							,				batch_size=-1							,				keep_in_memory=A__							,				remove_columns=train_dataset.column_names							,				)
			__lowerCamelCase					:	List[Any]	       =				train_dataset.map(
			    A__							,				batched=A__							,				batch_size=-1							,				keep_in_memory=A__							,				remove_columns=eval_dataset.column_names							,				)

			__lowerCamelCase					:	List[str]	       =				list(set(vocab_train['vocab'][0]     ) | set(vocab_test['vocab'][0]     )     )
			__lowerCamelCase					:	List[Any]	       =				{v: k for k, v in enumerate(A__     )}
			__lowerCamelCase					:	Optional[Any]	       =				vocab_dict[' ']
			del vocab_dict[" "]
			__lowerCamelCase					:	List[str]	       =				len(A__     )
			__lowerCamelCase					:	int	       =				len(A__     )

			with open('vocab.json'							,				'w'     ) as vocab_file:
						json.dump(A__							,				A__     )

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.
			__lowerCamelCase					:	Optional[Any]	       =				WavaVecaCTCTokenizer(
			    'vocab.json'							,				unk_token='[UNK]'							,				pad_token='[PAD]'							,				word_delimiter_token='|'							,				)
			__lowerCamelCase					:	Any	       =				WavaVecaFeatureExtractor(
			    feature_size=1							,				sampling_rate=16000							,				padding_value=0.0							,				do_normalize=A__							,				return_attention_mask=A__     )
			__lowerCamelCase					:	List[Any]	       =				WavaVecaProcessor(feature_extractor=A__							,				tokenizer=A__     )
			__lowerCamelCase					:	Union[str, Any]	       =				WavaVecaForCTC.from_pretrained(
			    model_args.model_name_or_path							,				cache_dir=model_args.cache_dir							,				activation_dropout=model_args.activation_dropout							,				attention_dropout=model_args.attention_dropout							,				hidden_dropout=model_args.hidden_dropout							,				feat_proj_dropout=model_args.feat_proj_dropout							,				mask_time_prob=model_args.mask_time_prob							,				gradient_checkpointing=training_args.gradient_checkpointing							,				layerdrop=model_args.layerdrop							,				ctc_loss_reduction='mean'							,				pad_token_id=processor.tokenizer.pad_token_id							,				vocab_size=len(processor.tokenizer     )							,				)

			if data_args.max_train_samples is not None:
						__lowerCamelCase					:	str	       =				min(len(A__     )							,				data_args.max_train_samples     )
						__lowerCamelCase					:	str	       =				train_dataset.select(range(A__     )     )

			if data_args.max_val_samples is not None:
						__lowerCamelCase					:	List[str]	       =				eval_dataset.select(range(data_args.max_val_samples     )     )

			__lowerCamelCase					:	int	       =				torchaudio.transforms.Resample(48000							,				16000     )

			# Preprocessing the datasets.
			# We need to read the aduio files as arrays and tokenize the targets.
			def speech_file_to_array_fn(A__:    Optional[int]     ):
						__lowerCamelCase					:	int	       =				torchaudio.load(batch['path']     )
						__lowerCamelCase					:	Any	       =				resampler(A__     ).squeeze().numpy()
						__lowerCamelCase					:	List[str]	       =				16000
						__lowerCamelCase					:	Union[str, Any]	       =				batch['text']
						return batch

			__lowerCamelCase					:	Union[str, Any]	       =				train_dataset.map(
			    A__							,				remove_columns=train_dataset.column_names							,				num_proc=data_args.preprocessing_num_workers							,				)
			__lowerCamelCase					:	List[str]	       =				eval_dataset.map(
			    A__							,				remove_columns=eval_dataset.column_names							,				num_proc=data_args.preprocessing_num_workers							,				)

			def prepare_dataset(A__:    Dict     ):
						# check that all files have the correct sampling rate
						assert (
						    len(set(batch['sampling_rate']     )     ) == 1
						), f'''Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.'''

						__lowerCamelCase					:	Optional[Any]	       =				processor(
						    audio=batch['speech']							,				text=batch['target_text']							,				sampling_rate=batch['sampling_rate'][0]     )
						batch.update(A__     )
						return batch

			__lowerCamelCase					:	str	       =				train_dataset.map(
			    A__							,				remove_columns=train_dataset.column_names							,				batch_size=training_args.per_device_train_batch_size							,				batched=A__							,				num_proc=data_args.preprocessing_num_workers							,				)
			__lowerCamelCase					:	Optional[int]	       =				eval_dataset.map(
			    A__							,				remove_columns=eval_dataset.column_names							,				batch_size=training_args.per_device_train_batch_size							,				batched=A__							,				num_proc=data_args.preprocessing_num_workers							,				)

			# Metric
			__lowerCamelCase					:	Optional[Any]	       =				datasets.load_metric('wer'     )

			def compute_metrics(A__:    Tuple     ):
						__lowerCamelCase					:	int	       =				pred.predictions
						__lowerCamelCase					:	Optional[Any]	       =				np.argmax(A__							,				axis=-1     )

						__lowerCamelCase					:	int	       =				processor.tokenizer.pad_token_id

						__lowerCamelCase					:	Optional[int]	       =				processor.batch_decode(A__     )
						# we do not want to group tokens when computing the metrics
						__lowerCamelCase					:	Tuple	       =				processor.batch_decode(pred.label_ids							,				group_tokens=A__     )

						__lowerCamelCase					:	Dict	       =				wer_metric.compute(predictions=A__							,				references=A__     )

						return {"wer": wer}

			if model_args.freeze_feature_extractor:
						model.freeze_feature_extractor()

			# Data collator
			__lowerCamelCase					:	Union[str, Any]	       =				DataCollatorCTCWithPadding(processor=A__							,				padding=A__     )

			# Initialize our Trainer
			__lowerCamelCase					:	int	       =				CTCTrainer(
			    model=A__							,				data_collator=A__							,				args=A__							,				compute_metrics=A__							,				train_dataset=train_dataset if training_args.do_train else None							,				eval_dataset=eval_dataset if training_args.do_eval else None							,				tokenizer=processor.feature_extractor							,				)

			# Training
			if training_args.do_train:
						if last_checkpoint is not None:
									__lowerCamelCase					:	Any	       =				last_checkpoint
						elif os.path.isdir(model_args.model_name_or_path     ):
									__lowerCamelCase					:	Optional[Any]	       =				model_args.model_name_or_path
						else:
									__lowerCamelCase					:	Any	       =				None

						# Save the feature_extractor and the tokenizer
						if is_main_process(training_args.local_rank     ):
									processor.save_pretrained(training_args.output_dir     )

						__lowerCamelCase					:	str	       =				trainer.train(resume_from_checkpoint=A__     )
						trainer.save_model()

						__lowerCamelCase					:	Dict	       =				train_result.metrics
						__lowerCamelCase					:	Optional[int]	       =				(
						    data_args.max_train_samples if data_args.max_train_samples is not None else len(A__     )
						)
						__lowerCamelCase					:	Optional[Any]	       =				min(A__							,				len(A__     )     )

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

			# Evaluation
			__lowerCamelCase					:	str	       =				{}
			if training_args.do_eval:
						logger.info('*** Evaluate ***'     )
						__lowerCamelCase					:	Dict	       =				trainer.evaluate()
						__lowerCamelCase					:	Optional[int]	       =				data_args.max_val_samples if data_args.max_val_samples is not None else len(A__     )
						__lowerCamelCase					:	Union[str, Any]	       =				min(A__							,				len(A__     )     )

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

			return results


if __name__ == "__main__":
					main()








"""simple docstring"""
import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class __lowercase(      lowercase__				):




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a = None						,	__a = None						,	__a = None						,	__a = False						,	__a = False						,	__a = None						,	__a = None						,	**__a						,	):
								super().__init__(
								    __a						,	split=__a						,	features=__a						,	cache_dir=__a						,	keep_in_memory=__a						,	streaming=__a						,	num_proc=__a						,	**__a						,	)
								__lowerCamelCase					:	List[Any]	       =				field
								__lowerCamelCase					:	Dict	       =				path_or_paths if isinstance(__a						,	__a     ) else {self.split: path_or_paths}
								__lowerCamelCase					:	str	       =				Json(
								    cache_dir=__a						,	data_files=__a						,	features=__a						,	field=__a						,	**__a						,	)





					def     snake_case_				(    self     ):
								# Build iterable dataset
								if self.streaming:
											__lowerCamelCase					:	str	       =				self.builder.as_streaming_dataset(split=self.split     )
								# Build regular (map-style) dataset
								else:
											__lowerCamelCase					:	Any	       =				None
											__lowerCamelCase					:	Any	       =				None
											__lowerCamelCase					:	str	       =				None
											__lowerCamelCase					:	int	       =				None

											self.builder.download_and_prepare(
											    download_config=__a						,	download_mode=__a						,	verification_mode=__a						,	base_path=__a						,	num_proc=self.num_proc						,	)
											__lowerCamelCase					:	List[Any]	       =				self.builder.as_dataset(
											    split=self.split						,	verification_mode=__a						,	in_memory=self.keep_in_memory     )
								return dataset



class __lowercase:




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a						,	__a = None						,	__a = None						,	**__a						,	):
								if num_proc is not None and num_proc <= 0:
											raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''     )

								__lowerCamelCase					:	Optional[int]	       =				dataset
								__lowerCamelCase					:	Optional[int]	       =				path_or_buf
								__lowerCamelCase					:	List[str]	       =				batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
								__lowerCamelCase					:	str	       =				num_proc
								__lowerCamelCase					:	Optional[Any]	       =				'utf-8'
								__lowerCamelCase					:	Optional[int]	       =				to_json_kwargs
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				self.to_json_kwargs.pop('path_or_buf'						,	__a     )
								__lowerCamelCase					:	Any	       =				self.to_json_kwargs.pop('orient'						,	'records'     )
								__lowerCamelCase					:	List[Any]	       =				self.to_json_kwargs.pop('lines'						,	True if orient == 'records' else False     )
								__lowerCamelCase					:	Tuple	       =				self.to_json_kwargs.pop('index'						,	False if orient in ['split', 'table'] else True     )
								__lowerCamelCase					:	int	       =				self.to_json_kwargs.pop('compression'						,	__a     )

								if compression not in [None, "infer", "gzip", "bz2", "xz"]:
											raise NotImplementedError(f'''`datasets` currently does not support {compression} compression'''     )

								if isinstance(self.path_or_buf						,	(str, bytes, os.PathLike)     ):
											with fsspec.open(self.path_or_buf						,	'wb'						,	compression=__a     ) as buffer:
														__lowerCamelCase					:	int	       =				self._write(file_obj=__a						,	orient=__a						,	lines=__a						,	index=__a						,	**self.to_json_kwargs     )
								else:
											if compression:
														raise NotImplementedError(
														    f'''The compression parameter is not supported when writing to a buffer, but compression={compression}'''
														    ' was passed. Please provide a local path instead.'     )
											__lowerCamelCase					:	Dict	       =				self._write(
											    file_obj=self.path_or_buf						,	orient=__a						,	lines=__a						,	index=__a						,	**self.to_json_kwargs     )
								return written
					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	Union[str, Any]	       =				args

								__lowerCamelCase					:	int	       =				query_table(
								    table=self.dataset.data						,	key=slice(__a						,	offset + self.batch_size     )						,	indices=self.dataset._indices						,	)
								__lowerCamelCase					:	List[str]	       =				batch.to_pandas().to_json(
								    path_or_buf=__a						,	orient=__a						,	lines=__a						,	index=__a						,	**__a     )
								if not json_str.endswith('\n'     ):
											json_str += "\n"
								return json_str.encode(self.encoding     )





					def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	**__a						,	):
								__lowerCamelCase					:	Any	       =				0

								if self.num_proc is None or self.num_proc == 1:
											for offset in logging.tqdm(
											    range(0						,	len(self.dataset     )						,	self.batch_size     )						,	unit='ba'						,	disable=not logging.is_progress_bar_enabled()						,	desc='Creating json from Arrow format'						,	):
														__lowerCamelCase					:	List[str]	       =				self._batch_json((offset, orient, lines, index, to_json_kwargs)     )
														written += file_obj.write(__a     )
								else:
											__lowerCamelCase       ,      __lowerCamelCase					:	Optional[int]	       =				len(self.dataset     ), self.batch_size
											with multiprocessing.Pool(self.num_proc     ) as pool:
														for json_str in logging.tqdm(
														    pool.imap(
														        self._batch_json						,	[(offset, orient, lines, index, to_json_kwargs) for offset in range(0						,	__a						,	__a     )]						,	)						,	total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size						,	unit='ba'						,	disable=not logging.is_progress_bar_enabled()						,	desc='Creating json from Arrow format'						,	):
																	written += file_obj.write(__a     )

								return written



