"""simple docstring"""



import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

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

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


_UpperCamelCase								=		get_tests_dir("fixtures/dummy_feature_extractor_config.json")
_UpperCamelCase								=		get_tests_dir("fixtures/vocab.json")
_UpperCamelCase								=		get_tests_dir("fixtures")
class 						__UpperCAmelCase    (unittest.TestCase     ):







			'''simple docstring'''
			_UpperCamelCase  :						Optional[Any]					     =				['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			0





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     int								=			AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""							)
										self.assertIsInstance(snake_case_   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with tempfile.TemporaryDirectory() as tmpdirname:
																	A__       :     List[str]								=			WavaVecaConfig()
																	A__       :     List[Any]								=			AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""							)

																	# save in new folder
																	model_config.save_pretrained(snake_case_							)
																	processor.save_pretrained(snake_case_							)

																	A__       :     Union[str, Any]								=			AutoProcessor.from_pretrained(snake_case_							)

										self.assertIsInstance(snake_case_   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with tempfile.TemporaryDirectory() as tmpdirname:
																	# copy relevant files
																	copyfile(snake_case_   ,      os.path.join(snake_case_   ,      snake_case_							)							)
																	copyfile(snake_case_   ,      os.path.join(snake_case_   ,      """vocab.json"""							)							)

																	A__       :     Tuple								=			AutoProcessor.from_pretrained(snake_case_							)

										self.assertIsInstance(snake_case_   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with tempfile.TemporaryDirectory() as tmpdirname:
																	A__       :     Any								=			WavaVecaFeatureExtractor()
																	A__       :     Optional[Any]								=			AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h"""							)

																	A__       :     Dict								=			WavaVecaProcessor(snake_case_   ,      snake_case_							)

																	# save in new folder
																	processor.save_pretrained(snake_case_							)

																	# drop `processor_class` in tokenizer
																	with open(os.path.join(snake_case_   ,      snake_case_							)   ,      """r"""							) as f:
																								A__       :     Optional[Any]								=			json.load(snake_case_							)
																								config_dict.pop("""processor_class"""							)

																	with open(os.path.join(snake_case_   ,      snake_case_							)   ,      """w"""							) as f:
																								f.write(json.dumps(snake_case_							)							)

																	A__       :     Union[str, Any]								=			AutoProcessor.from_pretrained(snake_case_							)

										self.assertIsInstance(snake_case_   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with tempfile.TemporaryDirectory() as tmpdirname:
																	A__       :     List[Any]								=			WavaVecaFeatureExtractor()
																	A__       :     str								=			AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h"""							)

																	A__       :     List[Any]								=			WavaVecaProcessor(snake_case_   ,      snake_case_							)

																	# save in new folder
																	processor.save_pretrained(snake_case_							)

																	# drop `processor_class` in feature extractor
																	with open(os.path.join(snake_case_   ,      snake_case_							)   ,      """r"""							) as f:
																								A__       :     Tuple								=			json.load(snake_case_							)
																								config_dict.pop("""processor_class"""							)

																	with open(os.path.join(snake_case_   ,      snake_case_							)   ,      """w"""							) as f:
																								f.write(json.dumps(snake_case_							)							)

																	A__       :     Union[str, Any]								=			AutoProcessor.from_pretrained(snake_case_							)

										self.assertIsInstance(snake_case_   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with tempfile.TemporaryDirectory() as tmpdirname:
																	A__       :     List[Any]								=			WavaVecaConfig(processor_class="""Wav2Vec2Processor"""							)
																	model_config.save_pretrained(snake_case_							)
																	# copy relevant files
																	copyfile(snake_case_   ,      os.path.join(snake_case_   ,      """vocab.json"""							)							)
																	# create emtpy sample processor
																	with open(os.path.join(snake_case_   ,      snake_case_							)   ,      """w"""							) as f:
																								f.write("""{}"""							)

																	A__       :     List[str]								=			AutoProcessor.from_pretrained(snake_case_							)

										self.assertIsInstance(snake_case_   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with self.assertRaises(snake_case_							):
																	A__       :     List[Any]								=			AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""							)
										# If remote code is disabled, we can't load this config.
										with self.assertRaises(snake_case_							):
																	A__       :     List[Any]								=			AutoProcessor.from_pretrained(
																	    """hf-internal-testing/test_dynamic_processor"""   ,      trust_remote_code=snake_case_							)

										A__       :     Any								=			AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""   ,      trust_remote_code=snake_case_							)
										self.assertTrue(processor.special_attribute_present							)
										self.assertEqual(processor.__class__.__name__   ,      """NewProcessor"""							)

										A__       :     int								=			processor.feature_extractor
										self.assertTrue(feature_extractor.special_attribute_present							)
										self.assertEqual(feature_extractor.__class__.__name__   ,      """NewFeatureExtractor"""							)

										A__       :     Optional[Any]								=			processor.tokenizer
										self.assertTrue(tokenizer.special_attribute_present							)
										if is_tokenizers_available():
																	self.assertEqual(tokenizer.__class__.__name__   ,      """NewTokenizerFast"""							)

																	# Test we can also load the slow version
																	A__       :     Optional[int]								=			AutoProcessor.from_pretrained(
																	    """hf-internal-testing/test_dynamic_processor"""   ,      trust_remote_code=snake_case_   ,      use_fast=snake_case_							)
																	A__       :     Dict								=			new_processor.tokenizer
																	self.assertTrue(new_tokenizer.special_attribute_present							)
																	self.assertEqual(new_tokenizer.__class__.__name__   ,      """NewTokenizer"""							)
										else:
																	self.assertEqual(tokenizer.__class__.__name__   ,      """NewTokenizer"""							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										try:
																	AutoConfig.register("""custom"""   ,      snake_case_							)
																	AutoFeatureExtractor.register(snake_case_   ,      snake_case_							)
																	AutoTokenizer.register(snake_case_   ,      slow_tokenizer_class=snake_case_							)
																	AutoProcessor.register(snake_case_   ,      snake_case_							)
																	# Trying to register something existing in the Transformers library will raise an error
																	with self.assertRaises(snake_case_							):
																								AutoProcessor.register(snake_case_   ,      snake_case_							)

																	# Now that the config is registered, it can be used as any other config with the auto-API
																	A__       :     Optional[Any]								=			CustomFeatureExtractor.from_pretrained(snake_case_							)

																	with tempfile.TemporaryDirectory() as tmp_dir:
																								A__       :     str								=			os.path.join(snake_case_   ,      """vocab.txt"""							)
																								with open(snake_case_   ,      """w"""   ,      encoding="""utf-8"""							) as vocab_writer:
																															vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]							)							)
																								A__       :     str								=			CustomTokenizer(snake_case_							)

																	A__       :     Union[str, Any]								=			CustomProcessor(snake_case_   ,      snake_case_							)

																	with tempfile.TemporaryDirectory() as tmp_dir:
																								processor.save_pretrained(snake_case_							)
																								A__       :     Optional[int]								=			AutoProcessor.from_pretrained(snake_case_							)
																								self.assertIsInstance(snake_case_   ,      snake_case_							)

										finally:
																	if "custom" in CONFIG_MAPPING._extra_content:
																								del CONFIG_MAPPING._extra_content["custom"]
																	if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																								del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
																	if CustomConfig in TOKENIZER_MAPPING._extra_content:
																								del TOKENIZER_MAPPING._extra_content[CustomConfig]
																	if CustomConfig in PROCESSOR_MAPPING._extra_content:
																								del PROCESSOR_MAPPING._extra_content[CustomConfig]





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										class 						__UpperCAmelCase    (__A     ):







													'''simple docstring'''
													_UpperCamelCase  :						int					     =				False





										class 						__UpperCAmelCase    (__A     ):







													'''simple docstring'''
													_UpperCamelCase  :						Any					     =				False





										class 						__UpperCAmelCase    (__A     ):







													'''simple docstring'''
													_UpperCamelCase  :						str					     =				'AutoFeatureExtractor'
													_UpperCamelCase  :						Optional[Any]					     =				'AutoTokenizer'
													_UpperCamelCase  :						List[Any]					     =				False

										try:
																	AutoConfig.register("""custom"""   ,      snake_case_							)
																	AutoFeatureExtractor.register(snake_case_   ,      snake_case_							)
																	AutoTokenizer.register(snake_case_   ,      slow_tokenizer_class=snake_case_							)
																	AutoProcessor.register(snake_case_   ,      snake_case_							)
																	# If remote code is not set, the default is to use local classes.
																	A__       :     Dict								=			AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""							)
																	self.assertEqual(processor.__class__.__name__   ,      """NewProcessor"""							)
																	self.assertFalse(processor.special_attribute_present							)
																	self.assertFalse(processor.feature_extractor.special_attribute_present							)
																	self.assertFalse(processor.tokenizer.special_attribute_present							)

																	# If remote code is disabled, we load the local ones.
																	A__       :     Tuple								=			AutoProcessor.from_pretrained(
																	    """hf-internal-testing/test_dynamic_processor"""   ,      trust_remote_code=snake_case_							)
																	self.assertEqual(processor.__class__.__name__   ,      """NewProcessor"""							)
																	self.assertFalse(processor.special_attribute_present							)
																	self.assertFalse(processor.feature_extractor.special_attribute_present							)
																	self.assertFalse(processor.tokenizer.special_attribute_present							)

																	# If remote is enabled, we load from the Hub.
																	A__       :     List[str]								=			AutoProcessor.from_pretrained(
																	    """hf-internal-testing/test_dynamic_processor"""   ,      trust_remote_code=snake_case_							)
																	self.assertEqual(processor.__class__.__name__   ,      """NewProcessor"""							)
																	self.assertTrue(processor.special_attribute_present							)
																	self.assertTrue(processor.feature_extractor.special_attribute_present							)
																	self.assertTrue(processor.tokenizer.special_attribute_present							)

										finally:
																	if "custom" in CONFIG_MAPPING._extra_content:
																								del CONFIG_MAPPING._extra_content["custom"]
																	if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																								del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
																	if CustomConfig in TOKENIZER_MAPPING._extra_content:
																								del TOKENIZER_MAPPING._extra_content[CustomConfig]
																	if CustomConfig in PROCESSOR_MAPPING._extra_content:
																								del PROCESSOR_MAPPING._extra_content[CustomConfig]





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     int								=			AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-bert"""							)
										self.assertEqual(processor.__class__.__name__   ,      """BertTokenizerFast"""							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Tuple								=			AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-convnext"""							)
										self.assertEqual(processor.__class__.__name__   ,      """ConvNextImageProcessor"""							)





@is_staging_test
class 						__UpperCAmelCase    (unittest.TestCase     ):







			'''simple docstring'''
			_UpperCamelCase  :						Optional[int]					     =				['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']





			@classmethod
			def 		lowerCamelCase					(			cls							):







										'''simple docstring'''


										A__       :     List[Any]								=			TOKEN
										HfFolder.save_token(snake_case_							)





			@classmethod
			def 		lowerCamelCase					(			cls							):







										'''simple docstring'''


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

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

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





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[str]								=			WavaVecaProcessor.from_pretrained(snake_case_							)
										with tempfile.TemporaryDirectory() as tmp_dir:
																	processor.save_pretrained(
																	    os.path.join(snake_case_   ,      """test-processor"""							)   ,      push_to_hub=snake_case_   ,      use_auth_token=self._token							)

																	A__       :     Optional[int]								=			WavaVecaProcessor.from_pretrained(F'''{USER}/test-processor'''							)
																	for k, v in processor.feature_extractor.__dict__.items():
																								self.assertEqual(snake_case_   ,      getattr(new_processor.feature_extractor   ,      snake_case_							)							)
																	self.assertDictEqual(new_processor.tokenizer.get_vocab()   ,      processor.tokenizer.get_vocab()							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Tuple								=			WavaVecaProcessor.from_pretrained(snake_case_							)

										with tempfile.TemporaryDirectory() as tmp_dir:
																	processor.save_pretrained(
																	    os.path.join(snake_case_   ,      """test-processor-org"""							)   ,      push_to_hub=snake_case_   ,      use_auth_token=self._token   ,      organization="""valid_org"""   ,      )

																	A__       :     Union[str, Any]								=			WavaVecaProcessor.from_pretrained("""valid_org/test-processor-org"""							)
																	for k, v in processor.feature_extractor.__dict__.items():
																								self.assertEqual(snake_case_   ,      getattr(new_processor.feature_extractor   ,      snake_case_							)							)
																	self.assertDictEqual(new_processor.tokenizer.get_vocab()   ,      processor.tokenizer.get_vocab()							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										CustomFeatureExtractor.register_for_auto_class()
										CustomTokenizer.register_for_auto_class()
										CustomProcessor.register_for_auto_class()

										A__       :     int								=			CustomFeatureExtractor.from_pretrained(snake_case_							)

										with tempfile.TemporaryDirectory() as tmp_dir:
																	A__       :     Optional[Any]								=			os.path.join(snake_case_   ,      """vocab.txt"""							)
																	with open(snake_case_   ,      """w"""   ,      encoding="""utf-8"""							) as vocab_writer:
																								vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]							)							)
																	A__       :     Optional[int]								=			CustomTokenizer(snake_case_							)

										A__       :     Union[str, Any]								=			CustomProcessor(snake_case_   ,      snake_case_							)

										with tempfile.TemporaryDirectory() as tmp_dir:
																	create_repo(F'''{USER}/test-dynamic-processor'''   ,      token=self._token							)
																	A__       :     List[Any]								=			Repository(snake_case_   ,      clone_from=F'''{USER}/test-dynamic-processor'''   ,      token=self._token							)
																	processor.save_pretrained(snake_case_							)

																	# This has added the proper auto_map field to the feature extractor config
																	self.assertDictEqual(
																	    processor.feature_extractor.auto_map   ,      {
																	        """AutoFeatureExtractor""": """custom_feature_extraction.CustomFeatureExtractor""",
																	        """AutoProcessor""": """custom_processing.CustomProcessor""",
																	    }   ,      )

																	# This has added the proper auto_map field to the tokenizer config
																	with open(os.path.join(snake_case_   ,      """tokenizer_config.json"""							)							) as f:
																								A__       :     List[str]								=			json.load(snake_case_							)
																	self.assertDictEqual(
																	    tokenizer_config["""auto_map"""]   ,      {
																	        """AutoTokenizer""": ["""custom_tokenization.CustomTokenizer""", None],
																	        """AutoProcessor""": """custom_processing.CustomProcessor""",
																	    }   ,      )

																	# The code has been copied from fixtures
																	self.assertTrue(os.path.isfile(os.path.join(snake_case_   ,      """custom_feature_extraction.py"""							)							)							)
																	self.assertTrue(os.path.isfile(os.path.join(snake_case_   ,      """custom_tokenization.py"""							)							)							)
																	self.assertTrue(os.path.isfile(os.path.join(snake_case_   ,      """custom_processing.py"""							)							)							)

																	repo.push_to_hub()

										A__       :     Union[str, Any]								=			AutoProcessor.from_pretrained(F'''{USER}/test-dynamic-processor'''   ,      trust_remote_code=snake_case_							)
										# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
										self.assertEqual(new_processor.__class__.__name__   ,      """CustomProcessor"""							)






"""simple docstring"""



import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


_UpperCamelCase								=		logging.getLogger(__name__)

_UpperCamelCase								=		"pytorch_model.bin"
@dataclasses.dataclass
class 						__UpperCAmelCase    :







			'''simple docstring'''
			_UpperCamelCase  :						str					     =				dataclasses.field(
			    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'}     )
			_UpperCamelCase  :						Optional[str]					     =				dataclasses.field(
			    default=__A							,   metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'}							,   )





@dataclasses.dataclass
class 						__UpperCAmelCase    :







			'''simple docstring'''
			_UpperCamelCase  :						str					     =				dataclasses.field(metadata={'help': 'A csv or a json file containing the training data.'}     )
			_UpperCamelCase  :						str					     =				dataclasses.field(metadata={'help': 'A csv or a json file containing the data to predict on.'}     )
			_UpperCamelCase  :						Optional[str]					     =				dataclasses.field(
			    default=__A							,   metadata={'help': 'A csv or a json file containing the validation data.'}     )
			_UpperCamelCase  :						Optional[str]					     =				dataclasses.field(
			    default=__A							,   metadata={'help': 'The name of the task to train on.'}							,   )
			_UpperCamelCase  :						Optional[List[str]]					     =				dataclasses.field(
			    default=__A							,   metadata={'help': 'The list of labels for the task.'}     )





@dataclasses.dataclass
class 						__UpperCAmelCase    :







			'''simple docstring'''
			_UpperCamelCase  :						str					     =				dataclasses.field(
			    metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'}     )
			_UpperCamelCase  :						Optional[str]					     =				dataclasses.field(
			    default='accuracy'							,   metadata={'help': 'The evaluation metric used for the task.'}     )
			_UpperCamelCase  :						Optional[str]					     =				dataclasses.field(
			    default='no'							,   metadata={
			        'help': 'The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'
			    }							,   )
			_UpperCamelCase  :						Optional[int]					     =				dataclasses.field(
			    default=10							,   metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'}							,   )
			_UpperCamelCase  :						Optional[float]					     =				dataclasses.field(
			    default=0.0							,   metadata={
			        'help': 'How much the specified evaluation metric must improve to satisfy early stopping conditions.'
			    }							,   )
			_UpperCamelCase  :						Optional[bool]					     =				dataclasses.field(
			    default=__A							,   metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'}							,   )
			_UpperCamelCase  :						Optional[bool]					     =				dataclasses.field(
			    default=__A							,   metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'}							,   )
			_UpperCamelCase  :						Optional[bool]					     =				dataclasses.field(
			    default=__A							,   metadata={'help': 'Whether to fine-tune on labeled data after pseudo training.'}							,   )
			_UpperCamelCase  :						Optional[float]					     =				dataclasses.field(
			    default=0.0							,   metadata={'help': 'Confidence threshold for pseudo-labeled data filtering.'}							,   )
			_UpperCamelCase  :						Optional[int]					     =				dataclasses.field(
			    default=100							,   metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'}							,   )
			_UpperCamelCase  :						Optional[int]					     =				dataclasses.field(
			    default=__A							,   metadata={'help': 'Random seed for initialization.'}							,   )





def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Optional[int]								=			datasets.concatenate_datasets([infer_input, infer_output]			,  axis=1							)

							if args.do_filter_by_confidence:
														A__       :     Union[str, Any]								=			dataset.filter(lambda lowerCAmelCase							: example["probability"] > args.confidence_threshold							)

							if args.do_filter_by_val_performance:
														assert eval_result >= 0.0 and eval_result <= 1.0
														A__       :     Dict								=			int(eval_result * len(lowerCAmelCase							)							)
														print(lowerCAmelCase							)
														A__       :     Dict								=			dataset.sort("""probability"""			,  reverse=lowerCAmelCase							)
														A__       :     Tuple								=			dataset.select(range(lowerCAmelCase							)							)

							A__       :     Optional[int]								=			dataset.remove_columns(["""label""", """probability"""]							)
							A__       :     Dict								=			dataset.rename_column("""prediction"""			,  """label"""							)
							A__       :     str								=			dataset.map(lambda lowerCAmelCase							: {"label": idalabel[example["label"]]}							)
							A__       :     Optional[Any]								=			dataset.shuffle(seed=args.seed							)

							A__       :     Any								=			os.path.join(lowerCAmelCase			,  F'''train_pseudo.{args.data_file_extension}'''							)
							if args.data_file_extension == "csv":
														dataset.to_csv(lowerCAmelCase			,  index=lowerCAmelCase							)
							else:
														dataset.to_json(lowerCAmelCase							)



def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  **lowerCAmelCase							):
							A__       :     Dict								=			Accelerator()
							# Make one log on every process with the configuration for debugging.
							logging.basicConfig(
							    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""			,  datefmt="""%m/%d/%Y %H:%M:%S"""			,  level=logging.INFO			,  )
							logger.info(accelerator.state							)

							# Setup logging, we only want one process per machine to log things on the
							# screen. accelerator.is_local_main_process is only True for one process per
							# machine.
							logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR							)

							if accelerator.is_local_main_process:
														datasets.utils.logging.set_verbosity_warning()
														transformers.utils.logging.set_verbosity_info()
							else:
														datasets.utils.logging.set_verbosity_error()
														transformers.utils.logging.set_verbosity_error()

							A__       :     Union[str, Any]								=			STModelArguments(model_name_or_path=lowerCAmelCase							)
							A__       :     Tuple								=			STDataArguments(train_file=lowerCAmelCase			,  infer_file=lowerCAmelCase							)
							A__       :     List[str]								=			STTrainingArguments(output_dir=lowerCAmelCase							)
							A__       :     Tuple								=			argparse.Namespace()

							for arg_class in (model_args, data_args, training_args):
														for key, value in vars(lowerCAmelCase							).items():
																					setattr(lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							)

							for key, value in kwargs.items():
														if hasattr(lowerCAmelCase			,  lowerCAmelCase							):
																					setattr(lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							)

    # Sanity checks
							A__       :     Optional[int]								=			{}
							A__       :     Tuple								=			None

							# You need to provide the training data and the data to predict on
							assert args.train_file is not None
							assert args.infer_file is not None
							A__       :     Tuple								=			args.train_file
							A__       :     int								=			args.infer_file

							if args.evaluation_strategy != IntervalStrategy.NO.value:
														assert args.eval_file is not None
														A__       :     Optional[Any]								=			args.eval_file

							for key in data_files:
														A__       :     List[str]								=			data_files[key].split("""."""							)[-1]
														assert extension in ["csv", "json"], F'''`{key}_file` should be a csv or a json file.'''
														if args.data_file_extension is None:
																					A__       :     int								=			extension
														else:
																					assert extension == args.data_file_extension, F'''`{key}_file` should be a {args.data_file_extension} file`.'''

							assert (
							    args.eval_metric in datasets.list_metrics()
							), F'''{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'''

							# If passed along, set the training seed now.
							if args.seed is not None:
														set_seed(args.seed							)

							logger.info("""Creating the initial data directory for self-training..."""							)
							A__       :     Union[str, Any]								=			F'''{args.output_dir}/self-train_iter-{{}}'''.format
							A__       :     Any								=			data_dir_format(0							)

							if accelerator.is_main_process:
														if args.output_dir is not None:
																					os.makedirs(args.output_dir			,  exist_ok=lowerCAmelCase							)
																					os.makedirs(lowerCAmelCase			,  exist_ok=lowerCAmelCase							)
							accelerator.wait_for_everyone()

							A__       :     Union[str, Any]								=			None
							A__       :     str								=			None
							A__       :     Union[str, Any]								=			0
							A__       :     List[Any]								=			False
							# Show the progress bar
							A__       :     List[Any]								=			tqdm(range(args.max_selftrain_iterations							)			,  disable=not accelerator.is_local_main_process							)

							# Self-train
							for iteration in range(0			,  int(args.max_selftrain_iterations							)							):
														A__       :     List[str]								=			data_dir_format(lowerCAmelCase							)
														assert os.path.exists(lowerCAmelCase							)

														# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
														# iteration > 0
														A__       :     int								=			os.path.join(lowerCAmelCase			,  """stage-1"""							)
														A__       :     Any								=			{
														    """accelerator""": accelerator,
														    """model_name_or_path""": args.model_name_or_path,
														    """cache_dir""": args.cache_dir,
														    """do_train""": True,
														    """train_file""": data_files["""train"""] if iteration == 0 else data_files["""train_pseudo"""],
														    """do_eval""": True if args.eval_file is not None else False,
														    """eval_file""": data_files["""eval"""],
														    """do_predict""": True,
														    """infer_file""": data_files["""infer"""],
														    """task_name""": args.task_name,
														    """label_list""": args.label_list,
														    """output_dir""": current_output_dir,
														    """eval_metric""": args.eval_metric,
														    """evaluation_strategy""": args.evaluation_strategy,
														    """early_stopping_patience""": args.early_stopping_patience,
														    """early_stopping_threshold""": args.early_stopping_threshold,
														    """seed""": args.seed,
														}
														# Add additional training arguments
														for key, value in kwargs.items():
																					if key not in arguments_dict and not hasattr(lowerCAmelCase			,  lowerCAmelCase							):
																												arguments_dict.update({key: value}							)

														A__       :     Union[str, Any]								=			os.path.join(lowerCAmelCase			,  """best-checkpoint"""			,  lowerCAmelCase							)
														if os.path.exists(lowerCAmelCase							):
																					logger.info(
																					    """Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1."""			,  lowerCAmelCase			,  lowerCAmelCase			,  )
														else:
																					logger.info("""***** Running self-training: iteration: %d, stage: 1 *****"""			,  lowerCAmelCase							)
																					finetune(**lowerCAmelCase							)
																					accelerator.wait_for_everyone()
																					assert os.path.exists(lowerCAmelCase							)
																					logger.info("""Self-training job completed: iteration: %d, stage: 1."""			,  lowerCAmelCase							)

														if iteration > 0 and args.finetune_on_labeled_data:
																					# Stage 2 (optional): fine-tuning on the original labeled data
																					A__       :     Union[str, Any]								=			os.path.join(lowerCAmelCase			,  """best-checkpoint"""							)
																					A__       :     str								=			os.path.join(lowerCAmelCase			,  """stage-2"""							)
																					# Update arguments_dict
																					A__       :     Dict								=			model_path
																					A__       :     Optional[Any]								=			data_files["""train"""]
																					A__       :     Optional[Any]								=			current_output_dir

																					A__       :     Union[str, Any]								=			os.path.join(lowerCAmelCase			,  """best-checkpoint"""			,  lowerCAmelCase							)
																					if os.path.exists(lowerCAmelCase							):
																												logger.info(
																												    """Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2."""			,  lowerCAmelCase			,  lowerCAmelCase			,  )
																					else:
																												logger.info("""***** Running self-training: iteration: %d, stage: 2 *****"""			,  lowerCAmelCase							)
																												finetune(**lowerCAmelCase							)
																												accelerator.wait_for_everyone()
																												assert os.path.exists(lowerCAmelCase							)
																												logger.info("""Self-training job completed: iteration: %d, stage: 2."""			,  lowerCAmelCase							)

														A__       :     List[str]								=			iteration
														A__       :     List[str]								=			data_dir_format(iteration + 1							)

														A__       :     List[Any]								=			AutoConfig.from_pretrained(os.path.join(lowerCAmelCase			,  """best-checkpoint"""							)							)
														A__       :     int								=			config.idalabel
														A__       :     Tuple								=			os.path.join(lowerCAmelCase			,  """eval_results_best-checkpoint.json"""							)
														A__       :     str								=			os.path.join(lowerCAmelCase			,  """test_results_best-checkpoint.json"""							)
														assert os.path.exists(lowerCAmelCase							)

														with open(lowerCAmelCase			,  """r"""							) as f:
																					A__       :     List[str]								=			float(json.load(lowerCAmelCase							)[args.eval_metric]							)
														A__       :     List[Any]								=			os.path.join(lowerCAmelCase			,  """infer_output_best-checkpoint.csv"""							)
														assert os.path.exists(lowerCAmelCase							)
														# Loading the dataset from local csv or json files.
														A__       :     List[str]								=			load_dataset(args.data_file_extension			,  data_files={"""data""": data_files["""infer"""]}							)["""data"""]
														A__       :     List[Any]								=			load_dataset("""csv"""			,  data_files={"""data""": infer_output_file}							)["""data"""]

														if accelerator.is_main_process:
																					os.makedirs(lowerCAmelCase			,  exist_ok=lowerCAmelCase							)
																					shutil.copy(lowerCAmelCase			,  os.path.join(lowerCAmelCase			,  F'''eval_results_iter-{iteration}.json'''							)							)
																					if os.path.exists(lowerCAmelCase							):
																												shutil.copy(lowerCAmelCase			,  os.path.join(lowerCAmelCase			,  F'''test_results_iter-{iteration}.json'''							)							)
																					create_pseudo_labeled_data(lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							)
														accelerator.wait_for_everyone()

														A__       :     int								=			os.path.join(lowerCAmelCase			,  F'''train_pseudo.{args.data_file_extension}'''							)

														if args.evaluation_strategy != IntervalStrategy.NO.value:
																					A__       :     Optional[int]								=			eval_result

																					if best_iteration is None:
																												A__       :     List[str]								=			new_iteration
																												A__       :     List[str]								=			new_eval_result
																					else:
																												if new_eval_result - best_eval_result > args.early_stopping_threshold:
																																			A__       :     List[Any]								=			new_iteration
																																			A__       :     str								=			new_eval_result
																																			A__       :     Dict								=			0
																												else:
																																			if new_eval_result == best_eval_result:
																																										A__       :     Optional[int]								=			new_iteration
																																										A__       :     Optional[int]								=			new_eval_result
																																			early_stopping_patience_counter += 1

																												if early_stopping_patience_counter >= args.early_stopping_patience:
																																			A__       :     Tuple								=			True

														progress_bar.update(1							)

														if should_training_stop:
																					break

							if best_iteration is not None:
														# Save the best iteration
														logger.info("""Best iteration: %d"""			,  lowerCAmelCase							)
														logger.info("""Best evaluation result: %s = %f"""			,  args.eval_metric			,  lowerCAmelCase							)
														accelerator.wait_for_everyone()
														if accelerator.is_main_process:
																					shutil.copy(
																					    os.path.join(lowerCAmelCase			,  F'''eval_results_iter-{iteration}.json'''							)			,  os.path.join(lowerCAmelCase			,  """eval_results_best-iteration.json"""							)			,  )
							else:
														# Assume that the last iteration is the best
														logger.info("""Best iteration: %d"""			,  args.max_selftrain_iterations - 1							)
														logger.info("""Best evaluation result: %s = %f"""			,  args.eval_metric			,  lowerCAmelCase							)
														accelerator.wait_for_everyone()
														if accelerator.is_main_process:
																					shutil.copy(
																					    os.path.join(lowerCAmelCase			,  F'''eval_results_iter-{args.max_selftrain_iterations - 1}.json'''							)			,  os.path.join(lowerCAmelCase			,  """eval_results_best-iteration.json"""							)			,  )





