

import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
	import torch


lowerCAmelCase__		      =       random.Random()



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							str		,				SCREAMING_SNAKE_CASE_:							Optional[int]=1.0		,				SCREAMING_SNAKE_CASE_:							Tuple=None		,				SCREAMING_SNAKE_CASE_:							Dict=None					)	->					List[Any]:




				'''simple docstring'''





				if rng is None:
								A__						=	global_rng

				A__						=	[]
				for batch_idx in range(shape[0]					):
								values.append([]					)
								for _ in range(shape[1]					):
												values[-1].append(rng.random() * scale					)

				return values

@require_torch
class     a__  (    unittest.TestCase	):
			"""simple docstring"""







			def __init__(			self					,					lowercase					,					lowercase=7					,					lowercase=400					,					lowercase=2000					,					lowercase=1					,					lowercase=0.0					,					lowercase=16000					,					lowercase=True					,					lowercase=80					,					lowercase=16					,					lowercase=64					,					lowercase="hann_window"					,					lowercase=80					,					lowercase=7600					,					lowercase=1e-10					,					lowercase=True					,					)     ->  Union[str, Any]:
							'''simple docstring'''






							A__						=	parent
							A__						=	batch_size
							A__						=	min_seq_length
							A__						=	max_seq_length
							A__						=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							A__						=	feature_size
							A__						=	padding_value
							A__						=	sampling_rate
							A__						=	do_normalize
							A__						=	num_mel_bins
							A__						=	hop_length
							A__						=	win_length
							A__						=	win_function
							A__						=	fmin
							A__						=	fmax
							A__						=	mel_floor
							A__						=	return_attention_mask


			def    UpperCamelCase						(			self				)     ->  str:
							'''simple docstring'''






							return {
							    "feature_size": self.feature_size,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "do_normalize": self.do_normalize,
							    "num_mel_bins": self.num_mel_bins,
							    "hop_length": self.hop_length,
							    "win_length": self.win_length,
							    "win_function": self.win_function,
							    "fmin": self.fmin,
							    "fmax": self.fmax,
							    "mel_floor": self.mel_floor,
							    "return_attention_mask": self.return_attention_mask,
							}


			def    UpperCamelCase						(			self					,					lowercase=False					,					lowercase=False				)     ->  Union[str, Any]:
							'''simple docstring'''






							def _flatten(lowercase				):
											return list(itertools.chain(*lowercase				)				)

							if equal_length:
											A__						=	floats_list((self.batch_size, self.max_seq_length)				)
							else:
											# make sure that inputs increase in size
											A__						=	[
											    _flatten(floats_list((x, self.feature_size)				)				)
											    for x in range(self.min_seq_length					,					self.max_seq_length					,					self.seq_length_diff				)
											]

							if numpify:
											A__						=	[np.asarray(lowercase				) for x in speech_inputs]

							return speech_inputs


			def    UpperCamelCase						(			self					,					lowercase=False					,					lowercase=False				)     ->  Union[str, Any]:
							'''simple docstring'''






							if equal_length:
											A__						=	[floats_list((self.max_seq_length, self.num_mel_bins)				) for _ in range(self.batch_size				)]
							else:
											# make sure that inputs increase in size
											A__						=	[
											    floats_list((x, self.num_mel_bins)				)
											    for x in range(self.min_seq_length					,					self.max_seq_length					,					self.seq_length_diff				)
											]

							if numpify:
											A__						=	[np.asarray(lowercase				) for x in speech_inputs]

							return speech_inputs

@require_torch
class     a__  (    snake_case							,		unittest.TestCase	):
			"""simple docstring"""







			__lowerCamelCase       				=				SpeechTaFeatureExtractor


			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							A__						=	SpeechTaFeatureExtractionTester(self				)


			def    UpperCamelCase						(			self					,					lowercase				)     ->  Dict:
							'''simple docstring'''






							self.assertTrue(np.all(np.mean(lowercase					,					axis=0				) < 1e-3				)				)
							self.assertTrue(np.all(np.abs(np.var(lowercase					,					axis=0				) - 1				) < 1e-3				)				)


			def    UpperCamelCase						(			self				)     ->  Optional[Any]:
							'''simple docstring'''






							A__						=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
							# create three inputs of length 800, 1000, and 1200
							A__						=	[floats_list((1, x)				)[0] for x in range(800					,					1400					,					200				)]
							A__						=	[np.asarray(lowercase				) for speech_input in speech_inputs]

							# Test not batched input
							A__						=	feat_extract(speech_inputs[0]					,					return_tensors="np"				).input_values
							A__						=	feat_extract(np_speech_inputs[0]					,					return_tensors="np"				).input_values
							self.assertTrue(np.allclose(lowercase					,					lowercase					,					atol=1e-3				)				)

							# Test batched
							A__						=	feat_extract(lowercase					,					return_tensors="np"				).input_values
							A__						=	feat_extract(lowercase					,					return_tensors="np"				).input_values
							for enc_seq_a, enc_seq_a in zip(lowercase					,					lowercase				):
											self.assertTrue(np.allclose(lowercase					,					lowercase					,					atol=1e-3				)				)


			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






							A__						=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
							A__						=	[floats_list((1, x)				)[0] for x in range(800					,					1400					,					200				)]

							A__						=	["longest", "max_length", "do_not_pad"]
							A__						=	[None, 1600, None]
							for max_length, padding in zip(lowercase					,					lowercase				):
											A__						=	feat_extract(lowercase					,					padding=lowercase					,					max_length=lowercase					,					return_tensors="np"				)
											A__						=	processed.input_values

											self._check_zero_mean_unit_variance(input_values[0][:800]				)
											self.assertTrue(input_values[0][800:].sum() < 1e-6				)
											self._check_zero_mean_unit_variance(input_values[1][:1000]				)
											self.assertTrue(input_values[0][1000:].sum() < 1e-6				)
											self._check_zero_mean_unit_variance(input_values[2][:1200]				)


			def    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






							A__						=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
							A__						=	range(800					,					1400					,					200				)
							A__						=	[floats_list((1, x)				)[0] for x in lengths]

							A__						=	["longest", "max_length", "do_not_pad"]
							A__						=	[None, 1600, None]

							for max_length, padding in zip(lowercase					,					lowercase				):
											A__						=	feat_extract(lowercase					,					max_length=lowercase					,					padding=lowercase				)
											A__						=	processed.input_values

											self._check_zero_mean_unit_variance(input_values[0][:800]				)
											self._check_zero_mean_unit_variance(input_values[1][:1000]				)
											self._check_zero_mean_unit_variance(input_values[2][:1200]				)


			def    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''simple docstring'''






							A__						=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
							A__						=	[floats_list((1, x)				)[0] for x in range(800					,					1400					,					200				)]
							A__						=	feat_extract(
							    lowercase					,					truncation=lowercase					,					max_length=1000					,					padding="max_length"					,					return_tensors="np"				)
							A__						=	processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :800]				)
							self._check_zero_mean_unit_variance(input_values[1]				)
							self._check_zero_mean_unit_variance(input_values[2]				)


			def    UpperCamelCase						(			self				)     ->  str:
							'''simple docstring'''






							A__						=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
							A__						=	[floats_list((1, x)				)[0] for x in range(800					,					1400					,					200				)]
							A__						=	feat_extract(
							    lowercase					,					truncation=lowercase					,					max_length=1000					,					padding="longest"					,					return_tensors="np"				)
							A__						=	processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :800]				)
							self._check_zero_mean_unit_variance(input_values[1, :1000]				)
							self._check_zero_mean_unit_variance(input_values[2]				)

							# make sure that if max_length < longest -> then pad to max_length
							self.assertTrue(input_values.shape == (3, 1000)				)

							A__						=	[floats_list((1, x)				)[0] for x in range(800					,					1400					,					200				)]
							A__						=	feat_extract(
							    lowercase					,					truncation=lowercase					,					max_length=2000					,					padding="longest"					,					return_tensors="np"				)
							A__						=	processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :800]				)
							self._check_zero_mean_unit_variance(input_values[1, :1000]				)
							self._check_zero_mean_unit_variance(input_values[2]				)

							# make sure that if max_length > longest -> then pad to longest
							self.assertTrue(input_values.shape == (3, 1200)				)


			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							A__						=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
							A__						=	np.random.rand(100				).astype(np.floataa				)
							A__						=	np_speech_inputs.tolist()

							for inputs in [py_speech_inputs, np_speech_inputs]:
											A__						=	feature_extractor.pad([{"input_values": inputs}]					,					return_tensors="np"				)
											self.assertTrue(np_processed.input_values.dtype == np.floataa				)
											A__						=	feature_extractor.pad([{"input_values": inputs}]					,					return_tensors="pt"				)
											self.assertTrue(pt_processed.input_values.dtype == torch.floataa				)


			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






							A__						=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
							# create three inputs of length 800, 1000, and 1200
							A__						=	[floats_list((1, x)				)[0] for x in range(800					,					1400					,					200				)]
							A__						=	[np.asarray(lowercase				) for speech_input in speech_inputs]

							# Test feature size
							A__						=	feature_extractor(audio_target=lowercase					,					padding=lowercase					,					return_tensors="np"				).input_values
							self.assertTrue(input_values.ndim == 3				)
							self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins				)

							# Test not batched input
							A__						=	feature_extractor(speech_inputs[0]					,					return_tensors="np"				).input_values
							A__						=	feature_extractor(np_speech_inputs[0]					,					return_tensors="np"				).input_values
							self.assertTrue(np.allclose(lowercase					,					lowercase					,					atol=1e-3				)				)

							# Test batched
							A__						=	feature_extractor(lowercase					,					return_tensors="np"				).input_values
							A__						=	feature_extractor(lowercase					,					return_tensors="np"				).input_values
							for enc_seq_a, enc_seq_a in zip(lowercase					,					lowercase				):
											self.assertTrue(np.allclose(lowercase					,					lowercase					,					atol=1e-3				)				)

							# Test 2-D numpy arrays are batched.
							A__						=	[floats_list((1, x)				)[0] for x in (800, 800, 800)]
							A__						=	np.asarray(lowercase				)
							A__						=	feature_extractor(lowercase					,					return_tensors="np"				).input_values
							A__						=	feature_extractor(lowercase					,					return_tensors="np"				).input_values
							for enc_seq_a, enc_seq_a in zip(lowercase					,					lowercase				):
											self.assertTrue(np.allclose(lowercase					,					lowercase					,					atol=1e-3				)				)


			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






							A__						=	self.feat_extract_tester.prepare_inputs_for_target()
							A__						=	self.feature_extraction_class(**self.feat_extract_dict				)
							A__						=	feat_extract.model_input_names[0]

							A__						=	BatchFeature({input_name: speech_inputs}				)

							self.assertTrue(all(len(lowercase				) == len(lowercase				) for x, y in zip(lowercase					,					processed_features[input_name]				)				)				)

							A__						=	self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowercase				)
							A__						=	BatchFeature({input_name: speech_inputs}					,					tensor_type="np"				)

							A__						=	processed_features[input_name]

							if len(batch_features_input.shape				) < 3:
											A__						=	batch_features_input[:, :, None]

							self.assertTrue(
							    batch_features_input.shape
							    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]				), self.feat_extract_tester.num_mel_bins)				)


			@require_torch
			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






							A__						=	self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowercase				)
							A__						=	self.feature_extraction_class(**self.feat_extract_dict				)
							A__						=	feat_extract.model_input_names[0]

							A__						=	BatchFeature({input_name: speech_inputs}					,					tensor_type="pt"				)

							A__						=	processed_features[input_name]

							if len(batch_features_input.shape				) < 3:
											A__						=	batch_features_input[:, :, None]

							self.assertTrue(
							    batch_features_input.shape
							    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]				), self.feat_extract_tester.num_mel_bins)				)


			@require_torch
			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							A__						=	self.feature_extraction_class(**self.feat_extract_dict				)
							A__						=	self.feat_extract_tester.prepare_inputs_for_target()
							A__						=	feat_extract.model_input_names[0]

							A__						=	BatchFeature({input_name: speech_inputs}				)

							A__						=	feat_extract.num_mel_bins  # hack!

							A__						=	feat_extract.pad(lowercase					,					padding="longest"					,					return_tensors="np"				)[input_name]
							A__						=	feat_extract.pad(lowercase					,					padding="longest"					,					return_tensors="pt"				)[input_name]

							self.assertTrue(abs(input_np.astype(np.floataa				).sum() - input_pt.numpy().astype(np.floataa				).sum()				) < 1e-2				)


			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							A__						=	self.feat_extract_dict
							A__						=	True
							A__						=	self.feature_extraction_class(**lowercase				)
							A__						=	self.feat_extract_tester.prepare_inputs_for_target()
							A__						=	[len(lowercase				) for x in speech_inputs]
							A__						=	feat_extract.model_input_names[0]

							A__						=	BatchFeature({input_name: speech_inputs}				)

							A__						=	feat_extract.num_mel_bins  # hack!

							A__						=	feat_extract.pad(lowercase					,					padding="longest"					,					return_tensors="np"				)
							self.assertIn("attention_mask"					,					lowercase				)
							self.assertListEqual(list(processed.attention_mask.shape				)					,					list(processed[input_name].shape[:2]				)				)
							self.assertListEqual(processed.attention_mask.sum(-1				).tolist()					,					lowercase				)


			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							A__						=	self.feat_extract_dict
							A__						=	True
							A__						=	self.feature_extraction_class(**lowercase				)
							A__						=	self.feat_extract_tester.prepare_inputs_for_target()
							A__						=	[len(lowercase				) for x in speech_inputs]
							A__						=	feat_extract.model_input_names[0]

							A__						=	BatchFeature({input_name: speech_inputs}				)
							A__						=	min(lowercase				)

							A__						=	feat_extract.num_mel_bins  # hack!

							A__						=	feat_extract.pad(
							    lowercase					,					padding="max_length"					,					max_length=lowercase					,					truncation=lowercase					,					return_tensors="np"				)
							self.assertIn("attention_mask"					,					lowercase				)
							self.assertListEqual(
							    list(processed_pad.attention_mask.shape				)					,					[processed_pad[input_name].shape[0], max_length]				)
							self.assertListEqual(
							    processed_pad.attention_mask[:, :max_length].sum(-1				).tolist()					,					[max_length for x in speech_inputs]				)


			def    UpperCamelCase						(			self					,					lowercase				)     ->  Tuple:
							'''simple docstring'''






							from datasets import load_dataset

							A__						=	load_dataset("hf-internal-testing/librispeech_asr_dummy"					,					"clean"					,					split="validation"				)
							# automatic decoding with librispeech
							A__						=	ds.sort("id"				).select(range(lowercase				)				)[:num_samples]["audio"]

							return [x["array"] for x in speech_samples]


			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							A__						=	torch.tensor(
							    [2.38_04e-03, 2.07_52e-03, 1.98_36e-03, 2.10_57e-03, 1.61_74e-03,
							     3.05_18e-04, 9.15_53e-05, 3.35_69e-04, 9.76_56e-04, 1.83_11e-03,
							     2.01_42e-03, 2.10_57e-03, 1.73_95e-03, 4.57_76e-04, -3.96_73e-04,
							     4.57_76e-04, 1.00_71e-03, 9.15_53e-05, 4.88_28e-04, 1.15_97e-03,
							     7.32_42e-04, 9.46_04e-04, 1.80_05e-03, 1.83_11e-03, 8.85_01e-04,
							     4.27_25e-04, 4.88_28e-04, 7.32_42e-04, 1.09_86e-03, 2.10_57e-03]				)
							# fmt: on

							A__						=	self._load_datasamples(1				)
							A__						=	SpeechTaFeatureExtractor()
							A__						=	feature_extractor(lowercase					,					return_tensors="pt"				).input_values
							self.assertEquals(input_values.shape					,					(1, 93680)				)
							self.assertTrue(torch.allclose(input_values[0, :30]					,					lowercase					,					atol=1e-6				)				)


			def    UpperCamelCase						(			self				)     ->  List[str]:
							'''simple docstring'''






							A__						=	torch.tensor(
							    [-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
							     -3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
							     -3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
							     -3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998]				)
							# fmt: on

							A__						=	self._load_datasamples(1				)
							A__						=	SpeechTaFeatureExtractor()
							A__						=	feature_extractor(audio_target=lowercase					,					return_tensors="pt"				).input_values
							self.assertEquals(input_values.shape					,					(1, 366, 80)				)
							self.assertTrue(torch.allclose(input_values[0, 0, :30]					,					lowercase					,					atol=1e-4				)				)



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


__lowerCamelCase							=       logging.getLogger(__name__)

__lowerCamelCase							=       """pytorch_model.bin"""






@dataclasses.dataclass
class   UpperCAmelCase		:
   A__					:		str						     =     dataclasses.field(
       metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."} )
   A__					:		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		:
   A__					:		str						     =     dataclasses.field(metadata={"help": "A csv or a json file containing the training data."} )
   A__					:		str						     =     dataclasses.field(metadata={"help": "A csv or a json file containing the data to predict on."} )
   A__					:		Optional[str]						     =     dataclasses.field(
       default=A_       ,metadata={"help": "A csv or a json file containing the validation data."} )
   A__					:		Optional[str]						     =     dataclasses.field(
       default=A_       ,metadata={"help": "The name of the task to train on."}       ,)
   A__					:		Optional[List[str]]						     =     dataclasses.field(
       default=A_       ,metadata={"help": "The list of labels for the task."} )






@dataclasses.dataclass
class   UpperCAmelCase		:
   A__					:		str						     =     dataclasses.field(
       metadata={"help": "The output directory where the model predictions and checkpoints will be written."} )
   A__					:		Optional[str]						     =     dataclasses.field(
       default="accuracy"       ,metadata={"help": "The evaluation metric used for the task."} )
   A__					:		Optional[str]						     =     dataclasses.field(
       default="no"       ,metadata={
           "help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
       }       ,)
   A__					:		Optional[int]						     =     dataclasses.field(
       default=10       ,metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}       ,)
   A__					:		Optional[float]						     =     dataclasses.field(
       default=0.0       ,metadata={
           "help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
       }       ,)
   A__					:		Optional[bool]						     =     dataclasses.field(
       default=A_       ,metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."}       ,)
   A__					:		Optional[bool]						     =     dataclasses.field(
       default=A_       ,metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."}       ,)
   A__					:		Optional[bool]						     =     dataclasses.field(
       default=A_       ,metadata={"help": "Whether to fine-tune on labeled data after pseudo training."}       ,)
   A__					:		Optional[float]						     =     dataclasses.field(
       default=0.0       ,metadata={"help": "Confidence threshold for pseudo-labeled data filtering."}       ,)
   A__					:		Optional[int]						     =     dataclasses.field(
       default=1_00       ,metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}       ,)
   A__					:		Optional[int]						     =     dataclasses.field(
       default=A_       ,metadata={"help": "Random seed for initialization."}       ,)



def 			UpperCamelCase					(    __lowerCamelCase     : int				,					__lowerCamelCase     : Tuple				,					__lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : Tuple				,					__lowerCamelCase     : Dict				,					__lowerCamelCase     : Optional[int]    ):
       snake_case       :   Tuple							=      datasets.concatenate_datasets([infer_input, infer_output]				,					axis=1    )

       if args.do_filter_by_confidence:
              snake_case       :   Optional[int]							=      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
              snake_case       :   int							=      int(eval_result * len(__lowerCamelCase    )    )
              print(__lowerCamelCase    )
              snake_case       :   List[str]							=      dataset.sort("probability"				,					reverse=__lowerCamelCase    )
              snake_case       :   Tuple							=      dataset.select(range(__lowerCamelCase    )    )

       snake_case       :   List[Any]							=      dataset.remove_columns(["label", "probability"]    )
       snake_case       :   Any							=      dataset.rename_column("prediction"				,					"label"    )
       snake_case       :   str							=      dataset.map(lambda __lowerCamelCase    : {"label": idalabel[example["label"]]}    )
       snake_case       :   List[str]							=      dataset.shuffle(seed=args.seed    )

       snake_case       :   int							=      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 			UpperCamelCase					(    __lowerCamelCase     : Optional[int]				,					__lowerCamelCase     : str				,					__lowerCamelCase     : Optional[int]				,					__lowerCamelCase     : Tuple				,					**__lowerCamelCase     : List[Any]    ):
       snake_case       :   int							=      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()

       snake_case       :   Dict							=      STModelArguments(model_name_or_path=__lowerCamelCase    )
       snake_case       :   Tuple							=      STDataArguments(train_file=__lowerCamelCase				,					infer_file=__lowerCamelCase    )
       snake_case       :   str							=      STTrainingArguments(output_dir=__lowerCamelCase    )
       snake_case       :   int							=      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
       snake_case       :   List[str]							=      {}
       snake_case       :   Optional[int]							=      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
       snake_case       :   str							=      args.train_file
       snake_case       :   Tuple							=      args.infer_file

       if args.evaluation_strategy != IntervalStrategy.NO.value:
              assert args.eval_file is not None
              snake_case       :   Tuple							=      args.eval_file

       for key in data_files:
              snake_case       :   List[Any]							=      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:
                     snake_case       :   Union[str, Any]							=      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..."    )
       snake_case       :   List[Any]							=      f"""{args.output_dir}/self-train_iter-{{}}""".format
       snake_case       :   Optional[int]							=      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()

       snake_case       :   Dict							=      None
       snake_case       :   Union[str, Any]							=      None
       snake_case       :   Tuple							=      0
       snake_case       :   List[Any]							=      False
       # Show the progress bar
       snake_case       :   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    )    ):
              snake_case       :   str							=      data_dir_format(__lowerCamelCase    )
              assert os.path.exists(__lowerCamelCase    )

              # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
              # iteration > 0
              snake_case       :   Dict							=      os.path.join(__lowerCamelCase				,					"stage-1"    )
              snake_case       :   Optional[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}    )

              snake_case       :   int							=      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
                     snake_case       :   str							=      os.path.join(__lowerCamelCase				,					"best-checkpoint"    )
                     snake_case       :   Dict							=      os.path.join(__lowerCamelCase				,					"stage-2"    )
                     # Update arguments_dict
                     snake_case       :   List[str]							=      model_path
                     snake_case       :   Optional[Any]							=      data_files["train"]
                     snake_case       :   Optional[Any]							=      current_output_dir

                     snake_case       :   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    )

              snake_case       :   int							=      iteration
              snake_case       :   Tuple							=      data_dir_format(iteration + 1    )

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

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

              snake_case       :   str							=      os.path.join(__lowerCamelCase				,					f"""train_pseudo.{args.data_file_extension}"""    )

              if args.evaluation_strategy != IntervalStrategy.NO.value:
                     snake_case       :   List[Any]							=      eval_result

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

                            if early_stopping_patience_counter >= args.early_stopping_patience:
                                   snake_case       :   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"    )				,					)





