





'''simple docstring'''




from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor






class 				__snake_case     :





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_=13      ,       UpperCamelCase_=30      ,       UpperCamelCase_=2      ,       UpperCamelCase_=3      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=32      ,       UpperCamelCase_=2      ,       UpperCamelCase_=4      ,       UpperCamelCase_=37      ,       UpperCamelCase_="gelu"      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=10      ,       UpperCamelCase_=0.0_2      ,       UpperCamelCase_=3      ,       UpperCamelCase_=0.6      ,       UpperCamelCase_=None      ,       )      ->		Optional[Any]:
		snake_case__	       =   parent
		snake_case__	       =   batch_size
		snake_case__	       =   image_size
		snake_case__	       =   patch_size
		snake_case__	       =   num_channels
		snake_case__	       =   is_training
		snake_case__	       =   use_labels
		snake_case__	       =   hidden_size
		snake_case__	       =   num_hidden_layers
		snake_case__	       =   num_attention_heads
		snake_case__	       =   intermediate_size
		snake_case__	       =   hidden_act
		snake_case__	       =   hidden_dropout_prob
		snake_case__	       =   attention_probs_dropout_prob
		snake_case__	       =   type_sequence_label_size
		snake_case__	       =   initializer_range
		snake_case__	       =   mask_ratio
		snake_case__	       =   scope

		# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
		# (we add 1 for the [CLS] token)
		snake_case__	       =   (image_size // patch_size) ** 2
		snake_case__	       =   int(math.ceil((1 - mask_ratio) * (num_patches + 1)		)		)





	def   _snake_case   (		self		)      ->		List[str]:
		snake_case__	       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

		snake_case__	       =   None
		if self.use_labels:
			snake_case__	       =   ids_tensor([self.batch_size]      ,       self.type_sequence_label_size		)

		snake_case__	       =   self.get_config()

		return config, pixel_values, labels





	def   _snake_case   (		self		)      ->		Dict:
		return ViTMAEConfig(
		    image_size=self.image_size      ,       patch_size=self.patch_size      ,       num_channels=self.num_channels      ,       hidden_size=self.hidden_size      ,       num_hidden_layers=self.num_hidden_layers      ,       num_attention_heads=self.num_attention_heads      ,       intermediate_size=self.intermediate_size      ,       decoder_hidden_size=self.hidden_size      ,       decoder_num_hidden_layers=self.num_hidden_layers      ,       decoder_num_attention_heads=self.num_attention_heads      ,       decoder_intermediate_size=self.intermediate_size      ,       hidden_act=self.hidden_act      ,       hidden_dropout_prob=self.hidden_dropout_prob      ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,       is_decoder=UpperCamelCase_      ,       initializer_range=self.initializer_range      ,       mask_ratio=self.mask_ratio      ,       )





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Optional[Any]:
		snake_case__	       =   TFViTMAEModel(config=UpperCamelCase_		)
		snake_case__	       =   model(UpperCamelCase_      ,       training=UpperCamelCase_		)
		self.parent.assertEqual(result.last_hidden_state.shape      ,       (self.batch_size, self.seq_length, self.hidden_size)		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Optional[int]:
		snake_case__	       =   TFViTMAEForPreTraining(UpperCamelCase_		)
		snake_case__	       =   model(UpperCamelCase_      ,       training=UpperCamelCase_		)
		# expected sequence length = num_patches
		snake_case__	       =   (self.image_size // self.patch_size) ** 2
		snake_case__	       =   self.patch_size**2 * self.num_channels
		self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, num_patches, expected_num_channels)		)

		# test greyscale images
		snake_case__	       =   1
		snake_case__	       =   TFViTMAEForPreTraining(UpperCamelCase_		)

		snake_case__	       =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
		snake_case__	       =   model(UpperCamelCase_      ,       training=UpperCamelCase_		)
		snake_case__	       =   self.patch_size**2
		self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, num_patches, expected_num_channels)		)





	def   _snake_case   (		self		)      ->		Tuple:
		snake_case__	       =   self.prepare_config_and_inputs()
		((snake_case__)       ,  (snake_case__)       ,  (snake_case__))	       =   config_and_inputs
		snake_case__	       =   {'pixel_values': pixel_values}
		return config, inputs_dict






@require_tf
class 				__snake_case     (					__magic_name__       ,       __magic_name__       ,       unittest.TestCase							):
	__lowerCAmelCase       						=  (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
	__lowerCAmelCase       						=  {'''feature-extraction''': TFViTMAEModel} if is_tf_available() else {}

	__lowerCAmelCase       						=  False
	__lowerCAmelCase       						=  False
	__lowerCAmelCase       						=  False
	__lowerCAmelCase       						=  False





	def   _snake_case   (		self		)      ->		List[str]:
		snake_case__	       =   TFViTMAEModelTester(self		)
		snake_case__	       =   ConfigTester(self      ,       config_class=UpperCamelCase_      ,       has_text_modality=UpperCamelCase_      ,       hidden_size=37		)





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		self.config_tester.run_common_tests()





	@unittest.skip(reason='ViTMAE does not use inputs_embeds'		)
	def   _snake_case   (		self		)      ->		Dict:
		pass





	def   _snake_case   (		self		)      ->		str:
		snake_case__       ,  snake_case__	       =   self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			snake_case__	       =   model_class(UpperCamelCase_		)
			self.assertIsInstance(model.get_input_embeddings()      ,       (tf.keras.layers.Layer)		)
			snake_case__	       =   model.get_output_embeddings()
			self.assertTrue(x is None or isinstance(UpperCamelCase_      ,       tf.keras.layers.Layer		)		)





	def   _snake_case   (		self		)      ->		Any:
		snake_case__       ,  snake_case__	       =   self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			snake_case__	       =   model_class(UpperCamelCase_		)
			snake_case__	       =   inspect.signature(model.call		)
			# signature.parameters is an OrderedDict => so arg_names order is deterministic
			snake_case__	       =   [*signature.parameters.keys()]

			snake_case__	       =   ['pixel_values']
			self.assertListEqual(arg_names[:1]      ,       UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		List[str]:
		snake_case__	       =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		List[Any]:
		snake_case__	       =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_pretraining(*UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		Optional[Any]:
		# make the mask reproducible
		np.random.seed(2		)

		snake_case__       ,  snake_case__	       =   self.model_tester.prepare_config_and_inputs_for_common()

		snake_case__	       =   int((config.image_size // config.patch_size) ** 2		)
		snake_case__	       =   np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)

		for model_class in self.all_model_classes:
			snake_case__	       =   model_class(UpperCamelCase_		)
			snake_case__	       =   self._prepare_for_class(UpperCamelCase_      ,       UpperCamelCase_		)

			snake_case__	       =   model(UpperCamelCase_      ,       noise=UpperCamelCase_		)

			snake_case__	       =   copy.deepcopy(self._prepare_for_class(UpperCamelCase_      ,       UpperCamelCase_		)		)
			snake_case__	       =   model(**UpperCamelCase_      ,       noise=UpperCamelCase_		)
			snake_case__	       =   outputs_dict[0].numpy()
			snake_case__	       =   outputs_keywords[0].numpy()

			self.assertLess(np.sum(np.abs(output_dict - output_keywords		)		)      ,       1E-6		)





	def   _snake_case   (		self		)      ->		Optional[int]:
		# make the mask reproducible
		np.random.seed(2		)

		snake_case__       ,  snake_case__	       =   self.model_tester.prepare_config_and_inputs_for_common()

		snake_case__	       =   int((config.image_size // config.patch_size) ** 2		)
		snake_case__	       =   np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)

		def prepare_numpy_arrays(UpperCamelCase_		):
			snake_case__	       =   {}
			for k, v in inputs_dict.items():
				if tf.is_tensor(UpperCamelCase_		):
					snake_case__	       =   v.numpy()
				else:
					snake_case__	       =   np.array(UpperCamelCase_		)

			return inputs_np_dict

		for model_class in self.all_model_classes:
			snake_case__	       =   model_class(UpperCamelCase_		)

			snake_case__	       =   self._prepare_for_class(UpperCamelCase_      ,       UpperCamelCase_		)
			snake_case__	       =   prepare_numpy_arrays(UpperCamelCase_		)

			snake_case__	       =   model(UpperCamelCase_      ,       noise=UpperCamelCase_		)
			snake_case__	       =   model(**UpperCamelCase_      ,       noise=UpperCamelCase_		)
			self.assert_outputs_same(UpperCamelCase_      ,       UpperCamelCase_		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Dict:
		# make masks reproducible
		np.random.seed(2		)

		snake_case__	       =   int((tf_model.config.image_size // tf_model.config.patch_size) ** 2		)
		snake_case__	       =   np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)
		snake_case__	       =   tf.constant(UpperCamelCase_		)

		# Add `noise` argument.
		# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
		snake_case__	       =   tf_noise

		super().check_pt_tf_models(UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		int:
		# make mask reproducible
		np.random.seed(2		)

		snake_case__       ,  snake_case__	       =   self.model_tester.prepare_config_and_inputs_for_common()

		snake_case__	       =   {
		    module_member
		    for model_class in self.all_model_classes
		    for module in (import_module(model_class.__module__		),)
		    for module_member_name in dir(UpperCamelCase_		)
		    if module_member_name.endswith('MainLayer'		)
		    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
		    and module_member_name[: -len('MainLayer'		)] == model_class.__name__[: -len('Model'		)]
		    for module_member in (getattr(UpperCamelCase_      ,       UpperCamelCase_		),)
		    if isinstance(UpperCamelCase_      ,       UpperCamelCase_		)
		    and tf.keras.layers.Layer in module_member.__bases__
		    and getattr(UpperCamelCase_      ,       '_keras_serializable'      ,       UpperCamelCase_		)
		}

		snake_case__	       =   int((config.image_size // config.patch_size) ** 2		)
		snake_case__	       =   np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)
		snake_case__	       =   tf.convert_to_tensor(UpperCamelCase_		)
		inputs_dict.update({'noise': noise}		)

		for main_layer_class in tf_main_layer_classes:
			snake_case__	       =   main_layer_class(UpperCamelCase_		)

			snake_case__	       =   {
			    name: tf.keras.Input(tensor.shape[1:]      ,       dtype=tensor.dtype		) for name, tensor in inputs_dict.items()
			}

			snake_case__	       =   tf.keras.Model(UpperCamelCase_      ,       outputs=main_layer(UpperCamelCase_		)		)
			snake_case__	       =   model(UpperCamelCase_		)

			with tempfile.TemporaryDirectory() as tmpdirname:
				snake_case__	       =   os.path.join(UpperCamelCase_      ,       'keras_model.h5'		)
				model.save(UpperCamelCase_		)
				snake_case__	       =   tf.keras.models.load_model(
				    UpperCamelCase_      ,       custom_objects={main_layer_class.__name__: main_layer_class}		)
				assert isinstance(UpperCamelCase_      ,       tf.keras.Model		)
				snake_case__	       =   model(UpperCamelCase_		)
				self.assert_outputs_same(UpperCamelCase_      ,       UpperCamelCase_		)





	@slow
	def   _snake_case   (		self		)      ->		Optional[int]:
		# make mask reproducible
		np.random.seed(2		)

		snake_case__       ,  snake_case__	       =   self.model_tester.prepare_config_and_inputs_for_common()

		snake_case__	       =   int((config.image_size // config.patch_size) ** 2		)
		snake_case__	       =   np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)

		for model_class in self.all_model_classes:
			snake_case__	       =   model_class(UpperCamelCase_		)
			snake_case__	       =   self._prepare_for_class(UpperCamelCase_      ,       UpperCamelCase_		)
			snake_case__	       =   model(UpperCamelCase_      ,       noise=UpperCamelCase_		)

			if model_class.__name__ == "TFViTMAEModel":
				snake_case__	       =   outputs.last_hidden_state.numpy()
				snake_case__	       =   0
			else:
				snake_case__	       =   outputs.logits.numpy()
				snake_case__	       =   0

			with tempfile.TemporaryDirectory() as tmpdirname:
				model.save_pretrained(UpperCamelCase_      ,       saved_model=UpperCamelCase_		)
				snake_case__	       =   model_class.from_pretrained(UpperCamelCase_		)
				snake_case__	       =   model(UpperCamelCase_      ,       noise=UpperCamelCase_		)

				if model_class.__name__ == "TFViTMAEModel":
					snake_case__	       =   after_outputs['last_hidden_state'].numpy()
					snake_case__	       =   0
				else:
					snake_case__	       =   after_outputs['logits'].numpy()
					snake_case__	       =   0

				snake_case__	       =   np.amax(np.abs(out_a - out_a		)		)
				self.assertLessEqual(UpperCamelCase_      ,       1E-5		)





	def   _snake_case   (		self		)      ->		Dict:
		# make mask reproducible
		np.random.seed(2		)

		snake_case__       ,  snake_case__	       =   self.model_tester.prepare_config_and_inputs_for_common()

		snake_case__	       =   int((config.image_size // config.patch_size) ** 2		)
		snake_case__	       =   np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)

		for model_class in self.all_model_classes:
			snake_case__	       =   model_class(UpperCamelCase_		)
			snake_case__	       =   self._prepare_for_class(UpperCamelCase_      ,       UpperCamelCase_		)

			snake_case__	       =   model(UpperCamelCase_      ,       noise=UpperCamelCase_		)
			snake_case__	       =   model.get_config()
			# make sure that returned config is jsonifiable, which is required by keras
			json.dumps(UpperCamelCase_		)
			snake_case__	       =   model_class.from_config(model.get_config()		)
			# make sure it also accepts a normal config
			snake_case__	       =   model_class.from_config(model.config		)
			snake_case__	       =   new_model(UpperCamelCase_		)  # Build model
			new_model.set_weights(model.get_weights()		)
			snake_case__	       =   new_model(UpperCamelCase_      ,       noise=UpperCamelCase_		)

			self.assert_outputs_same(UpperCamelCase_      ,       UpperCamelCase_		)





	@unittest.skip(
	    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'		)
	def   _snake_case   (		self		)      ->		Optional[Any]:
		pass





	@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'		)
	def   _snake_case   (		self		)      ->		Union[str, Any]:
		pass





	@slow
	def   _snake_case   (		self		)      ->		Optional[Any]:
		snake_case__	       =   TFViTMAEModel.from_pretrained('google/vit-base-patch16-224'		)
		self.assertIsNotNone(UpperCamelCase_		)





def 						__lowerCamelCase			(	)   ->Union[str, Any]:
	snake_case__	       =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
	return image







@require_tf
@require_vision
class 				__snake_case     (					unittest.TestCase							):





	@cached_property
	def   _snake_case   (		self		)      ->		List[Any]:
		return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'		) if is_vision_available() else None





	@slow
	def   _snake_case   (		self		)      ->		str:
		# make random mask reproducible across the PT and TF model
		np.random.seed(2		)

		snake_case__	       =   TFViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'		)

		snake_case__	       =   self.default_image_processor
		snake_case__	       =   prepare_img()
		snake_case__	       =   image_processor(images=UpperCamelCase_      ,       return_tensors='tf'		)

		# prepare a noise vector that will be also used for testing the TF model
		# (this way we can ensure that the PT and TF models operate on the same inputs)
		snake_case__	       =   ViTMAEConfig()
		snake_case__	       =   int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2		)
		snake_case__	       =   np.random.uniform(size=(1, num_patches)		)

		# forward pass
		snake_case__	       =   model(**UpperCamelCase_      ,       noise=UpperCamelCase_		)

		# verify the logits
		snake_case__	       =   tf.convert_to_tensor([1, 196, 768]		)
		self.assertEqual(outputs.logits.shape      ,       UpperCamelCase_		)

		snake_case__	       =   tf.convert_to_tensor(
		    [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]		)

		tf.debugging.assert_near(outputs.logits[0, :3, :3]      ,       UpperCamelCase_      ,       atol=1E-4		)










'''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


a__					:			str          =     logging.getLogger(__name__)

a__					:			Tuple          =     '''pytorch_model.bin'''






@dataclasses.dataclass
class 				__snake_case     :
	__lowerCAmelCase       						=  dataclasses.field(
	    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models.'''}							)
	__lowerCAmelCase       						=  dataclasses.field(
	    default=__magic_name__       ,       metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co.'''}       ,       )






@dataclasses.dataclass
class 				__snake_case     :
	__lowerCAmelCase       						=  dataclasses.field(metadata={'''help''': '''A csv or a json file containing the training data.'''}							)
	__lowerCAmelCase       						=  dataclasses.field(metadata={'''help''': '''A csv or a json file containing the data to predict on.'''}							)
	__lowerCAmelCase       						=  dataclasses.field(
	    default=__magic_name__       ,       metadata={'''help''': '''A csv or a json file containing the validation data.'''}							)
	__lowerCAmelCase       						=  dataclasses.field(
	    default=__magic_name__       ,       metadata={'''help''': '''The name of the task to train on.'''}       ,       )
	__lowerCAmelCase       						=  dataclasses.field(
	    default=__magic_name__       ,       metadata={'''help''': '''The list of labels for the task.'''}							)






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





def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->int:
	snake_case__	       =   datasets.concatenate_datasets([infer_input, infer_output]					,				axis=1							)

	if args.do_filter_by_confidence:
		snake_case__	       =   dataset.filter(lambda UpperCAmelCase_							: 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(eval_result * len(UpperCAmelCase_							)							)
		print(UpperCAmelCase_							)
		snake_case__	       =   dataset.sort('probability'					,				reverse=UpperCAmelCase_							)
		snake_case__	       =   dataset.select(range(UpperCAmelCase_							)							)

	snake_case__	       =   dataset.remove_columns(['label', 'probability']							)
	snake_case__	       =   dataset.rename_column('prediction'					,				'label'							)
	snake_case__	       =   dataset.map(lambda UpperCAmelCase_							: {"label": idalabel[example["label"]]}							)
	snake_case__	       =   dataset.shuffle(seed=args.seed							)

	snake_case__	       =   os.path.join(UpperCAmelCase_					,				f'''train_pseudo.{args.data_file_extension}'''							)
	if args.data_file_extension == "csv":
		dataset.to_csv(UpperCAmelCase_					,				index=UpperCAmelCase_							)
	else:
		dataset.to_json(UpperCAmelCase_							)







def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				**UpperCAmelCase_							)   ->Tuple:
	snake_case__	       =   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__	       =   STModelArguments(model_name_or_path=UpperCAmelCase_							)
	snake_case__	       =   STDataArguments(train_file=UpperCAmelCase_					,				infer_file=UpperCAmelCase_							)
	snake_case__	       =   STTrainingArguments(output_dir=UpperCAmelCase_							)
	snake_case__	       =   argparse.Namespace()

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

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

    # Sanity checks
	snake_case__	       =   {}
	snake_case__	       =   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__	       =   args.train_file
	snake_case__	       =   args.infer_file

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

	for key in data_files:
		snake_case__	       =   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__	       =   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__	       =   f'''{args.output_dir}/self-train_iter-{{}}'''.format
	snake_case__	       =   data_dir_format(0							)

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

	snake_case__	       =   None
	snake_case__	       =   None
	snake_case__	       =   0
	snake_case__	       =   False
	# Show the progress bar
	snake_case__	       =   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__	       =   data_dir_format(UpperCAmelCase_							)
		assert os.path.exists(UpperCAmelCase_							)

		# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
		# iteration > 0
		snake_case__	       =   os.path.join(UpperCAmelCase_					,				'stage-1'							)
		snake_case__	       =   {
		    '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(UpperCAmelCase_					,				UpperCAmelCase_							):
				arguments_dict.update({key: value}							)

		snake_case__	       =   os.path.join(UpperCAmelCase_					,				'best-checkpoint'					,				UpperCAmelCase_							)
		if os.path.exists(UpperCAmelCase_							):
			logger.info(
			    'Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.'					,				UpperCAmelCase_					,				UpperCAmelCase_					,				)
		else:
			logger.info('***** Running self-training: iteration: %d, stage: 1 *****'					,				UpperCAmelCase_							)
			finetune(**UpperCAmelCase_							)
			accelerator.wait_for_everyone()
			assert os.path.exists(UpperCAmelCase_							)
			logger.info('Self-training job completed: iteration: %d, stage: 1.'					,				UpperCAmelCase_							)

		if iteration > 0 and args.finetune_on_labeled_data:
			# Stage 2 (optional): fine-tuning on the original labeled data
			snake_case__	       =   os.path.join(UpperCAmelCase_					,				'best-checkpoint'							)
			snake_case__	       =   os.path.join(UpperCAmelCase_					,				'stage-2'							)
			# Update arguments_dict
			snake_case__	       =   model_path
			snake_case__	       =   data_files['train']
			snake_case__	       =   current_output_dir

			snake_case__	       =   os.path.join(UpperCAmelCase_					,				'best-checkpoint'					,				UpperCAmelCase_							)
			if os.path.exists(UpperCAmelCase_							):
				logger.info(
				    'Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.'					,				UpperCAmelCase_					,				UpperCAmelCase_					,				)
			else:
				logger.info('***** Running self-training: iteration: %d, stage: 2 *****'					,				UpperCAmelCase_							)
				finetune(**UpperCAmelCase_							)
				accelerator.wait_for_everyone()
				assert os.path.exists(UpperCAmelCase_							)
				logger.info('Self-training job completed: iteration: %d, stage: 2.'					,				UpperCAmelCase_							)

		snake_case__	       =   iteration
		snake_case__	       =   data_dir_format(iteration + 1							)

		snake_case__	       =   AutoConfig.from_pretrained(os.path.join(UpperCAmelCase_					,				'best-checkpoint'							)							)
		snake_case__	       =   config.idalabel
		snake_case__	       =   os.path.join(UpperCAmelCase_					,				'eval_results_best-checkpoint.json'							)
		snake_case__	       =   os.path.join(UpperCAmelCase_					,				'test_results_best-checkpoint.json'							)
		assert os.path.exists(UpperCAmelCase_							)

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

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

		snake_case__	       =   os.path.join(UpperCAmelCase_					,				f'''train_pseudo.{args.data_file_extension}'''							)

		if args.evaluation_strategy != IntervalStrategy.NO.value:
			snake_case__	       =   eval_result

			if best_iteration is None:
				snake_case__	       =   new_iteration
				snake_case__	       =   new_eval_result
			else:
				if new_eval_result - best_eval_result > args.early_stopping_threshold:
					snake_case__	       =   new_iteration
					snake_case__	       =   new_eval_result
					snake_case__	       =   0
				else:
					if new_eval_result == best_eval_result:
						snake_case__	       =   new_iteration
						snake_case__	       =   new_eval_result
					early_stopping_patience_counter += 1

				if early_stopping_patience_counter >= args.early_stopping_patience:
					snake_case__	       =   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'					,				UpperCAmelCase_							)
		logger.info('Best evaluation result: %s = %f'					,				args.eval_metric					,				UpperCAmelCase_							)
		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
			shutil.copy(
			    os.path.join(UpperCAmelCase_					,				f'''eval_results_iter-{iteration}.json'''							)					,				os.path.join(UpperCAmelCase_					,				'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					,				UpperCAmelCase_							)
		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
			shutil.copy(
			    os.path.join(UpperCAmelCase_					,				f'''eval_results_iter-{args.max_selftrain_iterations - 1}.json'''							)					,				os.path.join(UpperCAmelCase_					,				'eval_results_best-iteration.json'							)					,				)



