






"""simple docstring"""
import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
							    MODEL_MAPPING,
							    LevitForImageClassification,
							    LevitForImageClassificationWithTeacher,
							    LevitModel,
							)
							from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import LevitImageProcessor



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	def  lowerCamelCase(self				):
		A_		:    Optional[Any]						     = self.config_class(**self.inputs_dict				)
		self.parent.assertTrue(hasattr(lowerCAmelCase_  ,  """hidden_sizes"""				)				)
		self.parent.assertTrue(hasattr(lowerCAmelCase_  ,  """num_attention_heads"""				)				)






class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	def __init__(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_=13  ,  lowerCAmelCase_=64  ,  lowerCAmelCase_=3  ,  lowerCAmelCase_=3  ,  lowerCAmelCase_=2  ,  lowerCAmelCase_=1  ,  lowerCAmelCase_=16  ,  lowerCAmelCase_=[128, 256, 384]  ,  lowerCAmelCase_=[4, 6, 8]  ,  lowerCAmelCase_=[2, 3, 4]  ,  lowerCAmelCase_=[16, 16, 16]  ,  lowerCAmelCase_=0  ,  lowerCAmelCase_=[2, 2, 2]  ,  lowerCAmelCase_=[2, 2, 2]  ,  lowerCAmelCase_=0.02  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_=2  ,  ):
		A_		:    Dict						     = parent
		A_		:    Dict						     = batch_size
		A_		:    List[str]						     = image_size
		A_		:    Tuple						     = num_channels
		A_		:    List[str]						     = kernel_size
		A_		:    Union[str, Any]						     = stride
		A_		:    Optional[int]						     = padding
		A_		:    Dict						     = hidden_sizes
		A_		:    Dict						     = num_attention_heads
		A_		:    str						     = depths
		A_		:    Any						     = key_dim
		A_		:    Optional[int]						     = drop_path_rate
		A_		:    List[Any]						     = patch_size
		A_		:    List[Any]						     = attention_ratio
		A_		:    Union[str, Any]						     = mlp_ratio
		A_		:    List[str]						     = initializer_range
		A_		:    Optional[Any]						     = [
		    ["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
		    ["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
		]
		A_		:    Optional[int]						     = is_training
		A_		:    Dict						     = use_labels
		A_		:    List[str]						     = num_labels
		A_		:    Any						     = initializer_range





	def  lowerCamelCase(self				):
		A_		:    int						     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

		A_		:    int						     = None
		if self.use_labels:
			A_		:    Tuple						     = ids_tensor([self.batch_size]  ,  self.num_labels				)

		A_		:    List[Any]						     = self.get_config()
		return config, pixel_values, labels





	def  lowerCamelCase(self				):
		return LevitConfig(
		    image_size=self.image_size  ,  num_channels=self.num_channels  ,  kernel_size=self.kernel_size  ,  stride=self.stride  ,  padding=self.padding  ,  patch_size=self.patch_size  ,  hidden_sizes=self.hidden_sizes  ,  num_attention_heads=self.num_attention_heads  ,  depths=self.depths  ,  key_dim=self.key_dim  ,  drop_path_rate=self.drop_path_rate  ,  mlp_ratio=self.mlp_ratio  ,  attention_ratio=self.attention_ratio  ,  initializer_range=self.initializer_range  ,  down_ops=self.down_ops  ,  )





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    str						     = LevitModel(config=lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    Dict						     = model(lowerCAmelCase_				)
		A_		:    Union[str, Any]						     = (self.image_size, self.image_size)
		A_     ,  A_		:    List[str]						     = image_size[0], image_size[1]
		for _ in range(4				):
			A_		:    str						     = floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1				)
			A_		:    List[Any]						     = floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1				)
		self.parent.assertEqual(
		    result.last_hidden_state.shape  ,  (self.batch_size, ceil(height / 4				) * ceil(width / 4				), self.hidden_sizes[-1])  ,  )





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    int						     = self.num_labels
		A_		:    int						     = LevitForImageClassification(lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    Optional[Any]						     = model(lowerCAmelCase_  ,  labels=lowerCAmelCase_				)
		self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.num_labels)				)





	def  lowerCamelCase(self				):
		A_		:    Tuple						     = self.prepare_config_and_inputs()
		A_     ,  A_     ,  A_		:    List[Any]						     = config_and_inputs
		A_		:    List[str]						     = {"""pixel_values""": pixel_values}
		return config, inputs_dict






@require_torch
class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE							,							unittest.TestCase    ):





	"""simple docstring"""




	_A							:					List[Any] 			=					(
	    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
	    if is_torch_available()
	    else ()
	)
	_A							:					Tuple 			=					(
	    {
	        """feature-extraction""": LevitModel,
	        """image-classification""": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
	    }
	    if is_torch_available()
	    else {}
	)

	_A							:					Optional[Any] 			=					False
	_A							:					List[str] 			=					False
	_A							:					List[str] 			=					False
	_A							:					Dict 			=					False
	_A							:					Dict 			=					False





	def  lowerCamelCase(self				):
		A_		:    Any						     = LevitModelTester(self				)
		A_		:    Dict						     = ConfigTester(self  ,  config_class=lowerCAmelCase_  ,  has_text_modality=lowerCAmelCase_  ,  hidden_size=37				)





	def  lowerCamelCase(self				):
		self.create_and_test_config_common_properties()
		self.config_tester.create_and_test_config_to_json_string()
		self.config_tester.create_and_test_config_to_json_file()
		self.config_tester.create_and_test_config_from_and_save_pretrained()
		self.config_tester.create_and_test_config_with_num_labels()
		self.config_tester.check_config_can_be_init_without_params()
		self.config_tester.check_config_arguments_init()





	def  lowerCamelCase(self				):
		return





	@unittest.skip(reason="""Levit does not use inputs_embeds"""				)
	def  lowerCamelCase(self				):
		pass





	@unittest.skip(reason="""Levit does not support input and output embeddings"""				)
	def  lowerCamelCase(self				):
		pass





	@unittest.skip(reason="""Levit does not output attentions"""				)
	def  lowerCamelCase(self				):
		pass





	def  lowerCamelCase(self				):
		A_     ,  A_		:    List[Any]						     = self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			A_		:    Dict						     = model_class(lowerCAmelCase_				)
			A_		:    Union[str, Any]						     = inspect.signature(model.forward				)
			# signature.parameters is an OrderedDict => so arg_names order is deterministic
			A_		:    Optional[int]						     = [*signature.parameters.keys()]

			A_		:    List[str]						     = ["""pixel_values"""]
			self.assertListEqual(arg_names[:1]  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		def check_hidden_states_output(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
			A_		:    str						     = model_class(lowerCAmelCase_				)
			model.to(lowerCAmelCase_				)
			model.eval()

			with torch.no_grad():
				A_		:    Any						     = model(**self._prepare_for_class(lowerCAmelCase_  ,  lowerCAmelCase_				)				)

			A_		:    Optional[int]						     = outputs.hidden_states

			A_		:    List[Any]						     = len(self.model_tester.depths				) + 1
			self.assertEqual(len(lowerCAmelCase_				)  ,  lowerCAmelCase_				)

			A_		:    List[str]						     = (self.model_tester.image_size, self.model_tester.image_size)
			A_     ,  A_		:    Optional[Any]						     = image_size[0], image_size[1]
			for _ in range(4				):
				A_		:    List[Any]						     = floor(
				    (
				        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
				        / self.model_tester.stride
				    )
				    + 1				)
				A_		:    str						     = floor(
				    (
				        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
				        / self.model_tester.stride
				    )
				    + 1				)
			# verify the first hidden states (first block)
			self.assertListEqual(
			    list(hidden_states[0].shape[-2:]				)  ,  [
			        height * width,
			        self.model_tester.hidden_sizes[0],
			    ]  ,  )

		A_     ,  A_		:    str						     = self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			A_		:    List[Any]						     = True
			check_hidden_states_output(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				)

			# check that output_hidden_states also work using config
			del inputs_dict["output_hidden_states"]
			A_		:    Dict						     = True

			check_hidden_states_output(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				)





	@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""				)
	def  lowerCamelCase(self				):
		pass





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_=False				):
		A_		:    Optional[int]						     = super()._prepare_for_class(lowerCAmelCase_  ,  lowerCAmelCase_  ,  return_labels=lowerCAmelCase_				)

		if return_labels:
			if model_class.__name__ == "LevitForImageClassificationWithTeacher":
				del inputs_dict["labels"]

		return inputs_dict





	def  lowerCamelCase(self				):
		A_		:    List[str]						     = self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    Any						     = self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		if not self.model_tester.is_training:
			return

		A_     ,  A_		:    Optional[int]						     = self.model_tester.prepare_config_and_inputs_for_common()
		A_		:    Dict						     = True

		for model_class in self.all_model_classes:
			# LevitForImageClassificationWithTeacher supports inference-only
			if (
			    model_class in get_values(lowerCAmelCase_				)
			    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
			):
				continue
			A_		:    int						     = model_class(lowerCAmelCase_				)
			model.to(lowerCAmelCase_				)
			model.train()
			A_		:    Dict						     = self._prepare_for_class(lowerCAmelCase_  ,  lowerCAmelCase_  ,  return_labels=lowerCAmelCase_				)
			A_		:    Union[str, Any]						     = model(**lowerCAmelCase_				).loss
			loss.backward()





	def  lowerCamelCase(self				):
		A_     ,  A_		:    List[str]						     = self.model_tester.prepare_config_and_inputs_for_common()
		if not self.model_tester.is_training:
			return

		A_		:    List[Any]						     = False
		A_		:    List[Any]						     = True

		for model_class in self.all_model_classes:
			if model_class in get_values(lowerCAmelCase_				) or not model_class.supports_gradient_checkpointing:
				continue
			# LevitForImageClassificationWithTeacher supports inference-only
			if model_class.__name__ == "LevitForImageClassificationWithTeacher":
				continue
			A_		:    str						     = model_class(lowerCAmelCase_				)
			model.gradient_checkpointing_enable()
			model.to(lowerCAmelCase_				)
			model.train()
			A_		:    Union[str, Any]						     = self._prepare_for_class(lowerCAmelCase_  ,  lowerCAmelCase_  ,  return_labels=lowerCAmelCase_				)
			A_		:    Optional[int]						     = model(**lowerCAmelCase_				).loss
			loss.backward()





	def  lowerCamelCase(self				):
		A_     ,  A_		:    Dict						     = self.model_tester.prepare_config_and_inputs_for_common()

		A_		:    Optional[int]						     = [
		    {"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
		    {"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
		    {"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
		]

		for model_class in self.all_model_classes:
			if (
			    model_class
			    not in [
			        *get_values(lowerCAmelCase_				),
			    ]
			    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
			):
				continue

			for problem_type in problem_types:
				with self.subTest(msg=f"""Testing {model_class} with {problem_type["title"]}"""				):
					A_		:    str						     = problem_type["""title"""]
					A_		:    Optional[int]						     = problem_type["""num_labels"""]

					A_		:    str						     = model_class(lowerCAmelCase_				)
					model.to(lowerCAmelCase_				)
					model.train()

					A_		:    int						     = self._prepare_for_class(lowerCAmelCase_  ,  lowerCAmelCase_  ,  return_labels=lowerCAmelCase_				)

					if problem_type["num_labels"] > 1:
						A_		:    List[str]						     = inputs["""labels"""].unsqueeze(1				).repeat(1  ,  problem_type["""num_labels"""]				)

					A_		:    Union[str, Any]						     = inputs["""labels"""].to(problem_type["""dtype"""]				)

					# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
					# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
					# they have the same size." which is a symptom something in wrong for the regression problem.
					# See https://github.com/huggingface/transformers/issues/11780
					with warnings.catch_warnings(record=lowerCAmelCase_				) as warning_list:
						A_		:    List[str]						     = model(**lowerCAmelCase_				).loss
					for w in warning_list:
						if "Using a target size that is different to the input size" in str(w.message				):
							raise ValueError(
							    f"""Something is going wrong in the regression problem: intercepted {w.message}"""				)

					loss.backward()





	@slow
	def  lowerCamelCase(self				):
		for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			A_		:    Any						     = LevitModel.from_pretrained(lowerCAmelCase_				)
			self.assertIsNotNone(lowerCAmelCase_				)






def       __UpperCamelCase						(		):
	A_		:    str						     = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
	return image




@require_torch
@require_vision
class   SCREAMING_SNAKE_CASE	(					unittest.TestCase    ):





	"""simple docstring"""




	@cached_property
	def  lowerCamelCase(self				):
		return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)





	@slow
	def  lowerCamelCase(self				):
		A_		:    Optional[Any]						     = LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(
		    lowerCAmelCase_				)

		A_		:    str						     = self.default_image_processor
		A_		:    Tuple						     = prepare_img()
		A_		:    Tuple						     = image_processor(images=lowerCAmelCase_  ,  return_tensors="""pt"""				).to(lowerCAmelCase_				)

		# forward pass
		with torch.no_grad():
			A_		:    Any						     = model(**lowerCAmelCase_				)

		# verify the logits
		A_		:    Union[str, Any]						     = torch.Size((1, 1000)				)
		self.assertEqual(outputs.logits.shape  ,  lowerCAmelCase_				)

		A_		:    Optional[Any]						     = torch.tensor([1.0448, -0.3745, -1.8317]				).to(lowerCAmelCase_				)

		self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,  lowerCAmelCase_  ,  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


_lowerCAmelCase       =				logging.getLogger(__name__)

_lowerCAmelCase       =				"pytorch_model.bin"



@dataclasses.dataclass
class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




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






@dataclasses.dataclass
class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	_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=_SCREAMING_SNAKE_CASE							,							metadata={"""help""": """A csv or a json file containing the validation data."""}    )
	_A							:					Optional[str] 			=					dataclasses.field(
	    default=_SCREAMING_SNAKE_CASE							,							metadata={"""help""": """The name of the task to train on."""}							,							)
	_A							:					Optional[List[str]] 			=					dataclasses.field(
	    default=_SCREAMING_SNAKE_CASE							,							metadata={"""help""": """The list of labels for the task."""}    )






@dataclasses.dataclass
class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	_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=_SCREAMING_SNAKE_CASE							,							metadata={"""help""": """Whether to filter the pseudo-labeled data based on the confidence score."""}							,							)
	_A							:					Optional[bool] 			=					dataclasses.field(
	    default=_SCREAMING_SNAKE_CASE							,							metadata={"""help""": """Whether to filter the pseudo-labeled data based on the validation performance."""}							,							)
	_A							:					Optional[bool] 			=					dataclasses.field(
	    default=_SCREAMING_SNAKE_CASE							,							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=100							,							metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""}							,							)
	_A							:					Optional[int] 			=					dataclasses.field(
	    default=_SCREAMING_SNAKE_CASE							,							metadata={"""help""": """Random seed for initialization."""}							,							)






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__       ):
	A_		:    Tuple						     = datasets.concatenate_datasets([infer_input, infer_output] , axis=1       )

	if args.do_filter_by_confidence:
		A_		:    Tuple						     = dataset.filter(lambda snake_case__       : example["probability"] > args.confidence_threshold       )

	if args.do_filter_by_val_performance:
		assert eval_result >= 0.0 and eval_result <= 1.0
		A_		:    List[Any]						     = int(eval_result * len(snake_case__       )       )
		print(snake_case__       )
		A_		:    int						     = dataset.sort("""probability""" , reverse=snake_case__       )
		A_		:    Union[str, Any]						     = dataset.select(range(snake_case__       )       )

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

	A_		:    Dict						     = os.path.join(snake_case__ , F"""train_pseudo.{args.data_file_extension}"""       )
	if args.data_file_extension == "csv":
		dataset.to_csv(snake_case__ , index=snake_case__       )
	else:
		dataset.to_json(snake_case__       )






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ , snake_case__ , **snake_case__       ):
	A_		:    Tuple						     = 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_		:    Optional[int]						     = STModelArguments(model_name_or_path=snake_case__       )
	A_		:    int						     = STDataArguments(train_file=snake_case__ , infer_file=snake_case__       )
	A_		:    Dict						     = STTrainingArguments(output_dir=snake_case__       )
	A_		:    Any						     = argparse.Namespace()

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

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

    # Sanity checks
	A_		:    Any						     = {}
	A_		:    List[Any]						     = 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_		:    Any						     = args.train_file
	A_		:    Optional[Any]						     = 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_		:    Union[str, 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:
			A_		:    str						     = 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_		:    Tuple						     = F"""{args.output_dir}/self-train_iter-{{}}""".format
	A_		:    Optional[Any]						     = data_dir_format(0       )

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

	A_		:    Dict						     = None
	A_		:    Optional[int]						     = None
	A_		:    List[Any]						     = 0
	A_		:    List[Any]						     = False
	# Show the progress bar
	A_		:    Optional[int]						     = 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_		:    int						     = data_dir_format(snake_case__       )
		assert os.path.exists(snake_case__       )

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

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

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

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

		A_		:    Optional[Any]						     = iteration
		A_		:    Optional[Any]						     = data_dir_format(iteration + 1       )

		A_		:    Optional[int]						     = AutoConfig.from_pretrained(os.path.join(snake_case__ , """best-checkpoint"""       )       )
		A_		:    str						     = config.idalabel
		A_		:    List[str]						     = os.path.join(snake_case__ , """eval_results_best-checkpoint.json"""       )
		A_		:    Dict						     = os.path.join(snake_case__ , """test_results_best-checkpoint.json"""       )
		assert os.path.exists(snake_case__       )

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

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

		A_		:    str						     = os.path.join(snake_case__ , F"""train_pseudo.{args.data_file_extension}"""       )

		if args.evaluation_strategy != IntervalStrategy.NO.value:
			A_		:    List[str]						     = eval_result

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

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

