




'''simple docstring'''


import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case : Optional[int]  				=						logging.get_logger(__name__)
def 						snake_case_       (UpperCamelCase			:    int					,		UpperCamelCase			:    Tuple ):





							'''simple docstring'''


							_a      	=							[]
							for i in range(encoder_config.num_hidden_layers ):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append(
														    (f'encoder.deit.blocks.{i}.norm1.weight', f'encoder.encoder.layer.{i}.layernorm_before.weight') )
														rename_keys.append((f'encoder.deit.blocks.{i}.norm1.bias', f'encoder.encoder.layer.{i}.layernorm_before.bias') )
														rename_keys.append(
														    (f'encoder.deit.blocks.{i}.attn.proj.weight', f'encoder.encoder.layer.{i}.attention.output.dense.weight') )
														rename_keys.append(
														    (f'encoder.deit.blocks.{i}.attn.proj.bias', f'encoder.encoder.layer.{i}.attention.output.dense.bias') )
														rename_keys.append(
														    (f'encoder.deit.blocks.{i}.norm2.weight', f'encoder.encoder.layer.{i}.layernorm_after.weight') )
														rename_keys.append((f'encoder.deit.blocks.{i}.norm2.bias', f'encoder.encoder.layer.{i}.layernorm_after.bias') )
														rename_keys.append(
														    (f'encoder.deit.blocks.{i}.mlp.fc1.weight', f'encoder.encoder.layer.{i}.intermediate.dense.weight') )
														rename_keys.append(
														    (f'encoder.deit.blocks.{i}.mlp.fc1.bias', f'encoder.encoder.layer.{i}.intermediate.dense.bias') )
														rename_keys.append(
														    (f'encoder.deit.blocks.{i}.mlp.fc2.weight', f'encoder.encoder.layer.{i}.output.dense.weight') )
														rename_keys.append((f'encoder.deit.blocks.{i}.mlp.fc2.bias', f'encoder.encoder.layer.{i}.output.dense.bias') )

							# cls token, position embeddings and patch embeddings of encoder
							rename_keys.extend(
							    [
							        ('''encoder.deit.cls_token''', '''encoder.embeddings.cls_token'''),
							        ('''encoder.deit.pos_embed''', '''encoder.embeddings.position_embeddings'''),
							        ('''encoder.deit.patch_embed.proj.weight''', '''encoder.embeddings.patch_embeddings.projection.weight'''),
							        ('''encoder.deit.patch_embed.proj.bias''', '''encoder.embeddings.patch_embeddings.projection.bias'''),
							        ('''encoder.deit.norm.weight''', '''encoder.layernorm.weight'''),
							        ('''encoder.deit.norm.bias''', '''encoder.layernorm.bias'''),
							    ] )

							return rename_keys
def 						snake_case_       (UpperCamelCase			:    Dict					,		UpperCamelCase			:    Tuple ):





							'''simple docstring'''


							for i in range(encoder_config.num_hidden_layers ):
														# queries, keys and values (only weights, no biases)
														_a      	=							state_dict.pop(f'encoder.deit.blocks.{i}.attn.qkv.weight' )

														_a      	=							in_proj_weight[
														    : encoder_config.hidden_size, :
														]
														_a      	=							in_proj_weight[
														    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
														]
														_a      	=							in_proj_weight[
														    -encoder_config.hidden_size :, :
														]
def 						snake_case_       (UpperCamelCase			:    List[str]					,		UpperCamelCase			:    int					,		UpperCamelCase			:    List[Any] ):





							'''simple docstring'''


							_a      	=							dct.pop(UpperCamelCase )
							_a      	=							val
def 						snake_case_       (UpperCamelCase			:    Dict ):





							'''simple docstring'''


							if "handwritten" in checkpoint_url:
														_a      	=							"""https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg"""  # industry
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
							elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
														_a      	=							"""https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"""
							_a      	=							Image.open(requests.get(UpperCamelCase					,		stream=UpperCamelCase ).raw ).convert('''RGB''' )
							return im
@torch.no_grad()
def 						snake_case_       (UpperCamelCase			:    Optional[int]					,		UpperCamelCase			:    str ):





							'''simple docstring'''


							_a      	=							ViTConfig(image_size=384					,		qkv_bias=UpperCamelCase )
							_a      	=							TrOCRConfig()

							# size of the architecture
							if "base" in checkpoint_url:
														_a      	=							768
							elif "large" in checkpoint_url:
														# use ViT-large encoder
														_a      	=							1024
														_a      	=							4096
														_a      	=							24
														_a      	=							16
														_a      	=							1024
							else:
														raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL''' )

							# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
							if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
														_a      	=							False
														_a      	=							"""relu"""
														_a      	=							1024
														_a      	=							True
														_a      	=							False
														_a      	=							False

							# load HuggingFace model
							_a      	=							ViTModel(UpperCamelCase					,		add_pooling_layer=UpperCamelCase )
							_a      	=							TrOCRForCausalLM(UpperCamelCase )
							_a      	=							VisionEncoderDecoderModel(encoder=UpperCamelCase					,		decoder=UpperCamelCase )
							model.eval()

							# load state_dict of original model, rename some keys
							_a      	=							torch.hub.load_state_dict_from_url(UpperCamelCase					,		map_location='''cpu'''					,		check_hash=UpperCamelCase )["""model"""]

							_a      	=							create_rename_keys(UpperCamelCase					,		UpperCamelCase )
							for src, dest in rename_keys:
														rename_key(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase )
							read_in_q_k_v(UpperCamelCase					,		UpperCamelCase )

							# remove parameters we don't need
							del state_dict["encoder.deit.head.weight"]
							del state_dict["encoder.deit.head.bias"]
							del state_dict["decoder.version"]

							# add prefix to decoder keys
							for key, val in state_dict.copy().items():
														_a      	=							state_dict.pop(UpperCamelCase )
														if key.startswith('''decoder''' ) and "output_projection" not in key:
																					_a      	=							val
														else:
																					_a      	=							val

    # load state dict
							model.load_state_dict(UpperCamelCase )

							# Check outputs on an image
							_a      	=							ViTImageProcessor(size=encoder_config.image_size )
							_a      	=							RobertaTokenizer.from_pretrained('''roberta-large''' )
							_a      	=							TrOCRProcessor(UpperCamelCase					,		UpperCamelCase )

							_a      	=							processor(images=prepare_img(UpperCamelCase )					,		return_tensors='''pt''' ).pixel_values

							# verify logits
							_a      	=							torch.tensor([[model.config.decoder.decoder_start_token_id]] )
							_a      	=							model(pixel_values=UpperCamelCase					,		decoder_input_ids=UpperCamelCase )
							_a      	=							outputs.logits

							_a      	=							torch.Size([1, 1, 5_0265] )
							if "trocr-base-handwritten" in checkpoint_url:
														_a      	=							torch.tensor(
														    [-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311] )
							elif "trocr-large-handwritten" in checkpoint_url:
														_a      	=							torch.tensor(
														    [-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702, 5.6113, 2.0170] )
							elif "trocr-base-printed" in checkpoint_url:
														_a      	=							torch.tensor(
														    [-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210] )
							elif "trocr-large-printed" in checkpoint_url:
														_a      	=							torch.tensor(
														    [-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466, -0.3081, -0.8106, -1.7535] )

							if "stage1" not in checkpoint_url:
														assert logits.shape == expected_shape, "Shape of logits not as expected"
														assert torch.allclose(logits[0, 0, :10]					,		UpperCamelCase					,		atol=1e-3 ), "First elements of logits not as expected"

							Path(UpperCamelCase ).mkdir(exist_ok=UpperCamelCase )
							print(f'Saving model to {pytorch_dump_folder_path}' )
							model.save_pretrained(UpperCamelCase )
							print(f'Saving processor to {pytorch_dump_folder_path}' )
							processor.save_pretrained(UpperCamelCase )


if __name__ == "__main__":
				_snake_case : Optional[Any]  				=						argparse.ArgumentParser()

				parser.add_argument(
				    '--checkpoint_url',
				    default='https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt',
				    type=str,
				    help='URL to the original PyTorch checkpoint (.pth file).',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
				)
				_snake_case : Dict  				=						parser.parse_args()
				convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


"""simple docstring"""



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

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

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


_UpperCamelCase								=		logging.getLogger(__name__)

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







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





@dataclasses.dataclass
class 						__UpperCAmelCase    :







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





@dataclasses.dataclass
class 						__UpperCAmelCase    :







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





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

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

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

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

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



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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

														progress_bar.update(1							)

														if should_training_stop:
																					break

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





