
import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_A   =			logging.get_logger(__name__)

_A   =			{
    "b0": efficientnet.EfficientNetBa,
    "b1": efficientnet.EfficientNetBa,
    "b2": efficientnet.EfficientNetBa,
    "b3": efficientnet.EfficientNetBa,
    "b4": efficientnet.EfficientNetBa,
    "b5": efficientnet.EfficientNetBa,
    "b6": efficientnet.EfficientNetBa,
    "b7": efficientnet.EfficientNetBa,
}

_A   =			{
    "b0": {
        "hidden_dim": 12_80,
        "width_coef": 1.0,
        "depth_coef": 1.0,
        "image_size": 2_24,
        "dropout_rate": 0.2,
        "dw_padding": [],
    },
    "b1": {
        "hidden_dim": 12_80,
        "width_coef": 1.0,
        "depth_coef": 1.1,
        "image_size": 2_40,
        "dropout_rate": 0.2,
        "dw_padding": [16],
    },
    "b2": {
        "hidden_dim": 14_08,
        "width_coef": 1.1,
        "depth_coef": 1.2,
        "image_size": 2_60,
        "dropout_rate": 0.3,
        "dw_padding": [5, 8, 16],
    },
    "b3": {
        "hidden_dim": 15_36,
        "width_coef": 1.2,
        "depth_coef": 1.4,
        "image_size": 3_00,
        "dropout_rate": 0.3,
        "dw_padding": [5, 18],
    },
    "b4": {
        "hidden_dim": 17_92,
        "width_coef": 1.4,
        "depth_coef": 1.8,
        "image_size": 3_80,
        "dropout_rate": 0.4,
        "dw_padding": [6],
    },
    "b5": {
        "hidden_dim": 20_48,
        "width_coef": 1.6,
        "depth_coef": 2.2,
        "image_size": 4_56,
        "dropout_rate": 0.4,
        "dw_padding": [13, 27],
    },
    "b6": {
        "hidden_dim": 23_04,
        "width_coef": 1.8,
        "depth_coef": 2.6,
        "image_size": 5_28,
        "dropout_rate": 0.5,
        "dw_padding": [31],
    },
    "b7": {
        "hidden_dim": 25_60,
        "width_coef": 2.0,
        "depth_coef": 3.1,
        "image_size": 6_00,
        "dropout_rate": 0.5,
        "dw_padding": [18],
    },
}



def 						lowercase_      (       A__		)							->							int:
    """simple docstring"""

    snake_case								= EfficientNetConfig()
    snake_case								= CONFIG_MAP[model_name]["hidden_dim"]
    snake_case								= CONFIG_MAP[model_name]["width_coef"]
    snake_case								= CONFIG_MAP[model_name]["depth_coef"]
    snake_case								= CONFIG_MAP[model_name]["image_size"]
    snake_case								= CONFIG_MAP[model_name]["dropout_rate"]
    snake_case								= CONFIG_MAP[model_name]["dw_padding"]

    snake_case								= "huggingface/label-files"
    snake_case								= "imagenet-1k-id2label.json"
    snake_case								= 1000
    snake_case								= json.load(open(hf_hub_download(A__			,		A__			,		repo_type="dataset"		)			,		"r"		)		)
    snake_case								= {int(A__		): v for k, v in idalabel.items()}

    snake_case								= idalabel
    snake_case								= {v: k for k, v in idalabel.items()}
    return config



def 						lowercase_      (       )							->							str:
    """simple docstring"""

    snake_case								= "http://images.cocodataset.org/val2017/000000039769.jpg"
    snake_case								= Image.open(requests.get(A__			,		stream=A__		).raw		)
    return im



def 						lowercase_      (       A__		)							->							Tuple:
    """simple docstring"""

    snake_case								= CONFIG_MAP[model_name]["image_size"]
    snake_case								= EfficientNetImageProcessor(
        size={"height": size, "width": size}			,		image_mean=[0.485, 0.456, 0.406]			,		image_std=[0.47853944, 0.4732864, 0.47434163]			,		do_center_crop=A__			,		)
    return preprocessor



def 						lowercase_      (       A__		)							->							Optional[int]:
    """simple docstring"""

    snake_case								= [v.split("_"		)[0].split("block"		)[1] for v in original_param_names if v.startswith("block"		)]
    snake_case								= sorted(set(A__		)		)
    snake_case								= len(A__		)
    snake_case								= {b: str(A__		) for b, i in zip(A__			,		range(A__		)		)}

    snake_case								= []
    rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight")		)
    rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight")		)
    rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias")		)
    rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean")		)
    rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var")		)

    for b in block_names:
        snake_case								= block_name_mapping[b]
        rename_keys.append((F'block{b}_expand_conv/kernel:0', F'encoder.blocks.{hf_b}.expansion.expand_conv.weight')		)
        rename_keys.append((F'block{b}_expand_bn/gamma:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.weight')		)
        rename_keys.append((F'block{b}_expand_bn/beta:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.bias')		)
        rename_keys.append(
            (F'block{b}_expand_bn/moving_mean:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.running_mean')		)
        rename_keys.append(
            (F'block{b}_expand_bn/moving_variance:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.running_var')		)
        rename_keys.append(
            (F'block{b}_dwconv/depthwise_kernel:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight')		)
        rename_keys.append((F'block{b}_bn/gamma:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight')		)
        rename_keys.append((F'block{b}_bn/beta:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias')		)
        rename_keys.append(
            (F'block{b}_bn/moving_mean:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean')		)
        rename_keys.append(
            (F'block{b}_bn/moving_variance:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var')		)

        rename_keys.append((F'block{b}_se_reduce/kernel:0', F'encoder.blocks.{hf_b}.squeeze_excite.reduce.weight')		)
        rename_keys.append((F'block{b}_se_reduce/bias:0', F'encoder.blocks.{hf_b}.squeeze_excite.reduce.bias')		)
        rename_keys.append((F'block{b}_se_expand/kernel:0', F'encoder.blocks.{hf_b}.squeeze_excite.expand.weight')		)
        rename_keys.append((F'block{b}_se_expand/bias:0', F'encoder.blocks.{hf_b}.squeeze_excite.expand.bias')		)
        rename_keys.append(
            (F'block{b}_project_conv/kernel:0', F'encoder.blocks.{hf_b}.projection.project_conv.weight')		)
        rename_keys.append((F'block{b}_project_bn/gamma:0', F'encoder.blocks.{hf_b}.projection.project_bn.weight')		)
        rename_keys.append((F'block{b}_project_bn/beta:0', F'encoder.blocks.{hf_b}.projection.project_bn.bias')		)
        rename_keys.append(
            (F'block{b}_project_bn/moving_mean:0', F'encoder.blocks.{hf_b}.projection.project_bn.running_mean')		)
        rename_keys.append(
            (F'block{b}_project_bn/moving_variance:0', F'encoder.blocks.{hf_b}.projection.project_bn.running_var')		)

    rename_keys.append(("top_conv/kernel:0", "encoder.top_conv.weight")		)
    rename_keys.append(("top_bn/gamma:0", "encoder.top_bn.weight")		)
    rename_keys.append(("top_bn/beta:0", "encoder.top_bn.bias")		)
    rename_keys.append(("top_bn/moving_mean:0", "encoder.top_bn.running_mean")		)
    rename_keys.append(("top_bn/moving_variance:0", "encoder.top_bn.running_var")		)

    snake_case								= {}
    for item in rename_keys:
        if item[0] in original_param_names:
            snake_case								= "efficientnet." + item[1]

    snake_case								= "classifier.weight"
    snake_case								= "classifier.bias"
    return key_mapping



def 						lowercase_      (       A__			,		A__			,		A__		)							->							Optional[Any]:
    """simple docstring"""

    for key, value in tf_params.items():
        if "normalization" in key:
            continue

        snake_case								= key_mapping[key]
        if "_conv" in key and "kernel" in key:
            snake_case								= torch.from_numpy(A__		).permute(3			,		2			,		0			,		1		)
        elif "depthwise_kernel" in key:
            snake_case								= torch.from_numpy(A__		).permute(2			,		3			,		0			,		1		)
        elif "kernel" in key:
            snake_case								= torch.from_numpy(np.transpose(A__		)		)
        else:
            snake_case								= torch.from_numpy(A__		)

        # Replace HF parameters with original TF model parameters
        assert hf_params[hf_key].shape == new_hf_value.shape
        hf_params[hf_key].copy_(A__		)



@torch.no_grad()
def 						lowercase_      (       A__			,		A__			,		A__			,		A__		)							->							Union[str, Any]:
    """simple docstring"""

    snake_case								= model_classes[model_name](
        include_top=A__			,		weights="imagenet"			,		input_tensor=A__			,		input_shape=A__			,		pooling=A__			,		classes=1000			,		classifier_activation="softmax"			,		)

    snake_case								= original_model.trainable_variables
    snake_case								= original_model.non_trainable_variables
    snake_case								= {param.name: param.numpy() for param in tf_params}
    for param in tf_non_train_params:
        snake_case								= param.numpy()
    snake_case								= list(tf_params.keys()		)

    # Load HuggingFace model
    snake_case								= get_efficientnet_config(A__		)
    snake_case								= EfficientNetForImageClassification(A__		).eval()
    snake_case								= hf_model.state_dict()

    # Create src-to-dst parameter name mapping dictionary
    print("Converting parameters..."		)
    snake_case								= rename_keys(A__		)
    replace_params(A__			,		A__			,		A__		)

    # Initialize preprocessor and preprocess input image
    snake_case								= convert_image_processor(A__		)
    snake_case								= preprocessor(images=prepare_img()			,		return_tensors="pt"		)

    # HF model inference
    hf_model.eval()
    with torch.no_grad():
        snake_case								= hf_model(**A__		)
    snake_case								= outputs.logits.detach().numpy()

    # Original model inference
    snake_case								= False
    snake_case								= CONFIG_MAP[model_name]["image_size"]
    snake_case								= prepare_img().resize((image_size, image_size)			,		resample=PIL.Image.NEAREST		)
    snake_case								= image.img_to_array(A__		)
    snake_case								= np.expand_dims(A__			,		axis=0		)
    snake_case								= original_model.predict(A__		)

    # Check whether original and HF model outputs match  -> np.allclose
    assert np.allclose(A__			,		A__			,		atol=1e-3		), "The predicted logits are not the same."
    print("Model outputs match!"		)

    if save_model:
        # Create folder to save model
        if not os.path.isdir(A__		):
            os.mkdir(A__		)
        # Save converted model and image processor
        hf_model.save_pretrained(A__		)
        preprocessor.save_pretrained(A__		)

    if push_to_hub:
        # Push model and image processor to hub
        print(F'Pushing converted {model_name} to the hub...'		)
        snake_case								= F'efficientnet-{model_name}'
        preprocessor.push_to_hub(A__		)
        hf_model.push_to_hub(A__		)


if __name__ == "__main__":
   _A   =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--model_name",
       default="b0",
       type=str,
       help="Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path",
       default="hf_model",
       type=str,
       help="Path to the output PyTorch model directory.",
   )
   parser.add_argument("--save_model", action="store_true", help="Save model to local")
   parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")

   _A   =			parser.parse_args()
   convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)



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   =			logging.getLogger(__name__)

_A   =			"pytorch_model.bin"







@dataclasses.dataclass
class 					lowerCamelCase		:
     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 					lowerCamelCase		:
     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 					lowerCamelCase		:
     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=1_00							,			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 						lowercase_      (       A__			,		A__			,		A__			,		A__			,		A__			,		A__		)							->							Union[str, Any]:
    """simple docstring"""

    snake_case								= datasets.concatenate_datasets([infer_input, infer_output]			,		axis=1		)

    if args.do_filter_by_confidence:
        snake_case								= dataset.filter(lambda A__		: 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(A__		)		)
        print(A__		)
        snake_case								= dataset.sort("probability"			,		reverse=A__		)
        snake_case								= dataset.select(range(A__		)		)

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

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





def 						lowercase_      (       A__			,		A__			,		A__			,		A__			,		**A__		)							->							List[Any]:
    """simple docstring"""

    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=A__		)
    snake_case								= STDataArguments(train_file=A__			,		infer_file=A__		)
    snake_case								= STTrainingArguments(output_dir=A__		)
    snake_case								= argparse.Namespace()

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

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

    # 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=A__		)
            os.makedirs(A__			,		exist_ok=A__		)
    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(A__		)
        assert os.path.exists(A__		)

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

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

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

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

        snake_case								= iteration
        snake_case								= data_dir_format(iteration + 1		)

        snake_case								= AutoConfig.from_pretrained(os.path.join(A__			,		"best-checkpoint"		)		)
        snake_case								= config.idalabel
        snake_case								= os.path.join(A__			,		"eval_results_best-checkpoint.json"		)
        snake_case								= os.path.join(A__			,		"test_results_best-checkpoint.json"		)
        assert os.path.exists(A__		)

        with open(A__			,		"r"		) as f:
            snake_case								= float(json.load(A__		)[args.eval_metric]		)
        snake_case								= os.path.join(A__			,		"infer_output_best-checkpoint.csv"		)
        assert os.path.exists(A__		)
        # 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(A__			,		exist_ok=A__		)
            shutil.copy(A__			,		os.path.join(A__			,		F'eval_results_iter-{iteration}.json'		)		)
            if os.path.exists(A__		):
                shutil.copy(A__			,		os.path.join(A__			,		F'test_results_iter-{iteration}.json'		)		)
            create_pseudo_labeled_data(A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
        accelerator.wait_for_everyone()

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

