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


__lowercase      			= logging.getLogger(__name__)

__lowercase      			= '''pytorch_model.bin'''


@dataclasses.dataclass
class      lowerCamelCase_					:






    '''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      lowerCamelCase_					:






    '''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      lowerCamelCase_					:






    '''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=1_0						,     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=1_0_0						,     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 lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:List[str]        =		datasets.concatenate_datasets([infer_input, infer_output]  ,      axis=1     )

    if args.do_filter_by_confidence:
        __UpperCamelCase					:Optional[Any]        =		dataset.filter(lambda SCREAMING_SNAKE_CASE     : example["probability"] > args.confidence_threshold     )

    if args.do_filter_by_val_performance:
        assert eval_result >= 0.0 and eval_result <= 1.0
        __UpperCamelCase					:Tuple        =		int(eval_result * len(_SCREAMING_SNAKE_CASE     )     )
        print(_SCREAMING_SNAKE_CASE     )
        __UpperCamelCase					:Optional[Any]        =		dataset.sort('''probability'''  ,      reverse=_SCREAMING_SNAKE_CASE     )
        __UpperCamelCase					:Dict        =		dataset.select(range(_SCREAMING_SNAKE_CASE     )     )

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

    __UpperCamelCase					:int        =		os.path.join(_SCREAMING_SNAKE_CASE  ,      f"""train_pseudo.{args.data_file_extension}"""     )
    if args.data_file_extension == "csv":
        dataset.to_csv(_SCREAMING_SNAKE_CASE  ,      index=_SCREAMING_SNAKE_CASE     )
    else:
        dataset.to_json(_SCREAMING_SNAKE_CASE     )



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      **SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					: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()

    __UpperCamelCase					:Optional[Any]        =		STModelArguments(model_name_or_path=_SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Optional[int]        =		STDataArguments(train_file=_SCREAMING_SNAKE_CASE  ,      infer_file=_SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Union[str, Any]        =		STTrainingArguments(output_dir=_SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:str        =		argparse.Namespace()

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

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

    # Sanity checks
    __UpperCamelCase					:str        =		{}
    __UpperCamelCase					:List[str]        =		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
    __UpperCamelCase					:Tuple        =		args.train_file
    __UpperCamelCase					:List[str]        =		args.infer_file

    if args.evaluation_strategy != IntervalStrategy.NO.value:
        assert args.eval_file is not None
        __UpperCamelCase					:List[str]        =		args.eval_file

    for key in data_files:
        __UpperCamelCase					: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:
            __UpperCamelCase					:List[Any]        =		extension
        else:
            assert extension == args.data_file_extension, f"""`{key}_file` should be a {args.data_file_extension} file`."""

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

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

    logger.info('''Creating the initial data directory for self-training...'''     )
    __UpperCamelCase					:Optional[int]        =		f"""{args.output_dir}/self-train_iter-{{}}""".format
    __UpperCamelCase					:Any        =		data_dir_format(0     )

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

    __UpperCamelCase					:int        =		None
    __UpperCamelCase					:Any        =		None
    __UpperCamelCase					:List[str]        =		0
    __UpperCamelCase					:Optional[Any]        =		False
    # Show the progress bar
    __UpperCamelCase					: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     )     ):
        __UpperCamelCase					:List[Any]        =		data_dir_format(_SCREAMING_SNAKE_CASE     )
        assert os.path.exists(_SCREAMING_SNAKE_CASE     )

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

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

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

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

        __UpperCamelCase					:int        =		iteration
        __UpperCamelCase					:int        =		data_dir_format(iteration + 1     )

        __UpperCamelCase					:Dict        =		AutoConfig.from_pretrained(os.path.join(_SCREAMING_SNAKE_CASE  ,      '''best-checkpoint'''     )     )
        __UpperCamelCase					:str        =		config.idalabel
        __UpperCamelCase					:Dict        =		os.path.join(_SCREAMING_SNAKE_CASE  ,      '''eval_results_best-checkpoint.json'''     )
        __UpperCamelCase					:Union[str, Any]        =		os.path.join(_SCREAMING_SNAKE_CASE  ,      '''test_results_best-checkpoint.json'''     )
        assert os.path.exists(_SCREAMING_SNAKE_CASE     )

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

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

        __UpperCamelCase					:Dict        =		os.path.join(_SCREAMING_SNAKE_CASE  ,      f"""train_pseudo.{args.data_file_extension}"""     )

        if args.evaluation_strategy != IntervalStrategy.NO.value:
            __UpperCamelCase					:int        =		eval_result

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

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






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union

from packaging import version

from ..utils import is_torch_available, logging


if is_torch_available():
    import torch


__lowercase										= logging.get_logger(__name__)


@dataclass
class      lowerCamelCase_					:






    '''simple docstring'''






    def __init__(     self			,			__lowercase=False			,			__lowercase=False			,			__lowercase=6.0			,			__lowercase=None			,			__lowercase=False			,			__lowercase=False			,			__lowercase=None			,			__lowercase="fp4"			,			__lowercase=False			,			**__lowercase			,			)	->     Tuple:
        __UpperCamelCase					:List[str]        =		load_in_abit
        __UpperCamelCase					:Union[str, Any]        =		load_in_abit
        __UpperCamelCase					:str        =		llm_inta_threshold
        __UpperCamelCase					:List[str]        =		llm_inta_skip_modules
        __UpperCamelCase					:Any        =		llm_inta_enable_fpaa_cpu_offload
        __UpperCamelCase					:List[Any]        =		llm_inta_has_fpaa_weight
        __UpperCamelCase					:str        =		bnb_abit_quant_type
        __UpperCamelCase					:Optional[int]        =		bnb_abit_use_double_quant

        if bnb_abit_compute_dtype is None:
            __UpperCamelCase					:Tuple        =		torch.floataa
        elif isinstance(__lowercase			,			__lowercase):
            __UpperCamelCase					:Union[str, Any]        =		getattr(__lowercase			,			__lowercase)
        elif isinstance(__lowercase			,			torch.dtype):
            __UpperCamelCase					:int        =		bnb_abit_compute_dtype
        else:
            raise ValueError('''bnb_4bit_compute_dtype must be a string or a torch.dtype''')

        self.post_init()







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        if not isinstance(self.llm_inta_threshold			,			__lowercase):
            raise ValueError('''llm_int8_threshold must be a float''')

        if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules			,			__lowercase):
            raise ValueError('''llm_int8_skip_modules must be a list of strings''')
        if not isinstance(self.llm_inta_enable_fpaa_cpu_offload			,			__lowercase):
            raise ValueError('''llm_int8_enable_fp32_cpu_offload must be a boolean''')

        if not isinstance(self.llm_inta_has_fpaa_weight			,			__lowercase):
            raise ValueError('''llm_int8_has_fp16_weight must be a boolean''')

        if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype			,			torch.dtype):
            raise ValueError('''bnb_4bit_compute_dtype must be torch.dtype''')

        if not isinstance(self.bnb_abit_quant_type			,			__lowercase):
            raise ValueError('''bnb_4bit_quant_type must be a string''')

        if not isinstance(self.bnb_abit_use_double_quant			,			__lowercase):
            raise ValueError('''bnb_4bit_use_double_quant must be a boolean''')

        if self.load_in_abit and not version.parse(importlib.metadata.version('''bitsandbytes''')) >= version.parse(
            '''0.39.0'''):
            raise ValueError(
                '''4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version''')







    def       UpperCamelCase__       (     self)	->     Any:
        return self.load_in_abit or self.load_in_abit







    def       UpperCamelCase__       (     self)	->     List[Any]:
        if self.load_in_abit:
            return "llm_int8"
        elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
            return "fp4"
        elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
            return "nf4"
        else:
            return None







    @classmethod
    def       UpperCamelCase__       (     cls			,			__lowercase			,			__lowercase			,			**__lowercase)	->     List[str]:
        __UpperCamelCase					:Optional[int]        =		cls(**__lowercase)

        __UpperCamelCase					:Optional[Any]        =		[]
        for key, value in kwargs.items():
            if hasattr(__lowercase			,			__lowercase):
                setattr(__lowercase			,			__lowercase			,			__lowercase)
                to_remove.append(__lowercase)
        for key in to_remove:
            kwargs.pop(__lowercase			,			__lowercase)

        if return_unused_kwargs:
            return config, kwargs
        else:
            return config







    def       UpperCamelCase__       (     self			,			__lowercase)	->     Union[str, Any]:
        with open(__lowercase			,			'''w'''			,			encoding='''utf-8''') as writer:
            __UpperCamelCase					:Optional[int]        =		self.to_dict()
            __UpperCamelCase					:Optional[int]        =		json.dumps(__lowercase			,			indent=2			,			sort_keys=__lowercase) + '''\n'''

            writer.write(__lowercase)







    def       UpperCamelCase__       (     self)	->     Dict[str, Any]:
        __UpperCamelCase					:Optional[Any]        =		copy.deepcopy(self.__dict__)
        __UpperCamelCase					:Optional[int]        =		str(output['''bnb_4bit_compute_dtype''']).split('''.''')[1]

        return output







    def __repr__(     self)	->     Dict:
        return f"""{self.__class__.__name__} {self.to_json_string()}"""







    def       UpperCamelCase__       (     self			,			__lowercase = True)	->     str:
        if use_diff is True:
            __UpperCamelCase					:Union[str, Any]        =		self.to_diff_dict()
        else:
            __UpperCamelCase					:Dict        =		self.to_dict()
        return json.dumps(__lowercase			,			indent=2			,			sort_keys=__lowercase) + "\n"







    def       UpperCamelCase__       (     self)	->     Dict[str, Any]:
        __UpperCamelCase					:Union[str, Any]        =		self.to_dict()

        # get the default config dict
        __UpperCamelCase					:Optional[Any]        =		BitsAndBytesConfig().to_dict()

        __UpperCamelCase					:str        =		{}

        # only serialize values that differ from the default config
        for key, value in config_dict.items():
            if value != default_config_dict[key]:
                __UpperCamelCase					:str        =		value

        return serializable_config_dict





