






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 			A						:







    """simple docstring"""





    def __init__(				self			:	Dict,lowercase_			:	Any=False,lowercase_			:	Optional[Any]=False,lowercase_			:	int=6.0,lowercase_			:	Any=None,lowercase_			:	List[Any]=False,lowercase_			:	Any=False,lowercase_			:	Dict=None,lowercase_			:	Dict="fp4",lowercase_			:	Dict=False,**lowercase_			:	int,)->   List[Any]:





        '''simple docstring'''







        A__   				=       load_in_abit
        A__   				=       load_in_abit
        A__   				=       llm_inta_threshold
        A__   				=       llm_inta_skip_modules
        A__   				=       llm_inta_enable_fpaa_cpu_offload
        A__   				=       llm_inta_has_fpaa_weight
        A__   				=       bnb_abit_quant_type
        A__   				=       bnb_abit_use_double_quant

        if bnb_abit_compute_dtype is None:
            A__   				=       torch.floataa
        elif isinstance(lowercase_,lowercase_		):
            A__   				=       getattr(lowercase_,lowercase_		)
        elif isinstance(lowercase_,torch.dtype		):
            A__   				=       bnb_abit_compute_dtype
        else:
            raise ValueError('bnb_4bit_compute_dtype must be a string or a torch.dtype'		)

        self.post_init()


    def 						snake_case__					(				self			:	str		)->   Optional[int]:





        '''simple docstring'''







        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 						snake_case__					(				self			:	Dict		)->   int:





        '''simple docstring'''







        return self.load_in_abit or self.load_in_abit


    def 						snake_case__					(				self			:	Dict		)->   int:





        '''simple docstring'''







        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 						snake_case__					(				cls			:	str,lowercase_			:	int,lowercase_			:	Tuple,**lowercase_			:	List[str]		)->   List[Any]:





        '''simple docstring'''







        A__   				=       cls(**lowercase_		)

        A__   				=       []
        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 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	Union[str, os.PathLike]		)->   Any:





        '''simple docstring'''







        with open(lowercase_,'w',encoding='utf-8'		) as writer:
            A__   				=       self.to_dict()
            A__   				=       json.dumps(lowercase_,indent=2,sort_keys=lowercase_		) + '\n'

            writer.write(lowercase_		)


    def 						snake_case__					(				self			:	Optional[Any]		)->   Dict[str, Any]:





        '''simple docstring'''







        A__   				=       copy.deepcopy(self.__dict__		)
        A__   				=       str(output['bnb_4bit_compute_dtype']		).split('.'		)[1]

        return output


    def __repr__(				self			:	Any		)->   Tuple:





        '''simple docstring'''







        return F'{self.__class__.__name__} {self.to_json_string()}'


    def 						snake_case__					(				self			:	Dict,lowercase_			:	bool = True		)->   str:





        '''simple docstring'''







        if use_diff is True:
            A__   				=       self.to_diff_dict()
        else:
            A__   				=       self.to_dict()
        return json.dumps(lowercase_,indent=2,sort_keys=lowercase_		) + "\n"


    def 						snake_case__					(				self			:	Optional[Any]		)->   Dict[str, Any]:





        '''simple docstring'''







        A__   				=       self.to_dict()

        # get the default config dict
        A__   				=       BitsAndBytesConfig().to_dict()

        A__   				=       {}

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

        return serializable_config_dict




# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


lowercase_			=  Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

lowercase_			=  {"base": "patrickvonplaten/wav2vec2_tiny_random", "robust": "patrickvonplaten/wav2vec2_tiny_random_robust"}

lowercase_			=  "zero2"
lowercase_			=  "zero3"
lowercase_			=  [ZEROa, ZEROa]
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     Tuple   ,      SCREAMING_SNAKE_CASE__      :     List[Any]				)							->				Dict:





    '''simple docstring'''






    A__   				=       parameterized.to_safe_name('_'.join(str(SCREAMING_SNAKE_CASE__				) for x in param.args				)				)
    return f'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
lowercase_			=  list(itertools.product(stages, models.keys()))


@slow
@require_deepspeed
@require_torch_gpu
class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	int,lowercase_			:	str,lowercase_			:	Any		)->   Optional[int]:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    @require_torch_multi_gpu
    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	Optional[Any],lowercase_			:	List[Any]		)->   int:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	List[str],lowercase_			:	List[str],lowercase_			:	List[Any]		)->   Any:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    @require_torch_multi_gpu
    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	Dict,lowercase_			:	Optional[Any],lowercase_			:	List[Any]		)->   Optional[int]:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    def 						snake_case__					(				self			:	Tuple,lowercase_			:	Any		)->   Union[str, Any]:





        '''simple docstring'''







        pass


    def 						snake_case__					(				self			:	int,lowercase_			:	str,lowercase_			:	str,lowercase_			:	int = 1_0,lowercase_			:	bool = True,lowercase_			:	bool = True,lowercase_			:	bool = True,)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       models[model]

        A__   				=       self.run_trainer(
            stage=lowercase_,model_name=lowercase_,eval_steps=lowercase_,num_train_epochs=1,distributed=lowercase_,fpaa=lowercase_,)

        self.do_checks(lowercase_		)

        return output_dir


    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	str,lowercase_			:	str,lowercase_			:	int = 1_0,lowercase_			:	int = 1,lowercase_			:	bool = True,lowercase_			:	bool = True,)->   Any:





        '''simple docstring'''







        A__   				=       self.get_auto_remove_tmp_dir('./xxx',after=lowercase_		)
        A__   				=       F'\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(lowercase_		)}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        '.split()

        if fpaa:
            args.extend(['--fp16']		)

        # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
        # hence the separate config files
        A__   				=       F'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
        A__   				=       [F'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
        A__   				=       self.get_launcher(lowercase_		)

        A__   				=       launcher + script + args + ds_args
        # keep for quick debug
        # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
        execute_subprocess_async(lowercase_,env=self.get_env()		)

        return output_dir


    def 						snake_case__					(				self			:	Any,lowercase_			:	int=False		)->   Tuple:





        '''simple docstring'''







        A__   				=       min(2,get_gpu_count()		) if distributed else 1
        return F'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()

