



import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


a__    =	logging.getLogger(__name__)
a__    =	list(MODEL_FOR_MASKED_LM_MAPPING.keys())
a__    =	tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class 		snake_case						:



   '''simple docstring'''






   snake_case_					: Optional[str]          =		field(
       default=SCREAMING_SNAKE_CASE_					,metadata={
           """help""": (
               """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
           )
       }					,)
   snake_case_					: Optional[str]          =		field(
       default=SCREAMING_SNAKE_CASE_					,metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(SCREAMING_SNAKE_CASE_  )}					,)
   snake_case_					: Optional[str]          =		field(
       default=SCREAMING_SNAKE_CASE_					,metadata={
           """help""": (
               """Override some existing default config settings when a model is trained from scratch. Example: """
               """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
           )
       }					,)
   snake_case_					: Optional[str]          =		field(
       default=SCREAMING_SNAKE_CASE_					,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}  )
   snake_case_					: Optional[str]          =		field(
       default=SCREAMING_SNAKE_CASE_					,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}  )
   snake_case_					: Optional[str]          =		field(
       default=SCREAMING_SNAKE_CASE_					,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}					,)
   snake_case_					: bool          =		field(
       default=SCREAMING_SNAKE_CASE_					,metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}					,)
   snake_case_					: str          =		field(
       default="""main"""					,metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}					,)
   snake_case_					: bool          =		field(
       default=SCREAMING_SNAKE_CASE_					,metadata={
           """help""": (
               """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
               """with private models)."""
           )
       }					,)

   def      UpperCamelCase_  (					self							:			Optional[Any])				->				str:

    """simple docstring"""







    if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
     raise ValueError(
         """--config_overrides can't be used in combination with --config_name or --model_name_or_path""")







@dataclass
class 		snake_case						:



   '''simple docstring'''






   snake_case_					: Optional[str]          =		field(
       default=SCREAMING_SNAKE_CASE_					,metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}  )
   snake_case_					: Optional[str]          =		field(
       default=SCREAMING_SNAKE_CASE_					,metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}  )
   snake_case_					: Optional[str]          =		field(default=SCREAMING_SNAKE_CASE_					,metadata={"""help""": """The input training data file (a text file)."""}  )
   snake_case_					: Optional[str]          =		field(
       default=SCREAMING_SNAKE_CASE_					,metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}					,)
   snake_case_					: Optional[str]          =		field(
       default=SCREAMING_SNAKE_CASE_					,metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}					,)
   snake_case_					: Optional[str]          =		field(
       default=SCREAMING_SNAKE_CASE_					,metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}					,)
   snake_case_					: bool          =		field(
       default=SCREAMING_SNAKE_CASE_					,metadata={"""help""": """Overwrite the cached training and evaluation sets"""}  )
   snake_case_					: Optional[int]          =		field(
       default=5					,metadata={
           """help""": """The percentage of the train set used as validation set in case there's no validation split"""
       }					,)
   snake_case_					: Optional[int]          =		field(
       default=SCREAMING_SNAKE_CASE_					,metadata={
           """help""": (
               """The maximum total input sequence length after tokenization. Sequences longer """
               """than this will be truncated. Default to the max input length of the model."""
           )
       }					,)
   snake_case_					: Optional[int]          =		field(
       default=SCREAMING_SNAKE_CASE_					,metadata={"""help""": """The number of processes to use for the preprocessing."""}					,)
   snake_case_					: float          =		field(
       default=0.15					,metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""}  )
   snake_case_					: bool          =		field(
       default=SCREAMING_SNAKE_CASE_					,metadata={
           """help""": (
               """Whether to pad all samples to `max_seq_length`. """
               """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
           )
       }					,)

   def      UpperCamelCase_  (					self							:			List[Any])				->				Any:

    """simple docstring"""







    if self.train_file is not None:
     _snake_case    :  Any					=					self.train_file.split(""".""")[-1]
     assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
    if self.validation_file is not None:
     _snake_case    :  Optional[Any]					=					self.validation_file.split(""".""")[-1]
     assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Any				,       SCREAMING_SNAKE_CASE__       :	List[Any]				)  ->     Tuple:
 with open(SCREAMING_SNAKE_CASE__				,       """r"""				,       encoding="""utf-8"""				) as f:
  _snake_case    :  str					=					[json.loads(SCREAMING_SNAKE_CASE__				) for line in f.read().splitlines() if (len(SCREAMING_SNAKE_CASE__				) > 0 and not line.isspace())]
 assert len(SCREAMING_SNAKE_CASE__				) == len(SCREAMING_SNAKE_CASE__				)

 _snake_case    :  Optional[Any]					=					{c: dataset[c] for c in dataset.column_names}
 _snake_case    :  List[Any]					=					refs
 return Dataset.from_dict(SCREAMING_SNAKE_CASE__				)



def       lowercase       (   )  ->     Dict:
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.

 _snake_case    :  Dict					=					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
 if len(sys.argv				) == 2 and sys.argv[1].endswith(""".json"""				):
  # If we pass only one argument to the script and it's the path to a json file,
  # let's parse it to get our arguments.
  _snake_case    :  Dict					=					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
 else:
  _snake_case    :  Optional[Any]					=					parser.parse_args_into_dataclasses()

 # Detecting last checkpoint.
 _snake_case    :  List[Any]					=					None
 if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
  _snake_case    :  Dict					=					get_last_checkpoint(training_args.output_dir				)
  if last_checkpoint is None and len(os.listdir(training_args.output_dir				)				) > 0:
   raise ValueError(
       F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
       """Use --overwrite_output_dir to overcome."""				)
  elif last_checkpoint is not None:
   logger.info(
       F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
       """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""				)

    # Setup logging
 logging.basicConfig(
     format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""				,       datefmt="""%m/%d/%Y %H:%M:%S"""				,       handlers=[logging.StreamHandler(sys.stdout				)]				,       )
 logger.setLevel(logging.INFO if is_main_process(training_args.local_rank				) else logging.WARN				)

 # Log on each process the small summary:
 logger.warning(
     F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
     + F'''distributed training: {bool(training_args.local_rank != -1				)}, 16-bits training: {training_args.fpaa}'''				)
 # Set the verbosity to info of the Transformers logger (on main process only):
 if is_main_process(training_args.local_rank				):
  transformers.utils.logging.set_verbosity_info()
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
 logger.info("""Training/evaluation parameters %s"""				,       SCREAMING_SNAKE_CASE__				)

 # Set seed before initializing model.
 set_seed(training_args.seed				)

 # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
 # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
 # (the dataset will be downloaded automatically from the datasets Hub).
 #
 # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
 # 'text' is found. You can easily tweak this behavior (see below).
 #
 # In distributed training, the load_dataset function guarantee that only one local process can concurrently
 # download the dataset.
 if data_args.dataset_name is not None:
  # Downloading and loading a dataset from the hub.
  _snake_case    :  Dict					=					load_dataset(data_args.dataset_name				,       data_args.dataset_config_name				)
  if "validation" not in datasets.keys():
   _snake_case    :  List[str]					=					load_dataset(
       data_args.dataset_name				,       data_args.dataset_config_name				,       split=F'''train[:{data_args.validation_split_percentage}%]'''				,       )
   _snake_case    :  Union[str, Any]					=					load_dataset(
       data_args.dataset_name				,       data_args.dataset_config_name				,       split=F'''train[{data_args.validation_split_percentage}%:]'''				,       )
 else:
  _snake_case    :  Optional[int]					=					{}
  if data_args.train_file is not None:
   _snake_case    :  int					=					data_args.train_file
  if data_args.validation_file is not None:
   _snake_case    :  List[str]					=					data_args.validation_file
  _snake_case    :  List[str]					=					data_args.train_file.split("""."""				)[-1]
  if extension == "txt":
   _snake_case    :  Dict					=					"""text"""
  _snake_case    :  Any					=					load_dataset(SCREAMING_SNAKE_CASE__				,       data_files=SCREAMING_SNAKE_CASE__				)
 # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
 # https://huggingface.co/docs/datasets/loading_datasets.html.

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.
 _snake_case    :  Optional[Any]					=					{
     """cache_dir""": model_args.cache_dir,
     """revision""": model_args.model_revision,
     """use_auth_token""": True if model_args.use_auth_token else None,
 }
 if model_args.config_name:
  _snake_case    :  str					=					AutoConfig.from_pretrained(model_args.config_name				,       **SCREAMING_SNAKE_CASE__				)
 elif model_args.model_name_or_path:
  _snake_case    :  int					=					AutoConfig.from_pretrained(model_args.model_name_or_path				,       **SCREAMING_SNAKE_CASE__				)
 else:
  _snake_case    :  List[Any]					=					CONFIG_MAPPING[model_args.model_type]()
  logger.warning("""You are instantiating a new config instance from scratch."""				)
  if model_args.config_overrides is not None:
   logger.info(F'''Overriding config: {model_args.config_overrides}'''				)
   config.update_from_string(model_args.config_overrides				)
   logger.info(F'''New config: {config}'''				)

 _snake_case    :  Optional[int]					=					{
     """cache_dir""": model_args.cache_dir,
     """use_fast""": model_args.use_fast_tokenizer,
     """revision""": model_args.model_revision,
     """use_auth_token""": True if model_args.use_auth_token else None,
 }
 if model_args.tokenizer_name:
  _snake_case    :  List[str]					=					AutoTokenizer.from_pretrained(model_args.tokenizer_name				,       **SCREAMING_SNAKE_CASE__				)
 elif model_args.model_name_or_path:
  _snake_case    :  List[str]					=					AutoTokenizer.from_pretrained(model_args.model_name_or_path				,       **SCREAMING_SNAKE_CASE__				)
 else:
  raise ValueError(
      """You are instantiating a new tokenizer from scratch. This is not supported by this script."""
      """You can do it from another script, save it, and load it from here, using --tokenizer_name."""				)

 if model_args.model_name_or_path:
  _snake_case    :  Dict					=					AutoModelForMaskedLM.from_pretrained(
      model_args.model_name_or_path				,       from_tf=bool(""".ckpt""" in model_args.model_name_or_path				)				,       config=SCREAMING_SNAKE_CASE__				,       cache_dir=model_args.cache_dir				,       revision=model_args.model_revision				,       use_auth_token=True if model_args.use_auth_token else None				,       )
 else:
  logger.info("""Training new model from scratch"""				)
  _snake_case    :  Optional[int]					=					AutoModelForMaskedLM.from_config(SCREAMING_SNAKE_CASE__				)

 model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE__				)				)

 # Preprocessing the datasets.
 # First we tokenize all the texts.
 if training_args.do_train:
  _snake_case    :  int					=					datasets["""train"""].column_names
 else:
  _snake_case    :  Any					=					datasets["""validation"""].column_names
 _snake_case    :  Optional[Any]					=					"""text""" if """text""" in column_names else column_names[0]

 _snake_case    :  Optional[int]					=					"""max_length""" if data_args.pad_to_max_length else False

 def tokenize_function(SCREAMING_SNAKE_CASE__       :	int				):
  # Remove empty lines
  _snake_case    :  List[str]					=					[line for line in examples["""text"""] if len(SCREAMING_SNAKE_CASE__				) > 0 and not line.isspace()]
  return tokenizer(examples["""text"""]				,       padding=SCREAMING_SNAKE_CASE__				,       truncation=SCREAMING_SNAKE_CASE__				,       max_length=data_args.max_seq_length				)

 _snake_case    :  int					=					datasets.map(
     SCREAMING_SNAKE_CASE__				,       batched=SCREAMING_SNAKE_CASE__				,       num_proc=data_args.preprocessing_num_workers				,       remove_columns=[text_column_name]				,       load_from_cache_file=not data_args.overwrite_cache				,       )

 # Add the chinese references if provided
 if data_args.train_ref_file is not None:
  _snake_case    :  Optional[Any]					=					add_chinese_references(tokenized_datasets["""train"""]				,       data_args.train_ref_file				)
 if data_args.validation_ref_file is not None:
  _snake_case    :  int					=					add_chinese_references(
      tokenized_datasets["""validation"""]				,       data_args.validation_ref_file				)
 # If we have ref files, need to avoid it removed by trainer
 _snake_case    :  Union[str, Any]					=					data_args.train_ref_file or data_args.validation_ref_file
 if has_ref:
  _snake_case    :  List[Any]					=					False

 # Data collator
 # This one will take care of randomly masking the tokens.
 _snake_case    :  Dict					=					DataCollatorForWholeWordMask(tokenizer=SCREAMING_SNAKE_CASE__				,       mlm_probability=data_args.mlm_probability				)

 # Initialize our Trainer
 _snake_case    :  Any					=					Trainer(
     model=SCREAMING_SNAKE_CASE__				,       args=SCREAMING_SNAKE_CASE__				,       train_dataset=tokenized_datasets["""train"""] if training_args.do_train else None				,       eval_dataset=tokenized_datasets["""validation"""] if training_args.do_eval else None				,       tokenizer=SCREAMING_SNAKE_CASE__				,       data_collator=SCREAMING_SNAKE_CASE__				,       )

 # Training
 if training_args.do_train:
  if last_checkpoint is not None:
   _snake_case    :  Tuple					=					last_checkpoint
  elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path				):
   _snake_case    :  Any					=					model_args.model_name_or_path
  else:
   _snake_case    :  Optional[Any]					=					None
  _snake_case    :  str					=					trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE__				)
  trainer.save_model()  # Saves the tokenizer too for easy upload

  _snake_case    :  Any					=					os.path.join(training_args.output_dir				,       """train_results.txt"""				)
  if trainer.is_world_process_zero():
   with open(SCREAMING_SNAKE_CASE__				,       """w"""				) as writer:
    logger.info("""***** Train results *****"""				)
    for key, value in sorted(train_result.metrics.items()				):
     logger.info(F'''  {key} = {value}'''				)
     writer.write(F'''{key} = {value}\n'''				)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
   trainer.state.save_to_json(os.path.join(training_args.output_dir				,       """trainer_state.json"""				)				)

    # Evaluation
 _snake_case    :  Optional[Any]					=					{}
 if training_args.do_eval:
  logger.info("""*** Evaluate ***"""				)

  _snake_case    :  Union[str, Any]					=					trainer.evaluate()

  _snake_case    :  Dict					=					math.exp(eval_output["""eval_loss"""]				)
  _snake_case    :  Tuple					=					perplexity

  _snake_case    :  Tuple					=					os.path.join(training_args.output_dir				,       """eval_results_mlm_wwm.txt"""				)
  if trainer.is_world_process_zero():
   with open(SCREAMING_SNAKE_CASE__				,       """w"""				) as writer:
    logger.info("""***** Eval results *****"""				)
    for key, value in sorted(results.items()				):
     logger.info(F'''  {key} = {value}'''				)
     writer.write(F'''{key} = {value}\n'''				)

 return results



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Union[str, Any]				)  ->     Optional[int]:
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
  main()





import logging
import os
from typing import Dict, List, Optional, Union

import torch
import torch.nn as nn

from accelerate.utils.imports import (
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_bnb_available,
)

from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
    find_tied_parameters,
    get_balanced_memory,
    infer_auto_device_map,
    load_checkpoint_in_model,
    offload_weight,
    set_module_tensor_to_device,
)


if is_bnb_available():
  import bitsandbytes as bnb

from copy import deepcopy


a__    =	logging.getLogger(__name__)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	torch.nn.Module				,       SCREAMING_SNAKE_CASE__       :	BnbQuantizationConfig				,       SCREAMING_SNAKE_CASE__       :	Union[str, os.PathLike] = None				,       SCREAMING_SNAKE_CASE__       :	Optional[Dict[str, Union[int, str, torch.device]]] = None				,       SCREAMING_SNAKE_CASE__       :	Optional[List[str]] = None				,       SCREAMING_SNAKE_CASE__       :	Optional[Dict[Union[int, str], Union[int, str]]] = None				,       SCREAMING_SNAKE_CASE__       :	Optional[Union[str, os.PathLike]] = None				,       SCREAMING_SNAKE_CASE__       :	bool = False				,       )  ->     int:
 _snake_case    :  int					=					bnb_quantization_config.load_in_abit
 _snake_case    :  Tuple					=					bnb_quantization_config.load_in_abit

 if load_in_abit and not is_abit_bnb_available():
  raise ImportError(
      """You have a version of `bitsandbytes` that is not compatible with 8bit quantization,"""
      """ make sure you have the latest version of `bitsandbytes` installed."""				)
 if load_in_abit and not is_abit_bnb_available():
  raise ValueError(
      """You have a version of `bitsandbytes` that is not compatible with 4bit quantization,"""
      """make sure you have the latest version of `bitsandbytes` installed."""				)

 _snake_case    :  List[Any]					=					[]
 # custom device map
 if isinstance(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				) and len(device_map.keys()				) > 1:
  _snake_case    :  Tuple					=					[key for key, value in device_map.items() if value in ["""disk""", """cpu"""]]

 # We keep some modules such as the lm_head in their original dtype for numerical stability reasons
 if bnb_quantization_config.skip_modules is None:
  _snake_case    :  Union[str, Any]					=					get_keys_to_not_convert(SCREAMING_SNAKE_CASE__				)

 # add cpu modules to skip modules only for 4-bit modules
 if load_in_abit:
  bnb_quantization_config.skip_modules.extend(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Union[str, Any]					=					bnb_quantization_config.skip_modules

 # We add the modules we want to keep in full precision
 if bnb_quantization_config.keep_in_fpaa_modules is None:
  _snake_case    :  Optional[Any]					=					[]
 _snake_case    :  Dict					=					bnb_quantization_config.keep_in_fpaa_modules
 modules_to_not_convert.extend(SCREAMING_SNAKE_CASE__				)

 # compatibility with peft
 _snake_case    :  Union[str, Any]					=					load_in_abit
 _snake_case    :  Any					=					load_in_abit

 _snake_case    :  Optional[int]					=					get_parameter_device(SCREAMING_SNAKE_CASE__				)
 if model_device.type != "meta":
  # quantization of an already loaded model
  logger.warning(
      """It is not recommended to quantize a loaded model. """
      """The model should be instantiated under the `init_empty_weights` context manager."""				)
  _snake_case    :  int					=					replace_with_bnb_layers(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       modules_to_not_convert=SCREAMING_SNAKE_CASE__				)
  # convert param to the right dtype
  _snake_case    :  Any					=					bnb_quantization_config.torch_dtype
  for name, param in model.state_dict().items():
   if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules				):
    param.to(torch.floataa				)
    if param.dtype != torch.floataa:
     _snake_case    :  Union[str, Any]					=					name.replace(""".weight"""				,       """"""				).replace(""".bias"""				,       """"""				)
     _snake_case    :  Any					=					getattr(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
     if param is not None:
      param.to(torch.floataa				)
   elif torch.is_floating_point(SCREAMING_SNAKE_CASE__				):
    param.to(SCREAMING_SNAKE_CASE__				)
  if model_device.type == "cuda":
   # move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
   model.cuda(torch.cuda.current_device()				)
   torch.cuda.empty_cache()
  elif torch.cuda.is_available():
   model.to(torch.cuda.current_device()				)
  else:
   raise RuntimeError("""No GPU found. A GPU is needed for quantization."""				)
  logger.info(
      F'''The model device type is {model_device.type}. However, cuda is needed for quantization.'''
      """We move the model to cuda."""				)
  return model

 elif weights_location is None:
  raise RuntimeError(
      F'''`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} '''				)

 else:
  with init_empty_weights():
   _snake_case    :  Optional[int]					=					replace_with_bnb_layers(
       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       modules_to_not_convert=SCREAMING_SNAKE_CASE__				)

  _snake_case    :  List[Any]					=					get_quantized_model_device_map(
      SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       max_memory=SCREAMING_SNAKE_CASE__				,       no_split_module_classes=SCREAMING_SNAKE_CASE__				,       )
  if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
   _snake_case    :  Union[str, Any]					=					True

  _snake_case    :  Any					=					any(x in list(device_map.values()				) for x in ["""cpu""", """disk"""]				)

  load_checkpoint_in_model(
      SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       dtype=bnb_quantization_config.torch_dtype				,       offload_folder=SCREAMING_SNAKE_CASE__				,       offload_state_dict=SCREAMING_SNAKE_CASE__				,       keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules				,       offload_abit_bnb=load_in_abit and offload				,       )
  return dispatch_model(SCREAMING_SNAKE_CASE__				,       device_map=SCREAMING_SNAKE_CASE__				,       offload_dir=SCREAMING_SNAKE_CASE__				)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Any				,       SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	int=None				,       SCREAMING_SNAKE_CASE__       :	List[str]=None				,       SCREAMING_SNAKE_CASE__       :	Any=None				)  ->     List[Any]:
 if device_map is None:
  if torch.cuda.is_available():
   _snake_case    :  Dict					=					{"""""": torch.cuda.current_device()}
  else:
   raise RuntimeError("""No GPU found. A GPU is needed for quantization."""				)
  logger.info("""The device_map was not initialized.""" """Setting device_map to `{'':torch.cuda.current_device()}`."""				)

 if isinstance(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				):
  if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
   raise ValueError(
       """If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or """
       """'sequential'."""				)

  _snake_case    :  int					=					{}
  special_dtypes.update(
      {
          name: bnb_quantization_config.torch_dtype
          for name, _ in model.named_parameters()
          if any(m in name for m in bnb_quantization_config.skip_modules				)
      }				)
  special_dtypes.update(
      {
          name: torch.floataa
          for name, _ in model.named_parameters()
          if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules				)
      }				)

  _snake_case    :  Tuple					=					{}
  _snake_case    :  List[str]					=					special_dtypes
  _snake_case    :  int					=					no_split_module_classes
  _snake_case    :  List[Any]					=					bnb_quantization_config.target_dtype

  # get max_memory for each device.
  if device_map != "sequential":
   _snake_case    :  Optional[int]					=					get_balanced_memory(
       SCREAMING_SNAKE_CASE__				,       low_zero=(device_map == """balanced_low_0""")				,       max_memory=SCREAMING_SNAKE_CASE__				,       **SCREAMING_SNAKE_CASE__				,       )

  _snake_case    :  str					=					max_memory
  _snake_case    :  Optional[int]					=					infer_auto_device_map(SCREAMING_SNAKE_CASE__				,       **SCREAMING_SNAKE_CASE__				)

 if isinstance(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				):
  # check if don't have any quantized module on the cpu
  _snake_case    :  List[str]					=					bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules

  _snake_case    :  Dict					=					{
      key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
  }
  for device in ["cpu", "disk"]:
   if device in device_map_without_some_modules.values():
    if bnb_quantization_config.load_in_abit:
     raise ValueError(
         """
                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit
                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping
                        these modules in `torch_dtype`, you need to pass a custom `device_map` to
                        `load_and_quantize_model`. Check
                        https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk
                        for more details.
                        """				)
    else:
     logger.info(
         """Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit"""				)
  del device_map_without_some_modules
 return device_map



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]=None				,       SCREAMING_SNAKE_CASE__       :	Any=None				)  ->     List[Any]:

 if modules_to_not_convert is None:
  _snake_case    :  Tuple					=					[]

 _snake_case	,    _snake_case    :  str					=					_replace_with_bnb_layers(
     SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
 if not has_been_replaced:
  logger.warning(
      """You are loading your model in 8bit or 4bit but no linear modules were found in your model."""
      """ this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers."""
      """ Please double check your model architecture, or submit an issue on github if you think this is"""
      """ a bug."""				)
 return model



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				,       SCREAMING_SNAKE_CASE__       :	str=None				,       SCREAMING_SNAKE_CASE__       :	Tuple=None				,       )  ->     Optional[Any]:
 _snake_case    :  List[str]					=					False
 for name, module in model.named_children():
  if current_key_name is None:
   _snake_case    :  List[str]					=					[]
  current_key_name.append(SCREAMING_SNAKE_CASE__				)
  if isinstance(SCREAMING_SNAKE_CASE__				,       nn.Linear				) and name not in modules_to_not_convert:
   # Check if the current key is not in the `modules_to_not_convert`
   _snake_case    :  int					=					""".""".join(SCREAMING_SNAKE_CASE__				)
   _snake_case    :  List[str]					=					True
   for key in modules_to_not_convert:
    if (
        (key in current_key_name_str) and (key + "." in current_key_name_str)
    ) or key == current_key_name_str:
     _snake_case    :  Optional[int]					=					False
     break
   if proceed:
    # Load bnb module with empty weight and replace ``nn.Linear` module
    if bnb_quantization_config.load_in_abit:
     _snake_case    :  List[Any]					=					bnb.nn.LinearabitLt(
         module.in_features				,       module.out_features				,       module.bias is not None				,       has_fpaa_weights=SCREAMING_SNAKE_CASE__				,       threshold=bnb_quantization_config.llm_inta_threshold				,       )
    elif bnb_quantization_config.load_in_abit:
     _snake_case    :  Any					=					bnb.nn.Linearabit(
         module.in_features				,       module.out_features				,       module.bias is not None				,       bnb_quantization_config.bnb_abit_compute_dtype				,       compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant				,       quant_type=bnb_quantization_config.bnb_abit_quant_type				,       )
    else:
     raise ValueError("""load_in_8bit and load_in_4bit can't be both False"""				)
    _snake_case    :  List[str]					=					module.weight.data
    if module.bias is not None:
     _snake_case    :  List[Any]					=					module.bias.data
    bnb_module.requires_grad_(SCREAMING_SNAKE_CASE__				)
    setattr(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
    _snake_case    :  List[str]					=					True
  if len(list(module.children()				)				) > 0:
   _snake_case	,    _snake_case    :  Optional[int]					=					_replace_with_bnb_layers(
       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
   _snake_case    :  List[str]					=					has_been_replaced | _has_been_replaced
  # Remove the last key for recursion
  current_key_name.pop(-1				)
 return model, has_been_replaced



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	str				)  ->     int:
 # Create a copy of the model
 with init_empty_weights():
  _snake_case    :  Optional[Any]					=					deepcopy(SCREAMING_SNAKE_CASE__				)  # this has 0 cost since it is done inside `init_empty_weights` context manager`

 _snake_case    :  Tuple					=					find_tied_parameters(SCREAMING_SNAKE_CASE__				)
 # For compatibility with Accelerate < 0.18
 if isinstance(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				):
  _snake_case    :  List[Any]					=					sum(list(tied_params.values()				)				,       []				) + list(tied_params.keys()				)
 else:
  _snake_case    :  Optional[Any]					=					sum(SCREAMING_SNAKE_CASE__				,       []				)
 _snake_case    :  Optional[Any]					=					len(SCREAMING_SNAKE_CASE__				) > 0

 # Check if it is a base model
 _snake_case    :  str					=					False
 if hasattr(SCREAMING_SNAKE_CASE__				,       """base_model_prefix"""				):
  _snake_case    :  List[Any]					=					not hasattr(SCREAMING_SNAKE_CASE__				,       model.base_model_prefix				)

 # Ignore this for base models (BertModel, GPT2Model, etc.)
 if (not has_tied_params) and is_base_model:
  return []

 # otherwise they have an attached head
 _snake_case    :  str					=					list(model.named_children()				)
 _snake_case    :  Dict					=					[list_modules[-1][0]]

 # add last module together with tied weights
 _snake_case    :  Optional[int]					=					set(SCREAMING_SNAKE_CASE__				) - set(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Dict					=					list(set(SCREAMING_SNAKE_CASE__				)				) + list(SCREAMING_SNAKE_CASE__				)

 # remove ".weight" from the keys
 _snake_case    :  Union[str, Any]					=					[""".weight""", """.bias"""]
 _snake_case    :  List[str]					=					[]
 for name in list_untouched:
  for name_to_remove in names_to_remove:
   if name_to_remove in name:
    _snake_case    :  Optional[Any]					=					name.replace(SCREAMING_SNAKE_CASE__				,       """"""				)
  filtered_module_names.append(SCREAMING_SNAKE_CASE__				)

 return filtered_module_names



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict				)  ->     Tuple:
 for m in model.modules():
  if isinstance(SCREAMING_SNAKE_CASE__				,       bnb.nn.Linearabit				):
   return True
 return False



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	nn.Module				)  ->     Union[str, Any]:
 return next(parameter.parameters()				).device



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Any				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]				,       SCREAMING_SNAKE_CASE__       :	List[str]				,       SCREAMING_SNAKE_CASE__       :	Any				,       SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	List[Any]				,       SCREAMING_SNAKE_CASE__       :	Dict				)  ->     Any:
 # if it is not quantized, we quantize and offload the quantized weights and the SCB stats
 if fpaa_statistics is None:
  set_module_tensor_to_device(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       0				,       dtype=SCREAMING_SNAKE_CASE__				,       value=SCREAMING_SNAKE_CASE__				)
  _snake_case    :  Optional[Any]					=					param_name
  _snake_case    :  List[Any]					=					model
  if "." in tensor_name:
   _snake_case    :  str					=					tensor_name.split("""."""				)
   for split in splits[:-1]:
    _snake_case    :  Tuple					=					getattr(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
    if new_module is None:
     raise ValueError(F'''{module} has no attribute {split}.'''				)
    _snake_case    :  Tuple					=					new_module
   _snake_case    :  Dict					=					splits[-1]
  # offload weights
  _snake_case    :  List[str]					=					False
  offload_weight(module._parameters[tensor_name]				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       index=SCREAMING_SNAKE_CASE__				)
  if hasattr(module._parameters[tensor_name]				,       """SCB"""				):
   offload_weight(
       module._parameters[tensor_name].SCB				,       param_name.replace("""weight"""				,       """SCB"""				)				,       SCREAMING_SNAKE_CASE__				,       index=SCREAMING_SNAKE_CASE__				,       )
 else:
  offload_weight(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       index=SCREAMING_SNAKE_CASE__				)
  offload_weight(SCREAMING_SNAKE_CASE__				,       param_name.replace("""weight"""				,       """SCB"""				)				,       SCREAMING_SNAKE_CASE__				,       index=SCREAMING_SNAKE_CASE__				)

 set_module_tensor_to_device(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       """meta"""				,       dtype=SCREAMING_SNAKE_CASE__				,       value=torch.empty(*param.size()				)				)
