






"""simple docstring"""






from typing import Any, Callable, Dict, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker


UpperCAmelCase_     :				List[str]         =				"""CompVis/stable-diffusion-v1-1"""
UpperCAmelCase_     :				List[str]         =				"""CompVis/stable-diffusion-v1-2"""
UpperCAmelCase_     :				Optional[int]         =				"""CompVis/stable-diffusion-v1-3"""
UpperCAmelCase_     :				int         =				"""CompVis/stable-diffusion-v1-4"""




class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      def __init__(		self  :						Optional[Any]		,			lowercase_  :						AutoencoderKL		,			lowercase_  :						CLIPTextModel		,			lowercase_  :						CLIPTokenizer		,			lowercase_  :						UNetaDConditionModel		,			lowercase_  :						Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]		,			lowercase_  :						StableDiffusionSafetyChecker		,			lowercase_  :						CLIPImageProcessor		,			lowercase_  :						bool = True		,			):




          '''simple docstring'''


          super()._init_()

          SCREAMING_SNAKE_CASE_   :						Tuple							=	StableDiffusionPipeline.from_pretrained(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	StableDiffusionPipeline.from_pretrained(lowercase_)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	StableDiffusionPipeline.from_pretrained(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	StableDiffusionPipeline(
              vae=lowercase_		,			text_encoder=lowercase_		,			tokenizer=lowercase_		,			unet=lowercase_		,			scheduler=lowercase_		,			safety_checker=lowercase_		,			feature_extractor=lowercase_		,			requires_safety_checker=lowercase_		,			)

          self.register_modules(pipelinea=self.pipea		,			pipelinea=self.pipea		,			pipelinea=self.pipea		,			pipelinea=self.pipea)
      @property
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]):




          '''simple docstring'''


          return {k: getattr(self		,			lowercase_) for k in self.config.keys() if not k.startswith('''_''')}
      def _SCREAMING_SNAKE_CASE				(		self  :						str		,			lowercase_  :						Optional[Union[str, int]] = "auto"):




          '''simple docstring'''


          if slice_size == "auto":
              # half the attention head size is usually a good trade-off between
              # speed and memory
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	self.unet.config.attention_head_dim // 2
          self.unet.set_attention_slice(lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          self.enable_attention_slicing(lowercase_)
      @torch.no_grad()
      def _SCREAMING_SNAKE_CASE				(		self  :						str		,			lowercase_  :						Union[str, List[str]]		,			lowercase_  :						int = 512		,			lowercase_  :						int = 512		,			lowercase_  :						int = 50		,			lowercase_  :						float = 7.5		,			lowercase_  :						Optional[Union[str, List[str]]] = None		,			lowercase_  :						Optional[int] = 1		,			lowercase_  :						float = 0.0		,			lowercase_  :						Optional[torch.Generator] = None		,			lowercase_  :						Optional[torch.FloatTensor] = None		,			lowercase_  :						Optional[str] = "pil"		,			lowercase_  :						bool = True		,			lowercase_  :						Optional[Callable[[int, int, torch.FloatTensor], None]] = None		,			lowercase_  :						int = 1		,			**lowercase_  :						List[Any]		,			):




          '''simple docstring'''


          return self.pipea(
              prompt=lowercase_		,			height=lowercase_		,			width=lowercase_		,			num_inference_steps=lowercase_		,			guidance_scale=lowercase_		,			negative_prompt=lowercase_		,			num_images_per_prompt=lowercase_		,			eta=lowercase_		,			generator=lowercase_		,			latents=lowercase_		,			output_type=lowercase_		,			return_dict=lowercase_		,			callback=lowercase_		,			callback_steps=lowercase_		,			**lowercase_		,			)
      @torch.no_grad()
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple		,			lowercase_  :						Union[str, List[str]]		,			lowercase_  :						int = 512		,			lowercase_  :						int = 512		,			lowercase_  :						int = 50		,			lowercase_  :						float = 7.5		,			lowercase_  :						Optional[Union[str, List[str]]] = None		,			lowercase_  :						Optional[int] = 1		,			lowercase_  :						float = 0.0		,			lowercase_  :						Optional[torch.Generator] = None		,			lowercase_  :						Optional[torch.FloatTensor] = None		,			lowercase_  :						Optional[str] = "pil"		,			lowercase_  :						bool = True		,			lowercase_  :						Optional[Callable[[int, int, torch.FloatTensor], None]] = None		,			lowercase_  :						int = 1		,			**lowercase_  :						Optional[int]		,			):




          '''simple docstring'''


          return self.pipea(
              prompt=lowercase_		,			height=lowercase_		,			width=lowercase_		,			num_inference_steps=lowercase_		,			guidance_scale=lowercase_		,			negative_prompt=lowercase_		,			num_images_per_prompt=lowercase_		,			eta=lowercase_		,			generator=lowercase_		,			latents=lowercase_		,			output_type=lowercase_		,			return_dict=lowercase_		,			callback=lowercase_		,			callback_steps=lowercase_		,			**lowercase_		,			)
      @torch.no_grad()
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]		,			lowercase_  :						Union[str, List[str]]		,			lowercase_  :						int = 512		,			lowercase_  :						int = 512		,			lowercase_  :						int = 50		,			lowercase_  :						float = 7.5		,			lowercase_  :						Optional[Union[str, List[str]]] = None		,			lowercase_  :						Optional[int] = 1		,			lowercase_  :						float = 0.0		,			lowercase_  :						Optional[torch.Generator] = None		,			lowercase_  :						Optional[torch.FloatTensor] = None		,			lowercase_  :						Optional[str] = "pil"		,			lowercase_  :						bool = True		,			lowercase_  :						Optional[Callable[[int, int, torch.FloatTensor], None]] = None		,			lowercase_  :						int = 1		,			**lowercase_  :						Optional[Any]		,			):




          '''simple docstring'''


          return self.pipea(
              prompt=lowercase_		,			height=lowercase_		,			width=lowercase_		,			num_inference_steps=lowercase_		,			guidance_scale=lowercase_		,			negative_prompt=lowercase_		,			num_images_per_prompt=lowercase_		,			eta=lowercase_		,			generator=lowercase_		,			latents=lowercase_		,			output_type=lowercase_		,			return_dict=lowercase_		,			callback=lowercase_		,			callback_steps=lowercase_		,			**lowercase_		,			)
      @torch.no_grad()
      def _SCREAMING_SNAKE_CASE				(		self  :						Any		,			lowercase_  :						Union[str, List[str]]		,			lowercase_  :						int = 512		,			lowercase_  :						int = 512		,			lowercase_  :						int = 50		,			lowercase_  :						float = 7.5		,			lowercase_  :						Optional[Union[str, List[str]]] = None		,			lowercase_  :						Optional[int] = 1		,			lowercase_  :						float = 0.0		,			lowercase_  :						Optional[torch.Generator] = None		,			lowercase_  :						Optional[torch.FloatTensor] = None		,			lowercase_  :						Optional[str] = "pil"		,			lowercase_  :						bool = True		,			lowercase_  :						Optional[Callable[[int, int, torch.FloatTensor], None]] = None		,			lowercase_  :						int = 1		,			**lowercase_  :						Optional[Any]		,			):




          '''simple docstring'''


          return self.pipea(
              prompt=lowercase_		,			height=lowercase_		,			width=lowercase_		,			num_inference_steps=lowercase_		,			guidance_scale=lowercase_		,			negative_prompt=lowercase_		,			num_images_per_prompt=lowercase_		,			eta=lowercase_		,			generator=lowercase_		,			latents=lowercase_		,			output_type=lowercase_		,			return_dict=lowercase_		,			callback=lowercase_		,			callback_steps=lowercase_		,			**lowercase_		,			)



      @torch.no_grad()
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]		,			lowercase_  :						Union[str, List[str]]		,			lowercase_  :						int = 512		,			lowercase_  :						int = 512		,			lowercase_  :						int = 50		,			lowercase_  :						float = 7.5		,			lowercase_  :						Optional[Union[str, List[str]]] = None		,			lowercase_  :						Optional[int] = 1		,			lowercase_  :						float = 0.0		,			lowercase_  :						Optional[torch.Generator] = None		,			lowercase_  :						Optional[torch.FloatTensor] = None		,			lowercase_  :						Optional[str] = "pil"		,			lowercase_  :						bool = True		,			lowercase_  :						Optional[Callable[[int, int, torch.FloatTensor], None]] = None		,			lowercase_  :						int = 1		,			**lowercase_  :						int		,			):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	'''cuda''' if torch.cuda.is_available() else '''cpu'''
          self.to(lowercase_)

          # Checks if the height and width are divisible by 8 or not
          if height % 8 != 0 or width % 8 != 0:
              raise ValueError(F'`height` and `width` must be divisible by 8 but are {height} and {width}.')

          # Get first result from Stable Diffusion Checkpoint v1.1
          SCREAMING_SNAKE_CASE_   :						Dict							=	self.textaimg_sda_a(
              prompt=lowercase_		,			height=lowercase_		,			width=lowercase_		,			num_inference_steps=lowercase_		,			guidance_scale=lowercase_		,			negative_prompt=lowercase_		,			num_images_per_prompt=lowercase_		,			eta=lowercase_		,			generator=lowercase_		,			latents=lowercase_		,			output_type=lowercase_		,			return_dict=lowercase_		,			callback=lowercase_		,			callback_steps=lowercase_		,			**lowercase_		,			)

          # Get first result from Stable Diffusion Checkpoint v1.2
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.textaimg_sda_a(
              prompt=lowercase_		,			height=lowercase_		,			width=lowercase_		,			num_inference_steps=lowercase_		,			guidance_scale=lowercase_		,			negative_prompt=lowercase_		,			num_images_per_prompt=lowercase_		,			eta=lowercase_		,			generator=lowercase_		,			latents=lowercase_		,			output_type=lowercase_		,			return_dict=lowercase_		,			callback=lowercase_		,			callback_steps=lowercase_		,			**lowercase_		,			)

          # Get first result from Stable Diffusion Checkpoint v1.3
          SCREAMING_SNAKE_CASE_   :						str							=	self.textaimg_sda_a(
              prompt=lowercase_		,			height=lowercase_		,			width=lowercase_		,			num_inference_steps=lowercase_		,			guidance_scale=lowercase_		,			negative_prompt=lowercase_		,			num_images_per_prompt=lowercase_		,			eta=lowercase_		,			generator=lowercase_		,			latents=lowercase_		,			output_type=lowercase_		,			return_dict=lowercase_		,			callback=lowercase_		,			callback_steps=lowercase_		,			**lowercase_		,			)

          # Get first result from Stable Diffusion Checkpoint v1.4
          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.textaimg_sda_a(
              prompt=lowercase_		,			height=lowercase_		,			width=lowercase_		,			num_inference_steps=lowercase_		,			guidance_scale=lowercase_		,			negative_prompt=lowercase_		,			num_images_per_prompt=lowercase_		,			eta=lowercase_		,			generator=lowercase_		,			latents=lowercase_		,			output_type=lowercase_		,			return_dict=lowercase_		,			callback=lowercase_		,			callback_steps=lowercase_		,			**lowercase_		,			)

          # Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
          return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]])













"""simple docstring"""






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


UpperCAmelCase_     :				str         =				logging.getLogger(__name__)



def       _A    (__a					,       __a					,       __a = None					,       __a = None					,       __a = None					,       __a = None					,       __a = None					,       __a = False					,       )				-> Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						List[Any]							=	bnb_quantization_config.load_in_abit
    SCREAMING_SNAKE_CASE_   :						Dict							=	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.'''							)

    SCREAMING_SNAKE_CASE_   :						str							=	[]
    # custom device map
    if isinstance(__a					,       __a							) and len(device_map.keys()							) > 1:
        SCREAMING_SNAKE_CASE_   :						int							=	[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:
        SCREAMING_SNAKE_CASE_   :						str							=	get_keys_to_not_convert(__a							)

    # add cpu modules to skip modules only for 4-bit modules
    if load_in_abit:
        bnb_quantization_config.skip_modules.extend(__a							)
    SCREAMING_SNAKE_CASE_   :						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:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	[]
    SCREAMING_SNAKE_CASE_   :						Dict							=	bnb_quantization_config.keep_in_fpaa_modules
    modules_to_not_convert.extend(__a							)

    # compatibility with peft
    SCREAMING_SNAKE_CASE_   :						Tuple							=	load_in_abit
    SCREAMING_SNAKE_CASE_   :						Any							=	load_in_abit

    SCREAMING_SNAKE_CASE_   :						Dict							=	get_parameter_device(__a							)
    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.'''							)
        SCREAMING_SNAKE_CASE_   :						int							=	replace_with_bnb_layers(__a					,       __a					,       modules_to_not_convert=__a							)
        # convert param to the right dtype
        SCREAMING_SNAKE_CASE_   :						Dict							=	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:
                    SCREAMING_SNAKE_CASE_   :						Tuple							=	name.replace('''.weight'''					,       ''''''							).replace('''.bias'''					,       ''''''							)
                    SCREAMING_SNAKE_CASE_   :						List[Any]							=	getattr(__a					,       __a					,       __a							)
                    if param is not None:
                        param.to(torch.floataa							)
            elif torch.is_floating_point(__a							):
                param.to(__a							)
        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():
            SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	replace_with_bnb_layers(
                __a					,       __a					,       modules_to_not_convert=__a							)

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

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

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



def       _A    (__a					,       __a					,       __a=None					,       __a=None					,       __a=None							)				-> Union[str, Any]:
    """simple docstring"""






    if device_map is None:
        if torch.cuda.is_available():
            SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	{'''''': 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(__a					,       __a							):
        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\'.'''							)

        SCREAMING_SNAKE_CASE_   :						str							=	{}
        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							)
            }							)

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

        # get max_memory for each device.
        if device_map != "sequential":
            SCREAMING_SNAKE_CASE_   :						str							=	get_balanced_memory(
                __a					,       low_zero=(device_map == '''balanced_low_0''')					,       max_memory=__a					,       **__a					,       )

        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	max_memory
        SCREAMING_SNAKE_CASE_   :						Tuple							=	infer_auto_device_map(__a					,       **__a							)

    if isinstance(__a					,       __a							):
        # check if don't have any quantized module on the cpu
        SCREAMING_SNAKE_CASE_   :						Dict							=	bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules

        SCREAMING_SNAKE_CASE_   :						List[Any]							=	{
            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       _A    (__a					,       __a					,       __a=None					,       __a=None							)				-> Optional[Any]:
    """simple docstring"""






    if modules_to_not_convert is None:
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	[]

    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	_replace_with_bnb_layers(
        __a					,       __a					,       __a					,       __a							)
    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       _A    (__a					,       __a					,       __a=None					,       __a=None					,       )				-> List[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						List[Any]							=	False
    for name, module in model.named_children():
        if current_key_name is None:
            SCREAMING_SNAKE_CASE_   :						Any							=	[]
        current_key_name.append(__a							)
        if isinstance(__a					,       nn.Linear							) and name not in modules_to_not_convert:
            # Check if the current key is not in the `modules_to_not_convert`
            SCREAMING_SNAKE_CASE_   :						Optional[int]							=	'''.'''.join(__a							)
            SCREAMING_SNAKE_CASE_   :						Dict							=	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:
                    SCREAMING_SNAKE_CASE_   :						List[str]							=	False
                    break
            if proceed:
                # Load bnb module with empty weight and replace ``nn.Linear` module
                if bnb_quantization_config.load_in_abit:
                    SCREAMING_SNAKE_CASE_   :						Tuple							=	bnb.nn.LinearabitLt(
                        module.in_features					,       module.out_features					,       module.bias is not None					,       has_fpaa_weights=__a					,       threshold=bnb_quantization_config.llm_inta_threshold					,       )
                elif bnb_quantization_config.load_in_abit:
                    SCREAMING_SNAKE_CASE_   :						Tuple							=	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'''							)
                SCREAMING_SNAKE_CASE_   :						int							=	module.weight.data
                if module.bias is not None:
                    SCREAMING_SNAKE_CASE_   :						str							=	module.bias.data
                bnb_module.requires_grad_(__a							)
                setattr(__a					,       __a					,       __a							)
                SCREAMING_SNAKE_CASE_   :						List[Any]							=	True
        if len(list(module.children()							)							) > 0:
            SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	_replace_with_bnb_layers(
                __a					,       __a					,       __a					,       __a							)
            SCREAMING_SNAKE_CASE_   :						List[Any]							=	has_been_replaced | _has_been_replaced
        # Remove the last key for recursion
        current_key_name.pop(-1							)
    return model, has_been_replaced



def       _A    (__a							)				-> List[str]:
    """simple docstring"""






    with init_empty_weights():
        SCREAMING_SNAKE_CASE_   :						Any							=	deepcopy(__a							)  # this has 0 cost since it is done inside `init_empty_weights` context manager`

    SCREAMING_SNAKE_CASE_   :						List[str]							=	find_tied_parameters(__a							)
    # For compatibility with Accelerate < 0.18
    if isinstance(__a					,       __a							):
        SCREAMING_SNAKE_CASE_   :						Tuple							=	sum(list(tied_params.values()							)					,       []							) + list(tied_params.keys()							)
    else:
        SCREAMING_SNAKE_CASE_   :						Dict							=	sum(__a					,       []							)
    SCREAMING_SNAKE_CASE_   :						int							=	len(__a							) > 0

    # Check if it is a base model
    SCREAMING_SNAKE_CASE_   :						str							=	False
    if hasattr(__a					,       '''base_model_prefix'''							):
        SCREAMING_SNAKE_CASE_   :						str							=	not hasattr(__a					,       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
    SCREAMING_SNAKE_CASE_   :						int							=	list(model.named_children()							)
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	[list_modules[-1][0]]

    # add last module together with tied weights
    SCREAMING_SNAKE_CASE_   :						str							=	set(__a							) - set(__a							)
    SCREAMING_SNAKE_CASE_   :						List[str]							=	list(set(__a							)							) + list(__a							)

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

    return filtered_module_names



def       _A    (__a							)				-> Union[str, Any]:
    """simple docstring"""






    for m in model.modules():
        if isinstance(__a					,       bnb.nn.Linearabit							):
            return True
    return False



def       _A    (__a							)				-> Dict:
    """simple docstring"""






    return next(parameter.parameters()							).device





def       _A    (__a					,       __a					,       __a					,       __a					,       __a					,       __a					,       __a							)				-> Tuple:
    """simple docstring"""






    if fpaa_statistics is None:
        set_module_tensor_to_device(__a					,       __a					,       0					,       dtype=__a					,       value=__a							)
        SCREAMING_SNAKE_CASE_   :						str							=	param_name
        SCREAMING_SNAKE_CASE_   :						List[str]							=	model
        if "." in tensor_name:
            SCREAMING_SNAKE_CASE_   :						str							=	tensor_name.split('''.'''							)
            for split in splits[:-1]:
                SCREAMING_SNAKE_CASE_   :						Any							=	getattr(__a					,       __a							)
                if new_module is None:
                    raise ValueError(f'{module} has no attribute {split}.'							)
                SCREAMING_SNAKE_CASE_   :						Optional[int]							=	new_module
            SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	splits[-1]
        # offload weights
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	False
        offload_weight(module._parameters[tensor_name]					,       __a					,       __a					,       index=__a							)
        if hasattr(module._parameters[tensor_name]					,       '''SCB'''							):
            offload_weight(
                module._parameters[tensor_name].SCB					,       param_name.replace('''weight'''					,       '''SCB'''							)					,       __a					,       index=__a					,       )
    else:
        offload_weight(__a					,       __a					,       __a					,       index=__a							)
        offload_weight(__a					,       param_name.replace('''weight'''					,       '''SCB'''							)					,       __a					,       index=__a							)

    set_module_tensor_to_device(__a					,       __a					,       '''meta'''					,       dtype=__a					,       value=torch.empty(*param.size()							)							)


