


'''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					       =							logging.getLogger(__name__)


def       _snake_case     (  A				,					A				,					A = None				,					A = None				,					A = None				,					A = None				,					A = None				,					A = False				,					)		->					int:
							lowerCAmelCase__      =			bnb_quantization_config.load_in_abit
							lowerCAmelCase__      =			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.''' )

							lowerCAmelCase__      =			[]
							# custom device map
							if isinstance(A				,					A ) and len(device_map.keys() ) > 1:
														lowerCAmelCase__      =			[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:
														lowerCAmelCase__      =			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 )
							lowerCAmelCase__      =			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:
														lowerCAmelCase__      =			[]
							lowerCAmelCase__      =			bnb_quantization_config.keep_in_fpaa_modules
							modules_to_not_convert.extend(A )

							# compatibility with peft
							lowerCAmelCase__      =			load_in_abit
							lowerCAmelCase__      =			load_in_abit

							lowerCAmelCase__      =			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.''' )
														lowerCAmelCase__      =			replace_with_bnb_layers(A				,					A				,					modules_to_not_convert=A )
														# convert param to the right dtype
														lowerCAmelCase__      =			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:
																																			lowerCAmelCase__      =			name.replace('''.weight'''				,					'''''' ).replace('''.bias'''				,					'''''' )
																																			lowerCAmelCase__      =			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():
																					lowerCAmelCase__      =			replace_with_bnb_layers(
																					    A				,					A				,					modules_to_not_convert=A )

														lowerCAmelCase__      =			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():
																					lowerCAmelCase__      =			True

														lowerCAmelCase__      =			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       _snake_case     (  A				,					A				,					A=None				,					A=None				,					A=None )		->					Dict:
							if device_map is None:
														if torch.cuda.is_available():
																					lowerCAmelCase__      =			{'''''': 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\'.''' )

														lowerCAmelCase__      =			{}
														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 )
														    } )

														lowerCAmelCase__      =			{}
														lowerCAmelCase__      =			special_dtypes
														lowerCAmelCase__      =			no_split_module_classes
														lowerCAmelCase__      =			bnb_quantization_config.target_dtype

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

														lowerCAmelCase__      =			max_memory
														lowerCAmelCase__      =			infer_auto_device_map(A				,					**A )

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

														lowerCAmelCase__      =			{
														    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       _snake_case     (  A				,					A				,					A=None				,					A=None )		->					str:

							if modules_to_not_convert is None:
														lowerCAmelCase__      =			[]

							lowerCAmelCase__     ,      lowerCAmelCase__      =			_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       _snake_case     (  A				,					A				,					A=None				,					A=None				,					)		->					Any:
							lowerCAmelCase__      =			False
							for name, module in model.named_children():
														if current_key_name is None:
																					lowerCAmelCase__      =			[]
														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`
																					lowerCAmelCase__      =			'''.'''.join(A )
																					lowerCAmelCase__      =			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:
																																			lowerCAmelCase__      =			False
																																			break
																					if proceed:
																												# Load bnb module with empty weight and replace ``nn.Linear` module
																												if bnb_quantization_config.load_in_abit:
																																			lowerCAmelCase__      =			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:
																																			lowerCAmelCase__      =			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''' )
																												lowerCAmelCase__      =			module.weight.data
																												if module.bias is not None:
																																			lowerCAmelCase__      =			module.bias.data
																												bnb_module.requires_grad_(A )
																												setattr(A				,					A				,					A )
																												lowerCAmelCase__      =			True
														if len(list(module.children() ) ) > 0:
																					lowerCAmelCase__     ,      lowerCAmelCase__      =			_replace_with_bnb_layers(
																					    A				,					A				,					A				,					A )
																					lowerCAmelCase__      =			has_been_replaced | _has_been_replaced
														# Remove the last key for recursion
														current_key_name.pop(-1 )
							return model, has_been_replaced


def       _snake_case     (  A )		->					Dict:
							# Create a copy of the model
							with init_empty_weights():
														lowerCAmelCase__      =			deepcopy(A )  # this has 0 cost since it is done inside `init_empty_weights` context manager`

							lowerCAmelCase__      =			find_tied_parameters(A )
							# For compatibility with Accelerate < 0.18
							if isinstance(A				,					A ):
														lowerCAmelCase__      =			sum(list(tied_params.values() )				,					[] ) + list(tied_params.keys() )
							else:
														lowerCAmelCase__      =			sum(A				,					[] )
							lowerCAmelCase__      =			len(A ) > 0

							# Check if it is a base model
							lowerCAmelCase__      =			False
							if hasattr(A				,					'''base_model_prefix''' ):
														lowerCAmelCase__      =			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
							lowerCAmelCase__      =			list(model.named_children() )
							lowerCAmelCase__      =			[list_modules[-1][0]]

							# add last module together with tied weights
							lowerCAmelCase__      =			set(A ) - set(A )
							lowerCAmelCase__      =			list(set(A ) ) + list(A )

							# remove ".weight" from the keys
							lowerCAmelCase__      =			['''.weight''', '''.bias''']
							lowerCAmelCase__      =			[]
							for name in list_untouched:
														for name_to_remove in names_to_remove:
																					if name_to_remove in name:
																												lowerCAmelCase__      =			name.replace(A				,					'''''' )
														filtered_module_names.append(A )

							return filtered_module_names


def       _snake_case     (  A )		->					str:
							for m in model.modules():
														if isinstance(A				,					bnb.nn.Linearabit ):
																					return True
							return False


def       _snake_case     (  A )		->					int:
							return next(parameter.parameters() ).device






def       _snake_case     (  A				,					A				,					A				,					A				,					A				,					A				,					A )		->					int:
							# 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(A				,					A				,					0				,					dtype=A				,					value=A )
														lowerCAmelCase__      =			param_name
														lowerCAmelCase__      =			model
														if "." in tensor_name:
																					lowerCAmelCase__      =			tensor_name.split('''.''' )
																					for split in splits[:-1]:
																												lowerCAmelCase__      =			getattr(A				,					A )
																												if new_module is None:
																																			raise ValueError(F"""{module} has no attribute {split}.""" )
																												lowerCAmelCase__      =			new_module
																					lowerCAmelCase__      =			splits[-1]
														# offload weights
														lowerCAmelCase__      =			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() ) )



'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


__UpperCAmelCase					       =							False




class 	a__       (							unittest.TestCase						):





							'''simple docstring'''






							pass




@slow
@require_torch_gpu
class 	a__       (							unittest.TestCase						):





							'''simple docstring'''






							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__      =			VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''     )
														pipe.to(lowerCamelCase_     )
														pipe.set_progress_bar_config(disable=lowerCamelCase_     )

														lowerCAmelCase__      =			load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''     )
														lowerCAmelCase__      =			torch.manual_seed(0     )
														lowerCAmelCase__      =			pipe(
														    image=lowerCamelCase_      ,					generator=lowerCamelCase_      ,					guidance_scale=7.5      ,					num_inference_steps=50      ,					output_type='''numpy'''      ,					).images

														lowerCAmelCase__      =			image[0, 2_53:2_56, 2_53:2_56, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__      =			np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]     )

														assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2