
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


lowercase__		:Dict   =    logging.getLogger(__name__)



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ = None ,			lowerCAmelCase__ = None ,			lowerCAmelCase__ = None ,			lowerCAmelCase__ = None ,			lowerCAmelCase__ = None ,			lowerCAmelCase__ = False ,			):
					'''simple docstring'''
					lowercase							=						bnb_quantization_config.load_in_abit
					lowercase							=						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.'''		)

					lowercase							=						[]
					# custom device map
					if isinstance(lowerCAmelCase__ ,			lowerCAmelCase__		) and len(device_map.keys()		) > 1:
										lowercase							=						[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:
										lowercase							=						get_keys_to_not_convert(lowerCAmelCase__		)

					# add cpu modules to skip modules only for 4-bit modules
					if load_in_abit:
										bnb_quantization_config.skip_modules.extend(lowerCAmelCase__		)
					lowercase							=						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:
										lowercase							=						[]
					lowercase							=						bnb_quantization_config.keep_in_fpaa_modules
					modules_to_not_convert.extend(lowerCAmelCase__		)

					# compatibility with peft
					lowercase							=						load_in_abit
					lowercase							=						load_in_abit

					lowercase							=						get_parameter_device(lowerCAmelCase__		)
					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.'''		)
										lowercase							=						replace_with_bnb_layers(lowerCAmelCase__ ,			lowerCAmelCase__ ,			modules_to_not_convert=lowerCAmelCase__		)
										# convert param to the right dtype
										lowercase							=						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:
																									lowercase							=						name.replace('''.weight''' ,			''''''		).replace('''.bias''' ,			''''''		)
																									lowercase							=						getattr(lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		)
																									if param is not None:
																														param.to(torch.floataa		)
															elif torch.is_floating_point(lowerCAmelCase__		):
																				param.to(lowerCAmelCase__		)
										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():
															lowercase							=						replace_with_bnb_layers(
															    lowerCAmelCase__ ,			lowerCAmelCase__ ,			modules_to_not_convert=lowerCAmelCase__		)

										lowercase							=						get_quantized_model_device_map(
										    lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			max_memory=lowerCAmelCase__ ,			no_split_module_classes=lowerCAmelCase__ ,			)
										if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
															lowercase							=						True

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

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



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__=None ,			lowerCAmelCase__=None ,			lowerCAmelCase__=None		):
					'''simple docstring'''
					if device_map is None:
										if torch.cuda.is_available():
															lowercase							=						{'''''': 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(lowerCAmelCase__ ,			lowerCAmelCase__		):
										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\'.'''		)

										lowercase							=						{}
										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		)
										    }		)

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

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

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

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

										lowercase							=						{
										    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        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__=None ,			lowerCAmelCase__=None		):
					'''simple docstring'''

					if modules_to_not_convert is None:
										lowercase							=						[]

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



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					# Create a copy of the model
					with init_empty_weights():
										lowercase							=						deepcopy(lowerCAmelCase__		)  # this has 0 cost since it is done inside `init_empty_weights` context manager`

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

					# Check if it is a base model
					lowercase							=						False
					if hasattr(lowerCAmelCase__ ,			'''base_model_prefix'''		):
										lowercase							=						not hasattr(lowerCAmelCase__ ,			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
					lowercase							=						list(model.named_children()		)
					lowercase							=						[list_modules[-1][0]]

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

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

					return filtered_module_names



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					for m in model.modules():
										if isinstance(lowerCAmelCase__ ,			bnb.nn.Linearabit		):
															return True
					return False



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return next(parameter.parameters()		).device



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					# 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(lowerCAmelCase__ ,			lowerCAmelCase__ ,			0 ,			dtype=lowerCAmelCase__ ,			value=lowerCAmelCase__		)
										lowercase							=						param_name
										lowercase							=						model
										if "." in tensor_name:
															lowercase							=						tensor_name.split('''.'''		)
															for split in splits[:-1]:
																				lowercase							=						getattr(lowerCAmelCase__ ,			lowerCAmelCase__		)
																				if new_module is None:
																									raise ValueError(f'{module} has no attribute {split}.'		)
																				lowercase							=						new_module
															lowercase							=						splits[-1]
										# offload weights
										lowercase							=						False
										offload_weight(module._parameters[tensor_name] ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			index=lowerCAmelCase__		)
										if hasattr(module._parameters[tensor_name] ,			'''SCB'''		):
															offload_weight(
															    module._parameters[tensor_name].SCB ,			param_name.replace('''weight''' ,			'''SCB'''		) ,			lowerCAmelCase__ ,			index=lowerCAmelCase__ ,			)
					else:
										offload_weight(lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			index=lowerCAmelCase__		)
										offload_weight(lowerCAmelCase__ ,			param_name.replace('''weight''' ,			'''SCB'''		) ,			lowerCAmelCase__ ,			index=lowerCAmelCase__		)

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












import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class   lowerCamelCase      (_snake_case ,  _snake_case ,  unittest.TestCase			):







		'''simple docstring'''



		_snake_case    :    Union[str, Any]       						=  IFImgaImgSuperResolutionPipeline
		_snake_case    :    Union[str, Any]       						=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
		_snake_case    :    List[Any]       						=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''original_image'''}			)
		_snake_case    :    List[str]       						=  PipelineTesterMixin.required_optional_params - {'''latents'''}



		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							return self._get_superresolution_dummy_components()



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase=0   )	->							Any:
							if str(_UpperCamelCase   ).startswith('mps'   ):
												UpperCAmelCase_    :      List[Any]            =						torch.manual_seed(_UpperCamelCase   )
							else:
												UpperCAmelCase_    :      int            =						torch.Generator(device=_UpperCamelCase   ).manual_seed(_UpperCamelCase   )

							UpperCAmelCase_    :      List[Any]            =						floats_tensor((1, 3, 3_2, 3_2)				,		rng=random.Random(_UpperCamelCase   )   ).to(_UpperCamelCase   )
							UpperCAmelCase_    :      Dict            =						floats_tensor((1, 3, 1_6, 1_6)				,		rng=random.Random(_UpperCamelCase   )   ).to(_UpperCamelCase   )

							UpperCAmelCase_    :      Tuple            =						{
							    'prompt': 'A painting of a squirrel eating a burger',
							    'image': image,
							    'original_image': original_image,
							    'generator': generator,
							    'num_inference_steps': 2,
							    'output_type': 'numpy',
							}

							return inputs



		@unittest.skipIf(
		    torch_device != 'cuda' or not is_xformers_available()				,		reason='XFormers attention is only available with CUDA and `xformers` installed'				,		)
		def 					__UpperCAmelCase			(							self   )	->							Any:
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							self._test_save_load_optional_components()



		@unittest.skipIf(torch_device != 'cuda'				,		reason='float16 requires CUDA'   )
		def 					__UpperCAmelCase			(							self   )	->							str:
							# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
							super().test_save_load_floataa(expected_max_diff=1E-1   )



		def 					__UpperCAmelCase			(							self   )	->							List[Any]:
							self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )



		def 					__UpperCAmelCase			(							self   )	->							Union[str, Any]:
							self._test_save_load_local()



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							self._test_inference_batch_single_identical(
							    expected_max_diff=1E-2				,		)






