


import importlib.metadata
import warnings
from copy import deepcopy

from packaging import version

from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available


if is_bitsandbytes_available():
		import bitsandbytes as bnb
		import torch
		import torch.nn as nn

		from ..pytorch_utils import ConvaD

if is_accelerate_available():
		from accelerate import init_empty_weights
		from accelerate.utils import find_tied_parameters

a__    :							Dict            =					logging.get_logger(__name__)




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None       ):




					'''simple docstring'''


					if "." in tensor_name:
										__SCREAMING_SNAKE_CASE						       =    tensor_name.split("."       )
										for split in splits[:-1]:
															__SCREAMING_SNAKE_CASE						       =    getattr(lowerCAmelCase_  ,						lowerCAmelCase_       )
															if new_module is None:
																				raise ValueError(f"""{module} has no attribute {split}."""       )
															__SCREAMING_SNAKE_CASE						       =    new_module
										__SCREAMING_SNAKE_CASE						       =    splits[-1]

					if tensor_name not in module._parameters and tensor_name not in module._buffers:
										raise ValueError(f"""{module} does not have a parameter or a buffer named {tensor_name}."""       )
					__SCREAMING_SNAKE_CASE						       =    tensor_name in module._buffers
					__SCREAMING_SNAKE_CASE						       =    getattr(lowerCAmelCase_  ,						lowerCAmelCase_       )

					if old_value.device == torch.device("meta"       ) and device not in ["meta", torch.device("meta"       )] and value is None:
										raise ValueError(f"""{tensor_name} is on the meta device, we need a `value` to put in on {device}."""       )

					__SCREAMING_SNAKE_CASE						       =    False
					__SCREAMING_SNAKE_CASE						       =    False
					if is_buffer or not is_bitsandbytes_available():
										__SCREAMING_SNAKE_CASE						       =    False
										__SCREAMING_SNAKE_CASE						       =    False
					else:
										__SCREAMING_SNAKE_CASE						       =    hasattr(bnb.nn  ,						"Params4bit"       ) and isinstance(module._parameters[tensor_name]  ,						bnb.nn.Paramsabit       )
										__SCREAMING_SNAKE_CASE						       =    isinstance(module._parameters[tensor_name]  ,						bnb.nn.IntaParams       )

					if is_abit or is_abit:
										__SCREAMING_SNAKE_CASE						       =    module._parameters[tensor_name]
										if param.device.type != "cuda":
															if value is None:
																				__SCREAMING_SNAKE_CASE						       =    old_value.to(lowerCAmelCase_       )
															elif isinstance(lowerCAmelCase_  ,						torch.Tensor       ):
																				__SCREAMING_SNAKE_CASE						       =    value.to("cpu"       )
																				if value.dtype == torch.inta:
																									__SCREAMING_SNAKE_CASE						       =    version.parse(importlib.metadata.version("bitsandbytes"       )       ) > version.parse(
																									    "0.37.2"       )
																									if not is_abit_serializable:
																														raise ValueError(
																														    "Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. "
																														    "Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`."       )
															else:
																				__SCREAMING_SNAKE_CASE						       =    torch.tensor(lowerCAmelCase_  ,						device="cpu"       )

															# Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
															# Since weights are saved in the correct "orientation", we skip transposing when loading.
															if issubclass(module.source_cls  ,						lowerCAmelCase_       ) and fpaa_statistics is None:
																				__SCREAMING_SNAKE_CASE						       =    new_value.T

															__SCREAMING_SNAKE_CASE						       =    old_value.__dict__
															if is_abit:
																				__SCREAMING_SNAKE_CASE						       =    bnb.nn.IntaParams(lowerCAmelCase_  ,						requires_grad=lowerCAmelCase_  ,						**lowerCAmelCase_       ).to(lowerCAmelCase_       )
															elif is_abit:
																				__SCREAMING_SNAKE_CASE						       =    bnb.nn.Paramsabit(lowerCAmelCase_  ,						requires_grad=lowerCAmelCase_  ,						**lowerCAmelCase_       ).to(lowerCAmelCase_       )

															__SCREAMING_SNAKE_CASE						       =    new_value
															if fpaa_statistics is not None:
																				setattr(module.weight  ,						"SCB"  ,						fpaa_statistics.to(lowerCAmelCase_       )       )

					else:
										if value is None:
															__SCREAMING_SNAKE_CASE						       =    old_value.to(lowerCAmelCase_       )
										elif isinstance(lowerCAmelCase_  ,						torch.Tensor       ):
															__SCREAMING_SNAKE_CASE						       =    value.to(lowerCAmelCase_       )
										else:
															__SCREAMING_SNAKE_CASE						       =    torch.tensor(lowerCAmelCase_  ,						device=lowerCAmelCase_       )

										if is_buffer:
															__SCREAMING_SNAKE_CASE						       =    new_value
										else:
															__SCREAMING_SNAKE_CASE						       =    nn.Parameter(lowerCAmelCase_  ,						requires_grad=old_value.requires_grad       )
															__SCREAMING_SNAKE_CASE						       =    new_value




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=False       ):




					'''simple docstring'''


					for name, module in model.named_children():
										if current_key_name is None:
															__SCREAMING_SNAKE_CASE						       =    []
										current_key_name.append(lowerCAmelCase_       )

										if (isinstance(lowerCAmelCase_  ,						nn.Linear       ) or isinstance(lowerCAmelCase_  ,						lowerCAmelCase_       )) and name not in modules_to_not_convert:
															# Check if the current key is not in the `modules_to_not_convert`
															if not any(key in ".".join(lowerCAmelCase_       ) for key in modules_to_not_convert       ):
																				with init_empty_weights():
																									if isinstance(lowerCAmelCase_  ,						lowerCAmelCase_       ):
																														__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    module.weight.shape
																									else:
																														__SCREAMING_SNAKE_CASE						       =    module.in_features
																														__SCREAMING_SNAKE_CASE						       =    module.out_features

																									if quantization_config.quantization_method() == "llm_int8":
																														__SCREAMING_SNAKE_CASE						       =    bnb.nn.LinearabitLt(
																														    lowerCAmelCase_  ,						lowerCAmelCase_  ,						module.bias is not None  ,						has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight  ,						threshold=quantization_config.llm_inta_threshold  ,						)
																														__SCREAMING_SNAKE_CASE						       =    True
																									else:
																														if (
																														    quantization_config.llm_inta_skip_modules is not None
																														    and name in quantization_config.llm_inta_skip_modules
																														):
																																			pass
																														else:
																																			__SCREAMING_SNAKE_CASE						       =    bnb.nn.Linearabit(
																																			    lowerCAmelCase_  ,						lowerCAmelCase_  ,						module.bias is not None  ,						quantization_config.bnb_abit_compute_dtype  ,						compress_statistics=quantization_config.bnb_abit_use_double_quant  ,						quant_type=quantization_config.bnb_abit_quant_type  ,						)
																																			__SCREAMING_SNAKE_CASE						       =    True
                    # Store the module class in case we need to transpose the weight later
																									__SCREAMING_SNAKE_CASE						       =    type(lowerCAmelCase_       )
																									# Force requires grad to False to avoid unexpected errors
																									model._modules[name].requires_grad_(lowerCAmelCase_       )
										if len(list(module.children()       )       ) > 0:
															__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    _replace_with_bnb_linear(
															    lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						has_been_replaced=lowerCAmelCase_  ,						)
										# Remove the last key for recursion
										current_key_name.pop(-1       )
					return model, has_been_replaced




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    ["lm_head"] if modules_to_not_convert is None else modules_to_not_convert
					__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    _replace_with_bnb_linear(
					    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."
										    " 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_       ):




					'''simple docstring'''


					warnings.warn(
					    "`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead"  ,						lowerCAmelCase_  ,						)
					return replace_with_bnb_linear(*lowerCAmelCase_  ,						**lowerCAmelCase_       )




def        UpperCAmelCase__	(*lowerCAmelCase_  ,						**lowerCAmelCase_       ):




					'''simple docstring'''


					warnings.warn(
					    "`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead"  ,						lowerCAmelCase_  ,						)
					return set_module_quantized_tensor_to_device(*lowerCAmelCase_  ,						**lowerCAmelCase_       )




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    deepcopy(lowerCAmelCase_       )  # this has 0 cost since it is done inside `init_empty_weights` context manager`
					tied_model.tie_weights()

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

					# Check if it is a base model
					__SCREAMING_SNAKE_CASE						       =    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
					__SCREAMING_SNAKE_CASE						       =    list(model.named_children()       )
					__SCREAMING_SNAKE_CASE						       =    [list_modules[-1][0]]

					# add last module together with tied weights
					__SCREAMING_SNAKE_CASE						       =    set(lowerCAmelCase_       ) - set(lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    list(set(lowerCAmelCase_       )       ) + list(lowerCAmelCase_       )

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

					return filtered_module_names




"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin




@skip_mps
class 			UpperCamelCase_     (			UpperCamelCase      ,  UpperCamelCase      ,  unittest.TestCase):

	"""simple docstring"""





	snake_case__    :  str    =      IFInpaintingSuperResolutionPipeline
	snake_case__    :  Optional[Any]    =      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
	snake_case__    :  Union[str, Any]    =      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"})
	snake_case__    :  Dict    =      PipelineTesterMixin.required_optional_params - {"latents"}




	def   UpperCAmelCase_				(			self      :    List[Any]       )	->     Optional[int]:
						return self._get_superresolution_dummy_components()




	def   UpperCAmelCase_				(			self      :    List[str]      ,      UpperCAmelCase__      :    List[Any]      ,      UpperCAmelCase__      :    List[str]=0       )	->     List[str]:
						if str(UpperCAmelCase__       ).startswith("mps"       ):
											__SCREAMING_SNAKE_CASE						       =    torch.manual_seed(UpperCAmelCase__       )
						else:
											__SCREAMING_SNAKE_CASE						       =    torch.Generator(device=UpperCAmelCase__       ).manual_seed(UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    floats_tensor((1, 3, 1_6, 1_6)      ,      rng=random.Random(UpperCAmelCase__       )       ).to(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    floats_tensor((1, 3, 3_2, 3_2)      ,      rng=random.Random(UpperCAmelCase__       )       ).to(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    floats_tensor((1, 3, 3_2, 3_2)      ,      rng=random.Random(UpperCAmelCase__       )       ).to(UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    {
						    "prompt": "A painting of a squirrel eating a burger",
						    "image": image,
						    "original_image": original_image,
						    "mask_image": mask_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      :    str       )	->     Dict:
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3       )




	def   UpperCAmelCase_				(			self      :    str       )	->     Tuple:
						self._test_save_load_optional_components()




	@unittest.skipIf(torch_device != "cuda"      ,      reason="float16 requires CUDA"       )
	def   UpperCAmelCase_				(			self      :    Tuple       )	->     Tuple:
						# 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      :    Optional[Any]       )	->     List[Any]:
						self._test_attention_slicing_forward_pass(expected_max_diff=1E-2       )




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




	def   UpperCAmelCase_				(			self      :    Union[str, Any]       )	->     Optional[Any]:
						self._test_inference_batch_single_identical(
						    expected_max_diff=1E-2      ,      )
