


from ....configuration_utils import PretrainedConfig
from ....utils import logging


_snake_case  :	Any			      =       logging.get_logger(__name__)

_snake_case  :	Union[str, Any]			      =       {
    "Visual-Attention-Network/van-base": (
        "https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
    ),
}

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							str  						=						"van"

							def __init__(							self :						Any					,			lowerCamelCase :						int=224					,			lowerCamelCase :						Dict=3					,			lowerCamelCase :						Optional[Any]=[7, 3, 3, 3]					,			lowerCamelCase :						Dict=[4, 2, 2, 2]					,			lowerCamelCase :						List[str]=[64, 128, 320, 512]					,			lowerCamelCase :						str=[3, 3, 12, 3]					,			lowerCamelCase :						List[Any]=[8, 8, 4, 4]					,			lowerCamelCase :						Optional[int]="gelu"					,			lowerCamelCase :						str=0.02					,			lowerCamelCase :						Union[str, Any]=1E-6					,			lowerCamelCase :						Dict=1E-2					,			lowerCamelCase :						Dict=0.0					,			lowerCamelCase :						Any=0.0					,			**lowerCamelCase :						Union[str, Any]					,			)			->					List[Any]:
														super().__init__(**lowerCamelCase       )
														__snake_case	:    Optional[int]											=     image_size
														__snake_case	:    Union[str, Any]											=     num_channels
														__snake_case	:    Optional[Any]											=     patch_sizes
														__snake_case	:    Tuple											=     strides
														__snake_case	:    Tuple											=     hidden_sizes
														__snake_case	:    int											=     depths
														__snake_case	:    Optional[int]											=     mlp_ratios
														__snake_case	:    Optional[Any]											=     hidden_act
														__snake_case	:    Union[str, Any]											=     initializer_range
														__snake_case	:    str											=     layer_norm_eps
														__snake_case	:    int											=     layer_scale_init_value
														__snake_case	:    str											=     drop_path_rate
														__snake_case	:    Tuple											=     dropout_rate





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

_snake_case  :	Any			      =       logging.get_logger(__name__)







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=None							,			__lowerCamelCase=None			):
							# Recurse if needed
							if "." in tensor_name:
														__snake_case	:    Tuple											=     tensor_name.split("."			)
														for split in splits[:-1]:
																					__snake_case	:    List[str]											=     getattr(__lowerCamelCase							,			__lowerCamelCase			)
																					if new_module is None:
																												raise ValueError(F'{module} has no attribute {split}.'			)
																					__snake_case	:    Optional[int]											=     new_module
														__snake_case	:    Union[str, Any]											=     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}.'			)
							__snake_case	:    Optional[int]											=     tensor_name in module._buffers
							__snake_case	:    List[str]											=     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}.'			)

							__snake_case	:    List[Any]											=     False
							__snake_case	:    Optional[int]											=     False
							if is_buffer or not is_bitsandbytes_available():
														__snake_case	:    Dict											=     False
														__snake_case	:    Optional[int]											=     False
							else:
														__snake_case	:    Any											=     hasattr(bnb.nn							,			"Params4bit"			) and isinstance(module._parameters[tensor_name]							,			bnb.nn.Paramsabit			)
														__snake_case	:    Union[str, Any]											=     isinstance(module._parameters[tensor_name]							,			bnb.nn.IntaParams			)

							if is_abit or is_abit:
														__snake_case	:    Union[str, Any]											=     module._parameters[tensor_name]
														if param.device.type != "cuda":
																					if value is None:
																												__snake_case	:    Any											=     old_value.to(__lowerCamelCase			)
																					elif isinstance(__lowerCamelCase							,			torch.Tensor			):
																												__snake_case	:    int											=     value.to("cpu"			)
																												if value.dtype == torch.inta:
																																			__snake_case	:    Optional[int]											=     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:
																												__snake_case	:    Optional[int]											=     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:
																												__snake_case	:    List[Any]											=     new_value.T

																					__snake_case	:    Tuple											=     old_value.__dict__
																					if is_abit:
																												__snake_case	:    List[str]											=     bnb.nn.IntaParams(__lowerCamelCase							,			requires_grad=__lowerCamelCase							,			**__lowerCamelCase			).to(__lowerCamelCase			)
																					elif is_abit:
																												__snake_case	:    Optional[int]											=     bnb.nn.Paramsabit(__lowerCamelCase							,			requires_grad=__lowerCamelCase							,			**__lowerCamelCase			).to(__lowerCamelCase			)

																					__snake_case	:    Dict											=     new_value
																					if fpaa_statistics is not None:
																												setattr(module.weight							,			"SCB"							,			fpaa_statistics.to(__lowerCamelCase			)			)

							else:
														if value is None:
																					__snake_case	:    Tuple											=     old_value.to(__lowerCamelCase			)
														elif isinstance(__lowerCamelCase							,			torch.Tensor			):
																					__snake_case	:    Dict											=     value.to(__lowerCamelCase			)
														else:
																					__snake_case	:    List[Any]											=     torch.tensor(__lowerCamelCase							,			device=__lowerCamelCase			)

														if is_buffer:
																					__snake_case	:    Optional[Any]											=     new_value
														else:
																					__snake_case	:    int											=     nn.Parameter(__lowerCamelCase							,			requires_grad=old_value.requires_grad			)
																					__snake_case	:    Tuple											=     new_value







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase=None							,			__lowerCamelCase=None							,			__lowerCamelCase=None							,			__lowerCamelCase=False			):
							for name, module in model.named_children():
														if current_key_name is None:
																					__snake_case	:    List[Any]											=     []
														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			):
																																										__snake_case				,    __snake_case	:    Union[str, Any]											=     module.weight.shape
																																			else:
																																										__snake_case	:    List[Any]											=     module.in_features
																																										__snake_case	:    Tuple											=     module.out_features

																																			if quantization_config.quantization_method() == "llm_int8":
																																										__snake_case	:    Tuple											=     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							,			)
																																										__snake_case	:    List[str]											=     True
																																			else:
																																										if (
																																										    quantization_config.llm_inta_skip_modules is not None
																																										    and name in quantization_config.llm_inta_skip_modules
																																										):
																																																	pass
																																										else:
																																																	__snake_case	:    Optional[int]											=     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							,			)
																																																	__snake_case	:    int											=     True
                    # Store the module class in case we need to transpose the weight later
																																			__snake_case	:    str											=     type(__lowerCamelCase			)
																																			# Force requires grad to False to avoid unexpected errors
																																			model._modules[name].requires_grad_(__lowerCamelCase			)
														if len(list(module.children()			)			) > 0:
																					__snake_case				,    __snake_case	:    Tuple											=     _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        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase=None							,			__lowerCamelCase=None							,			__lowerCamelCase=None			):
							__snake_case	:    List[str]											=     ["lm_head"] if modules_to_not_convert is None else modules_to_not_convert
							__snake_case				,    __snake_case	:    Any											=     _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        lowerCAmelCase_  (      *__lowerCamelCase							,			**__lowerCamelCase			):
							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        lowerCAmelCase_  (      *__lowerCamelCase							,			**__lowerCamelCase			):
							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        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case	:    Optional[int]											=     deepcopy(__lowerCamelCase			)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
							tied_model.tie_weights()

							__snake_case	:    Any											=     find_tied_parameters(__lowerCamelCase			)
							# For compatibility with Accelerate < 0.18
							if isinstance(__lowerCamelCase							,			__lowerCamelCase			):
														__snake_case	:    Dict											=     sum(list(tied_params.values()			)							,			[]			) + list(tied_params.keys()			)
							else:
														__snake_case	:    Any											=     sum(__lowerCamelCase							,			[]			)
							__snake_case	:    Optional[int]											=     len(__lowerCamelCase			) > 0

							# Check if it is a base model
							__snake_case	:    Dict											=     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
							__snake_case	:    Optional[int]											=     list(model.named_children()			)
							__snake_case	:    List[str]											=     [list_modules[-1][0]]

							# add last module together with tied weights
							__snake_case	:    int											=     set(__lowerCamelCase			) - set(__lowerCamelCase			)
							__snake_case	:    Optional[Any]											=     list(set(__lowerCamelCase			)			) + list(__lowerCamelCase			)

							# remove ".weight" from the keys
							__snake_case	:    Optional[Any]											=     [".weight", ".bias"]
							__snake_case	:    Any											=     []
							for name in list_untouched:
														for name_to_remove in names_to_remove:
																					if name_to_remove in name:
																												__snake_case	:    Optional[int]											=     name.replace(__lowerCamelCase							,			""			)
														filtered_module_names.append(__lowerCamelCase			)

							return filtered_module_names

