


'''simple docstring'''
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

UpperCamelCase__    					=      logging.get_logger(__name__)





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__=None  ,					lowerCAmelCase__=None						)    ->	List[str]:
    # Recurse if needed
    if "." in tensor_name:
        UpperCAmelCase__			:		Optional[int]													=				tensor_name.split('''.'''						)
        for split in splits[:-1]:
            UpperCAmelCase__			:		str													=				getattr(lowerCAmelCase__  ,					lowerCAmelCase__						)
            if new_module is None:
                raise ValueError(F"""{module} has no attribute {split}."""						)
            UpperCAmelCase__			:		int													=				new_module
        UpperCAmelCase__			:		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}."""						)
    UpperCAmelCase__			:		Dict													=				tensor_name in module._buffers
    UpperCAmelCase__			:		Optional[int]													=				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}."""						)

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

    if is_abit or is_abit:
        UpperCAmelCase__			:		Any													=				module._parameters[tensor_name]
        if param.device.type != "cuda":
            if value is None:
                UpperCAmelCase__			:		List[str]													=				old_value.to(lowerCAmelCase__						)
            elif isinstance(lowerCAmelCase__  ,					torch.Tensor						):
                UpperCAmelCase__			:		int													=				value.to('''cpu'''						)
                if value.dtype == torch.inta:
                    UpperCAmelCase__			:		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:
                UpperCAmelCase__			:		Optional[Any]													=				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:
                UpperCAmelCase__			:		List[str]													=				new_value.T

            UpperCAmelCase__			:		List[str]													=				old_value.__dict__
            if is_abit:
                UpperCAmelCase__			:		Union[str, Any]													=				bnb.nn.IntaParams(lowerCAmelCase__  ,					requires_grad=lowerCAmelCase__  ,					**lowerCAmelCase__						).to(lowerCAmelCase__						)
            elif is_abit:
                UpperCAmelCase__			:		Optional[Any]													=				bnb.nn.Paramsabit(lowerCAmelCase__  ,					requires_grad=lowerCAmelCase__  ,					**lowerCAmelCase__						).to(lowerCAmelCase__						)

            UpperCAmelCase__			:		Optional[int]													=				new_value
            if fpaa_statistics is not None:
                setattr(module.weight  ,					'''SCB'''  ,					fpaa_statistics.to(lowerCAmelCase__						)						)

    else:
        if value is None:
            UpperCAmelCase__			:		Optional[Any]													=				old_value.to(lowerCAmelCase__						)
        elif isinstance(lowerCAmelCase__  ,					torch.Tensor						):
            UpperCAmelCase__			:		Union[str, Any]													=				value.to(lowerCAmelCase__						)
        else:
            UpperCAmelCase__			:		int													=				torch.tensor(lowerCAmelCase__  ,					device=lowerCAmelCase__						)

        if is_buffer:
            UpperCAmelCase__			:		Optional[Any]													=				new_value
        else:
            UpperCAmelCase__			:		Tuple													=				nn.Parameter(lowerCAmelCase__  ,					requires_grad=old_value.requires_grad						)
            UpperCAmelCase__			:		List[str]													=				new_value





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__=None  ,					lowerCAmelCase__=None  ,					lowerCAmelCase__=None  ,					lowerCAmelCase__=False						)    ->	Optional[int]:
    for name, module in model.named_children():
        if current_key_name is None:
            UpperCAmelCase__			:		int													=				[]
        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__						):
                        UpperCAmelCase__						,						UpperCAmelCase__			:		Tuple													=				module.weight.shape
                    else:
                        UpperCAmelCase__			:		Optional[int]													=				module.in_features
                        UpperCAmelCase__			:		List[Any]													=				module.out_features

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

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

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

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

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

    return filtered_module_names




'''simple docstring'''
import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script




@require_cpu
class 			lowerCamelCase_   (	unittest.TestCase      ):






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          debug_launcher(test_script.main )






      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          debug_launcher(test_ops.main )
