


import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


_A   =			logging.get_logger(__name__)







class 					lowerCamelCase		(  A_  ):





     def __init__(self				:   List[str]    ,   *_A				:   Optional[Any]    ,   **_A				:   Dict       )						->						None:
         warnings.warn(
             "The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
             " Please use SegformerImageProcessor instead."    ,   _A    ,   )
         super().__init__(*_A    ,   **_A       )



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   =			logging.get_logger(__name__)



def 						lowercase_      (       A__			,		A__			,		A__			,		A__=None			,		A__=None		)							->							str:
    """simple docstring"""

    if "." in tensor_name:
        snake_case								= tensor_name.split("."		)
        for split in splits[:-1]:
            snake_case								= getattr(A__			,		A__		)
            if new_module is None:
                raise ValueError(F'{module} has no attribute {split}.'		)
            snake_case								= new_module
        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}.'		)
    snake_case								= tensor_name in module._buffers
    snake_case								= getattr(A__			,		A__		)

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

    if is_abit or is_abit:
        snake_case								= module._parameters[tensor_name]
        if param.device.type != "cuda":
            if value is None:
                snake_case								= old_value.to(A__		)
            elif isinstance(A__			,		torch.Tensor		):
                snake_case								= value.to("cpu"		)
                if value.dtype == torch.inta:
                    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:
                snake_case								= torch.tensor(A__			,		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			,		A__		) and fpaa_statistics is None:
                snake_case								= new_value.T

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

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

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

        if is_buffer:
            snake_case								= new_value
        else:
            snake_case								= nn.Parameter(A__			,		requires_grad=old_value.requires_grad		)
            snake_case								= new_value



def 						lowercase_      (       A__			,		A__=None			,		A__=None			,		A__=None			,		A__=False		)							->							Optional[Any]:
    """simple docstring"""

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

        if (isinstance(A__			,		nn.Linear		) or isinstance(A__			,		A__		)) 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(A__		) for key in modules_to_not_convert		):
                with init_empty_weights():
                    if isinstance(A__			,		A__		):
                        snake_case					,							snake_case								= module.weight.shape
                    else:
                        snake_case								= module.in_features
                        snake_case								= module.out_features

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



def 						lowercase_      (       A__			,		A__=None			,		A__=None			,		A__=None		)							->							List[str]:
    """simple docstring"""

    snake_case								= ["lm_head"] if modules_to_not_convert is None else modules_to_not_convert
    snake_case					,							snake_case								= _replace_with_bnb_linear(
        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."
            " Please double check your model architecture, or submit an issue on github if you think this is"
            " a bug."		)

    return model



def 						lowercase_      (       *A__			,		**A__		)							->							Optional[int]:
    """simple docstring"""

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



def 						lowercase_      (       *A__			,		**A__		)							->							Any:
    """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"			,		A__			,		)
    return set_module_quantized_tensor_to_device(*A__			,		**A__		)





def 						lowercase_      (       A__		)							->							Union[str, Any]:
    """simple docstring"""

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

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

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

    # add last module together with tied weights
    snake_case								= set(A__		) - set(A__		)
    snake_case								= list(set(A__		)		) + list(A__		)

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

    return filtered_module_names

