




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	:						Optional[int]				=     logging.get_logger(__name__)



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

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

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

      lowercase    :Tuple      	=						old_value.__dict__
      if is_abit:
        lowercase    :int      	=						bnb.nn.IntaParams(lowerCamelCase,			requires_grad=lowerCamelCase,			**lowerCamelCase   ).to(lowerCamelCase   )
      elif is_abit:
        lowercase    :Any      	=						bnb.nn.Paramsabit(lowerCamelCase,			requires_grad=lowerCamelCase,			**lowerCamelCase   ).to(lowerCamelCase   )

      lowercase    :List[str]      	=						new_value
      if fpaa_statistics is not None:
        setattr(module.weight,			"SCB",			fpaa_statistics.to(lowerCamelCase   )   )

  else:
    if value is None:
      lowercase    :List[str]      	=						old_value.to(lowerCamelCase   )
    elif isinstance(lowerCamelCase,			torch.Tensor   ):
      lowercase    :str      	=						value.to(lowerCamelCase   )
    else:
      lowercase    :Any      	=						torch.tensor(lowerCamelCase,			device=lowerCamelCase   )

    if is_buffer:
      lowercase    :int      	=						new_value
    else:
      lowercase    :Optional[Any]      	=						nn.Parameter(lowerCamelCase,			requires_grad=old_value.requires_grad   )
      lowercase    :Optional[int]      	=						new_value



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase=None,			lowerCamelCase=None,			lowerCamelCase=None,			lowerCamelCase=False   ):
  for name, module in model.named_children():
    if current_key_name is None:
      lowercase    :Optional[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   ):
            lowercase    :List[str]      	=						module.weight.shape
          else:
            lowercase    :Tuple      	=						module.in_features
            lowercase    :Tuple      	=						module.out_features

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

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

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

  # add last module together with tied weights
  lowercase    :str      	=						set(lowerCamelCase   ) - set(lowerCamelCase   )
  lowercase    :List[Any]      	=						list(set(lowerCamelCase   )   ) + list(lowerCamelCase   )

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

  return filtered_module_names









from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst



def       UpperCAmelCase__  (     ):
  lowercase	,    lowercase    :Union[str, Any]      	=						9, 14  # noqa: F841
  lowercase    :Optional[Any]      	=						[
      [0, 1, 4],
      [0, 7, 8],
      [1, 2, 8],
      [7, 8, 7],
      [7, 6, 1],
      [2, 8, 2],
      [8, 6, 6],
      [2, 3, 7],
      [2, 5, 4],
      [6, 5, 2],
      [3, 5, 14],
      [3, 4, 9],
      [5, 4, 10],
      [1, 7, 11],
  ]

  lowercase    :Optional[Any]      	=						defaultdict(lowerCamelCase   )
  for nodea, nodea, cost in edges:
    adjancency[nodea].append([nodea, cost]   )
    adjancency[nodea].append([nodea, cost]   )

  lowercase    :Optional[int]      	=						mst(lowerCamelCase   )

  lowercase    :Any      	=						[
      [7, 6, 1],
      [2, 8, 2],
      [6, 5, 2],
      [0, 1, 4],
      [2, 5, 4],
      [2, 3, 7],
      [0, 7, 8],
      [3, 4, 9],
  ]

  for answer in expected:
    lowercase    :Any      	=						tuple(answer[:2]   )
    lowercase    :str      	=						tuple(edge[::-1]   )
    assert edge in result or reverse in result



