




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

lowerCAmelCase_					    = logging.get_logger(__name__)



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

  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}."""      )

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

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

      lowerCAmelCase__				:  Dict		       =     old_value.__dict__
      if is_abit:
        lowerCAmelCase__				:  List[str]		       =     bnb.nn.IntaParams(UpperCamelCase	,      requires_grad=UpperCamelCase	,      **UpperCamelCase      ).to(UpperCamelCase      )
      elif is_abit:
        lowerCAmelCase__				:  Tuple		       =     bnb.nn.Paramsabit(UpperCamelCase	,      requires_grad=UpperCamelCase	,      **UpperCamelCase      ).to(UpperCamelCase      )

      lowerCAmelCase__				:  Tuple		       =     new_value
      if fpaa_statistics is not None:
        setattr(module.weight	,      '''SCB'''	,      fpaa_statistics.to(UpperCamelCase      )      )

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

    if is_buffer:
      lowerCAmelCase__				:  Dict		       =     new_value
    else:
      lowerCAmelCase__				:  Any		       =     nn.Parameter(UpperCamelCase	,      requires_grad=old_value.requires_grad      )
      lowerCAmelCase__				:  Union[str, Any]		       =     new_value



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase=None	,      UpperCamelCase=None	,      UpperCamelCase=None	,      UpperCamelCase=False      )     ->	Optional[Any]:
  for name, module in model.named_children():
    if current_key_name is None:
      lowerCAmelCase__				:  str		       =     []
    current_key_name.append(UpperCamelCase      )

    if (isinstance(UpperCamelCase	,      nn.Linear      ) or isinstance(UpperCamelCase	,      UpperCamelCase      )) 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(UpperCamelCase      ) for key in modules_to_not_convert      ):
        with init_empty_weights():
          if isinstance(UpperCamelCase	,      UpperCamelCase      ):
            lowerCAmelCase__       ,			lowerCAmelCase__				:  Union[str, Any]		       =     module.weight.shape
          else:
            lowerCAmelCase__				:  Optional[int]		       =     module.in_features
            lowerCAmelCase__				:  Tuple		       =     module.out_features

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



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase=None	,      UpperCamelCase=None	,      UpperCamelCase=None      )     ->	Tuple:
  lowerCAmelCase__				:  str		       =     ['''lm_head'''] if modules_to_not_convert is None else modules_to_not_convert
  lowerCAmelCase__       ,			lowerCAmelCase__				:  Dict		       =     _replace_with_bnb_linear(
      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )

  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			(					*UpperCamelCase	,      **UpperCamelCase      )     ->	Dict:
  warnings.warn(
      '''`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead'''	,      UpperCamelCase	,      )
  return replace_with_bnb_linear(*UpperCamelCase	,      **UpperCamelCase      )



def      __lowerCAmelCase			(					*UpperCamelCase	,      **UpperCamelCase      )     ->	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'''	,      UpperCamelCase	,      )
  return set_module_quantized_tensor_to_device(*UpperCamelCase	,      **UpperCamelCase      )



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	Tuple:
  lowerCAmelCase__				:  Any		       =     deepcopy(UpperCamelCase      )  # this has 0 cost since it is done inside `init_empty_weights` context manager`
  tied_model.tie_weights()

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

  # Check if it is a base model
  lowerCAmelCase__				:  Optional[int]		       =     not hasattr(UpperCamelCase	,      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
  lowerCAmelCase__				:  Any		       =     list(model.named_children()      )
  lowerCAmelCase__				:  Union[str, Any]		       =     [list_modules[-1][0]]

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

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

  return filtered_module_names







from datetime import datetime

import matplotlib.pyplot as plt
import torch



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	str:
  for param in module.parameters():
    lowerCAmelCase__				:  int		       =     False



def      __lowerCAmelCase			(					)     ->	Optional[Any]:
  lowerCAmelCase__				:  List[Any]		       =     '''cuda''' if torch.cuda.is_available() else '''cpu'''
  if torch.backends.mps.is_available() and torch.backends.mps.is_built():
    lowerCAmelCase__				:  Optional[Any]		       =     '''mps'''
  if device == "mps":
    print(
        '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
        ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
        ''' with generations.'''      )
  return device



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	List[Any]:
  lowerCAmelCase__				:  str		       =     plt.imshow(UpperCamelCase      )
  fig.axes.get_xaxis().set_visible(UpperCamelCase      )
  fig.axes.get_yaxis().set_visible(UpperCamelCase      )
  plt.show()



def      __lowerCAmelCase			(					)     ->	str:
  lowerCAmelCase__				:  Dict		       =     datetime.now()
  lowerCAmelCase__				:  Optional[int]		       =     current_time.strftime('''%H:%M:%S'''      )
  return timestamp

