





import logging
import os
from typing import Dict, List, Optional, Union

import torch
import torch.nn as nn

from accelerate.utils.imports import (
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_bnb_available,
)

from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
    find_tied_parameters,
    get_balanced_memory,
    infer_auto_device_map,
    load_checkpoint_in_model,
    offload_weight,
    set_module_tensor_to_device,
)


if is_bnb_available():
    import bitsandbytes as bnb

from copy import deepcopy


snake_case_				=			logging.getLogger(__name__)






def lowerCamelCase__    (							snake_case_	:  torch.nn.Module       ,      snake_case_	:  BnbQuantizationConfig       ,      snake_case_	:  Union[str, os.PathLike] = None       ,      snake_case_	:  Optional[Dict[str, Union[int, str, torch.device]]] = None       ,      snake_case_	:  Optional[List[str]] = None       ,      snake_case_	:  Optional[Dict[Union[int, str], Union[int, str]]] = None       ,      snake_case_	:  Optional[Union[str, os.PathLike]] = None       ,      snake_case_	:  bool = False       ,      )      ->				Optional[Any]:
   __snake_case							       =   bnb_quantization_config.load_in_abit
   __snake_case							       =   bnb_quantization_config.load_in_abit

   if load_in_abit and not is_abit_bnb_available():
      raise ImportError(
          '''You have a version of `bitsandbytes` that is not compatible with 8bit quantization,'''
          ''' make sure you have the latest version of `bitsandbytes` installed.'''       )
   if load_in_abit and not is_abit_bnb_available():
      raise ValueError(
          '''You have a version of `bitsandbytes` that is not compatible with 4bit quantization,'''
          '''make sure you have the latest version of `bitsandbytes` installed.'''       )

   __snake_case							       =   []
   # custom device map
   if isinstance(snake_case_       ,      snake_case_       ) and len(device_map.keys()       ) > 1:
      __snake_case							       =   [key for key, value in device_map.items() if value in ['''disk''', '''cpu''']]

   # We keep some modules such as the lm_head in their original dtype for numerical stability reasons
   if bnb_quantization_config.skip_modules is None:
      __snake_case							       =   get_keys_to_not_convert(snake_case_       )

   # add cpu modules to skip modules only for 4-bit modules
   if load_in_abit:
      bnb_quantization_config.skip_modules.extend(snake_case_       )
   __snake_case							       =   bnb_quantization_config.skip_modules

   # We add the modules we want to keep in full precision
   if bnb_quantization_config.keep_in_fpaa_modules is None:
      __snake_case							       =   []
   __snake_case							       =   bnb_quantization_config.keep_in_fpaa_modules
   modules_to_not_convert.extend(snake_case_       )

   # compatibility with peft
   __snake_case							       =   load_in_abit
   __snake_case							       =   load_in_abit

   __snake_case							       =   get_parameter_device(snake_case_       )
   if model_device.type != "meta":
      # quantization of an already loaded model
      logger.warning(
          '''It is not recommended to quantize a loaded model. '''
          '''The model should be instantiated under the `init_empty_weights` context manager.'''       )
      __snake_case							       =   replace_with_bnb_layers(snake_case_       ,      snake_case_       ,      modules_to_not_convert=snake_case_       )
      # convert param to the right dtype
      __snake_case							       =   bnb_quantization_config.torch_dtype
      for name, param in model.state_dict().items():
         if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules       ):
            param.to(torch.floataa       )
            if param.dtype != torch.floataa:
               __snake_case							       =   name.replace('''.weight'''       ,      ''''''       ).replace('''.bias'''       ,      ''''''       )
               __snake_case							       =   getattr(snake_case_       ,      snake_case_       ,      snake_case_       )
               if param is not None:
                  param.to(torch.floataa       )
         elif torch.is_floating_point(snake_case_       ):
            param.to(snake_case_       )
      if model_device.type == "cuda":
         # move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
         model.cuda(torch.cuda.current_device()       )
         torch.cuda.empty_cache()
      elif torch.cuda.is_available():
         model.to(torch.cuda.current_device()       )
      else:
         raise RuntimeError('''No GPU found. A GPU is needed for quantization.'''       )
      logger.info(
          f"""The model device type is {model_device.type}. However, cuda is needed for quantization."""
          '''We move the model to cuda.'''       )
      return model

   elif weights_location is None:
      raise RuntimeError(
          f"""`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} """       )

   else:
      with init_empty_weights():
         __snake_case							       =   replace_with_bnb_layers(
             snake_case_       ,      snake_case_       ,      modules_to_not_convert=snake_case_       )

      __snake_case							       =   get_quantized_model_device_map(
          snake_case_       ,      snake_case_       ,      snake_case_       ,      max_memory=snake_case_       ,      no_split_module_classes=snake_case_       ,      )
      if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
         __snake_case							       =   True

      __snake_case							       =   any(x in list(device_map.values()       ) for x in ['''cpu''', '''disk''']       )

      load_checkpoint_in_model(
          snake_case_       ,      snake_case_       ,      snake_case_       ,      dtype=bnb_quantization_config.torch_dtype       ,      offload_folder=snake_case_       ,      offload_state_dict=snake_case_       ,      keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules       ,      offload_abit_bnb=load_in_abit and offload       ,      )
      return dispatch_model(snake_case_       ,      device_map=snake_case_       ,      offload_dir=snake_case_       )






def lowerCamelCase__    (							snake_case_	:  List[str]       ,      snake_case_	:  Any       ,      snake_case_	:  List[Any]=None       ,      snake_case_	:  int=None       ,      snake_case_	:  Dict=None       )      ->				Any:
   if device_map is None:
      if torch.cuda.is_available():
         __snake_case							       =   {'''''': torch.cuda.current_device()}
      else:
         raise RuntimeError('''No GPU found. A GPU is needed for quantization.'''       )
      logger.info('''The device_map was not initialized.''' '''Setting device_map to `{\'\':torch.cuda.current_device()}`.'''       )

   if isinstance(snake_case_       ,      snake_case_       ):
      if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
         raise ValueError(
             '''If passing a string for `device_map`, please choose \'auto\', \'balanced\', \'balanced_low_0\' or '''
             '''\'sequential\'.'''       )

      __snake_case							       =   {}
      special_dtypes.update(
          {
              name: bnb_quantization_config.torch_dtype
              for name, _ in model.named_parameters()
              if any(m in name for m in bnb_quantization_config.skip_modules       )
          }       )
      special_dtypes.update(
          {
              name: torch.floataa
              for name, _ in model.named_parameters()
              if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules       )
          }       )

      __snake_case							       =   {}
      __snake_case							       =   special_dtypes
      __snake_case							       =   no_split_module_classes
      __snake_case							       =   bnb_quantization_config.target_dtype

      # get max_memory for each device.
      if device_map != "sequential":
         __snake_case							       =   get_balanced_memory(
             snake_case_       ,      low_zero=(device_map == '''balanced_low_0''')       ,      max_memory=snake_case_       ,      **snake_case_       ,      )

      __snake_case							       =   max_memory
      __snake_case							       =   infer_auto_device_map(snake_case_       ,      **snake_case_       )

   if isinstance(snake_case_       ,      snake_case_       ):
      # check if don't have any quantized module on the cpu
      __snake_case							       =   bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules

      __snake_case							       =   {
          key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
      }
      for device in ["cpu", "disk"]:
         if device in device_map_without_some_modules.values():
            if bnb_quantization_config.load_in_abit:
               raise ValueError(
                   '''
                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit
                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping
                        these modules in `torch_dtype`, you need to pass a custom `device_map` to
                        `load_and_quantize_model`. Check
                        https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk
                        for more details.
                        '''       )
            else:
               logger.info(
                   '''Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit'''       )
      del device_map_without_some_modules
   return device_map






def lowerCamelCase__    (							snake_case_	:  Any       ,      snake_case_	:  Any       ,      snake_case_	:  Union[str, Any]=None       ,      snake_case_	:  Any=None       )      ->				int:

   if modules_to_not_convert is None:
      __snake_case							       =   []

   __snake_case       ,	__snake_case							       =   _replace_with_bnb_layers(
       snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       )
   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.'''
          ''' this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers.'''
          ''' Please double check your model architecture, or submit an issue on github if you think this is'''
          ''' a bug.'''       )
   return model






def lowerCamelCase__    (							snake_case_	:  List[str]       ,      snake_case_	:  Dict       ,      snake_case_	:  str=None       ,      snake_case_	:  int=None       ,      )      ->				Any:
   __snake_case							       =   False
   for name, module in model.named_children():
      if current_key_name is None:
         __snake_case							       =   []
      current_key_name.append(snake_case_       )
      if isinstance(snake_case_       ,      nn.Linear       ) and name not in modules_to_not_convert:
         # Check if the current key is not in the `modules_to_not_convert`
         __snake_case							       =   '''.'''.join(snake_case_       )
         __snake_case							       =   True
         for key in modules_to_not_convert:
            if (
                (key in current_key_name_str) and (key + "." in current_key_name_str)
            ) or key == current_key_name_str:
               __snake_case							       =   False
               break
         if proceed:
            # Load bnb module with empty weight and replace ``nn.Linear` module
            if bnb_quantization_config.load_in_abit:
               __snake_case							       =   bnb.nn.LinearabitLt(
                   module.in_features       ,      module.out_features       ,      module.bias is not None       ,      has_fpaa_weights=snake_case_       ,      threshold=bnb_quantization_config.llm_inta_threshold       ,      )
            elif bnb_quantization_config.load_in_abit:
               __snake_case							       =   bnb.nn.Linearabit(
                   module.in_features       ,      module.out_features       ,      module.bias is not None       ,      bnb_quantization_config.bnb_abit_compute_dtype       ,      compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant       ,      quant_type=bnb_quantization_config.bnb_abit_quant_type       ,      )
            else:
               raise ValueError('''load_in_8bit and load_in_4bit can\'t be both False'''       )
            __snake_case							       =   module.weight.data
            if module.bias is not None:
               __snake_case							       =   module.bias.data
            bnb_module.requires_grad_(snake_case_       )
            setattr(snake_case_       ,      snake_case_       ,      snake_case_       )
            __snake_case							       =   True
      if len(list(module.children()       )       ) > 0:
         __snake_case       ,	__snake_case							       =   _replace_with_bnb_layers(
             snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       )
         __snake_case							       =   has_been_replaced | _has_been_replaced
      # Remove the last key for recursion
      current_key_name.pop(-1       )
   return model, has_been_replaced






def lowerCamelCase__    (							snake_case_	:  int       )      ->				Dict:
   # Create a copy of the model
   with init_empty_weights():
      __snake_case							       =   deepcopy(snake_case_       )  # this has 0 cost since it is done inside `init_empty_weights` context manager`

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

   # Check if it is a base model
   __snake_case							       =   False
   if hasattr(snake_case_       ,      '''base_model_prefix'''       ):
      __snake_case							       =   not hasattr(snake_case_       ,      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(snake_case_       ) - set(snake_case_       )
   __snake_case							       =   list(set(snake_case_       )       ) + list(snake_case_       )

   # 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(snake_case_       ,      ''''''       )
      filtered_module_names.append(snake_case_       )

   return filtered_module_names






def lowerCamelCase__    (							snake_case_	:  str       )      ->				str:
   for m in model.modules():
      if isinstance(snake_case_       ,      bnb.nn.Linearabit       ):
         return True
   return False






def lowerCamelCase__    (							snake_case_	:  nn.Module       )      ->				Dict:
   return next(parameter.parameters()       ).device







def lowerCamelCase__    (							snake_case_	:  Union[str, Any]       ,      snake_case_	:  str       ,      snake_case_	:  List[Any]       ,      snake_case_	:  int       ,      snake_case_	:  List[Any]       ,      snake_case_	:  str       ,      snake_case_	:  Dict       )      ->				List[str]:
   # if it is not quantized, we quantize and offload the quantized weights and the SCB stats
   if fpaa_statistics is None:
      set_module_tensor_to_device(snake_case_       ,      snake_case_       ,      0       ,      dtype=snake_case_       ,      value=snake_case_       )
      __snake_case							       =   param_name
      __snake_case							       =   model
      if "." in tensor_name:
         __snake_case							       =   tensor_name.split('''.'''       )
         for split in splits[:-1]:
            __snake_case							       =   getattr(snake_case_       ,      snake_case_       )
            if new_module is None:
               raise ValueError(f"""{module} has no attribute {split}."""       )
            __snake_case							       =   new_module
         __snake_case							       =   splits[-1]
      # offload weights
      __snake_case							       =   False
      offload_weight(module._parameters[tensor_name]       ,      snake_case_       ,      snake_case_       ,      index=snake_case_       )
      if hasattr(module._parameters[tensor_name]       ,      '''SCB'''       ):
         offload_weight(
             module._parameters[tensor_name].SCB       ,      param_name.replace('''weight'''       ,      '''SCB'''       )       ,      snake_case_       ,      index=snake_case_       ,      )
   else:
      offload_weight(snake_case_       ,      snake_case_       ,      snake_case_       ,      index=snake_case_       )
      offload_weight(snake_case_       ,      param_name.replace('''weight'''       ,      '''SCB'''       )       ,      snake_case_       ,      index=snake_case_       )

   set_module_tensor_to_device(snake_case_       ,      snake_case_       ,      '''meta'''       ,      dtype=snake_case_       ,      value=torch.empty(*param.size()       )       )









"""simple docstring"""





from cva import destroyAllWindows, imread, imshow, waitKey







def      lowerCamelCase__       (							_lowerCamelCase       :		Tuple )      ->				Dict:
  # getting number of pixels in the image
  lowerCamelCase_     ,	lowerCamelCase_         =						img.shape[0], img.shape[1]

  # converting each pixel's color to its negative
  for i in range(_lowerCamelCase ):
    for j in range(_lowerCamelCase ):
      lowerCamelCase_         =						[255, 255, 255] - img[i][j]

  return img


if __name__ == "__main__":
  # read original image
  _SCREAMING_SNAKE_CASE					:				List[Any]					    =						imread('''image_data/lena.jpg''', 1)

  # convert to its negative
  _SCREAMING_SNAKE_CASE					:				List[Any]					    =						convert_to_negative(img)

  # show result image
  imshow('''negative of original image''', img)
  waitKey(0)
  destroyAllWindows()




