




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       :	List[Any]									=     logging.get_logger(__name__)

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int			,							lowercase			: Union[str, Any]			,							lowercase			: List[str]			,							lowercase			: Tuple=None			,							lowercase			: Tuple=None  ):


      '''simple docstring'''



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

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

      if is_abit or is_abit:
            lowerCamelCase_	  =					module._parameters[tensor_name]
            if param.device.type != "cuda":
                  if value is None:
                        lowerCamelCase_	  =					old_value.to(lowercase  )
                  elif isinstance(lowercase			,							torch.Tensor  ):
                        lowerCamelCase_	  =					value.to('cpu'  )
                        if value.dtype == torch.inta:
                              lowerCamelCase_	  =					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_	  =					torch.tensor(lowercase			,							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			,							lowercase  ) and fpaa_statistics is None:
                        lowerCamelCase_	  =					new_value.T

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

                  lowerCamelCase_	  =					new_value
                  if fpaa_statistics is not None:
                        setattr(module.weight			,							'SCB'			,							fpaa_statistics.to(lowercase  )  )

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

            if is_buffer:
                  lowerCamelCase_	  =					new_value
            else:
                  lowerCamelCase_	  =					nn.Parameter(lowercase			,							requires_grad=old_value.requires_grad  )
                  lowerCamelCase_	  =					new_value

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[int]			,							lowercase			: str=None			,							lowercase			: str=None			,							lowercase			: int=None			,							lowercase			: Optional[Any]=False  ):


      '''simple docstring'''



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

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

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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Tuple			,							lowercase			: Union[str, Any]=None			,							lowercase			: Any=None			,							lowercase			: Any=None  ):


      '''simple docstring'''



      lowerCamelCase_	  =					['lm_head'] if modules_to_not_convert is None else modules_to_not_convert
      lowerCamelCase_  ,       lowerCamelCase_	  =					_replace_with_bnb_linear(
          lowercase			,							lowercase			,							lowercase			,							lowercase  )

      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     _SCREAMING_SNAKE_CASE    (							*lowercase			: str			,							**lowercase			: int  ):


      '''simple docstring'''



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

def     _SCREAMING_SNAKE_CASE    (							*lowercase			: int			,							**lowercase			: Optional[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'			,							lowercase			,							)
      return set_module_quantized_tensor_to_device(*lowercase			,							**lowercase  )






def     _SCREAMING_SNAKE_CASE    (							lowercase			: str  ):


      '''simple docstring'''



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

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

      # Check if it is a base model
      lowerCamelCase_	  =					not hasattr(lowercase			,							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_	  =					list(model.named_children()  )
      lowerCamelCase_	  =					[list_modules[-1][0]]

      # add last module together with tied weights
      lowerCamelCase_	  =					set(lowercase  ) - set(lowercase  )
      lowerCamelCase_	  =					list(set(lowercase  )  ) + list(lowercase  )

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

      return filtered_module_names









import cva
import numpy as np







class 						A:



 '''simple docstring'''


 def __init__(       self      :						int		,							A_      :						float		,							A_      :						int						)     ->			List[Any]:

       """simple docstring"""






       if k in (0.04, 0.06):
             lowerCamelCase_	  =					k
             lowerCamelCase_	  =					window_size
       else:
             raise ValueError('invalid k value'						)

 def __str__(       self      :						str						)     ->			str:

       """simple docstring"""






       return str(self.k						)





 def        a__				(       self      :						Any		,							A_      :						str						)     ->			tuple[cva.Mat, list[list[int]]]:

       """simple docstring"""






       lowerCamelCase_	  =					cva.imread(A_		,							0						)
       lowerCamelCase_  ,       lowerCamelCase_	  =					img.shape
       lowerCamelCase_	  =					[]
       lowerCamelCase_	  =					img.copy()
       lowerCamelCase_	  =					cva.cvtColor(A_		,							cva.COLOR_GRAY2RGB						)
       lowerCamelCase_  ,       lowerCamelCase_	  =					np.gradient(A_						)
       lowerCamelCase_	  =					dx**2
       lowerCamelCase_	  =					dy**2
       lowerCamelCase_	  =					dx * dy
       lowerCamelCase_	  =					0.04
       lowerCamelCase_	  =					self.window_size // 2
       for y in range(A_		,							h - offset						):
             for x in range(A_		,							w - offset						):
                   lowerCamelCase_	  =					ixx[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()
                   lowerCamelCase_	  =					iyy[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()
                   lowerCamelCase_	  =					ixy[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()

                   lowerCamelCase_	  =					(wxx * wyy) - (wxy**2)
                   lowerCamelCase_	  =					wxx + wyy
                   lowerCamelCase_	  =					det - k * (trace**2)
                   # Can change the value
                   if r > 0.5:
                         corner_list.append([x, y, r]						)
                         color_img.itemset((y, x, 0)		,							0						)
                         color_img.itemset((y, x, 1)		,							0						)
                         color_img.itemset((y, x, 2)		,							255						)
       return color_img, corner_list


if __name__ == "__main__":
 lowerCamelCase       :	Optional[int]									=     HarrisCorner(0.04, 3)
 lowerCamelCase					,					lowerCamelCase       :	Optional[int]									=     edge_detect.detect("path_to_image")
 cva.imwrite("detect.png", color_img)



