





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

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



def _a				(	lowerCamelCase:   Tuple					,				lowerCamelCase:   int					,				lowerCamelCase:   Union[str, Any]					,				lowerCamelCase:   Dict=None					,				lowerCamelCase:   List[Any]=None )	->						Tuple:







      '''simple docstring'''






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

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

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

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

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

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

            if is_buffer:
                  __A          =				new_value
            else:
                  __A          =				nn.Parameter(lowerCamelCase					,				requires_grad=old_value.requires_grad )
                  __A          =				new_value



def _a				(	lowerCamelCase:   Optional[int]					,				lowerCamelCase:   str=None					,				lowerCamelCase:   Dict=None					,				lowerCamelCase:   Union[str, Any]=None					,				lowerCamelCase:   Tuple=False )	->						Dict:







      '''simple docstring'''






      for name, module in model.named_children():
            if current_key_name is None:
                  __A          =				[]
            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 ):
                                    __A   ,						__A          =				module.weight.shape
                              else:
                                    __A          =				module.in_features
                                    __A          =				module.out_features

                              if quantization_config.quantization_method() == "llm_int8":
                                    __A          =				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					,				)
                                    __A          =				True
                              else:
                                    if (
                                        quantization_config.llm_inta_skip_modules is not None
                                        and name in quantization_config.llm_inta_skip_modules
                                    ):
                                          pass
                                    else:
                                          __A          =				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					,				)
                                          __A          =				True
                    # Store the module class in case we need to transpose the weight later
                              __A          =				type(lowerCamelCase )
                              # Force requires grad to False to avoid unexpected errors
                              model._modules[name].requires_grad_(lowerCamelCase )
            if len(list(module.children() ) ) > 0:
                  __A   ,						__A          =				_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 _a				(	lowerCamelCase:   Dict					,				lowerCamelCase:   Optional[int]=None					,				lowerCamelCase:   Optional[int]=None					,				lowerCamelCase:   List[str]=None )	->						Dict:







      '''simple docstring'''






      __A          =				['''lm_head'''] if modules_to_not_convert is None else modules_to_not_convert
      __A   ,						__A          =				_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 _a				(	*lowerCamelCase:   Union[str, Any]					,				**lowerCamelCase:   int )	->						Optional[Any]:







      '''simple docstring'''






      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 _a				(	*lowerCamelCase:   List[Any]					,				**lowerCamelCase:   str )	->						Union[str, 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'''					,				lowerCamelCase					,				)
      return set_module_quantized_tensor_to_device(*lowerCamelCase					,				**lowerCamelCase )







def _a				(	lowerCamelCase:   Tuple )	->						Dict:







      '''simple docstring'''






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

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

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

      # add last module together with tied weights
      __A          =				set(lowerCamelCase ) - set(lowerCamelCase )
      __A          =				list(set(lowerCamelCase ) ) + list(lowerCamelCase )

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

      return filtered_module_names












from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt



def _a				(	lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   int					,				lowerCamelCase:   int )	->						np.ndarray:







      '''simple docstring'''






      __A          =				cva.getAffineTransform(lowerCamelCase					,				lowerCamelCase )
      return cva.warpAffine(lowerCamelCase					,				lowerCamelCase					,				(rows, cols) )


if __name__ == "__main__":
       # read original image
       snake_case__					:							List[Any]													=     cva.imread(
           str(Path(__file__).resolve().parent.parent / 'image_data' / 'lena.jpg')
       )
       # turn image in gray scale value
       snake_case__					:							List[str]													=     cva.cvtColor(image, cva.COLOR_BGR2GRAY)
       # get image shape
       snake_case__						,    snake_case__					:							str													=     gray_img.shape

       # set different points to rotate image
       snake_case__					:							Any													=     np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
       snake_case__					:							str													=     np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
       snake_case__					:							int													=     np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
       snake_case__					:							List[str]													=     np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

       # add all rotated images in a list
       snake_case__					:							Optional[Any]													=     [
           gray_img,
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
       ]

       # plot different image rotations
       snake_case__					:							Optional[Any]													=     plt.figure(1)
       snake_case__					:							Dict													=     ['Original', 'Rotation 1', 'Rotation 2', 'Rotation 3']
       for i, image in enumerate(images):
              plt.subplot(2, 2, i + 1), plt.imshow(image, 'gray')
              plt.title(titles[i])
              plt.axis('off')
              plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
       plt.show()





