






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

__magic_name__   :  str        =							logging.get_logger(__name__)
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase=None       ,							_UpperCamelCase=None) ->			str:
      """simple docstring"""

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

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

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

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

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

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

            if is_buffer:
                  UpperCamelCase					       =  new_value
            else:
                  UpperCamelCase					       =  nn.Parameter(_UpperCamelCase       ,							requires_grad=old_value.requires_grad)
                  UpperCamelCase					       =  new_value
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase=None       ,							_UpperCamelCase=None       ,							_UpperCamelCase=None       ,							_UpperCamelCase=False) ->			List[Any]:
      """simple docstring"""

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

                              if quantization_config.quantization_method() == "llm_int8":
                                    UpperCamelCase					       =  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       ,							)
                                    UpperCamelCase					       =  True
                              else:
                                    if (
                                        quantization_config.llm_inta_skip_modules is not None
                                        and name in quantization_config.llm_inta_skip_modules
                                    ):
                                          pass
                                    else:
                                          UpperCamelCase					       =  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       ,							)
                                          UpperCamelCase					       =  True
                    # Store the module class in case we need to transpose the weight later
                              UpperCamelCase					       =  type(_UpperCamelCase)
                              # Force requires grad to False to avoid unexpected errors
                              model._modules[name].requires_grad_(_UpperCamelCase)
            if len(list(module.children())) > 0:
                  UpperCamelCase       ,  UpperCamelCase					       =  _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 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase=None       ,							_UpperCamelCase=None       ,							_UpperCamelCase=None) ->			Union[str, Any]:
      """simple docstring"""

      UpperCamelCase					       =  ['lm_head'] if modules_to_not_convert is None else modules_to_not_convert
      UpperCamelCase       ,  UpperCamelCase					       =  _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 		lowercase__  (				*_UpperCamelCase       ,							**_UpperCamelCase) ->			Any:
      """simple docstring"""

      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 		lowercase__  (				*_UpperCamelCase       ,							**_UpperCamelCase) ->			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'       ,							_UpperCamelCase       ,							)
      return set_module_quantized_tensor_to_device(*_UpperCamelCase       ,							**_UpperCamelCase)
def 		lowercase__  (				_UpperCamelCase) ->			Any:
      """simple docstring"""

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

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

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

      # add last module together with tied weights
      UpperCamelCase					       =  set(_UpperCamelCase) - set(_UpperCamelCase)
      UpperCamelCase					       =  list(set(_UpperCamelCase)) + list(_UpperCamelCase)

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

      return filtered_module_names











def 		lowercase__  (				_UpperCamelCase) ->			Any:
      """simple docstring"""

      UpperCamelCase					       =  []
      UpperCamelCase					       =  []
      UpperCamelCase					       =  {
          '^': 3,
          '*': 2,
          '/': 2,
          '%': 2,
          '+': 1,
          '-': 1,
      }  # Priority of each operator
      UpperCamelCase					       =  len(_UpperCamelCase) if (len(_UpperCamelCase) > 7) else 7

      # Print table header for output
      print(
          'Symbol'.center(8)       ,							'Stack'.center(_UpperCamelCase)       ,							'Postfix'.center(_UpperCamelCase)       ,							sep=' | '       ,							)
      print('-' * (print_width * 3 + 7))

      for x in infix:
            if x.isalpha() or x.isdigit():
                  post_fix.append(_UpperCamelCase)  # if x is Alphabet / Digit, add it to Postfix
            elif x == "(":
                  stack.append(_UpperCamelCase)  # if x is "(" push to Stack
            elif x == ")":  # if x is ")" pop stack until "(" is encountered
                  while stack[-1] != "(":
                        post_fix.append(stack.pop())  # Pop stack & add the content to Postfix
                  stack.pop()
            else:
                  if len(_UpperCamelCase) == 0:
                        stack.append(_UpperCamelCase)  # If stack is empty, push x to stack
                  else:  # while priority of x is not > priority of element in the stack
                        while len(_UpperCamelCase) > 0 and priority[x] <= priority[stack[-1]]:
                              post_fix.append(stack.pop())  # pop stack & add to Postfix
                        stack.append(_UpperCamelCase)  # push x to stack

            print(
                x.center(8)       ,							(''.join(_UpperCamelCase)).ljust(_UpperCamelCase)       ,							(''.join(_UpperCamelCase)).ljust(_UpperCamelCase)       ,							sep=' | '       ,							)  # Output in tabular format

      while len(_UpperCamelCase) > 0:  # while stack is not empty
            post_fix.append(stack.pop())  # pop stack & add to Postfix
            print(
                ' '.center(8)       ,							(''.join(_UpperCamelCase)).ljust(_UpperCamelCase)       ,							(''.join(_UpperCamelCase)).ljust(_UpperCamelCase)       ,							sep=' | '       ,							)  # Output in tabular format

      return "".join(_UpperCamelCase)  # return Postfix as str
def 		lowercase__  (				_UpperCamelCase) ->			Optional[Any]:
      """simple docstring"""

      UpperCamelCase					       =  list(infix[::-1])  # reverse the infix equation

      for i in range(len(_UpperCamelCase)):
            if infix[i] == "(":
                  UpperCamelCase					       =  ')'  # change "(" to ")"
            elif infix[i] == ")":
                  UpperCamelCase					       =  '('  # change ")" to "("

      return (infix_2_postfix(''.join(_UpperCamelCase)))[
          ::-1
      ]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
       __magic_name__   :  int        =							input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
       __magic_name__   :  Optional[Any]        =							''''''.join(Infix.split())  # Remove spaces from the input
       print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')



