


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


UpperCAmelCase    =					logging.getLogger(__name__)






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE = None      ,      SCREAMING_SNAKE_CASE = None      ,      SCREAMING_SNAKE_CASE = None      ,      SCREAMING_SNAKE_CASE = None      ,      SCREAMING_SNAKE_CASE = None      ,      SCREAMING_SNAKE_CASE = False      ,      )->						Any:




      """simple docstring"""

      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(UpperCamelCase__      ,      UpperCamelCase__      ) 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(UpperCamelCase__      )

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

      # compatibility with peft
      snake_case_ =   load_in_abit
      snake_case_ =   load_in_abit

      snake_case_ =   get_parameter_device(UpperCamelCase__      )
      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(UpperCamelCase__      ,      UpperCamelCase__      ,      modules_to_not_convert=UpperCamelCase__      )
            # 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(UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      )
                              if param is not None:
                                    param.to(torch.floataa      )
                  elif torch.is_floating_point(UpperCamelCase__      ):
                        param.to(UpperCamelCase__      )
            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(
                      UpperCamelCase__      ,      UpperCamelCase__      ,      modules_to_not_convert=UpperCamelCase__      )

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=None      ,      SCREAMING_SNAKE_CASE=None      ,      SCREAMING_SNAKE_CASE=None      )->						Optional[int]:




      """simple docstring"""

      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(UpperCamelCase__      ,      UpperCamelCase__      ):
            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(
                      UpperCamelCase__      ,      low_zero=(device_map == '''balanced_low_0''')      ,      max_memory=UpperCamelCase__      ,      **UpperCamelCase__      ,      )

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

      if isinstance(UpperCamelCase__      ,      UpperCamelCase__      ):
            # 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					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=None      ,      SCREAMING_SNAKE_CASE=None      )->						Optional[Any]:




      """simple docstring"""

      if modules_to_not_convert is None:
            snake_case_ =   []

      snake_case_ ,					snake_case_ =   _replace_with_bnb_layers(
          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.'''
                ''' 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					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=None      ,      SCREAMING_SNAKE_CASE=None      ,      )->						Any:




      """simple docstring"""

      snake_case_ =   False
      for name, module in model.named_children():
            if current_key_name is None:
                  snake_case_ =   []
            current_key_name.append(UpperCamelCase__      )
            if isinstance(UpperCamelCase__      ,      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(UpperCamelCase__      )
                  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=UpperCamelCase__      ,      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_(UpperCamelCase__      )
                        setattr(UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      )
                        snake_case_ =   True
            if len(list(module.children()      )      ) > 0:
                  snake_case_ ,					snake_case_ =   _replace_with_bnb_layers(
                      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      )
                  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					(SCREAMING_SNAKE_CASE      )->						Optional[Any]:




      """simple docstring"""

      with init_empty_weights():
            snake_case_ =   deepcopy(UpperCamelCase__      )  # this has 0 cost since it is done inside `init_empty_weights` context manager`

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

      # Check if it is a base model
      snake_case_ =   False
      if hasattr(UpperCamelCase__      ,      '''base_model_prefix'''      ):
            snake_case_ =   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
      snake_case_ =   list(model.named_children()      )
      snake_case_ =   [list_modules[-1][0]]

      # add last module together with tied weights
      snake_case_ =   set(UpperCamelCase__      ) - set(UpperCamelCase__      )
      snake_case_ =   list(set(UpperCamelCase__      )      ) + list(UpperCamelCase__      )

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

      return filtered_module_names






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						List[Any]:




      """simple docstring"""

      for m in model.modules():
            if isinstance(UpperCamelCase__      ,      bnb.nn.Linearabit      ):
                  return True
      return False






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						List[Any]:




      """simple docstring"""

      return next(parameter.parameters()      ).device






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Dict:




      """simple docstring"""

      if fpaa_statistics is None:
            set_module_tensor_to_device(UpperCamelCase__      ,      UpperCamelCase__      ,      0      ,      dtype=UpperCamelCase__      ,      value=UpperCamelCase__      )
            snake_case_ =   param_name
            snake_case_ =   model
            if "." in tensor_name:
                  snake_case_ =   tensor_name.split('''.'''      )
                  for split in splits[:-1]:
                        snake_case_ =   getattr(UpperCamelCase__      ,      UpperCamelCase__      )
                        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]      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      index=UpperCamelCase__      )
            if hasattr(module._parameters[tensor_name]      ,      '''SCB'''      ):
                  offload_weight(
                      module._parameters[tensor_name].SCB      ,      param_name.replace('''weight'''      ,      '''SCB'''      )      ,      UpperCamelCase__      ,      index=UpperCamelCase__      ,      )
      else:
            offload_weight(UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      index=UpperCamelCase__      )
            offload_weight(UpperCamelCase__      ,      param_name.replace('''weight'''      ,      '''SCB'''      )      ,      UpperCamelCase__      ,      index=UpperCamelCase__      )

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



import copy
import re







class lowerCAmelCase_		:

 '''simple docstring'''

 __snake_case				=						"hp"
 __snake_case				=						{}
 __snake_case				=						None



 @classmethod
 def 		UpperCamelCase__  (			cls	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   prefix
       snake_case_ =   defaults
       cls.build_naming_info()



 @staticmethod
 def 		UpperCamelCase__  (			_UpperCAmelCase	,   _UpperCAmelCase      ):
       if len(_UpperCAmelCase      ) == 0:
             return ""
       snake_case_ =   None
       if any(char.isdigit() for char in word      ):
             raise Exception(F'''Parameters should not contain numbers: \'{word}\' contains a number'''      )
       if word in info["short_word"]:
             return info["short_word"][word]
       for prefix_len in range(1	,   len(_UpperCAmelCase      ) + 1      ):
             snake_case_ =   word[:prefix_len]
             if prefix in info["reverse_short_word"]:
                   continue
             else:
                   snake_case_ =   prefix
                   break

       if short_word is None:
             # Paranoid fallback
             def int_to_alphabetic(_UpperCAmelCase      ):
                   snake_case_ =   ''''''
                   while integer != 0:
                         snake_case_ =   chr(ord('''A'''      ) + integer % 10      ) + s
                         integer //= 10
                   return s

             snake_case_ =   0
             while True:
                   snake_case_ =   word + '''#''' + int_to_alphabetic(_UpperCAmelCase      )
                   if sword in info["reverse_short_word"]:
                         continue
                   else:
                         snake_case_ =   sword
                         break

       snake_case_ =   short_word
       snake_case_ =   word
       return short_word



 @staticmethod
 def 		UpperCamelCase__  (			_UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   param_name.split('''_'''      )

       snake_case_ =   [TrialShortNamer.shortname_for_word(_UpperCAmelCase	,   _UpperCAmelCase      ) for word in words]

       # We try to create a separatorless short name, but if there is a collision we have to fallback
       # to a separated short name
       snake_case_ =   ['''''', '''_''']

       for separator in separators:
             snake_case_ =   separator.join(_UpperCAmelCase      )
             if shortname not in info["reverse_short_param"]:
                   snake_case_ =   shortname
                   snake_case_ =   param_name
                   return shortname

       return param_name



 @staticmethod
 def 		UpperCamelCase__  (			_UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   TrialShortNamer.shortname_for_key(_UpperCAmelCase	,   _UpperCAmelCase      )
       snake_case_ =   short_name
       snake_case_ =   param_name



 @classmethod
 def 		UpperCamelCase__  (			cls      ):
       if cls.NAMING_INFO is not None:
             return

       snake_case_ =   {
           '''short_word''': {},
           '''reverse_short_word''': {},
           '''short_param''': {},
           '''reverse_short_param''': {},
       }

       snake_case_ =   list(cls.DEFAULTS.keys()      )

       for k in field_keys:
             cls.add_new_param_name(_UpperCAmelCase	,   _UpperCAmelCase      )

       snake_case_ =   info



 @classmethod
 def 		UpperCamelCase__  (			cls	,   _UpperCAmelCase      ):
       cls.build_naming_info()
       assert cls.PREFIX is not None
       snake_case_ =   [copy.copy(cls.PREFIX      )]

       for k, v in params.items():
             if k not in cls.DEFAULTS:
                   raise Exception(F'''You should provide a default value for the param name {k} with value {v}'''      )
             if v == cls.DEFAULTS[k]:
                   # The default value is not added to the name
                   continue

             snake_case_ =   cls.NAMING_INFO['''short_param'''][k]

             if isinstance(_UpperCAmelCase	,   _UpperCAmelCase      ):
                   snake_case_ =   1 if v else 0

             snake_case_ =   '''''' if isinstance(_UpperCAmelCase	,   (int, float)      ) else '''-'''
             snake_case_ =   F'''{key}{sep}{v}'''
             name.append(_UpperCAmelCase      )

       return "_".join(_UpperCAmelCase      )





 @classmethod
 def 		UpperCamelCase__  (			cls	,   _UpperCAmelCase      ):
       snake_case_ =   repr[len(cls.PREFIX      ) + 1 :]
       if repr == "":
             snake_case_ =   []
       else:
             snake_case_ =   repr.split('''_'''      )

       snake_case_ =   {}

       for value in values:
             if "-" in value:
                   snake_case_ ,					snake_case_ =   value.split('''-'''      )
             else:
                   snake_case_ =   re.sub('''[0-9.]'''	,   ''''''	,   _UpperCAmelCase      )
                   snake_case_ =   float(re.sub('''[^0-9.]'''	,   ''''''	,   _UpperCAmelCase      )      )

             snake_case_ =   cls.NAMING_INFO['''reverse_short_param'''][p_k]

             snake_case_ =   p_v

       for k in cls.DEFAULTS:
             if k not in parameters:
                   snake_case_ =   cls.DEFAULTS[k]

       return parameters