




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


lowerCAmelCase_						=			logging.getLogger(__name__)






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__ = None			,		__magic_name__ = None			,		__magic_name__ = None			,		__magic_name__ = None			,		__magic_name__ = None			,		__magic_name__ = False			,		)			->							Any:





     '''simple docstring'''


     lowercase :       Dict       =			bnb_quantization_config.load_in_abit
     lowercase :       str       =			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.'''   )

     lowercase :       Optional[int]       =			[]
     # custom device map
     if isinstance(__lowerCamelCase			,		__lowerCamelCase   ) and len(device_map.keys()   ) > 1:
          lowercase :       str       =			[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:
          lowercase :       Dict       =			get_keys_to_not_convert(__lowerCamelCase   )

     # add cpu modules to skip modules only for 4-bit modules
     if load_in_abit:
          bnb_quantization_config.skip_modules.extend(__lowerCamelCase   )
     lowercase :       Dict       =			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:
          lowercase :       int       =			[]
     lowercase :       Optional[Any]       =			bnb_quantization_config.keep_in_fpaa_modules
     modules_to_not_convert.extend(__lowerCamelCase   )

     # compatibility with peft
     lowercase :       int       =			load_in_abit
     lowercase :       Optional[int]       =			load_in_abit

     lowercase :       Any       =			get_parameter_device(__lowerCamelCase   )
     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.'''   )
          lowercase :       Dict       =			replace_with_bnb_layers(__lowerCamelCase			,		__lowerCamelCase			,		modules_to_not_convert=__lowerCamelCase   )
          # convert param to the right dtype
          lowercase :       Dict       =			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:
                         lowercase :       Tuple       =			name.replace('''.weight'''			,		''''''   ).replace('''.bias'''			,		''''''   )
                         lowercase :       Tuple       =			getattr(__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase   )
                         if param is not None:
                              param.to(torch.floataa   )
               elif torch.is_floating_point(__lowerCamelCase   ):
                    param.to(__lowerCamelCase   )
          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():
               lowercase :       int       =			replace_with_bnb_layers(
                   __lowerCamelCase			,		__lowerCamelCase			,		modules_to_not_convert=__lowerCamelCase   )

          lowercase :       Optional[Any]       =			get_quantized_model_device_map(
              __lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase			,		max_memory=__lowerCamelCase			,		no_split_module_classes=__lowerCamelCase			,		)
          if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
               lowercase :       List[Any]       =			True

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

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






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__=None			,		__magic_name__=None			,		__magic_name__=None   )			->							Union[str, Any]:





     '''simple docstring'''


     if device_map is None:
          if torch.cuda.is_available():
               lowercase :       str       =			{'''''': 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(__lowerCamelCase			,		__lowerCamelCase   ):
          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\'.'''   )

          lowercase :       int       =			{}
          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   )
              }   )

          lowercase :       str       =			{}
          lowercase :       List[Any]       =			special_dtypes
          lowercase :       List[str]       =			no_split_module_classes
          lowercase :       Tuple       =			bnb_quantization_config.target_dtype

          # get max_memory for each device.
          if device_map != "sequential":
               lowercase :       Optional[Any]       =			get_balanced_memory(
                   __lowerCamelCase			,		low_zero=(device_map == '''balanced_low_0''')			,		max_memory=__lowerCamelCase			,		**__lowerCamelCase			,		)

          lowercase :       List[Any]       =			max_memory
          lowercase :       str       =			infer_auto_device_map(__lowerCamelCase			,		**__lowerCamelCase   )

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

          lowercase :       List[Any]       =			{
              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  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__=None			,		__magic_name__=None   )			->							List[Any]:





     '''simple docstring'''


     if modules_to_not_convert is None:
          lowercase :       Tuple       =			[]

     lowercase      ,	lowercase :       Any       =			_replace_with_bnb_layers(
         __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.'''
              ''' 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  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__=None			,		__magic_name__=None			,		)			->							Dict:





     '''simple docstring'''


     lowercase :       List[Any]       =			False
     for name, module in model.named_children():
          if current_key_name is None:
               lowercase :       Union[str, Any]       =			[]
          current_key_name.append(__lowerCamelCase   )
          if isinstance(__lowerCamelCase			,		nn.Linear   ) and name not in modules_to_not_convert:
               # Check if the current key is not in the `modules_to_not_convert`
               lowercase :       int       =			'''.'''.join(__lowerCamelCase   )
               lowercase :       str       =			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:
                         lowercase :       List[str]       =			False
                         break
               if proceed:
                    # Load bnb module with empty weight and replace ``nn.Linear` module
                    if bnb_quantization_config.load_in_abit:
                         lowercase :       Optional[Any]       =			bnb.nn.LinearabitLt(
                             module.in_features			,		module.out_features			,		module.bias is not None			,		has_fpaa_weights=__lowerCamelCase			,		threshold=bnb_quantization_config.llm_inta_threshold			,		)
                    elif bnb_quantization_config.load_in_abit:
                         lowercase :       List[Any]       =			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'''   )
                    lowercase :       str       =			module.weight.data
                    if module.bias is not None:
                         lowercase :       Optional[int]       =			module.bias.data
                    bnb_module.requires_grad_(__lowerCamelCase   )
                    setattr(__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase   )
                    lowercase :       List[str]       =			True
          if len(list(module.children()   )   ) > 0:
               lowercase      ,	lowercase :       int       =			_replace_with_bnb_layers(
                   __lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase   )
               lowercase :       Any       =			has_been_replaced | _has_been_replaced
          # Remove the last key for recursion
          current_key_name.pop(-1   )
     return model, has_been_replaced






def  snake_case(    __magic_name__   )			->							str:





     '''simple docstring'''


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

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

     # Check if it is a base model
     lowercase :       Union[str, Any]       =			False
     if hasattr(__lowerCamelCase			,		'''base_model_prefix'''   ):
          lowercase :       int       =			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
     lowercase :       List[Any]       =			list(model.named_children()   )
     lowercase :       Optional[Any]       =			[list_modules[-1][0]]

     # add last module together with tied weights
     lowercase :       List[str]       =			set(__lowerCamelCase   ) - set(__lowerCamelCase   )
     lowercase :       Dict       =			list(set(__lowerCamelCase   )   ) + list(__lowerCamelCase   )

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

     return filtered_module_names






def  snake_case(    __magic_name__   )			->							Dict:





     '''simple docstring'''


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






def  snake_case(    __magic_name__   )			->							Tuple:





     '''simple docstring'''


     return next(parameter.parameters()   ).device






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__   )			->							List[Any]:





     '''simple docstring'''


     if fpaa_statistics is None:
          set_module_tensor_to_device(__lowerCamelCase			,		__lowerCamelCase			,		0			,		dtype=__lowerCamelCase			,		value=__lowerCamelCase   )
          lowercase :       List[str]       =			param_name
          lowercase :       Optional[int]       =			model
          if "." in tensor_name:
               lowercase :       str       =			tensor_name.split('''.'''   )
               for split in splits[:-1]:
                    lowercase :       Tuple       =			getattr(__lowerCamelCase			,		__lowerCamelCase   )
                    if new_module is None:
                         raise ValueError(F"""{module} has no attribute {split}."""   )
                    lowercase :       Optional[int]       =			new_module
               lowercase :       str       =			splits[-1]
          # offload weights
          lowercase :       Dict       =			False
          offload_weight(module._parameters[tensor_name]			,		__lowerCamelCase			,		__lowerCamelCase			,		index=__lowerCamelCase   )
          if hasattr(module._parameters[tensor_name]			,		'''SCB'''   ):
               offload_weight(
                   module._parameters[tensor_name].SCB			,		param_name.replace('''weight'''			,		'''SCB'''   )			,		__lowerCamelCase			,		index=__lowerCamelCase			,		)
     else:
          offload_weight(__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase			,		index=__lowerCamelCase   )
          offload_weight(__lowerCamelCase			,		param_name.replace('''weight'''			,		'''SCB'''   )			,		__lowerCamelCase			,		index=__lowerCamelCase   )

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






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_						=			logging.get_logger(__name__)

lowerCAmelCase_						=			{
    'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
    'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
    'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
    'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
    'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
    'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
    'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
    'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
    'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      Tuple			=      '''xmod'''






  def __init__(  self		:     Optional[Any]				,						_A		:     Union[str, Any]=30_522				,						_A		:     List[Any]=768				,						_A		:     Optional[Any]=12				,						_A		:     Any=12				,						_A		:     Tuple=3_072				,						_A		:     Optional[int]="gelu"				,						_A		:     List[Any]=0.1				,						_A		:     str=0.1				,						_A		:     List[Any]=512				,						_A		:     List[str]=2				,						_A		:     str=0.02				,						_A		:     Any=1E-12				,						_A		:     Union[str, Any]=1				,						_A		:     List[Any]=0				,						_A		:     Dict=2				,						_A		:     int="absolute"				,						_A		:     Dict=True				,						_A		:     int=None				,						_A		:     List[str]=False				,						_A		:     Dict=2				,						_A		:     int=False				,						_A		:     Optional[int]=True				,						_A		:     Any=True				,						_A		:     Optional[int]=("en_XX",)				,						_A		:     Any=None				,						**_A		:     Optional[int]				,						)		->   Union[str, Any]:






       """simple docstring"""







       super().__init__(pad_token_id=_A				,						bos_token_id=_A				,						eos_token_id=_A				,						**_A				)

       lowercase :       Optional[Any]       =			vocab_size
       lowercase :       Union[str, Any]       =			hidden_size
       lowercase :       Optional[Any]       =			num_hidden_layers
       lowercase :       Dict       =			num_attention_heads
       lowercase :       Union[str, Any]       =			hidden_act
       lowercase :       Tuple       =			intermediate_size
       lowercase :       List[str]       =			hidden_dropout_prob
       lowercase :       Union[str, Any]       =			attention_probs_dropout_prob
       lowercase :       Dict       =			max_position_embeddings
       lowercase :       Any       =			type_vocab_size
       lowercase :       Optional[Any]       =			initializer_range
       lowercase :       str       =			layer_norm_eps
       lowercase :       Tuple       =			position_embedding_type
       lowercase :       Optional[Any]       =			use_cache
       lowercase :       int       =			classifier_dropout
       lowercase :       Optional[int]       =			pre_norm
       lowercase :       Any       =			adapter_reduction_factor
       lowercase :       Union[str, Any]       =			adapter_layer_norm
       lowercase :       Optional[int]       =			adapter_reuse_layer_norm
       lowercase :       Optional[Any]       =			ln_before_adapter
       lowercase :       Union[str, Any]       =			list(_A				)
       lowercase :       List[Any]       =			default_language


class 						_A (  _lowerCamelCase				):






  @property
  def  __a      (  self		:     Union[str, Any]				)		->   Mapping[str, Mapping[int, str]]:






       """simple docstring"""







       if self.task == "multiple-choice":
            lowercase :       Any       =			{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
       else:
            lowercase :       Union[str, Any]       =			{0: '''batch''', 1: '''sequence'''}
       return OrderedDict(
           [
               ('''input_ids''', dynamic_axis),
               ('''attention_mask''', dynamic_axis),
           ]				)