





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


__A							   =							logging.getLogger(__name__)





def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a = None      ,       __a = None      ,       __a = None      ,       __a = None      ,       __a = None      ,       __a = False      ,       )	->						str:







  """simple docstring"""





  lowerCamelCase__: int         =bnb_quantization_config.load_in_abit
  lowerCamelCase__: Any         =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."						)

  lowerCamelCase__: List[Any]         =[]
  # custom device map
  if isinstance(__a      ,       __a						) and len(device_map.keys()						) > 1:
    lowerCamelCase__: Optional[int]         =[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:
    lowerCamelCase__: Any         =get_keys_to_not_convert(__a						)

  # add cpu modules to skip modules only for 4-bit modules
  if load_in_abit:
    bnb_quantization_config.skip_modules.extend(__a						)
  lowerCamelCase__: List[str]         =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:
    lowerCamelCase__: List[Any]         =[]
  lowerCamelCase__: int         =bnb_quantization_config.keep_in_fpaa_modules
  modules_to_not_convert.extend(__a						)

  # compatibility with peft
  lowerCamelCase__: List[str]         =load_in_abit
  lowerCamelCase__: int         =load_in_abit

  lowerCamelCase__: Tuple         =get_parameter_device(__a						)
  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."						)
    lowerCamelCase__: Tuple         =replace_with_bnb_layers(__a      ,       __a      ,       modules_to_not_convert=__a						)
    # convert param to the right dtype
    lowerCamelCase__: 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:
          lowerCamelCase__: str         =name.replace(".weight"      ,       ""						).replace(".bias"      ,       ""						)
          lowerCamelCase__: Optional[Any]         =getattr(__a      ,       __a      ,       __a						)
          if param is not None:
            param.to(torch.floataa						)
      elif torch.is_floating_point(__a						):
        param.to(__a						)
    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():
      lowerCamelCase__: str         =replace_with_bnb_layers(
          __a      ,       __a      ,       modules_to_not_convert=__a						)

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

    lowerCamelCase__: List[str]         =any(x in list(device_map.values()						) for x in ["cpu", "disk"]						)

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





def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a=None      ,       __a=None      ,       __a=None						)	->						str:







  """simple docstring"""





  if device_map is None:
    if torch.cuda.is_available():
      lowerCamelCase__: 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(__a      ,       __a						):
    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'."						)

    lowerCamelCase__: Optional[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						)
        }						)

    lowerCamelCase__: Optional[Any]         ={}
    lowerCamelCase__: str         =special_dtypes
    lowerCamelCase__: List[str]         =no_split_module_classes
    lowerCamelCase__: Dict         =bnb_quantization_config.target_dtype

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

    lowerCamelCase__: Union[str, Any]         =max_memory
    lowerCamelCase__: Dict         =infer_auto_device_map(__a      ,       **__a						)

  if isinstance(__a      ,       __a						):
    # check if don't have any quantized module on the cpu
    lowerCamelCase__: Union[str, Any]         =bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules

    lowerCamelCase__: 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(
              "\n                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit\n                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping\n                        these modules in `torch_dtype`, you need to pass a custom `device_map` to\n                        `load_and_quantize_model`. Check\n                        https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk\n                        for more details.\n                        "						)
        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_					(			__a      ,       __a      ,       __a=None      ,       __a=None						)	->						Optional[Any]:







  """simple docstring"""





  if modules_to_not_convert is None:
    lowerCamelCase__: List[Any]         =[]

  lowerCamelCase__    ,   lowerCamelCase__: Any         =_replace_with_bnb_layers(
      __a      ,       __a      ,       __a      ,       __a						)
  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_					(			__a      ,       __a      ,       __a=None      ,       __a=None      ,       )	->						List[Any]:







  """simple docstring"""





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





def 	lowerCAmelCase_					(			__a						)	->						List[Any]:







  """simple docstring"""





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

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

  # Check if it is a base model
  lowerCamelCase__: Optional[Any]         =False
  if hasattr(__a      ,       "base_model_prefix"						):
    lowerCamelCase__: Union[str, Any]         =not hasattr(__a      ,       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__: Optional[int]         =list(model.named_children()						)
  lowerCamelCase__: Optional[int]         =[list_modules[-1][0]]

  # add last module together with tied weights
  lowerCamelCase__: Union[str, Any]         =set(__a						) - set(__a						)
  lowerCamelCase__: List[str]         =list(set(__a						)						) + list(__a						)

  # remove ".weight" from the keys
  lowerCamelCase__: List[Any]         =[".weight", ".bias"]
  lowerCamelCase__: Tuple         =[]
  for name in list_untouched:
    for name_to_remove in names_to_remove:
      if name_to_remove in name:
        lowerCamelCase__: Optional[Any]         =name.replace(__a      ,       ""						)
    filtered_module_names.append(__a						)

  return filtered_module_names





def 	lowerCAmelCase_					(			__a						)	->						Tuple:







  """simple docstring"""





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





def 	lowerCAmelCase_					(			__a						)	->						List[str]:







  """simple docstring"""





  return next(parameter.parameters()						).device






def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a      ,       __a      ,       __a      ,       __a      ,       __a						)	->						Any:







  """simple docstring"""





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

  set_module_tensor_to_device(__a      ,       __a      ,       "meta"      ,       dtype=__a      ,       value=torch.empty(*param.size()						)						)




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_UpperCAmelCase						:  Union[str, Any]									=			logging.get_logger(__name__)

_UpperCAmelCase						:  Dict									=			{
    """microsoft/resnet-50""": """https://huggingface.co/microsoft/resnet-50/blob/main/config.json""",
}







class 					lowercase							(			lowercase_      ,    lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					List[str]			     =     '''resnet'''
				__SCREAMING_SNAKE_CASE   :					Dict			     =     ['''basic''', '''bottleneck''']




				def __init__(   self ,  snake_case=3 ,  snake_case=64 ,  snake_case=[256, 512, 1024, 2048] ,  snake_case=[3, 4, 6, 3] ,  snake_case="bottleneck" ,  snake_case="relu" ,  snake_case=False ,  snake_case=None ,  snake_case=None ,  **snake_case ,  ):
											super().__init__(**snake_case			)
											if layer_type not in self.layer_types:
																		raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types			)}'''			)
											snake_case_     							=       num_channels
											snake_case_     							=       embedding_size
											snake_case_     							=       hidden_sizes
											snake_case_     							=       depths
											snake_case_     							=       layer_type
											snake_case_     							=       hidden_act
											snake_case_     							=       downsample_in_first_stage
											snake_case_     							=       ['stem'] + [F'''stage{idx}''' for idx in range(1 ,  len(snake_case			) + 1			)]
											snake_case_							,       snake_case_     							=       get_aligned_output_features_output_indices(
											    out_features=snake_case ,  out_indices=snake_case ,  stage_names=self.stage_names			)







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					List[Any]			     =     version.parse('''1.11'''     )




				@property
				def        a				(   self			):
											return OrderedDict(
											    [
											        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
											    ]			)




				@property
				def        a				(   self			):
											return 1e-3




