



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


__snake_case						:Dict						=					logging.getLogger(__name__)





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase = None       ,			_UpperCAmelCase = None       ,			_UpperCAmelCase = None       ,			_UpperCAmelCase = None       ,			_UpperCAmelCase = None       ,			_UpperCAmelCase = False       ,			):
    __a							=    bnb_quantization_config.load_in_abit
    __a							=    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.'''    )

    __a							=    []
    # custom device map
    if isinstance(_UpperCAmelCase       ,			_UpperCAmelCase    ) and len(device_map.keys()    ) > 1:
        __a							=    [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:
        __a							=    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    )
    __a							=    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:
        __a							=    []
    __a							=    bnb_quantization_config.keep_in_fpaa_modules
    modules_to_not_convert.extend(_UpperCAmelCase    )

    # compatibility with peft
    __a							=    load_in_abit
    __a							=    load_in_abit

    __a							=    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.'''    )
        __a							=    replace_with_bnb_layers(_UpperCAmelCase       ,			_UpperCAmelCase       ,			modules_to_not_convert=_UpperCAmelCase    )
        # convert param to the right dtype
        __a							=    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:
                    __a							=    name.replace('''.weight'''       ,			''''''    ).replace('''.bias'''       ,			''''''    )
                    __a							=    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():
            __a							=    replace_with_bnb_layers(
                _UpperCAmelCase       ,			_UpperCAmelCase       ,			modules_to_not_convert=_UpperCAmelCase    )

        __a							=    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():
            __a							=    True

        __a							=    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 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=None    ):
    if device_map is None:
        if torch.cuda.is_available():
            __a							=    {'''''': 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\'.'''    )

        __a							=    {}
        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    )
            }    )

        __a							=    {}
        __a							=    special_dtypes
        __a							=    no_split_module_classes
        __a							=    bnb_quantization_config.target_dtype

        # get max_memory for each device.
        if device_map != "sequential":
            __a							=    get_balanced_memory(
                _UpperCAmelCase       ,			low_zero=(device_map == '''balanced_low_0''')       ,			max_memory=_UpperCAmelCase       ,			**_UpperCAmelCase       ,			)

        __a							=    max_memory
        __a							=    infer_auto_device_map(_UpperCAmelCase       ,			**_UpperCAmelCase    )

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

        __a							=    {
            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     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=None    ):

    if modules_to_not_convert is None:
        __a							=    []

    __a	,       __a							=    _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 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=None       ,			):
    __a							=    False
    for name, module in model.named_children():
        if current_key_name is None:
            __a							=    []
        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`
            __a							=    '''.'''.join(_UpperCAmelCase    )
            __a							=    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:
                    __a							=    False
                    break
            if proceed:
                # Load bnb module with empty weight and replace ``nn.Linear` module
                if bnb_quantization_config.load_in_abit:
                    __a							=    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:
                    __a							=    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'''    )
                __a							=    module.weight.data
                if module.bias is not None:
                    __a							=    module.bias.data
                bnb_module.requires_grad_(_UpperCAmelCase    )
                setattr(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    )
                __a							=    True
        if len(list(module.children()    )    ) > 0:
            __a	,       __a							=    _replace_with_bnb_layers(
                _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    )
            __a							=    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     (      _UpperCAmelCase    ):
    # Create a copy of the model
    with init_empty_weights():
        __a							=    deepcopy(_UpperCAmelCase    )  # this has 0 cost since it is done inside `init_empty_weights` context manager`

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

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

    # add last module together with tied weights
    __a							=    set(_UpperCAmelCase    ) - set(_UpperCAmelCase    )
    __a							=    list(set(_UpperCAmelCase    )    ) + list(_UpperCAmelCase    )

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

    return filtered_module_names





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





def 					__snake_case     (      _UpperCAmelCase    ):
    return next(parameter.parameters()    ).device





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    # if it is not quantized, we quantize and offload the quantized weights and the SCB stats
    if fpaa_statistics is None:
        set_module_tensor_to_device(_UpperCAmelCase       ,			_UpperCAmelCase       ,			0       ,			dtype=_UpperCAmelCase       ,			value=_UpperCAmelCase    )
        __a							=    param_name
        __a							=    model
        if "." in tensor_name:
            __a							=    tensor_name.split('''.'''    )
            for split in splits[:-1]:
                __a							=    getattr(_UpperCAmelCase       ,			_UpperCAmelCase    )
                if new_module is None:
                    raise ValueError(f'{module} has no attribute {split}.'    )
                __a							=    new_module
            __a							=    splits[-1]
        # offload weights
        __a							=    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()    )    )










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case						:Tuple						=					{
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case						:Optional[int]						=					[
     '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''LukeForEntityClassification''',
     '''LukeForEntityPairClassification''',
     '''LukeForEntitySpanClassification''',
     '''LukeForMultipleChoice''',
     '''LukeForQuestionAnswering''',
     '''LukeForSequenceClassification''',
     '''LukeForTokenClassification''',
     '''LukeForMaskedLM''',
     '''LukeModel''',
     '''LukePreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
 from .tokenization_luke import LukeTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_luke import (
      LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
      LukeForEntityClassification,
      LukeForEntityPairClassification,
      LukeForEntitySpanClassification,
      LukeForMaskedLM,
      LukeForMultipleChoice,
      LukeForQuestionAnswering,
      LukeForSequenceClassification,
      LukeForTokenClassification,
      LukeModel,
      LukePreTrainedModel,
  )

else:
 import sys

 __snake_case						:Dict						=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





