


'''simple docstring'''







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

A				:					Optional[Any]    						=			logging.get_logger(__name__)
def 				lowerCAmelCase__	(    lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      Tuple  ,lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      Optional[Any]=None  ,lowerCamelCase					:      str=None       ):
       # Recurse if needed
       if "." in tensor_name:
              _A					:     List[Any]     = tensor_name.split('.'       )
              for split in splits[:-1]:
                     _A					:     Dict     = getattr(__lowerCAmelCase  ,__lowerCAmelCase       )
                     if new_module is None:
                            raise ValueError(F'{module} has no attribute {split}.'       )
                     _A					:     Optional[Any]     = new_module
              _A					:     Any     = 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}.'       )
       _A					:     List[str]     = tensor_name in module._buffers
       _A					:     Dict     = getattr(__lowerCAmelCase  ,__lowerCAmelCase       )

       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}.'       )

       _A					:     List[str]     = False
       _A					:     str     = False
       if is_buffer or not is_bitsandbytes_available():
              _A					:     Optional[int]     = False
              _A					:     List[Any]     = False
       else:
              _A					:     str     = hasattr(bnb.nn  ,'Params4bit'       ) and isinstance(module._parameters[tensor_name]  ,bnb.nn.Paramsabit       )
              _A					:     str     = isinstance(module._parameters[tensor_name]  ,bnb.nn.IntaParams       )

       if is_abit or is_abit:
              _A					:     Dict     = module._parameters[tensor_name]
              if param.device.type != "cuda":
                     if value is None:
                            _A					:     Tuple     = old_value.to(__lowerCAmelCase       )
                     elif isinstance(__lowerCAmelCase  ,torch.Tensor       ):
                            _A					:     int     = value.to('cpu'       )
                            if value.dtype == torch.inta:
                                   _A					:     str     = 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:
                            _A					:     Optional[Any]     = torch.tensor(__lowerCAmelCase  ,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  ,__lowerCAmelCase       ) and fpaa_statistics is None:
                            _A					:     Optional[int]     = new_value.T

                     _A					:     Union[str, Any]     = old_value.__dict__
                     if is_abit:
                            _A					:     str     = bnb.nn.IntaParams(__lowerCAmelCase  ,requires_grad=__lowerCAmelCase  ,**__lowerCAmelCase       ).to(__lowerCAmelCase       )
                     elif is_abit:
                            _A					:     Union[str, Any]     = bnb.nn.Paramsabit(__lowerCAmelCase  ,requires_grad=__lowerCAmelCase  ,**__lowerCAmelCase       ).to(__lowerCAmelCase       )

                     _A					:     Optional[Any]     = new_value
                     if fpaa_statistics is not None:
                            setattr(module.weight  ,'SCB'  ,fpaa_statistics.to(__lowerCAmelCase       )       )

       else:
              if value is None:
                     _A					:     str     = old_value.to(__lowerCAmelCase       )
              elif isinstance(__lowerCAmelCase  ,torch.Tensor       ):
                     _A					:     List[str]     = value.to(__lowerCAmelCase       )
              else:
                     _A					:     Optional[int]     = torch.tensor(__lowerCAmelCase  ,device=__lowerCAmelCase       )

              if is_buffer:
                     _A					:     List[str]     = new_value
              else:
                     _A					:     List[Any]     = nn.Parameter(__lowerCAmelCase  ,requires_grad=old_value.requires_grad       )
                     _A					:     Dict     = new_value
def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      int=None  ,lowerCamelCase					:      List[Any]=None  ,lowerCamelCase					:      List[str]=None  ,lowerCamelCase					:      Optional[Any]=False       ):
       for name, module in model.named_children():
              if current_key_name is None:
                     _A					:     Union[str, Any]     = []
              current_key_name.append(__lowerCAmelCase       )

              if (isinstance(__lowerCAmelCase  ,nn.Linear       ) or isinstance(__lowerCAmelCase  ,__lowerCAmelCase       )) 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(__lowerCAmelCase       ) for key in modules_to_not_convert       ):
                            with init_empty_weights():
                                   if isinstance(__lowerCAmelCase  ,__lowerCAmelCase       ):
                                          _A					:     Tuple     = module.weight.shape
                                   else:
                                          _A					:     str     = module.in_features
                                          _A					:     Dict     = module.out_features

                                   if quantization_config.quantization_method() == "llm_int8":
                                          _A					:     Dict     = bnb.nn.LinearabitLt(
                                              __lowerCAmelCase  ,__lowerCAmelCase  ,module.bias is not None  ,has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight  ,threshold=quantization_config.llm_inta_threshold  ,)
                                          _A					:     Tuple     = True
                                   else:
                                          if (
                                              quantization_config.llm_inta_skip_modules is not None
                                              and name in quantization_config.llm_inta_skip_modules
                                          ):
                                                 pass
                                          else:
                                                 _A					:     Optional[int]     = bnb.nn.Linearabit(
                                                     __lowerCAmelCase  ,__lowerCAmelCase  ,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  ,)
                                                 _A					:     int     = True
                    # Store the module class in case we need to transpose the weight later
                                   _A					:     Dict     = type(__lowerCAmelCase       )
                                   # Force requires grad to False to avoid unexpected errors
                                   model._modules[name].requires_grad_(__lowerCAmelCase       )
              if len(list(module.children()       )       ) > 0:
                     _A					:     Tuple     = _replace_with_bnb_linear(
                         __lowerCAmelCase  ,__lowerCAmelCase  ,__lowerCAmelCase  ,__lowerCAmelCase  ,has_been_replaced=__lowerCAmelCase  ,)
              # Remove the last key for recursion
              current_key_name.pop(-1       )
       return model, has_been_replaced
def 				lowerCAmelCase__	(    lowerCamelCase					:      List[str]  ,lowerCamelCase					:      Any=None  ,lowerCamelCase					:      int=None  ,lowerCamelCase					:      List[str]=None       ):
       _A					:     int     = ["""lm_head"""] if modules_to_not_convert is None else modules_to_not_convert
       _A					:     Any     = _replace_with_bnb_linear(
           __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.'
                  ' Please double check your model architecture, or submit an issue on github if you think this is'
                  ' a bug.'       )

       return model
def 				lowerCAmelCase__	(    *lowerCamelCase					:      Optional[int]  ,**lowerCamelCase					:      Tuple       ):
       warnings.warn(
           '`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead'  ,__lowerCAmelCase  ,)
       return replace_with_bnb_linear(*__lowerCAmelCase  ,**__lowerCAmelCase       )
def 				lowerCAmelCase__	(    *lowerCamelCase					:      Dict  ,**lowerCamelCase					:      Dict       ):
       warnings.warn(
           '`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead'  ,__lowerCAmelCase  ,)
       return set_module_quantized_tensor_to_device(*__lowerCAmelCase  ,**__lowerCAmelCase       )


def 				lowerCAmelCase__	(    lowerCamelCase					:      Tuple       ):
       _A					:     List[Any]     = deepcopy(__lowerCAmelCase       )  # this has 0 cost since it is done inside `init_empty_weights` context manager`
       tied_model.tie_weights()

       _A					:     List[str]     = find_tied_parameters(__lowerCAmelCase       )
       # For compatibility with Accelerate < 0.18
       if isinstance(__lowerCAmelCase  ,__lowerCAmelCase       ):
              _A					:     Union[str, Any]     = sum(list(tied_params.values()       )  ,[]       ) + list(tied_params.keys()       )
       else:
              _A					:     List[Any]     = sum(__lowerCAmelCase  ,[]       )
       _A					:     str     = len(__lowerCAmelCase       ) > 0

       # Check if it is a base model
       _A					:     Optional[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
       _A					:     int     = list(model.named_children()       )
       _A					:     str     = [list_modules[-1][0]]

       # add last module together with tied weights
       _A					:     Any     = set(__lowerCAmelCase       ) - set(__lowerCAmelCase       )
       _A					:     Any     = list(set(__lowerCAmelCase       )       ) + list(__lowerCAmelCase       )

       # remove ".weight" from the keys
       _A					:     Any     = [""".weight""", """.bias"""]
       _A					:     Optional[Any]     = []
       for name in list_untouched:
              for name_to_remove in names_to_remove:
                     if name_to_remove in name:
                            _A					:     Optional[int]     = name.replace(__lowerCAmelCase  ,''       )
              filtered_module_names.append(__lowerCAmelCase       )

       return filtered_module_names










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A				:					List[str]    						=			{
    '''configuration_electra''': ['''ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ElectraConfig''', '''ElectraOnnxConfig'''],
    '''tokenization_electra''': ['''ElectraTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					int    						=			['''ElectraTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					List[str]    						=			[
       '''ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ElectraForCausalLM''',
       '''ElectraForMaskedLM''',
       '''ElectraForMultipleChoice''',
       '''ElectraForPreTraining''',
       '''ElectraForQuestionAnswering''',
       '''ElectraForSequenceClassification''',
       '''ElectraForTokenClassification''',
       '''ElectraModel''',
       '''ElectraPreTrainedModel''',
       '''load_tf_weights_in_electra''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Dict    						=			[
       '''TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFElectraForMaskedLM''',
       '''TFElectraForMultipleChoice''',
       '''TFElectraForPreTraining''',
       '''TFElectraForQuestionAnswering''',
       '''TFElectraForSequenceClassification''',
       '''TFElectraForTokenClassification''',
       '''TFElectraModel''',
       '''TFElectraPreTrainedModel''',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					List[Any]    						=			[
       '''FlaxElectraForCausalLM''',
       '''FlaxElectraForMaskedLM''',
       '''FlaxElectraForMultipleChoice''',
       '''FlaxElectraForPreTraining''',
       '''FlaxElectraForQuestionAnswering''',
       '''FlaxElectraForSequenceClassification''',
       '''FlaxElectraForTokenClassification''',
       '''FlaxElectraModel''',
       '''FlaxElectraPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
   from .tokenization_electra import ElectraTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_electra_fast import ElectraTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_electra import (
          ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
          ElectraForCausalLM,
          ElectraForMaskedLM,
          ElectraForMultipleChoice,
          ElectraForPreTraining,
          ElectraForQuestionAnswering,
          ElectraForSequenceClassification,
          ElectraForTokenClassification,
          ElectraModel,
          ElectraPreTrainedModel,
          load_tf_weights_in_electra,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_electra import (
          TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFElectraForMaskedLM,
          TFElectraForMultipleChoice,
          TFElectraForPreTraining,
          TFElectraForQuestionAnswering,
          TFElectraForSequenceClassification,
          TFElectraForTokenClassification,
          TFElectraModel,
          TFElectraPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_electra import (
          FlaxElectraForCausalLM,
          FlaxElectraForMaskedLM,
          FlaxElectraForMultipleChoice,
          FlaxElectraForPreTraining,
          FlaxElectraForQuestionAnswering,
          FlaxElectraForSequenceClassification,
          FlaxElectraForTokenClassification,
          FlaxElectraModel,
          FlaxElectraPreTrainedModel,
      )

else:
   import sys

   A				:					Tuple    						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






