
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

lowerCamelCase_						     =					logging.get_logger(__name__)






def 						__magic_name__			(   __a      :	Union[str, Any]	,		__a      :	Optional[Any]	,		__a      :	List[Any]	,		__a      :	List[Any]=None	,		__a      :	str=None						):



     '''simple docstring'''




     if "." in tensor_name:
          UpperCamelCase__				=    tensor_name.split("""."""						)
          for split in splits[:-1]:
               UpperCamelCase__				=    getattr(__a	,		__a						)
               if new_module is None:
                    raise ValueError(f"{module} has no attribute {split}."						)
               UpperCamelCase__				=    new_module
          UpperCamelCase__				=    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}."						)
     UpperCamelCase__				=    tensor_name in module._buffers
     UpperCamelCase__				=    getattr(__a	,		__a						)

     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}."						)

     UpperCamelCase__				=    False
     UpperCamelCase__				=    False
     if is_buffer or not is_bitsandbytes_available():
          UpperCamelCase__				=    False
          UpperCamelCase__				=    False
     else:
          UpperCamelCase__				=    hasattr(bnb.nn	,		"""Params4bit"""						) and isinstance(module._parameters[tensor_name]	,		bnb.nn.Paramsabit						)
          UpperCamelCase__				=    isinstance(module._parameters[tensor_name]	,		bnb.nn.IntaParams						)

     if is_abit or is_abit:
          UpperCamelCase__				=    module._parameters[tensor_name]
          if param.device.type != "cuda":
               if value is None:
                    UpperCamelCase__				=    old_value.to(__a						)
               elif isinstance(__a	,		torch.Tensor						):
                    UpperCamelCase__				=    value.to("""cpu"""						)
                    if value.dtype == torch.inta:
                         UpperCamelCase__				=    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:
                    UpperCamelCase__				=    torch.tensor(__a	,		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	,		__a						) and fpaa_statistics is None:
                    UpperCamelCase__				=    new_value.T

               UpperCamelCase__				=    old_value.__dict__
               if is_abit:
                    UpperCamelCase__				=    bnb.nn.IntaParams(__a	,		requires_grad=__a	,		**__a						).to(__a						)
               elif is_abit:
                    UpperCamelCase__				=    bnb.nn.Paramsabit(__a	,		requires_grad=__a	,		**__a						).to(__a						)

               UpperCamelCase__				=    new_value
               if fpaa_statistics is not None:
                    setattr(module.weight	,		"""SCB"""	,		fpaa_statistics.to(__a						)						)

     else:
          if value is None:
               UpperCamelCase__				=    old_value.to(__a						)
          elif isinstance(__a	,		torch.Tensor						):
               UpperCamelCase__				=    value.to(__a						)
          else:
               UpperCamelCase__				=    torch.tensor(__a	,		device=__a						)

          if is_buffer:
               UpperCamelCase__				=    new_value
          else:
               UpperCamelCase__				=    nn.Parameter(__a	,		requires_grad=old_value.requires_grad						)
               UpperCamelCase__				=    new_value






def 						__magic_name__			(   __a      :	List[Any]	,		__a      :	List[str]=None	,		__a      :	Tuple=None	,		__a      :	List[Any]=None	,		__a      :	Optional[Any]=False						):



     '''simple docstring'''




     for name, module in model.named_children():
          if current_key_name is None:
               UpperCamelCase__				=    []
          current_key_name.append(__a						)

          if (isinstance(__a	,		nn.Linear						) or isinstance(__a	,		__a						)) 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(__a						) for key in modules_to_not_convert						):
                    with init_empty_weights():
                         if isinstance(__a	,		__a						):
                              UpperCamelCase__		,	UpperCamelCase__				=    module.weight.shape
                         else:
                              UpperCamelCase__				=    module.in_features
                              UpperCamelCase__				=    module.out_features

                         if quantization_config.quantization_method() == "llm_int8":
                              UpperCamelCase__				=    bnb.nn.LinearabitLt(
                                  __a	,		__a	,		module.bias is not None	,		has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight	,		threshold=quantization_config.llm_inta_threshold	,		)
                              UpperCamelCase__				=    True
                         else:
                              if (
                                  quantization_config.llm_inta_skip_modules is not None
                                  and name in quantization_config.llm_inta_skip_modules
                              ):
                                   pass
                              else:
                                   UpperCamelCase__				=    bnb.nn.Linearabit(
                                       __a	,		__a	,		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	,		)
                                   UpperCamelCase__				=    True
                    # Store the module class in case we need to transpose the weight later
                         UpperCamelCase__				=    type(__a						)
                         # Force requires grad to False to avoid unexpected errors
                         model._modules[name].requires_grad_(__a						)
          if len(list(module.children()						)						) > 0:
               UpperCamelCase__		,	UpperCamelCase__				=    _replace_with_bnb_linear(
                   __a	,		__a	,		__a	,		__a	,		has_been_replaced=__a	,		)
          # Remove the last key for recursion
          current_key_name.pop(-1						)
     return model, has_been_replaced






def 						__magic_name__			(   __a      :	Dict	,		__a      :	List[str]=None	,		__a      :	Any=None	,		__a      :	Any=None						):



     '''simple docstring'''




     UpperCamelCase__				=    ["""lm_head"""] if modules_to_not_convert is None else modules_to_not_convert
     UpperCamelCase__		,	UpperCamelCase__				=    _replace_with_bnb_linear(
         __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."""
              """ Please double check your model architecture, or submit an issue on github if you think this is"""
              """ a bug."""						)

     return model






def 						__magic_name__			(   *__a      :	List[str]	,		**__a      :	List[Any]						):



     '''simple docstring'''




     warnings.warn(
         """`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead"""	,		__a	,		)
     return replace_with_bnb_linear(*__a	,		**__a						)






def 						__magic_name__			(   *__a      :	Tuple	,		**__a      :	Optional[int]						):



     '''simple docstring'''




     warnings.warn(
         """`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead"""	,		__a	,		)
     return set_module_quantized_tensor_to_device(*__a	,		**__a						)






def 						__magic_name__			(   __a      :	Optional[int]						):



     '''simple docstring'''




     UpperCamelCase__				=    deepcopy(__a						)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
     tied_model.tie_weights()

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

     # Check if it is a base model
     UpperCamelCase__				=    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
     UpperCamelCase__				=    list(model.named_children()						)
     UpperCamelCase__				=    [list_modules[-1][0]]

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

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

     return filtered_module_names







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_						     =					{
    '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
    '''processing_git''': ['''GitProcessor'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_						     =					[
        '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''GitForCausalLM''',
        '''GitModel''',
        '''GitPreTrainedModel''',
        '''GitVisionModel''',
    ]

if TYPE_CHECKING:
    from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
    from .processing_git import GitProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_git import (
            GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            GitForCausalLM,
            GitModel,
            GitPreTrainedModel,
            GitVisionModel,
        )

else:
    import sys

    lowerCamelCase_						     =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





