'''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

__lowercase        =    logging.get_logger(__name__)






def 							snake_case__				(     _A:   List[Any] ,  _A:   List[Any] ,  _A:   List[str] ,  _A:   Optional[Any]=None ,  _A:   List[str]=None					)       ->      str:

		'''simple docstring'''


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

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

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

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

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

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

				if is_buffer:
						lowerCAmelCase     =    new_value
				else:
						lowerCAmelCase     =    nn.Parameter(_A ,  requires_grad=old_value.requires_grad					)
						lowerCAmelCase     =    new_value






def 							snake_case__				(     _A:   str ,  _A:   Union[str, Any]=None ,  _A:   Optional[int]=None ,  _A:   Optional[int]=None ,  _A:   int=False					)       ->      List[Any]:

		'''simple docstring'''


		for name, module in model.named_children():
				if current_key_name is None:
						lowerCAmelCase     =    []
				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					):
												lowerCAmelCase   ,	lowerCAmelCase     =    module.weight.shape
										else:
												lowerCAmelCase     =    module.in_features
												lowerCAmelCase     =    module.out_features

										if quantization_config.quantization_method() == "llm_int8":
												lowerCAmelCase     =    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 ,  )
												lowerCAmelCase     =    True
										else:
												if (
												    quantization_config.llm_inta_skip_modules is not None
												    and name in quantization_config.llm_inta_skip_modules
												):
														pass
												else:
														lowerCAmelCase     =    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 ,  )
														lowerCAmelCase     =    True
                    # Store the module class in case we need to transpose the weight later
										lowerCAmelCase     =    type(_A					)
										# Force requires grad to False to avoid unexpected errors
										model._modules[name].requires_grad_(_A					)
				if len(list(module.children()					)					) > 0:
						lowerCAmelCase   ,	lowerCAmelCase     =    _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 							snake_case__				(     _A:   Union[str, Any] ,  _A:   List[str]=None ,  _A:   Optional[int]=None ,  _A:   str=None					)       ->      Any:

		'''simple docstring'''


		lowerCAmelCase     =    ["""lm_head"""] if modules_to_not_convert is None else modules_to_not_convert
		lowerCAmelCase   ,	lowerCAmelCase     =    _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 							snake_case__				(     *_A:   Tuple ,  **_A:   str					)       ->      Optional[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 							snake_case__				(     *_A:   Optional[Any] ,  **_A:   Tuple					)       ->      List[str]:

		'''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 							snake_case__				(     _A:   Optional[int]					)       ->      List[Any]:

		'''simple docstring'''


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

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

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

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

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

		return filtered_module_names





'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


__lowercase        =    {
    '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
    '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
    '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
    '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
    '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
    '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
    '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
    '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[str]						=			'''albert'''



			def __init__(  self			,       __lowerCAmelCase=30000			,       __lowerCAmelCase=128			,       __lowerCAmelCase=4096			,       __lowerCAmelCase=12			,       __lowerCAmelCase=1			,       __lowerCAmelCase=64			,       __lowerCAmelCase=16384			,       __lowerCAmelCase=1			,       __lowerCAmelCase="gelu_new"			,       __lowerCAmelCase=0			,       __lowerCAmelCase=0			,       __lowerCAmelCase=512			,       __lowerCAmelCase=2			,       __lowerCAmelCase=0.02			,       __lowerCAmelCase=1E-1_2			,       __lowerCAmelCase=0.1			,       __lowerCAmelCase="absolute"			,       __lowerCAmelCase=0			,       __lowerCAmelCase=2			,       __lowerCAmelCase=3			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


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

					lowerCAmelCase     =    vocab_size
					lowerCAmelCase     =    embedding_size
					lowerCAmelCase     =    hidden_size
					lowerCAmelCase     =    num_hidden_layers
					lowerCAmelCase     =    num_hidden_groups
					lowerCAmelCase     =    num_attention_heads
					lowerCAmelCase     =    inner_group_num
					lowerCAmelCase     =    hidden_act
					lowerCAmelCase     =    intermediate_size
					lowerCAmelCase     =    hidden_dropout_prob
					lowerCAmelCase     =    attention_probs_dropout_prob
					lowerCAmelCase     =    max_position_embeddings
					lowerCAmelCase     =    type_vocab_size
					lowerCAmelCase     =    initializer_range
					lowerCAmelCase     =    layer_norm_eps
					lowerCAmelCase     =    classifier_dropout_prob
					lowerCAmelCase     =    position_embedding_type







class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			@property
			def 				a_			(  self):




					"""simple docstring"""


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




