



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       :					int      					=					logging.get_logger(__name__)
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Tuple					, __UpperCamelCase      :			str					, __UpperCamelCase      :			List[Any]					, __UpperCamelCase      :			Optional[Any]=None					, __UpperCamelCase      :			Tuple=None    ):


						'''simple docstring'''






						if "." in tensor_name:
												snake_case_   : List[str]						=			tensor_name.split("""."""    )
												for split in splits[:-1]:
																		snake_case_   : Optional[int]						=			getattr(_lowercase					, _lowercase    )
																		if new_module is None:
																								raise ValueError(F'{module} has no attribute {split}.'    )
																		snake_case_   : int						=			new_module
												snake_case_   : List[str]						=			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}.'    )
						snake_case_   : Union[str, Any]						=			tensor_name in module._buffers
						snake_case_   : str						=			getattr(_lowercase					, _lowercase    )

						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}.'    )

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

						if is_abit or is_abit:
												snake_case_   : Any						=			module._parameters[tensor_name]
												if param.device.type != "cuda":
																		if value is None:
																								snake_case_   : Union[str, Any]						=			old_value.to(_lowercase    )
																		elif isinstance(_lowercase					, torch.Tensor    ):
																								snake_case_   : Dict						=			value.to("""cpu"""    )
																								if value.dtype == torch.inta:
																														snake_case_   : List[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:
																								snake_case_   : Union[str, Any]						=			torch.tensor(_lowercase					, 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					, _lowercase    ) and fpaa_statistics is None:
																								snake_case_   : Tuple						=			new_value.T

																		snake_case_   : List[str]						=			old_value.__dict__
																		if is_abit:
																								snake_case_   : List[Any]						=			bnb.nn.IntaParams(_lowercase					, requires_grad=_lowercase					, **_lowercase    ).to(_lowercase    )
																		elif is_abit:
																								snake_case_   : int						=			bnb.nn.Paramsabit(_lowercase					, requires_grad=_lowercase					, **_lowercase    ).to(_lowercase    )

																		snake_case_   : int						=			new_value
																		if fpaa_statistics is not None:
																								setattr(module.weight					, """SCB"""					, fpaa_statistics.to(_lowercase    )    )

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

												if is_buffer:
																		snake_case_   : int						=			new_value
												else:
																		snake_case_   : int						=			nn.Parameter(_lowercase					, requires_grad=old_value.requires_grad    )
																		snake_case_   : Tuple						=			new_value
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]					, __UpperCamelCase      :			Optional[Any]=None					, __UpperCamelCase      :			List[Any]=None					, __UpperCamelCase      :			int=None					, __UpperCamelCase      :			Any=False    ):


						'''simple docstring'''






						for name, module in model.named_children():
												if current_key_name is None:
																		snake_case_   : Dict						=			[]
												current_key_name.append(_lowercase    )

												if (isinstance(_lowercase					, nn.Linear    ) or isinstance(_lowercase					, _lowercase    )) 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(_lowercase    ) for key in modules_to_not_convert    ):
																								with init_empty_weights():
																														if isinstance(_lowercase					, _lowercase    ):
																																				snake_case_   : Optional[int]						=			module.weight.shape
																														else:
																																				snake_case_   : Any						=			module.in_features
																																				snake_case_   : str						=			module.out_features

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


						'''simple docstring'''






						snake_case_   : Optional[Any]						=			['lm_head'] if modules_to_not_convert is None else modules_to_not_convert
						snake_case_   : List[Any]						=			_replace_with_bnb_linear(
						    _lowercase					, _lowercase					, _lowercase					, _lowercase    )

						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						(		*__UpperCamelCase      :			Tuple					, **__UpperCamelCase      :			Tuple    ):


						'''simple docstring'''






						warnings.warn(
						    """`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead"""					, _lowercase					, )
						return replace_with_bnb_linear(*_lowercase					, **_lowercase    )
def 	__lowerCAmelCase						(		*__UpperCamelCase      :			List[str]					, **__UpperCamelCase      :			Dict    ):


						'''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"""					, _lowercase					, )
						return set_module_quantized_tensor_to_device(*_lowercase					, **_lowercase    )






def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[str]    ):


						'''simple docstring'''






						snake_case_   : List[Any]						=			deepcopy(_lowercase    )  # this has 0 cost since it is done inside `init_empty_weights` context manager`
						tied_model.tie_weights()

						snake_case_   : int						=			find_tied_parameters(_lowercase    )
						# For compatibility with Accelerate < 0.18
						if isinstance(_lowercase					, _lowercase    ):
												snake_case_   : Dict						=			sum(list(tied_params.values()    )					, []    ) + list(tied_params.keys()    )
						else:
												snake_case_   : List[Any]						=			sum(_lowercase					, []    )
						snake_case_   : Any						=			len(_lowercase    ) > 0

						# Check if it is a base model
						snake_case_   : str						=			not hasattr(_lowercase					, 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
						snake_case_   : int						=			list(model.named_children()    )
						snake_case_   : List[str]						=			[list_modules[-1][0]]

						# add last module together with tied weights
						snake_case_   : Optional[Any]						=			set(_lowercase    ) - set(_lowercase    )
						snake_case_   : Optional[Any]						=			list(set(_lowercase    )    ) + list(_lowercase    )

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

						return filtered_module_names






"""simple docstring"""





import math

import tensorflow as tf
from packaging import version
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]    ):


						'''simple docstring'''






						snake_case_   : int						=			tf.convert_to_tensor(__UpperCamelCase    )
						snake_case_   : int						=			0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0    )					, x.dtype    )    ))

						return x * cdf
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[str]    ):


						'''simple docstring'''






						snake_case_   : int						=			tf.convert_to_tensor(__UpperCamelCase    )
						snake_case_   : List[Any]						=			tf.cast(math.pi					, x.dtype    )
						snake_case_   : int						=			tf.cast(0.044_715					, x.dtype    )
						snake_case_   : Optional[int]						=			0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi    ) * (x + coeff * tf.pow(__UpperCamelCase					, 3    ))    ))

						return x * cdf
def 	__lowerCAmelCase						(		__UpperCamelCase      :			str    ):


						'''simple docstring'''






						snake_case_   : Optional[Any]						=			tf.convert_to_tensor(__UpperCamelCase    )

						return x * tf.tanh(tf.math.softplus(__UpperCamelCase    )    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any    ):


						'''simple docstring'''






						snake_case_   : str						=			tf.convert_to_tensor(__UpperCamelCase    )
						snake_case_   : int						=			tf.cast(0.044_715					, x.dtype    )
						snake_case_   : Optional[int]						=			tf.cast(0.7_978_845_608					, x.dtype    )

						return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)    ))
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[str]    ):


						'''simple docstring'''






						snake_case_   : Tuple						=			tf.convert_to_tensor(__UpperCamelCase    )
						snake_case_   : str						=			tf.cast(1.702					, x.dtype    )
						return x * tf.math.sigmoid(coeff * x    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int    ):


						'''simple docstring'''






						return tf.clip_by_value(_gelu(__UpperCamelCase    )					, -1_0					, 1_0    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[int]					, __UpperCamelCase      :			List[str]=-1    ):


						'''simple docstring'''






						snake_case_			,		snake_case_   : List[Any]						=			tf.split(__UpperCamelCase					, 2					, axis=__UpperCamelCase    )
						return a * tf.math.sigmoid(__UpperCamelCase    )


if version.parse(tf.version.VERSION) >= version.parse('''2.4'''):
					def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]    ):


											'''simple docstring'''






											return tf.keras.activations.gelu(__UpperCamelCase					, approximate=__UpperCamelCase    )

					__lowerCAmelCase       :					int      					=					tf.keras.activations.gelu






					__lowerCAmelCase       :					Optional[Any]      					=					approximate_gelu_wrap
else:
					__lowerCAmelCase       :					List[Any]      					=					_gelu
					__lowerCAmelCase       :					Any      					=					_gelu_new


__lowerCAmelCase       :					Dict      					=					{
    '''gelu''': gelu,
    '''gelu_10''': gelu_aa,
    '''gelu_fast''': gelu_fast,
    '''gelu_new''': gelu_new,
    '''glu''': glu,
    '''mish''': mish,
    '''quick_gelu''': quick_gelu,
    '''relu''': tf.keras.activations.relu,
    '''sigmoid''': tf.keras.activations.sigmoid,
    '''silu''': tf.keras.activations.swish,
    '''swish''': tf.keras.activations.swish,
    '''tanh''': tf.keras.activations.tanh,
}






def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any    ):


						'''simple docstring'''






						if activation_string in ACTaFN:
												return ACTaFN[activation_string]
						else:
												raise KeyError(F'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()    )}'    )

