
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   =			logging.get_logger(__name__)



def 						lowercase_      (       A__			,		A__			,		A__			,		A__=None			,		A__=None		)							->							str:
				"""simple docstring"""

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

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

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

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

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

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

								if is_buffer:
												snake_case								= new_value
								else:
												snake_case								= nn.Parameter(A__			,		requires_grad=old_value.requires_grad		)
												snake_case								= new_value



def 						lowercase_      (       A__			,		A__=None			,		A__=None			,		A__=None			,		A__=False		)							->							Optional[Any]:
				"""simple docstring"""

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

																				if quantization_config.quantization_method() == "llm_int8":
																								snake_case								= 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			,		)
																								snake_case								= 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								= 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			,		)
																												snake_case								= True
                    # Store the module class in case we need to transpose the weight later
																				snake_case								= type(A__		)
																				# Force requires grad to False to avoid unexpected errors
																				model._modules[name].requires_grad_(A__		)
								if len(list(module.children()		)		) > 0:
												snake_case					,							snake_case								= _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 						lowercase_      (       A__			,		A__=None			,		A__=None			,		A__=None		)							->							List[str]:
				"""simple docstring"""

				snake_case								= ["lm_head"] if modules_to_not_convert is None else modules_to_not_convert
				snake_case					,							snake_case								= _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 						lowercase_      (       *A__			,		**A__		)							->							Optional[int]:
				"""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 						lowercase_      (       *A__			,		**A__		)							->							Any:
				"""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 						lowercase_      (       A__		)							->							Union[str, Any]:
				"""simple docstring"""

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

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

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

				# add last module together with tied weights
				snake_case								= set(A__		) - set(A__		)
				snake_case								= list(set(A__		)		) + list(A__		)

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

				return filtered_module_names



from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt



def 						lowercase_      (       A__		)							->							bool:
    """simple docstring"""

    snake_case								= int(number**0.5		)
    return number == sq * sq



def 						lowercase_      (       A__			,		A__			,		A__			,		A__			,		A__			,		A__		)							->							tuple[int, int]:
    """simple docstring"""

    snake_case								= x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
    snake_case								= x_den * y_den * z_den
    snake_case								= gcd(A__			,		A__		)
    top //= hcf
    bottom //= hcf
    return top, bottom



def 						lowercase_      (       A__ = 35		)							->							int:
    """simple docstring"""

    snake_case								= set()
    snake_case								= 42
    snake_case								= Fraction(0		)
    snake_case								= 42

    for x_num in range(1			,		order + 1		):
        for x_den in range(x_num + 1			,		order + 1		):
            for y_num in range(1			,		order + 1		):
                for y_den in range(y_num + 1			,		order + 1		):
                    # n=1
                    snake_case								= x_num * y_den + x_den * y_num
                    snake_case								= x_den * y_den
                    snake_case								= gcd(A__			,		A__		)
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        snake_case								= add_three(
                            A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
                        unique_s.add(A__		)

                    # n=2
                    snake_case								= (
                        x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
                    )
                    snake_case								= x_den * x_den * y_den * y_den
                    if is_sq(A__		) and is_sq(A__		):
                        snake_case								= int(sqrt(A__		)		)
                        snake_case								= int(sqrt(A__		)		)
                        snake_case								= gcd(A__			,		A__		)
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            snake_case								= add_three(
                                A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
                            unique_s.add(A__		)

                    # n=-1
                    snake_case								= x_num * y_num
                    snake_case								= x_den * y_num + x_num * y_den
                    snake_case								= gcd(A__			,		A__		)
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        snake_case								= add_three(
                            A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
                        unique_s.add(A__		)

                    # n=2
                    snake_case								= x_num * x_num * y_num * y_num
                    snake_case								= (
                        x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
                    )
                    if is_sq(A__		) and is_sq(A__		):
                        snake_case								= int(sqrt(A__		)		)
                        snake_case								= int(sqrt(A__		)		)
                        snake_case								= gcd(A__			,		A__		)
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            snake_case								= add_three(
                                A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
                            unique_s.add(A__		)

    for num, den in unique_s:
        total += Fraction(A__			,		A__		)

    return total.denominator + total.numerator


if __name__ == "__main__":
   print(f"{solution() = }")

