"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
from collections import OrderedDict
from packaging import version
from torch import Tensor, nn
from .utils import logging
import torch
import math
logger = logging.get_logger(__name__)
class PytorchGELUTanh(nn.Module):
    def __init__(self):
        super().__init__()
        if version.parse(torch.__version__) < version.parse("1.12.0"): raise ImportError(f"You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use PytorchGELUTanh. Please upgrade torch.")
    def forward(self, input: Tensor) -> Tensor: return nn.functional.gelu(input, approximate="tanh")
class NewGELUActivation(nn.Module):
    def forward(self, input: Tensor) -> Tensor: return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044715 * torch.pow(input, 3.0))))
class GELUActivation(nn.Module):
    def __init__(self, use_gelu_python: bool = False):
        super().__init__()
        if use_gelu_python: self.act = self._gelu_python
        else: self.act = nn.functional.gelu
    def _gelu_python(self, input: Tensor) -> Tensor: return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0)))
    def forward(self, input: Tensor) -> Tensor: return self.act(input)
class FastGELUActivation(nn.Module):
    def forward(self, input: Tensor) -> Tensor:
        return 0.5 * input * (1.0 + torch.tanh(input * 0.7978845608 * (1.0 + 0.044715 * input * input)))
class QuickGELUActivation(nn.Module):
    def forward(self, input: Tensor) -> Tensor: return input * torch.sigmoid(1.702 * input)
class ClippedGELUActivation(nn.Module):
    def __init__(self, min: float, max: float):
        if min > max: raise ValueError(f"min should be < max (got min: {min}, max: {max})")
        super().__init__()
        self.min = min
        self.max = max
    def forward(self, x: Tensor) -> Tensor: return torch.clip(gelu(x), self.min, self.max)
class AccurateGELUActivation(nn.Module):
    def __init__(self):
        super().__init__()
        self.precomputed_constant = math.sqrt(2 / math.pi)
    def forward(self, input: Tensor) -> Tensor: return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.044715 * torch.pow(input, 3))))
class MishActivation(nn.Module):
    def __init__(self):
        super().__init__()
        if version.parse(torch.__version__) < version.parse("1.9.0"): self.act = self._mish_python
        else: self.act = nn.functional.mish
    def _mish_python(self, input: Tensor) -> Tensor: return input * torch.tanh(nn.functional.softplus(input))
    def forward(self, input: Tensor) -> Tensor: return self.act(input)
class LinearActivation(nn.Module):
    def forward(self, input: Tensor) -> Tensor: return input
class LaplaceActivation(nn.Module):
    def forward(self, input, mu=0.707107, sigma=0.282095):
        input = (input - mu).div(sigma * math.sqrt(2.0))
        return 0.5 * (1.0 + torch.erf(input))
class ReLUSquaredActivation(nn.Module):
    def forward(self, input):
        relu_applied = nn.functional.relu(input)
        squared = torch.square(relu_applied)
        return squared
class ClassInstantier(OrderedDict):
    def __getitem__(self, key):
        content = super().__getitem__(key)
        cls, kwargs = content if isinstance(content, tuple) else (content, {})
        return cls(**kwargs)
ACT2CLS = {"gelu": GELUActivation, "gelu_10": (ClippedGELUActivation, {"min": -10, "max": 10}), "gelu_fast": FastGELUActivation, "gelu_new": NewGELUActivation,
"gelu_python": (GELUActivation, {"use_gelu_python": True}), "gelu_pytorch_tanh": PytorchGELUTanh, "gelu_accurate": AccurateGELUActivation, "laplace": LaplaceActivation,
"leaky_relu": nn.LeakyReLU, "linear": LinearActivation, "mish": MishActivation, "quick_gelu": QuickGELUActivation, "relu": nn.ReLU, "relu2": ReLUSquaredActivation,
"relu6": nn.ReLU6, "sigmoid": nn.Sigmoid, "silu": nn.SiLU, "swish": nn.SiLU, "tanh": nn.Tanh}
ACT2FN = ClassInstantier(ACT2CLS)
def get_activation(activation_string):
    if activation_string in ACT2FN: return ACT2FN[activation_string]
    else: raise KeyError(f"function {activation_string} not found in ACT2FN mapping {list(ACT2FN.keys())}")
gelu_python = get_activation("gelu_python")
gelu_new = get_activation("gelu_new")
gelu = get_activation("gelu")
gelu_fast = get_activation("gelu_fast")
quick_gelu = get_activation("quick_gelu")
silu = get_activation("silu")
mish = get_activation("mish")
linear_act = get_activation("linear")
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
