
'''simple docstring'''





import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


a__		: Optional[Any]  							=							logging.get_logger(__name__)


class 			UpperCamelCase__	(     nn.Module):






 def __init__(      self	:Optional[Any]      )					->      Optional[Any]:







       '''simple docstring'''



       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 						lowercase_    (      self	:Optional[Any]						,					_A	:Tensor      )					->      Tensor:







       '''simple docstring'''



       return nn.functional.gelu(_A						,					approximate='tanh'      )


class 			UpperCamelCase__	(     nn.Module):






 def 						lowercase_    (      self	:Optional[Any]						,					_A	:Tensor      )					->      Tensor:







       '''simple docstring'''



       return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi      ) * (input + 0.044_715 * torch.pow(_A						,					3.0      ))      ))


class 			UpperCamelCase__	(     nn.Module):






 def __init__(      self	:Optional[int]						,					_A	:bool = False      )					->      Tuple:







       '''simple docstring'''



       super().__init__()
       if use_gelu_python:
             __A													=      self._gelu_python
       else:
             __A													=      nn.functional.gelu






 def 						lowercase_    (      self	:int						,					_A	:Tensor      )					->      Tensor:







       '''simple docstring'''



       return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0      )      ))






 def 						lowercase_    (      self	:Optional[int]						,					_A	:Tensor      )					->      Tensor:







       '''simple docstring'''



       return self.act(_A      )


class 			UpperCamelCase__	(     nn.Module):






 def 						lowercase_    (      self	:Tuple						,					_A	:Tensor      )					->      Tensor:







       '''simple docstring'''



       return 0.5 * input * (1.0 + torch.tanh(input * 0.7_978_845_608 * (1.0 + 0.044_715 * input * input)      ))


class 			UpperCamelCase__	(     nn.Module):






 def 						lowercase_    (      self	:Optional[int]						,					_A	:Tensor      )					->      Tensor:







       '''simple docstring'''



       return input * torch.sigmoid(1.702 * input      )


class 			UpperCamelCase__	(     nn.Module):






 def __init__(      self	:List[str]						,					_A	:float						,					_A	:float      )					->      List[Any]:







       '''simple docstring'''



       if min > max:
             raise ValueError(F'min should be < max (got min: {min}, max: {max})'      )

       super().__init__()
       __A													=      min
       __A													=      max






 def 						lowercase_    (      self	:Dict						,					_A	:Tensor      )					->      Tensor:







       '''simple docstring'''



       return torch.clip(gelu(_A      )						,					self.min						,					self.max      )


class 			UpperCamelCase__	(     nn.Module):






 def __init__(      self	:Dict      )					->      Optional[int]:







       '''simple docstring'''



       super().__init__()
       __A													=      math.sqrt(2 / math.pi      )






 def 						lowercase_    (      self	:Tuple						,					_A	:Tensor      )					->      Tensor:







       '''simple docstring'''



       return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.044_715 * torch.pow(_A						,					3      ))      ))


class 			UpperCamelCase__	(     nn.Module):






 def 						lowercase_    (      self	:int						,					_A	:Tensor      )					->      Tensor:







       '''simple docstring'''



       return nn.functional.silu(_A      )


class 			UpperCamelCase__	(     nn.Module):






 def __init__(      self	:Any      )					->      Optional[int]:







       '''simple docstring'''



       super().__init__()
       if version.parse(torch.__version__      ) < version.parse('1.9.0'      ):
             __A													=      self._mish_python
       else:
             __A													=      nn.functional.mish






 def 						lowercase_    (      self	:Optional[Any]						,					_A	:Tensor      )					->      Tensor:







       '''simple docstring'''



       return input * torch.tanh(nn.functional.softplus(_A      )      )






 def 						lowercase_    (      self	:Dict						,					_A	:Tensor      )					->      Tensor:







       '''simple docstring'''



       return self.act(_A      )


class 			UpperCamelCase__	(     nn.Module):






 def 						lowercase_    (      self	:str						,					_A	:Tensor      )					->      Tensor:







       '''simple docstring'''



       return input


class 			UpperCamelCase__	(     nn.Module):






 def 						lowercase_    (      self	:List[str]						,					_A	:Optional[Any]						,					_A	:Tuple=0.707_107						,					_A	:List[str]=0.282_095      )					->      Union[str, Any]:







       '''simple docstring'''



       __A													=      (input - mu).div(sigma * math.sqrt(2.0      )      )
       return 0.5 * (1.0 + torch.erf(_A      ))


class 			UpperCamelCase__	(     nn.Module):






 def 						lowercase_    (      self	:Tuple						,					_A	:Any      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      nn.functional.relu(_A      )
       __A													=      torch.square(_A      )
       return squared


class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE):






 def __getitem__(      self	:Any						,					_A	:Optional[int]      )					->      Dict:







       '''simple docstring'''



       __A													=      super().__getitem__(_A      )
       __A						,   __A													=      content if isinstance(_A						,					_A      ) else (content, {})
       return cls(**_A      )
a__		: Optional[int]  							=							{
    "gelu": GELUActivation,
    "gelu_10": (ClippedGELUActivation, {"min": -1_0, "max": 1_0}),
    "gelu_fast": FastGELUActivation,
    "gelu_new": NewGELUActivation,
    "gelu_python": (GELUActivation, {"use_gelu_python": True}),
    "gelu_pytorch_tanh": PytorchGELUTanh,
    "gelu_accurate": AccurateGELUActivation,
    "laplace": LaplaceActivation,
    "linear": LinearActivation,
    "mish": MishActivation,
    "quick_gelu": QuickGELUActivation,
    "relu": nn.ReLU,
    "relu2": ReLUSquaredActivation,
    "relu6": nn.ReLUa,
    "sigmoid": nn.Sigmoid,
    "silu": SiLUActivation,
    "swish": SiLUActivation,
    "tanh": nn.Tanh,
}
a__		: str  							=							ClassInstantier(ACTaCLS)





def 							snake_case				(		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()						)}'						)


# For backwards compatibility with: from activations import gelu_python
a__		: Dict  							=							get_activation("gelu_python")
a__		: int  							=							get_activation("gelu_new")
a__		: Optional[int]  							=							get_activation("gelu")
a__		: Optional[Any]  							=							get_activation("gelu_fast")
a__		: Optional[Any]  							=							get_activation("quick_gelu")
a__		: Dict  							=							get_activation("silu")
a__		: Dict  							=							get_activation("mish")
a__		: int  							=							get_activation("linear")










'''simple docstring'''

from __future__ import annotations



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					list[list[int]]     ,		_UpperCamelCase   :					list[list[int]] )		->       list[int]:





   '''simple docstring'''







   assert len(_UpperCamelCase ) == len(_UpperCamelCase )

   UpperCamelCase__			    =    len(_UpperCamelCase )
   UpperCamelCase__			    =    list(range(_UpperCamelCase ) )
   UpperCamelCase__			    =    [-1] * n  # who the donor has donated to
   UpperCamelCase__			    =    [-1] * n  # who the recipient has received from
   UpperCamelCase__			    =    [0] * n

   while unmatched_donors:
      UpperCamelCase__			    =    unmatched_donors[0]
      UpperCamelCase__			    =    donor_pref[donor]
      UpperCamelCase__			    =    donor_preference[num_donations[donor]]
      num_donations[donor] += 1
      UpperCamelCase__			    =    recipient_pref[recipient]
      UpperCamelCase__			    =    rec_record[recipient]

      if prev_donor != -1:
         if rec_preference.index(_UpperCamelCase ) > rec_preference.index(_UpperCamelCase ):
            UpperCamelCase__			    =    donor
            UpperCamelCase__			    =    recipient
            unmatched_donors.append(_UpperCamelCase )
            unmatched_donors.remove(_UpperCamelCase )
      else:
         UpperCamelCase__			    =    donor
         UpperCamelCase__			    =    recipient
         unmatched_donors.remove(_UpperCamelCase )
   return donor_record