






import math
from collections import OrderedDict

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

from .utils import logging


__UpperCAmelCase      =				logging.get_logger(__name__)
class 	a_(		nn.Module	):



     """simple docstring"""



     def __init__(		self					:	Tuple)							->	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     __UpperCamelCase						(		self					:	Tuple  ,  lowerCAmelCase__					:	Tensor)							->	Tensor:

      """simple docstring"""
      return nn.functional.gelu(lowerCAmelCase__  ,  approximate='tanh')

class 	a_(		nn.Module	):



     """simple docstring"""



     def     __UpperCamelCase						(		self					:	str  ,  lowerCAmelCase__					:	Tensor)							->	Tensor:

      """simple docstring"""
      return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.04_47_15 * torch.pow(lowerCAmelCase__  ,  3.0))))

class 	a_(		nn.Module	):



     """simple docstring"""



     def __init__(		self					:	str  ,  lowerCAmelCase__					:	bool = False)							->	List[Any]:

      """simple docstring"""
      super().__init__()
      if use_gelu_python:
       SCREAMING_SNAKE_CASE				    =		self._gelu_python
      else:
       SCREAMING_SNAKE_CASE				    =		nn.functional.gelu





     def     __UpperCamelCase						(		self					:	Dict  ,  lowerCAmelCase__					:	Tensor)							->	Tensor:

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





     def     __UpperCamelCase						(		self					:	Any  ,  lowerCAmelCase__					:	Tensor)							->	Tensor:

      """simple docstring"""
      return self.act(lowerCAmelCase__)

class 	a_(		nn.Module	):



     """simple docstring"""



     def     __UpperCamelCase						(		self					:	str  ,  lowerCAmelCase__					:	Tensor)							->	Tensor:

      """simple docstring"""
      return 0.5 * input * (1.0 + torch.tanh(input * 0.79_78_84_56_08 * (1.0 + 0.04_47_15 * input * input)))

class 	a_(		nn.Module	):



     """simple docstring"""



     def     __UpperCamelCase						(		self					:	Dict  ,  lowerCAmelCase__					:	Tensor)							->	Tensor:

      """simple docstring"""
      return input * torch.sigmoid(1.7_02 * input)

class 	a_(		nn.Module	):



     """simple docstring"""



     def __init__(		self					:	str  ,  lowerCAmelCase__					:	float  ,  lowerCAmelCase__					:	float)							->	Optional[Any]:

      """simple docstring"""
      if min > max:
       raise ValueError(f'''min should be < max (got min: {min}, max: {max})''')

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





     def     __UpperCamelCase						(		self					:	Optional[Any]  ,  lowerCAmelCase__					:	Tensor)							->	Tensor:

      """simple docstring"""
      return torch.clip(gelu(lowerCAmelCase__)  ,  self.min  ,  self.max)

class 	a_(		nn.Module	):



     """simple docstring"""



     def __init__(		self					:	List[str])							->	Dict:

      """simple docstring"""
      super().__init__()
      SCREAMING_SNAKE_CASE				    =		math.sqrt(2 / math.pi)





     def     __UpperCamelCase						(		self					:	List[Any]  ,  lowerCAmelCase__					:	Tensor)							->	Tensor:

      """simple docstring"""
      return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.04_47_15 * torch.pow(lowerCAmelCase__  ,  3))))

class 	a_(		nn.Module	):



     """simple docstring"""



     def     __UpperCamelCase						(		self					:	Optional[Any]  ,  lowerCAmelCase__					:	Tensor)							->	Tensor:

      """simple docstring"""
      return nn.functional.silu(lowerCAmelCase__)

class 	a_(		nn.Module	):



     """simple docstring"""



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

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





     def     __UpperCamelCase						(		self					:	Tuple  ,  lowerCAmelCase__					:	Tensor)							->	Tensor:

      """simple docstring"""
      return input * torch.tanh(nn.functional.softplus(lowerCAmelCase__))





     def     __UpperCamelCase						(		self					:	Any  ,  lowerCAmelCase__					:	Tensor)							->	Tensor:

      """simple docstring"""
      return self.act(lowerCAmelCase__)

class 	a_(		nn.Module	):



     """simple docstring"""



     def     __UpperCamelCase						(		self					:	List[Any]  ,  lowerCAmelCase__					:	Tensor)							->	Tensor:

      """simple docstring"""
      return input

class 	a_(		nn.Module	):



     """simple docstring"""



     def     __UpperCamelCase						(		self					:	Any  ,  lowerCAmelCase__					:	Optional[int]  ,  lowerCAmelCase__					:	Union[str, Any]=0.70_71_07  ,  lowerCAmelCase__					:	Tuple=0.28_20_95)							->	Optional[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		(input - mu).div(sigma * math.sqrt(2.0))
      return 0.5 * (1.0 + torch.erf(lowerCAmelCase__))

class 	a_(		nn.Module	):



     """simple docstring"""



     def     __UpperCamelCase						(		self					:	Dict  ,  lowerCAmelCase__					:	int)							->	List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		nn.functional.relu(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		torch.square(lowerCAmelCase__)
      return squared

class 	a_(		lowercase__	):



     """simple docstring"""



     def __getitem__(		self					:	List[Any]  ,  lowerCAmelCase__					:	Optional[int])							->	List[str]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		super().__getitem__(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		content if isinstance(lowerCAmelCase__  ,  lowerCAmelCase__) else (content, {})
      return cls(**lowerCAmelCase__)

__UpperCAmelCase      =				{
    "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,
}
__UpperCAmelCase      =				ClassInstantier(ACTaCLS)





def 						A_					(						lowercase_			)				->Optional[int]:







 """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
__UpperCAmelCase      =				get_activation("gelu_python")
__UpperCAmelCase      =				get_activation("gelu_new")
__UpperCAmelCase      =				get_activation("gelu")
__UpperCAmelCase      =				get_activation("gelu_fast")
__UpperCAmelCase      =				get_activation("quick_gelu")
__UpperCAmelCase      =				get_activation("silu")
__UpperCAmelCase      =				get_activation("mish")
__UpperCAmelCase      =				get_activation("linear")









"""simple docstring"""
from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest
@custom_tokenizers
class 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					def   lowerCAmelCase_							(			self:				Optional[int]		,	snake_case:				Optional[Any]		,	snake_case:				Optional[Any]		,	snake_case:				Tuple		,	snake_case:				int		,	snake_case:				Optional[Any]		)  ->      int:
											return True






					def   lowerCAmelCase_							(			self:				Tuple		)  ->      Optional[int]:
											snake_case_					:Dict		=     CpmTokenizer.from_pretrained("""TsinghuaAI/CPM-Generate"""		)
											snake_case_					:Any		=     """Hugging Face大法好，谁用谁知道。"""
											snake_case_					:Union[str, Any]		=     """Hugging Face大法好,谁用谁知道。<unk>"""
											snake_case_					:Dict		=     """▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。""".split()

											snake_case_					:str		=     tokenizer.tokenize(snake_case		)
											self.assertListEqual(snake_case		,	snake_case		)

											snake_case_					:List[Any]		=     tokens + [tokenizer.unk_token]

											snake_case_					:Optional[Any]		=     [13_789, 13_283, 1_421, 8, 10, 1_164, 13_608, 16_528, 63, 8, 9, 440, 108, 440, 121, 90, 8, 12, 0]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case		)		,	snake_case		)

											snake_case_					:int		=     tokenizer.decode(snake_case		)
											self.assertEqual(snake_case		,	snake_case		)
