




"""simple docstring"""

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		import torch

		from transformers import CamembertModel
@require_torch
@require_sentencepiece
@require_tokenizers
class     A__      (      unittest.TestCase     ):




	@slow
	def 							a_		(   self  :	List[Any]	)					-> str:
						__snake_case          =	CamembertModel.from_pretrained("""camembert-base"""	)
						model.to(lowercase	)

						__snake_case          =	torch.tensor(
						    [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]      ,    device=lowercase      ,    dtype=torch.long      ,    )  # J'aime le camembert !
						with torch.no_grad():
											__snake_case          =	model(lowercase	)["""last_hidden_state"""]
						__snake_case          =	torch.Size((1, 10, 768)	)
						self.assertEqual(output.shape      ,    lowercase	)
						# compare the actual values for a slice.
						__snake_case          =	torch.tensor(
						    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]      ,    device=lowercase      ,    dtype=torch.float      ,    )
						# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
						# camembert.eval()
						# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

						self.assertTrue(torch.allclose(output[:, :3, :3]      ,    lowercase      ,    atol=1e-4	)	)








"""simple docstring"""

import math
from collections import OrderedDict

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

from .utils import logging


UpperCamelCase__ :    Optional[int] 					=							logging.get_logger(__name__)
class     A__      (      nn.Module     ):




	def __init__(   self  :	List[str]	)					-> Tuple:
						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 							a_		(   self  :	Union[str, Any]      ,    lowercase  :	Tensor	)					-> Tensor:
						return nn.functional.gelu(lowercase      ,    approximate="""tanh"""	)

class     A__      (      nn.Module     ):




	def 							a_		(   self  :	Optional[int]      ,    lowercase  :	Tensor	)					-> Tensor:
						return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi	) * (input + 0.04_47_15 * torch.pow(lowercase      ,    3.0	))	))

class     A__      (      nn.Module     ):




	def __init__(   self  :	Any      ,    lowercase  :	bool = False	)					-> Optional[Any]:
						super().__init__()
						if use_gelu_python:
											__snake_case          =	self._gelu_python
						else:
											__snake_case          =	nn.functional.gelu




	def 							a_		(   self  :	Tuple      ,    lowercase  :	Tensor	)					-> Tensor:
						return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0	)	))




	def 							a_		(   self  :	str      ,    lowercase  :	Tensor	)					-> Tensor:
						return self.act(lowercase	)

class     A__      (      nn.Module     ):




	def 							a_		(   self  :	str      ,    lowercase  :	Tensor	)					-> Tensor:
						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     ):




	def 							a_		(   self  :	List[str]      ,    lowercase  :	Tensor	)					-> Tensor:
						return input * torch.sigmoid(1.7_02 * input	)

class     A__      (      nn.Module     ):




	def __init__(   self  :	Union[str, Any]      ,    lowercase  :	float      ,    lowercase  :	float	)					-> List[str]:
						if min > max:
											raise ValueError(f'''min should be < max (got min: {min}, max: {max})'''	)

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




	def 							a_		(   self  :	Any      ,    lowercase  :	Tensor	)					-> Tensor:
						return torch.clip(gelu(lowercase	)      ,    self.min      ,    self.max	)

class     A__      (      nn.Module     ):




	def __init__(   self  :	str	)					-> List[str]:
						super().__init__()
						__snake_case          =	math.sqrt(2 / math.pi	)




	def 							a_		(   self  :	Any      ,    lowercase  :	Tensor	)					-> Tensor:
						return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.04_47_15 * torch.pow(lowercase      ,    3	))	))

class     A__      (      nn.Module     ):




	def 							a_		(   self  :	Tuple      ,    lowercase  :	Tensor	)					-> Tensor:
						return nn.functional.silu(lowercase	)

class     A__      (      nn.Module     ):




	def __init__(   self  :	Union[str, Any]	)					-> int:
						super().__init__()
						if version.parse(torch.__version__	) < version.parse("""1.9.0"""	):
											__snake_case          =	self._mish_python
						else:
											__snake_case          =	nn.functional.mish




	def 							a_		(   self  :	Any      ,    lowercase  :	Tensor	)					-> Tensor:
						return input * torch.tanh(nn.functional.softplus(lowercase	)	)




	def 							a_		(   self  :	int      ,    lowercase  :	Tensor	)					-> Tensor:
						return self.act(lowercase	)

class     A__      (      nn.Module     ):




	def 							a_		(   self  :	Union[str, Any]      ,    lowercase  :	Tensor	)					-> Tensor:
						return input

class     A__      (      nn.Module     ):




	def 							a_		(   self  :	List[Any]      ,    lowercase  :	List[Any]      ,    lowercase  :	Optional[Any]=0.70_71_07      ,    lowercase  :	Union[str, Any]=0.28_20_95	)					-> Optional[int]:
						__snake_case          =	(input - mu).div(sigma * math.sqrt(2.0	)	)
						return 0.5 * (1.0 + torch.erf(lowercase	))

class     A__      (      nn.Module     ):




	def 							a_		(   self  :	List[str]      ,    lowercase  :	int	)					-> List[Any]:
						__snake_case          =	nn.functional.relu(lowercase	)
						__snake_case          =	torch.square(lowercase	)
						return squared

class     A__      (      lowercase_     ):




	def __getitem__(   self  :	int      ,    lowercase  :	Dict	)					-> Optional[Any]:
						__snake_case          =	super().__getitem__(lowercase	)
						__snake_case      ,      __snake_case          =	content if isinstance(lowercase      ,    lowercase	) else (content, {})
						return cls(**lowercase	)

UpperCamelCase__ :    List[Any] 					=							{
    "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,
    "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__ :    Any 					=							ClassInstantier(ACTaCLS)




def       snake_case__	(       lowerCAmelCase   :		str		):
					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__ :    List[Any] 					=							get_activation("gelu_python")
UpperCamelCase__ :    Optional[int] 					=							get_activation("gelu_new")
UpperCamelCase__ :    Dict 					=							get_activation("gelu")
UpperCamelCase__ :    int 					=							get_activation("gelu_fast")
UpperCamelCase__ :    Tuple 					=							get_activation("quick_gelu")
UpperCamelCase__ :    int 					=							get_activation("silu")
UpperCamelCase__ :    Union[str, Any] 					=							get_activation("mish")

UpperCamelCase__ :    Tuple 					=							get_activation("linear")


