
import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image


def 					__lowerCAmelCase    (    a__		,					a__=256						) ->						Tuple:
				__a    							=					min(img.size						)

				if s < target_image_size:
								raise ValueError(F"""min dim for image {s} < {target_image_size}"""						)

				__a    							=					target_image_size / s
				__a    							=					(round(r * img.size[1]						), round(r * img.size[0]						))
				__a    							=					TF.resize(a__		,					a__		,					interpolation=PIL.Image.LANCZOS						)
				__a    							=					TF.center_crop(a__		,					output_size=2 * [target_image_size]						)
				__a    							=					torch.unsqueeze(T.ToTensor()(a__						)		,					0						)
				return img


def 					__lowerCAmelCase    (    a__						) ->						Dict:
				__a    							=					2.0 * x - 1.0
				return x


def 					__lowerCAmelCase    (    a__		,					a__=True		,					a__="RGB"						) ->						Optional[Any]:
				__a    							=					x.detach().cpu()
				if process:
								__a    							=					post_process_tensor(a__						)
				__a    							=					x.numpy()
				if process:
								__a    							=					(255 * x).astype(np.uinta						)
				__a    							=					Image.fromarray(a__						)
				if not x.mode == mode:
								__a    							=					x.convert(a__						)
				return x


def 					__lowerCAmelCase    (    a__						) ->						List[Any]:
				__a    							=					torch.clamp(a__		,					-1.0		,					1.0						)
				__a    							=					(x + 1.0) / 2.0
				__a    							=					x.permute(1		,					2		,					0						)
				return x






def 					__lowerCAmelCase    (    a__						) ->						List[str]:
				__a    							=					post_process_tensor(x.squeeze()						)
				return x.permute(2		,					0		,					1						).unsqueeze(0						)

import math
from collections import OrderedDict

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

from .utils import logging


A  :			List[str]     				=				logging.get_logger(__name__)







class     __A(				nn.Module    ):




				def __init__(  self       )					->   Union[str, 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        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




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







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi       ) * (input + 0.04_4715 * torch.pow(_snake_case	,  3.0       ))       ))







class     __A(				nn.Module    ):




				def __init__(  self	,  _snake_case = False       )					->   Tuple:


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return self.act(_snake_case       )







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return 0.5 * input * (1.0 + torch.tanh(input * 0.79_7884_5608 * (1.0 + 0.04_4715 * input * input)       ))







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return input * torch.sigmoid(1.702 * input       )







class     __A(				nn.Module    ):




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


								'''simple docstring'''




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

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




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




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







class     __A(				nn.Module    ):




				def __init__(  self       )					->   str:


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.04_4715 * torch.pow(_snake_case	,  3       ))       ))







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return nn.functional.silu(_snake_case       )







class     __A(				nn.Module    ):




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


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return self.act(_snake_case       )







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return input







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case=0.70_7107	,  _snake_case=0.28_2095       )					->   int:


								'''simple docstring'''




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







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Any:


								'''simple docstring'''




								__a    							=					nn.functional.relu(_snake_case       )
								__a    							=					torch.square(_snake_case       )
								return squared







class     __A(				a    ):




				def __getitem__(  self	,  _snake_case       )					->   Dict:


								'''simple docstring'''




								__a    							=					super().__getitem__(_snake_case       )
								__a							,  __a    							=					content if isinstance(_snake_case	,  _snake_case       ) else (content, {})
								return cls(**_snake_case       )



A  :			str     				=				{
    '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  :			Any     				=				ClassInstantier(ACTaCLS)


def 					__lowerCAmelCase    (    a__						) ->						Optional[int]:
				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  :			Optional[Any]     				=				get_activation('gelu_python')
A  :			str     				=				get_activation('gelu_new')
A  :			Any     				=				get_activation('gelu')
A  :			int     				=				get_activation('gelu_fast')
A  :			Any     				=				get_activation('quick_gelu')
A  :			int     				=				get_activation('silu')
A  :			List[str]     				=				get_activation('mish')






A  :			Dict     				=				get_activation('linear')