
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


A  :			Any     				=				{
    'configuration_bark': [
        'BARK_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BarkCoarseConfig',
        'BarkConfig',
        'BarkFineConfig',
        'BarkSemanticConfig',
    ],
    'processing_bark': ['BarkProcessor'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A  :			Optional[int]     				=				[
					    'BARK_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'BarkFineModel',
					    'BarkSemanticModel',
					    'BarkCoarseModel',
					    'BarkModel',
					    'BarkPreTrainedModel',
					    'BarkCausalModel',
					]

if TYPE_CHECKING:
					from .configuration_bark import (
					    BARK_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BarkCoarseConfig,
					    BarkConfig,
					    BarkFineConfig,
					    BarkSemanticConfig,
					)
					from .processing_bark import BarkProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bark import (
										    BARK_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BarkCausalModel,
										    BarkCoarseModel,
										    BarkFineModel,
										    BarkModel,
										    BarkPreTrainedModel,
										    BarkSemanticModel,
										)

else:
					import sys

					A  :			str     				=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

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')