





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase__  :	Optional[Any]     ={'''configuration_pegasus''': ['''PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	Union[str, Any]     =['''PegasusTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	int     =['''PegasusTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	str     =[
							    '''PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''PegasusForCausalLM''',
							    '''PegasusForConditionalGeneration''',
							    '''PegasusModel''',
							    '''PegasusPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	Union[str, Any]     =[
							    '''TFPegasusForConditionalGeneration''',
							    '''TFPegasusModel''',
							    '''TFPegasusPreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	Dict     =[
							    '''FlaxPegasusForConditionalGeneration''',
							    '''FlaxPegasusModel''',
							    '''FlaxPegasusPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_pegasus import PegasusTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_pegasus_fast import PegasusTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_pegasus import (
														    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PegasusForCausalLM,
														    PegasusForConditionalGeneration,
														    PegasusModel,
														    PegasusPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_pegasus import (
														    FlaxPegasusForConditionalGeneration,
														    FlaxPegasusModel,
														    FlaxPegasusPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase__  :	Union[str, Any]     =_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


lowerCamelCase_						     =  logging.get_logger(__name__)




class 							a_ (						nn.Module		):


							'''simple docstring'''






							def __init__(		self   )					->					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			,			lowercase_   )					->					Tensor:
														'''simple docstring'''

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




class 							a_ (						nn.Module		):


							'''simple docstring'''






							def      _lowercase       (		self			,			lowercase_   )					->					Tensor:
														'''simple docstring'''

														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		):


							'''simple docstring'''






							def __init__(		self			,			lowercase_ = False   )					->					Optional[Any]:
														'''simple docstring'''

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

							def      _lowercase       (		self			,			lowercase_   )					->					Tensor:
														'''simple docstring'''

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






							def      _lowercase       (		self			,			lowercase_   )					->					Tensor:
														'''simple docstring'''

														return self.act(lowercase_   )




class 							a_ (						nn.Module		):


							'''simple docstring'''






							def      _lowercase       (		self			,			lowercase_   )					->					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      _lowercase       (		self			,			lowercase_   )					->					Tensor:
														'''simple docstring'''

														return input * torch.sigmoid(1.7_02 * input   )




class 							a_ (						nn.Module		):


							'''simple docstring'''






							def __init__(		self			,			lowercase_			,			lowercase_   )					->					Optional[int]:
														'''simple docstring'''

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

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






							def      _lowercase       (		self			,			lowercase_   )					->					Tensor:
														'''simple docstring'''

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




class 							a_ (						nn.Module		):


							'''simple docstring'''






							def __init__(		self   )					->					Union[str, Any]:
														'''simple docstring'''

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






							def      _lowercase       (		self			,			lowercase_   )					->					Tensor:
														'''simple docstring'''

														return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.04_47_15 * torch.pow(lowercase_			,			3   ))   ))




class 							a_ (						nn.Module		):


							'''simple docstring'''






							def      _lowercase       (		self			,			lowercase_   )					->					Tensor:
														'''simple docstring'''

														return nn.functional.silu(lowercase_   )




class 							a_ (						nn.Module		):


							'''simple docstring'''






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

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

							def      _lowercase       (		self			,			lowercase_   )					->					Tensor:
														'''simple docstring'''

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






							def      _lowercase       (		self			,			lowercase_   )					->					Tensor:
														'''simple docstring'''

														return self.act(lowercase_   )




class 							a_ (						nn.Module		):


							'''simple docstring'''






							def      _lowercase       (		self			,			lowercase_   )					->					Tensor:
														'''simple docstring'''

														return input




class 							a_ (						nn.Module		):


							'''simple docstring'''






							def      _lowercase       (		self			,			lowercase_			,			lowercase_=0.70_71_07			,			lowercase_=0.28_20_95   )					->					Optional[Any]:
														'''simple docstring'''

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




class 							a_ (						nn.Module		):


							'''simple docstring'''






							def      _lowercase       (		self			,			lowercase_   )					->					Any:
														'''simple docstring'''

														lowerCAmelCase_										=     nn.functional.relu(lowercase_   )
														lowerCAmelCase_										=     torch.square(lowercase_   )
														return squared




class 							a_ (						a_		):


							'''simple docstring'''






							def __getitem__(		self			,			lowercase_   )					->					Any:
														'''simple docstring'''

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


lowerCamelCase_						     =  {
    """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,
}
lowerCamelCase_						     =  ClassInstantier(ACTaCLS)
def      lowerCamelCase					(						a_	)	->			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
lowerCamelCase_						     =  get_activation("""gelu_python""")
lowerCamelCase_						     =  get_activation("""gelu_new""")
lowerCamelCase_						     =  get_activation("""gelu""")
lowerCamelCase_						     =  get_activation("""gelu_fast""")
lowerCamelCase_						     =  get_activation("""quick_gelu""")
lowerCamelCase_						     =  get_activation("""silu""")
lowerCamelCase_						     =  get_activation("""mish""")



lowerCamelCase_						     =  get_activation("""linear""")



