
'''simple docstring'''





import math
from collections import OrderedDict

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

from .utils import logging


a__		: Optional[Any]  							=							logging.get_logger(__name__)


class 			UpperCamelCase__	(     nn.Module):






 def __init__(      self	:Optional[Any]      )					->      Optional[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	:Optional[Any]						,					_A	:Tensor      )					->      Tensor:







       '''simple docstring'''



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


class 			UpperCamelCase__	(     nn.Module):






 def 						lowercase_    (      self	:Optional[Any]						,					_A	:Tensor      )					->      Tensor:







       '''simple docstring'''



       return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi      ) * (input + 0.044_715 * torch.pow(_A						,					3.0      ))      ))


class 			UpperCamelCase__	(     nn.Module):






 def __init__(      self	:Optional[int]						,					_A	:bool = False      )					->      Tuple:







       '''simple docstring'''



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






 def 						lowercase_    (      self	:int						,					_A	:Tensor      )					->      Tensor:







       '''simple docstring'''



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






 def 						lowercase_    (      self	:Optional[int]						,					_A	:Tensor      )					->      Tensor:







       '''simple docstring'''



       return self.act(_A      )


class 			UpperCamelCase__	(     nn.Module):






 def 						lowercase_    (      self	:Tuple						,					_A	:Tensor      )					->      Tensor:







       '''simple docstring'''



       return 0.5 * input * (1.0 + torch.tanh(input * 0.7_978_845_608 * (1.0 + 0.044_715 * input * input)      ))


class 			UpperCamelCase__	(     nn.Module):






 def 						lowercase_    (      self	:Optional[int]						,					_A	:Tensor      )					->      Tensor:







       '''simple docstring'''



       return input * torch.sigmoid(1.702 * input      )


class 			UpperCamelCase__	(     nn.Module):






 def __init__(      self	:List[str]						,					_A	:float						,					_A	:float      )					->      List[Any]:







       '''simple docstring'''



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

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






 def 						lowercase_    (      self	:Dict						,					_A	:Tensor      )					->      Tensor:







       '''simple docstring'''



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


class 			UpperCamelCase__	(     nn.Module):






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







       '''simple docstring'''



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






 def 						lowercase_    (      self	:Tuple						,					_A	:Tensor      )					->      Tensor:







       '''simple docstring'''



       return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.044_715 * torch.pow(_A						,					3      ))      ))


class 			UpperCamelCase__	(     nn.Module):






 def 						lowercase_    (      self	:int						,					_A	:Tensor      )					->      Tensor:







       '''simple docstring'''



       return nn.functional.silu(_A      )


class 			UpperCamelCase__	(     nn.Module):






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







       '''simple docstring'''



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






 def 						lowercase_    (      self	:Optional[Any]						,					_A	:Tensor      )					->      Tensor:







       '''simple docstring'''



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






 def 						lowercase_    (      self	:Dict						,					_A	:Tensor      )					->      Tensor:







       '''simple docstring'''



       return self.act(_A      )


class 			UpperCamelCase__	(     nn.Module):






 def 						lowercase_    (      self	:str						,					_A	:Tensor      )					->      Tensor:







       '''simple docstring'''



       return input


class 			UpperCamelCase__	(     nn.Module):






 def 						lowercase_    (      self	:List[str]						,					_A	:Optional[Any]						,					_A	:Tuple=0.707_107						,					_A	:List[str]=0.282_095      )					->      Union[str, Any]:







       '''simple docstring'''



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


class 			UpperCamelCase__	(     nn.Module):






 def 						lowercase_    (      self	:Tuple						,					_A	:Any      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      nn.functional.relu(_A      )
       __A													=      torch.square(_A      )
       return squared


class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE):






 def __getitem__(      self	:Any						,					_A	:Optional[int]      )					->      Dict:







       '''simple docstring'''



       __A													=      super().__getitem__(_A      )
       __A						,   __A													=      content if isinstance(_A						,					_A      ) else (content, {})
       return cls(**_A      )
a__		: Optional[int]  							=							{
    "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__		: str  							=							ClassInstantier(ACTaCLS)





def 							snake_case				(		UpperCAmelCase						)->      Any:







      """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
a__		: Dict  							=							get_activation("gelu_python")
a__		: int  							=							get_activation("gelu_new")
a__		: Optional[int]  							=							get_activation("gelu")
a__		: Optional[Any]  							=							get_activation("gelu_fast")
a__		: Optional[Any]  							=							get_activation("quick_gelu")
a__		: Dict  							=							get_activation("silu")
a__		: Dict  							=							get_activation("mish")
a__		: int  							=							get_activation("linear")











import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase							)		->		Dict:







						"""simple docstring"""

						A__						:				str         =			BertConfig.from_pretrained(
						    "bert-large-cased"				,  vocab_size=__lowercase				,  max_position_embeddings=5_1_2				,  is_decoder=__lowercase				,  add_cross_attention=__lowercase				,  )
						A__						:				List[str]         =			bert_config.to_dict()
						del bert_config_dict["type_vocab_size"]
						A__						:				Union[str, Any]         =			BertGenerationConfig(**__lowercase							)
						if is_encoder:
												A__						:				Optional[int]         =			BertGenerationEncoder(__lowercase							)
						else:
												A__						:				List[Any]         =			BertGenerationDecoder(__lowercase							)
						print(F'Building PyTorch model from configuration: {config}'							)

						# Load weights from tf checkpoint
						load_tf_weights_in_bert_generation(
						    __lowercase				,  __lowercase				,  model_class="bert"				,  is_encoder_named_decoder=__lowercase				,  is_encoder=__lowercase				,  )

						# Save pytorch-model
						print(F'Save PyTorch model and config to {pytorch_dump_path}'							)
						model.save_pretrained(__lowercase							)


if __name__ == "__main__":
		snake_case     :    List[Any]						=    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		parser.add_argument(
		    '--is_encoder_named_decoder',
		    action='store_true',
		    help='If decoder has to be renamed to encoder in PyTorch model.',
		)
		parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
		parser.add_argument('--vocab_size', default=5_0_3_5_8, type=int, help='Vocab size of model')
		snake_case     :    List[Any]						=    parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.tf_hub_path,
		    args.pytorch_dump_path,
		    args.is_encoder_named_decoder,
		    args.vocab_size,
		    is_encoder=args.is_encoder,
		)