




import math
from collections import OrderedDict

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

from .utils import logging


UpperCAmelCase_   							=      logging.get_logger(__name__)





class        lowerCamelCase__(   nn.Module):
      def __init__(							self:					Optional[int]  ):
             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      lowerCAmelCase__		(							self:					Optional[Any]       ,			UpperCamelCase_:					Tensor  ):
             return nn.functional.gelu(UpperCamelCase_       ,			approximate="""tanh"""  )





class        lowerCamelCase__(   nn.Module):
      def      lowerCAmelCase__		(							self:					Tuple       ,			UpperCamelCase_:					Tensor  ):
             return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi  ) * (input + 0.04_4715 * torch.pow(UpperCamelCase_       ,			3.0  ))  ))





class        lowerCamelCase__(   nn.Module):
      def __init__(							self:					Dict       ,			UpperCamelCase_:					bool = False  ):
             super().__init__()
             if use_gelu_python:
                    __lowerCamelCase  =		self._gelu_python
             else:
                    __lowerCamelCase  =		nn.functional.gelu
      def      lowerCAmelCase__		(							self:					str       ,			UpperCamelCase_:					Tensor  ):
             return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0  )  ))




      def      lowerCAmelCase__		(							self:					Union[str, Any]       ,			UpperCamelCase_:					Tensor  ):
             return self.act(UpperCamelCase_  )





class        lowerCamelCase__(   nn.Module):
      def      lowerCAmelCase__		(							self:					int       ,			UpperCamelCase_:					Tensor  ):
             return 0.5 * input * (1.0 + torch.tanh(input * 0.79_7884_5608 * (1.0 + 0.04_4715 * input * input)  ))





class        lowerCamelCase__(   nn.Module):
      def      lowerCAmelCase__		(							self:					List[Any]       ,			UpperCamelCase_:					Tensor  ):
             return input * torch.sigmoid(1.702 * input  )





class        lowerCamelCase__(   nn.Module):
      def __init__(							self:					Union[str, Any]       ,			UpperCamelCase_:					float       ,			UpperCamelCase_:					float  ):
             if min > max:
                    raise ValueError(F'min should be < max (got min: {min}, max: {max})'  )

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




      def      lowerCAmelCase__		(							self:					Tuple       ,			UpperCamelCase_:					Tensor  ):
             return torch.clip(gelu(UpperCamelCase_  )       ,			self.min       ,			self.max  )





class        lowerCamelCase__(   nn.Module):
      def __init__(							self:					Tuple  ):
             super().__init__()
             __lowerCamelCase  =		math.sqrt(2 / math.pi  )




      def      lowerCAmelCase__		(							self:					Optional[Any]       ,			UpperCamelCase_:					Tensor  ):
             return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.04_4715 * torch.pow(UpperCamelCase_       ,			3  ))  ))





class        lowerCamelCase__(   nn.Module):
      def      lowerCAmelCase__		(							self:					int       ,			UpperCamelCase_:					Tensor  ):
             return nn.functional.silu(UpperCamelCase_  )





class        lowerCamelCase__(   nn.Module):
      def __init__(							self:					Optional[int]  ):
             super().__init__()
             if version.parse(torch.__version__  ) < version.parse("""1.9.0"""  ):
                    __lowerCamelCase  =		self._mish_python
             else:
                    __lowerCamelCase  =		nn.functional.mish
      def      lowerCAmelCase__		(							self:					Tuple       ,			UpperCamelCase_:					Tensor  ):
             return input * torch.tanh(nn.functional.softplus(UpperCamelCase_  )  )




      def      lowerCAmelCase__		(							self:					int       ,			UpperCamelCase_:					Tensor  ):
             return self.act(UpperCamelCase_  )





class        lowerCamelCase__(   nn.Module):
      def      lowerCAmelCase__		(							self:					List[str]       ,			UpperCamelCase_:					Tensor  ):
             return input





class        lowerCamelCase__(   nn.Module):
      def      lowerCAmelCase__		(							self:					Optional[int]       ,			UpperCamelCase_:					List[str]       ,			UpperCamelCase_:					Optional[int]=0.70_7107       ,			UpperCamelCase_:					Optional[Any]=0.28_2095  ):
             __lowerCamelCase  =		(input - mu).div(sigma * math.sqrt(2.0  )  )
             return 0.5 * (1.0 + torch.erf(UpperCamelCase_  ))





class        lowerCamelCase__(   nn.Module):
      def      lowerCAmelCase__		(							self:					Tuple       ,			UpperCamelCase_:					List[str]  ):
             __lowerCamelCase  =		nn.functional.relu(UpperCamelCase_  )
             __lowerCamelCase  =		torch.square(UpperCamelCase_  )
             return squared





class        lowerCamelCase__(   __lowerCamelCase):
      def __getitem__(							self:					int       ,			UpperCamelCase_:					Optional[int]  ):
             __lowerCamelCase  =		super().__getitem__(UpperCamelCase_  )
             __lowerCamelCase,  __lowerCamelCase  =		content if isinstance(UpperCamelCase_       ,			UpperCamelCase_  ) else (content, {})
             return cls(**UpperCamelCase_  )



UpperCAmelCase_   							=      {
    '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_   							=      ClassInstantier(ACTaCLS)




def    lowerCamelCase__			(		A__			:   List[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
UpperCAmelCase_   							=      get_activation('gelu_python')
UpperCAmelCase_   							=      get_activation('gelu_new')
UpperCAmelCase_   							=      get_activation('gelu')
UpperCAmelCase_   							=      get_activation('gelu_fast')
UpperCAmelCase_   							=      get_activation('quick_gelu')
UpperCAmelCase_   							=      get_activation('silu')
UpperCAmelCase_   							=      get_activation('mish')




UpperCAmelCase_   							=      get_activation('linear')







import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()




def    lowerCamelCase__			(		A__			:   int    ,     A__			:   Tuple    ,     A__			:   str      ):





       '''simple docstring'''



       __lowerCamelCase  =		FNetConfig.from_json_file(A__      )
       print(f'Building PyTorch model from configuration: {config}'      )
       __lowerCamelCase  =		FNetForPreTraining(A__      )

       __lowerCamelCase  =		restore_checkpoint(A__    ,     A__      )
       __lowerCamelCase  =		checkpoint_dict["""target"""]

       # Embeddings
       # Position IDs
       __lowerCamelCase  =		fnet_pretraining_model.state_dict()

       __lowerCamelCase  =		state_dict["""fnet.embeddings.position_ids"""]
       __lowerCamelCase  =		{"""fnet.embeddings.position_ids""": position_ids}
       # Embedding Layers
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]      ).T
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]      )

       # Encoder Layers
       for layer in range(config.num_hidden_layers      ):
              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'encoder_{layer}']["""mixing_layer_norm"""]["""scale"""]      )
              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'encoder_{layer}']["""mixing_layer_norm"""]["""bias"""]      )

              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'feed_forward_{layer}']["""intermediate"""]["""kernel"""]      ).T
              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'feed_forward_{layer}']["""intermediate"""]["""bias"""]      )

              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'feed_forward_{layer}']["""output"""]["""kernel"""]      ).T
              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'feed_forward_{layer}']["""output"""]["""bias"""]      )

              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'encoder_{layer}']["""output_layer_norm"""]["""scale"""]      )
              __lowerCamelCase  =		torch.tensor(
                  pretrained_model_params["""encoder"""][f'encoder_{layer}']["""output_layer_norm"""]["""bias"""]      )

       # Pooler Layers
       __lowerCamelCase  =		torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]      ).T
       __lowerCamelCase  =		torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]      )

       # Masked LM Layers
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""predictions_dense"""]["""kernel"""]      ).T
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""predictions_dense"""]["""bias"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""predictions_layer_norm"""]["""scale"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""predictions_layer_norm"""]["""bias"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""predictions_output"""]["""output_bias"""]      )
       __lowerCamelCase  =		torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]      )

       # Seq Relationship Layers
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""classification"""]["""output_kernel"""]      )
       __lowerCamelCase  =		torch.tensor(
           pretrained_model_params["""classification"""]["""output_bias"""]      )

       # Load State Dict
       fnet_pretraining_model.load_state_dict(A__      )

       # Save PreTrained
       print(f'Saving pretrained model to {save_path}'      )
       fnet_pretraining_model.save_pretrained(A__      )


if __name__ == "__main__":
       UpperCAmelCase_   							=      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--flax_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--fnet_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained FNet model. \n'
               'This specifies the model architecture.'
           ),
       )
       parser.add_argument('--save_path', default=None, type=str, required=True, help='Path to the output model.')
       UpperCAmelCase_   							=      parser.parse_args()
       convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)

