




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


UpperCAmelCase_   							=      logging.get_logger(__name__)

UpperCAmelCase_   							=      {
    'uclanlp/plbart-base': 'https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}





class        lowerCamelCase__(   __lowerCamelCase):
      UpperCAmelCase__ :      Union[str, Any]       						=  'plbart'
      UpperCAmelCase__ :      Dict       						=  ['past_key_values']
      UpperCAmelCase__ :      int       						=  {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
      def __init__(							self:					Optional[int]       ,			UpperCamelCase_:					List[str]=5_00_05       ,			UpperCamelCase_:					List[Any]=10_24       ,			UpperCamelCase_:					Optional[int]=6       ,			UpperCamelCase_:					Union[str, Any]=30_72       ,			UpperCamelCase_:					Dict=12       ,			UpperCamelCase_:					str=6       ,			UpperCamelCase_:					Dict=30_72       ,			UpperCamelCase_:					Dict=12       ,			UpperCamelCase_:					List[str]=0.0       ,			UpperCamelCase_:					List[Any]=0.0       ,			UpperCamelCase_:					Dict=True       ,			UpperCamelCase_:					int=True       ,			UpperCamelCase_:					int="gelu"       ,			UpperCamelCase_:					Any=7_68       ,			UpperCamelCase_:					Any=0.1       ,			UpperCamelCase_:					Optional[Any]=0.1       ,			UpperCamelCase_:					Any=0.0       ,			UpperCamelCase_:					int=0.02       ,			UpperCamelCase_:					Union[str, Any]=0.0       ,			UpperCamelCase_:					str=True       ,			UpperCamelCase_:					int=1       ,			UpperCamelCase_:					int=0       ,			UpperCamelCase_:					Union[str, Any]=2       ,			UpperCamelCase_:					Union[str, Any]=2       ,			**UpperCamelCase_:					Any       ,			):
             __lowerCamelCase  =		vocab_size
             __lowerCamelCase  =		max_position_embeddings
             __lowerCamelCase  =		d_model
             __lowerCamelCase  =		encoder_ffn_dim
             __lowerCamelCase  =		encoder_layers
             __lowerCamelCase  =		encoder_attention_heads
             __lowerCamelCase  =		decoder_ffn_dim
             __lowerCamelCase  =		decoder_layers
             __lowerCamelCase  =		decoder_attention_heads
             __lowerCamelCase  =		dropout
             __lowerCamelCase  =		attention_dropout
             __lowerCamelCase  =		activation_dropout
             __lowerCamelCase  =		activation_function
             __lowerCamelCase  =		init_std
             __lowerCamelCase  =		encoder_layerdrop
             __lowerCamelCase  =		decoder_layerdrop
             __lowerCamelCase  =		classifier_dropout
             __lowerCamelCase  =		use_cache
             __lowerCamelCase  =		encoder_layers
             __lowerCamelCase  =		scale_embedding  # scale factor will be sqrt(d_model) if True
             super().__init__(
                 pad_token_id=UpperCamelCase_       ,			bos_token_id=UpperCamelCase_       ,			eos_token_id=UpperCamelCase_       ,			is_encoder_decoder=UpperCamelCase_       ,			forced_eos_token_id=UpperCamelCase_       ,			**UpperCamelCase_       ,			)





class        lowerCamelCase__(   __lowerCamelCase):
      @property
      def      lowerCAmelCase__		(							self:					int  ):
             return OrderedDict(
                 [
                     ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                     ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
                 ]  )




      @property
      def      lowerCAmelCase__		(							self:					List[Any]  ):
             if self.use_past:
                    return OrderedDict(
                        [
                            ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
                            ("""past_keys""", {0: """batch""", 2: """sequence"""}),
                            ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
                        ]  )
             else:
                    return OrderedDict(
                        [
                            ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
                            ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
                        ]  )







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)

