




import torch

from transformers import AutoTokenizer





class  __lowerCamelCase       (    UpperCamelCase__    ):




    """simple docstring"""

    def __init__(       self     :     int   ,	SCREAMING_SNAKE_CASE__     :     Optional[int]   )      ->       List[str]:
         lowerCAmelCase__		= AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__   )


    def    a      (       self     :     str   ,	SCREAMING_SNAKE_CASE__     :     Optional[Any]   )      ->       Any:

         if isinstance(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ) and all(isinstance(_x   ,	SCREAMING_SNAKE_CASE__   ) for _x in x   ):
              lowerCAmelCase__		= None
              for l in x:
                   lowerCAmelCase__		= self.tokenizer(
                       SCREAMING_SNAKE_CASE__   ,	padding="max_length"   ,	max_length=384   ,	truncation=SCREAMING_SNAKE_CASE__   ,	return_tensors="pt"   ,	)
                   lowerCAmelCase__		= torch.tensor([len(SCREAMING_SNAKE_CASE__   )]   )
                   if d is not None:
                        for k in d.keys():
                             lowerCAmelCase__		= torch.cat((d[k], t[k])   ,	0   )
                   else:
                        lowerCAmelCase__		= t

              lowerCAmelCase__		= torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"   )   )
              lowerCAmelCase__		= torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"   )   )

         elif isinstance(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ) and all(isinstance(_x   ,	SCREAMING_SNAKE_CASE__   ) for _x in x   ):
              lowerCAmelCase__		= self.tokenizer(
                  SCREAMING_SNAKE_CASE__   ,	padding="max_length"   ,	max_length=384   ,	truncation=SCREAMING_SNAKE_CASE__   ,	return_tensors="pt"   ,	)

         else:
              raise Exception(
                  "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"
                  " strings` for supports are supported."   )

         return d


    def    a      (       self     :     Optional[Any]   ,	SCREAMING_SNAKE_CASE__     :     List[Any]   ,	SCREAMING_SNAKE_CASE__     :     List[str]   ,	SCREAMING_SNAKE_CASE__     :     Tuple   ,	SCREAMING_SNAKE_CASE__     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     Optional[Any]=0.70   )      ->       Dict:
         lowerCAmelCase__		= []
         for idx in range(len(W_query["input_ids"]   )   ):
              lowerCAmelCase__		= lowerCAmelCase__		= range(p_start.shape[1]   )

              lowerCAmelCase__		= []
              for start_id in start_indexes:
                   for end_id in end_indexes:
                        if start_id < end_id:
                             output.append(
                                 (
                                     start_id,
                                     end_id,
                                     p_start[idx][start_id].item(),
                                     p_end[idx][end_id].item(),
                                 )   )

              output.sort(key=lambda SCREAMING_SNAKE_CASE__   : (tup[2] * tup[3])   ,	reverse=SCREAMING_SNAKE_CASE__   )
              lowerCAmelCase__		= []
              for k in range(len(SCREAMING_SNAKE_CASE__   )   ):
                   if output[k][2] * output[k][3] >= thresh:
                        lowerCAmelCase__	,		lowerCAmelCase__		= output[k][0], output[k][1]
                        lowerCAmelCase__		= self.tokenizer.decode(W_query["input_ids"][idx][c_start_pos:c_end_pos]   )
                        temp.append((decoded, output[k][2] * output[k][3])   )

              final_outputs.append(SCREAMING_SNAKE_CASE__   )

         return final_outputs






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 							_A   (						lowerCAmelCase_     :					Dict   ,				lowerCAmelCase_     :					List[str]   ,				lowerCAmelCase_     :					int			):
     """simple docstring"""

     lowerCAmelCase__		= FNetConfig.from_json_file(lowerCAmelCase_			)
     print(F'Building PyTorch model from configuration: {config}'			)
     lowerCAmelCase__		= FNetForPreTraining(lowerCAmelCase_			)

     lowerCAmelCase__		= restore_checkpoint(lowerCAmelCase_   ,				lowerCAmelCase_			)
     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(lowerCAmelCase_			)

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


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)
