





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 :       Any       , _a :       Dict       , _a :       Optional[Any]      )						->      Any:
     # Initialise PyTorch model
     lowerCAmelCase_       :							str			 =		FNetConfig.from_json_file(_a      )
     print(F'Building PyTorch model from configuration: {config}'      )
     lowerCAmelCase_       :							str			 =		FNetForPreTraining(_a      )

     lowerCAmelCase_       :							Tuple			 =		restore_checkpoint(_a       , _a      )
     lowerCAmelCase_       :							List[str]			 =		checkpoint_dict["""target"""]

     # Embeddings
     # Position IDs
     lowerCAmelCase_       :							Any			 =		fnet_pretraining_model.state_dict()

     lowerCAmelCase_       :							Tuple			 =		state_dict["""fnet.embeddings.position_ids"""]
     lowerCAmelCase_       :							Optional[int]			 =		{"""fnet.embeddings.position_ids""": position_ids}
     # Embedding Layers
     lowerCAmelCase_       :							str			 =		torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]      )
     lowerCAmelCase_       :							Tuple			 =		torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]      )
     lowerCAmelCase_       :							List[str]			 =		torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]      )
     lowerCAmelCase_       :							Any			 =		torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]      ).T
     lowerCAmelCase_       :							Dict			 =		torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]      )
     lowerCAmelCase_       :							List[Any]			 =		torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]      )
     lowerCAmelCase_       :							Union[str, Any]			 =		torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]      )

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

          lowerCAmelCase_       :							Any			 =		torch.tensor(
              pretrained_model_params["""encoder"""][F'feed_forward_{layer}']["""intermediate"""]["""kernel"""]      ).T
          lowerCAmelCase_       :							Optional[Any]			 =		torch.tensor(
              pretrained_model_params["""encoder"""][F'feed_forward_{layer}']["""intermediate"""]["""bias"""]      )

          lowerCAmelCase_       :							Any			 =		torch.tensor(
              pretrained_model_params["""encoder"""][F'feed_forward_{layer}']["""output"""]["""kernel"""]      ).T
          lowerCAmelCase_       :							Union[str, Any]			 =		torch.tensor(
              pretrained_model_params["""encoder"""][F'feed_forward_{layer}']["""output"""]["""bias"""]      )

          lowerCAmelCase_       :							List[str]			 =		torch.tensor(
              pretrained_model_params["""encoder"""][F'encoder_{layer}']["""output_layer_norm"""]["""scale"""]      )
          lowerCAmelCase_       :							Optional[int]			 =		torch.tensor(
              pretrained_model_params["""encoder"""][F'encoder_{layer}']["""output_layer_norm"""]["""bias"""]      )

     # Pooler Layers
     lowerCAmelCase_       :							List[Any]			 =		torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]      ).T
     lowerCAmelCase_       :							Tuple			 =		torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]      )

     # Masked LM Layers
     lowerCAmelCase_       :							Union[str, Any]			 =		torch.tensor(
         pretrained_model_params["""predictions_dense"""]["""kernel"""]      ).T
     lowerCAmelCase_       :							Optional[int]			 =		torch.tensor(
         pretrained_model_params["""predictions_dense"""]["""bias"""]      )
     lowerCAmelCase_       :							Optional[int]			 =		torch.tensor(
         pretrained_model_params["""predictions_layer_norm"""]["""scale"""]      )
     lowerCAmelCase_       :							Union[str, Any]			 =		torch.tensor(
         pretrained_model_params["""predictions_layer_norm"""]["""bias"""]      )
     lowerCAmelCase_       :							Any			 =		torch.tensor(
         pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]      )
     lowerCAmelCase_       :							List[str]			 =		torch.tensor(
         pretrained_model_params["""predictions_output"""]["""output_bias"""]      )
     lowerCAmelCase_       :							Optional[Any]			 =		torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]      )

     # Seq Relationship Layers
     lowerCAmelCase_       :							str			 =		torch.tensor(
         pretrained_model_params["""classification"""]["""output_kernel"""]      )
     lowerCAmelCase_       :							List[Any]			 =		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_       : Optional[int]							=    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_       : Any							=    parser.parse_args()
      convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)







import math





class   lowercase__     :







  def __init__(						self     ,    _lowercase    ):
       lowerCAmelCase_       :							Optional[Any]			 =		len(_lowercase    )
       lowerCAmelCase_       :							Union[str, Any]			 =		[0] * (
           4 * self.N
       )  # approximate the overall size of segment tree with array N
       self.build(1     ,    0     ,    self.N - 1    )







  def 						UpperCAmelCase__		(						self     ,    _lowercase    ):
       return idx * 2







  def 						UpperCAmelCase__		(						self     ,    _lowercase    ):
       return idx * 2 + 1







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase    ):  # noqa: E741
       if l == r:
            lowerCAmelCase_       :							Dict			 =		A[l]
       else:
            lowerCAmelCase_       :							List[str]			 =		(l + r) // 2
            self.build(self.left(_lowercase    )     ,    _lowercase     ,    _lowercase    )
            self.build(self.right(_lowercase    )     ,    mid + 1     ,    _lowercase    )
            lowerCAmelCase_       :							Dict			 =		max(self.st[self.left(_lowercase    )]     ,    self.st[self.right(_lowercase    )]    )







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase    ):
       return self.update_recursive(1     ,    0     ,    self.N - 1     ,    a - 1     ,    b - 1     ,    _lowercase    )







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    ):  # noqa: E741
       if r < a or l > b:
            return True
       if l == r:
            lowerCAmelCase_       :							List[str]			 =		val
            return True
       lowerCAmelCase_       :							List[str]			 =		(l + r) // 2
       self.update_recursive(self.left(_lowercase    )     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    )
       self.update_recursive(self.right(_lowercase    )     ,    mid + 1     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    )
       lowerCAmelCase_       :							List[Any]			 =		max(self.st[self.left(_lowercase    )]     ,    self.st[self.right(_lowercase    )]    )
       return True







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase    ):
       return self.query_recursive(1     ,    0     ,    self.N - 1     ,    a - 1     ,    b - 1    )







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    ):  # noqa: E741
       if r < a or l > b:
            return -math.inf
       if l >= a and r <= b:
            return self.st[idx]
       lowerCAmelCase_       :							int			 =		(l + r) // 2
       lowerCAmelCase_       :							List[Any]			 =		self.query_recursive(self.left(_lowercase    )     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    )
       lowerCAmelCase_       :							List[str]			 =		self.query_recursive(self.right(_lowercase    )     ,    mid + 1     ,    _lowercase     ,    _lowercase     ,    _lowercase    )
       return max(_lowercase     ,    _lowercase    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							List[Any]			 =		[]
       for i in range(1     ,    N + 1    ):
            show_list += [self.query(_lowercase     ,    _lowercase    )]
       print(_lowercase    )


if __name__ == "__main__":
      UpperCAmelCase_       : Any							=    [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
      UpperCAmelCase_       : Optional[Any]							=    15
      UpperCAmelCase_       : List[Any]							=    SegmentTree(A)
      print(segt.query(4, 6))
      print(segt.query(7, 11))
      print(segt.query(7, 12))
      segt.update(1, 3, 1_11)
      print(segt.query(1, 15))
      segt.update(7, 8, 2_35)
      segt.show_data()
