

import numpy as np







class    UpperCamelCase_			:


  """simple docstring"""



  def __init__(	self    :				List[Any]     ,  A_    :				str=5     ,  A_    :				Any=5	)					-> Optional[int]:
      SCREAMING_SNAKE_CASE__					     =						depth
      SCREAMING_SNAKE_CASE__					     =						0
      SCREAMING_SNAKE_CASE__					     =						None
      SCREAMING_SNAKE_CASE__					     =						None
      SCREAMING_SNAKE_CASE__					     =						min_leaf_size
      SCREAMING_SNAKE_CASE__					     =						None



  def      _UpperCamelCase    (	self    :				List[Any]     ,  A_    :				List[Any]     ,  A_    :				Tuple	)					-> Tuple:
      if labels.ndim != 1:
          print("Error: Input labels must be one dimensional"	)

      return np.mean((labels - prediction) ** 2	)



  def      _UpperCamelCase    (	self    :				Union[str, Any]     ,  A_    :				Optional[Any]     ,  A_    :				List[Any]	)					-> Dict:
      if x.ndim != 1:
          print("Error: Input data set must be one dimensional"	)
          return
      if len(A_	) != len(A_	):
          print("Error: X and y have different lengths"	)
          return
      if y.ndim != 1:
          print("Error: Data set labels must be one dimensional"	)
          return

      if len(A_	) < 2 * self.min_leaf_size:
          SCREAMING_SNAKE_CASE__					     =						np.mean(A_	)
          return

      if self.depth == 1:
          SCREAMING_SNAKE_CASE__					     =						np.mean(A_	)
          return

      SCREAMING_SNAKE_CASE__					     =						0
      SCREAMING_SNAKE_CASE__					     =						self.mean_squared_error(A_     ,  np.mean(A_	)	) * 2
      for i in range(len(A_	)	):
          if len(x[:i]	) < self.min_leaf_size:
              continue
          elif len(x[i:]	) < self.min_leaf_size:
              continue
          else:
              SCREAMING_SNAKE_CASE__					     =						self.mean_squared_error(x[:i]     ,  np.mean(y[:i]	)	)
              SCREAMING_SNAKE_CASE__					     =						self.mean_squared_error(x[i:]     ,  np.mean(y[i:]	)	)
              SCREAMING_SNAKE_CASE__					     =						error_left + error_right
              if error < min_error:
                  SCREAMING_SNAKE_CASE__					     =						i
                  SCREAMING_SNAKE_CASE__					     =						error

      if best_split != 0:
          SCREAMING_SNAKE_CASE__					     =						x[:best_split]
          SCREAMING_SNAKE_CASE__					     =						y[:best_split]
          SCREAMING_SNAKE_CASE__					     =						x[best_split:]
          SCREAMING_SNAKE_CASE__					     =						y[best_split:]

          SCREAMING_SNAKE_CASE__					     =						x[best_split]
          SCREAMING_SNAKE_CASE__					     =						DecisionTree(
              depth=self.depth - 1     ,  min_leaf_size=self.min_leaf_size	)
          SCREAMING_SNAKE_CASE__					     =						DecisionTree(
              depth=self.depth - 1     ,  min_leaf_size=self.min_leaf_size	)
          self.left.train(A_     ,  A_	)
          self.right.train(A_     ,  A_	)
      else:
          SCREAMING_SNAKE_CASE__					     =						np.mean(A_	)

      return







  def      _UpperCamelCase    (	self    :				str     ,  A_    :				Optional[Any]	)					-> int:
      if self.prediction is not None:
          return self.prediction
      elif self.left or self.right is not None:
          if x >= self.decision_boundary:
              return self.right.predict(A_	)
          else:
              return self.left.predict(A_	)
      else:
          print("Error: Decision tree not yet trained"	)
          return None







class    UpperCamelCase_			:


  """simple docstring"""



  @staticmethod
  def      _UpperCamelCase    (	A_    :				Optional[Any]     ,  A_    :				Optional[int]	)					-> Optional[int]:
      SCREAMING_SNAKE_CASE__					     =						float(0	)
      for label in labels:
          squared_error_sum += (label - prediction) ** 2

      return float(squared_error_sum / labels.size	)





def 							__a       (					):
    SCREAMING_SNAKE_CASE__					     =						np.arange(-1.0 ,       1.0 ,       0.005	)
    SCREAMING_SNAKE_CASE__					     =						np.sin(__A	)

    SCREAMING_SNAKE_CASE__					     =						DecisionTree(depth=10 ,       min_leaf_size=10	)
    tree.train(__A ,       __A	)

    SCREAMING_SNAKE_CASE__					     =						(np.random.rand(10	) * 2) - 1
    SCREAMING_SNAKE_CASE__					     =						np.array([tree.predict(__A	) for x in test_cases]	)
    SCREAMING_SNAKE_CASE__					     =						np.mean((predictions - test_cases) ** 2	)

    print("Test values: " + str(__A	)	)
    print("Predictions: " + str(__A	)	)
    print("Average error: " + str(__A	)	)


if __name__ == "__main__":
  main()
  import doctest

  doctest.testmod(name="mean_squarred_error", verbose=True)





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       (					__A ,       __A ,       __A	):
    # Initialise PyTorch model
    SCREAMING_SNAKE_CASE__					     =						FNetConfig.from_json_file(__A	)
    print(f"Building PyTorch model from configuration: {config}"	)
    SCREAMING_SNAKE_CASE__					     =						FNetForPreTraining(__A	)

    SCREAMING_SNAKE_CASE__					     =						restore_checkpoint(__A ,       __A	)
    SCREAMING_SNAKE_CASE__					     =						checkpoint_dict["target"]

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

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

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

        SCREAMING_SNAKE_CASE__					     =						torch.tensor(
            pretrained_model_params["encoder"][f"feed_forward_{layer}"]["intermediate"]["kernel"]	).T
        SCREAMING_SNAKE_CASE__					     =						torch.tensor(
            pretrained_model_params["encoder"][f"feed_forward_{layer}"]["intermediate"]["bias"]	)

        SCREAMING_SNAKE_CASE__					     =						torch.tensor(
            pretrained_model_params["encoder"][f"feed_forward_{layer}"]["output"]["kernel"]	).T
        SCREAMING_SNAKE_CASE__					     =						torch.tensor(
            pretrained_model_params["encoder"][f"feed_forward_{layer}"]["output"]["bias"]	)

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

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

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

    # Seq Relationship Layers
    SCREAMING_SNAKE_CASE__					     =						torch.tensor(
        pretrained_model_params["classification"]["output_kernel"]	)
    SCREAMING_SNAKE_CASE__					     =						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__":
  A__        =			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.")
  A__        =			parser.parse_args()
  convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)


