

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__							=			{
    "configuration_big_bird": ["BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdConfig", "BigBirdOnnxConfig"],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCAmelCase__							=			["BigBirdTokenizer"]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCAmelCase__							=			["BigBirdTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCAmelCase__							=			[
          "BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST",
          "BigBirdForCausalLM",
          "BigBirdForMaskedLM",
          "BigBirdForMultipleChoice",
          "BigBirdForPreTraining",
          "BigBirdForQuestionAnswering",
          "BigBirdForSequenceClassification",
          "BigBirdForTokenClassification",
          "BigBirdLayer",
          "BigBirdModel",
          "BigBirdPreTrainedModel",
          "load_tf_weights_in_big_bird",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCAmelCase__							=			[
          "FlaxBigBirdForCausalLM",
          "FlaxBigBirdForMaskedLM",
          "FlaxBigBirdForMultipleChoice",
          "FlaxBigBirdForPreTraining",
          "FlaxBigBirdForQuestionAnswering",
          "FlaxBigBirdForSequenceClassification",
          "FlaxBigBirdForTokenClassification",
          "FlaxBigBirdModel",
          "FlaxBigBirdPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_big_bird import BigBirdTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_big_bird_fast import BigBirdTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_big_bird import (
                BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
                BigBirdForCausalLM,
                BigBirdForMaskedLM,
                BigBirdForMultipleChoice,
                BigBirdForPreTraining,
                BigBirdForQuestionAnswering,
                BigBirdForSequenceClassification,
                BigBirdForTokenClassification,
                BigBirdLayer,
                BigBirdModel,
                BigBirdPreTrainedModel,
                load_tf_weights_in_big_bird,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_big_bird import (
                FlaxBigBirdForCausalLM,
                FlaxBigBirdForMaskedLM,
                FlaxBigBirdForMultipleChoice,
                FlaxBigBirdForPreTraining,
                FlaxBigBirdForQuestionAnswering,
                FlaxBigBirdForSequenceClassification,
                FlaxBigBirdForTokenClassification,
                FlaxBigBirdModel,
                FlaxBigBirdPreTrainedModel,
            )

else:
      import sys

      lowerCAmelCase__							=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






import matplotlib.pyplot as plt
import numpy as np
def   _lowerCAmelCase(						__A ,							__A ,							__A      ):
  UpperCAmelCase          =	len(__A      )  # Number of training samples
  UpperCAmelCase          =	np.eye(__A      )  # Initialize weights as identity matrix
  for j in range(__A      ):
    UpperCAmelCase          =	point - x_train[j]
    UpperCAmelCase          =	np.exp(diff @ diff.T / (-2.0 * tau**2)      )

  return weights
def   _lowerCAmelCase(						__A ,							__A ,							__A ,							__A      ):
  UpperCAmelCase          =	weight_matrix(__A ,							__A ,							__A      )
  UpperCAmelCase          =	np.linalg.inv(x_train.T @ weight_mat @ x_train      ) @ (
      x_train.T @ weight_mat @ y_train.T
  )

  return weight
def   _lowerCAmelCase(						__A ,							__A ,							__A      ):
  UpperCAmelCase          =	np.zeros(len(__A      )      )  # Initialize array of predictions
  for i, item in enumerate(__A      ):
    UpperCAmelCase          =	item @ local_weight(__A ,							__A ,							__A ,							__A      )

  return y_pred
def   _lowerCAmelCase(						__A ,							__A ,							__A      ):
  import seaborn as sns

  UpperCAmelCase          =	sns.load_dataset(__A      )
  UpperCAmelCase          =	np.array(data[x_name]      )
  UpperCAmelCase          =	np.array(data[y_name]      )

  UpperCAmelCase          =	np.ones(len(__A      )      )

  # pairing elements of one and x_data
  UpperCAmelCase          =	np.column_stack((one, x_data)      )

  return x_train, x_data, y_data
def   _lowerCAmelCase(						__A ,							__A ,							__A ,							__A ,							__A ,							__A ,							):
  UpperCAmelCase          =	np.sort(__A ,							axis=0      )
  plt.scatter(__A ,							__A ,							color="blue"      )
  plt.plot(
      x_train_sorted[:, 1] ,							preds[x_train[:, 1].argsort(0      )] ,							color="yellow" ,							linewidth=5 ,							)
  plt.title("Local Weighted Regression"      )
  plt.xlabel(__A      )
  plt.ylabel(__A      )
  plt.show()


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      # Demo with a dataset from the seaborn module
      lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__							=			load_data("tips", "total_bill", "tip")
      lowerCAmelCase__							=			local_weight_regression(training_data_x, tip, 5)
      plot_preds(training_data_x, predictions, total_bill, tip, "total_bill", "tip")



