



'''simple docstring'''


import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
  lowercase											=      []

  lowercase											=      os.path.join('''tests''', '''models''', '''**''', '''test_modeling_*.py''')
  lowercase											=      glob.glob(pattern)
  # TODO: deal with TF/Flax too
  lowercase											=      [
      x for x in test_files if not (x.startswith('''test_modeling_tf_''') or x.startswith('''test_modeling_flax_'''))
  ]

  for test_file in test_files:
    lowercase											=      get_tester_classes(test_file)
    for tester_class in tester_classes:
      # A few tester classes don't have `parent` parameter in `__init__`.
      # TODO: deal this better
      try:
        lowercase											=      tester_class(parent=None)
      except Exception:
        continue
      if hasattr(tester, '''get_config'''):
        lowercase											=      tester.get_config()
        for k, v in config.to_dict().items():
          if isinstance(v, int):
            lowercase											=      None
            if k in ["vocab_size"]:
              lowercase											=      100
            elif k in ["max_position_embeddings"]:
              lowercase											=      128
            elif k in ["hidden_size", "d_model"]:
              lowercase											=      40
            elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
              lowercase											=      5
            if target is not None and v > target:
              failures.append(
                  F"""{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`"""
                  F""" with config[\"{k}\"] = {v} which is too large for testing! Set its value to be smaller"""
                  F""" than {target}."""
              )

  if len(failures) > 0:
    raise Exception(F"""There were {len(failures)} failures:\n""" + '''\n'''.join(failures))




from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
lowerCamelCase__        =						load_iris()
iris.keys()


print(F"""Target names: \n {iris.target_names} """)
print(F"""\n Features: \n {iris.feature_names}""")

# Train set e Test set
lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__        =						train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

lowerCamelCase__        =						KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
lowerCamelCase__        =						[[1, 2, 1, 4], [2, 3, 4, 5]]

lowerCamelCase__        =						knn.predict(X_new)

print(
    F"""\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"""
    F""" {iris["target_names"][prediction]}"""
)