





import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  assert isinstance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  assert dataset.num_rows == 4
  assert dataset.num_columns == 1
  assert dataset.column_names == ["text"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("keep_in_memory"					,	[False, True]       )
def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				tmp_path / "cache"
  _snake_case     		=				{"text": "string"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    _snake_case     		=				TextDatasetReader(SCREAMING_SNAKE_CASE__					,	cache_dir=SCREAMING_SNAKE_CASE__					,	keep_in_memory=SCREAMING_SNAKE_CASE__       ).read()
  _check_text_dataset(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )




@pytest.mark.parametrize(
    "features"					,	[
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]					,	)
def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				tmp_path / "cache"
  _snake_case     		=				{"text": "string"}
  _snake_case     		=				features.copy() if features else default_expected_features
  _snake_case     		=				(
      Features({feature: Value(SCREAMING_SNAKE_CASE__       ) for feature, dtype in features.items()}       ) if features is not None else None
  )
  _snake_case     		=				TextDatasetReader(SCREAMING_SNAKE_CASE__					,	features=SCREAMING_SNAKE_CASE__					,	cache_dir=SCREAMING_SNAKE_CASE__       ).read()
  _check_text_dataset(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )




@pytest.mark.parametrize("split"					,	[None, NamedSplit("train"       ), "train", "test"]       )
def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				tmp_path / "cache"
  _snake_case     		=				{"text": "string"}
  _snake_case     		=				TextDatasetReader(SCREAMING_SNAKE_CASE__					,	cache_dir=SCREAMING_SNAKE_CASE__					,	split=SCREAMING_SNAKE_CASE__       ).read()
  _check_text_dataset(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  assert dataset.split == split if split else "train"




@pytest.mark.parametrize("path_type"					,	[str, list]       )
def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  if issubclass(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):
    _snake_case     		=				text_path
  elif issubclass(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):
    _snake_case     		=				[text_path]
  _snake_case     		=				tmp_path / "cache"
  _snake_case     		=				{"text": "string"}
  _snake_case     		=				TextDatasetReader(SCREAMING_SNAKE_CASE__					,	cache_dir=SCREAMING_SNAKE_CASE__       ).read()
  _check_text_dataset(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=("train",)       ):







  '''simple docstring'''



  assert isinstance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  for split in splits:
    _snake_case     		=				dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 1
    assert dataset.column_names == ["text"]
    for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("keep_in_memory"					,	[False, True]       )
def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				tmp_path / "cache"
  _snake_case     		=				{"text": "string"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    _snake_case     		=				TextDatasetReader({"train": text_path}					,	cache_dir=SCREAMING_SNAKE_CASE__					,	keep_in_memory=SCREAMING_SNAKE_CASE__       ).read()
  _check_text_datasetdict(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )




@pytest.mark.parametrize(
    "features"					,	[
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]					,	)
def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				tmp_path / "cache"
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  _snake_case     		=				{"text": "string"}
  _snake_case     		=				features.copy() if features else default_expected_features
  _snake_case     		=				(
      Features({feature: Value(SCREAMING_SNAKE_CASE__       ) for feature, dtype in features.items()}       ) if features is not None else None
  )
  _snake_case     		=				TextDatasetReader({"train": text_path}					,	features=SCREAMING_SNAKE_CASE__					,	cache_dir=SCREAMING_SNAKE_CASE__       ).read()
  _check_text_datasetdict(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )




@pytest.mark.parametrize("split"					,	[None, NamedSplit("train"       ), "train", "test"]       )
def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  if split:
    _snake_case     		=				{split: text_path}
  else:
    _snake_case     		=				"train"
    _snake_case     		=				{"train": text_path, "test": text_path}
  _snake_case     		=				tmp_path / "cache"
  _snake_case     		=				{"text": "string"}
  _snake_case     		=				TextDatasetReader(SCREAMING_SNAKE_CASE__					,	cache_dir=SCREAMING_SNAKE_CASE__       ).read()
  _check_text_datasetdict(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	splits=list(path.keys()       )       )
  assert all(dataset[split].split == split for split in path.keys()       )









'''simple docstring'''



import math


class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				0.0
       _snake_case     		=				0.0
       for i in range(len(lowerCamelCase	)	):
         da += math.pow((sample[i] - weights[0][i])						,   2	)
         da += math.pow((sample[i] - weights[1][i])						,   2	)
         return 0 if da > da else 1
       return 0






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       for i in range(len(lowerCamelCase	)	):
         weights[j][i] += alpha * (sample[i] - weights[j][i])
       return weights







def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

  # weight initialization ( n, C )
  _snake_case     		=				[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

  # training
  _snake_case     		=				SelfOrganizingMap()
  _snake_case     		=				3
  _snake_case     		=				0.5

  for _ in range(SCREAMING_SNAKE_CASE__       ):
    for j in range(len(SCREAMING_SNAKE_CASE__       )       ):
      # training sample
      _snake_case     		=				training_samples[j]

      # Compute the winning vector
      _snake_case     		=				self_organizing_map.get_winner(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

      # Update the winning vector
      _snake_case     		=				self_organizing_map.update(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

    # classify test sample
  _snake_case     		=				[0, 0, 0, 1]
  _snake_case     		=				self_organizing_map.get_winner(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

  # results
  print(f'''Clusters that the test sample belongs to : {winner}'''       )
  print(f'''Weights that have been trained : {weights}'''       )


# running the main() function
if __name__ == "__main__":
 main()


