
import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase				):
    assert isinstance(__lowerCAmelCase     ,		__lowerCAmelCase				)
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
        assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("keep_in_memory"     ,		[False, True]				)
def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    snake_case__    =					tmp_path / "cache"
    snake_case__    =					{"col_1": "string", "col_2": "int64", "col_3": "float64"}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        snake_case__    =					JsonDatasetReader(__lowerCAmelCase     ,		cache_dir=__lowerCAmelCase     ,		keep_in_memory=__lowerCAmelCase				).read()
    _check_json_dataset(__lowerCAmelCase     ,		__lowerCAmelCase				)





@pytest.mark.parametrize(
    "features"     ,		[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]     ,		)
def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    snake_case__    =					tmp_path / "cache"
    snake_case__    =					{"col_1": "string", "col_2": "int64", "col_3": "float64"}
    snake_case__    =					features.copy() if features else default_expected_features
    snake_case__    =					(
        Features({feature: Value(__lowerCAmelCase				) for feature, dtype in features.items()}				) if features is not None else None
    )
    snake_case__    =					JsonDatasetReader(__lowerCAmelCase     ,		features=__lowerCAmelCase     ,		cache_dir=__lowerCAmelCase				).read()
    _check_json_dataset(__lowerCAmelCase     ,		__lowerCAmelCase				)





@pytest.mark.parametrize(
    "features"     ,		[
        None,
        {"col_3": "float64", "col_1": "string", "col_2": "int64"},
    ]     ,		)
def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    snake_case__    =					tmp_path / "cache"
    snake_case__    =					{"col_3": "float64", "col_1": "string", "col_2": "int64"}
    snake_case__    =					features.copy() if features else default_expected_features
    snake_case__    =					(
        Features({feature: Value(__lowerCAmelCase				) for feature, dtype in features.items()}				) if features is not None else None
    )
    snake_case__    =					JsonDatasetReader(__lowerCAmelCase     ,		features=__lowerCAmelCase     ,		cache_dir=__lowerCAmelCase				).read()
    assert isinstance(__lowerCAmelCase     ,		__lowerCAmelCase				)
    assert dataset.num_rows == 2
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_3", "col_1", "col_2"]
    for feature, expected_dtype in expected_features.items():
        assert dataset.features[feature].dtype == expected_dtype





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase				):
    # jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
    snake_case__    =					{"col_2": "int64", "col_3": "float64", "col_1": "string"}
    snake_case__    =					features.copy()
    snake_case__    =					(
        Features({feature: Value(__lowerCAmelCase				) for feature, dtype in features.items()}				) if features is not None else None
    )
    snake_case__    =					tmp_path / "cache"
    snake_case__    =					JsonDatasetReader(__lowerCAmelCase     ,		features=__lowerCAmelCase     ,		cache_dir=__lowerCAmelCase				).read()
    assert isinstance(__lowerCAmelCase     ,		__lowerCAmelCase				)
    assert dataset.num_rows == 2
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_2", "col_3", "col_1"]
    for feature, expected_dtype in expected_features.items():
        assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("split"     ,		[None, NamedSplit("train"				), "train", "test"]				)
def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    snake_case__    =					tmp_path / "cache"
    snake_case__    =					{"col_1": "string", "col_2": "int64", "col_3": "float64"}
    snake_case__    =					JsonDatasetReader(__lowerCAmelCase     ,		cache_dir=__lowerCAmelCase     ,		split=__lowerCAmelCase				).read()
    _check_json_dataset(__lowerCAmelCase     ,		__lowerCAmelCase				)
    assert dataset.split == split if split else "train"





@pytest.mark.parametrize("path_type"     ,		[str, list]				)
def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    if issubclass(__lowerCAmelCase     ,		__lowerCAmelCase				):
        snake_case__    =					jsonl_path
    elif issubclass(__lowerCAmelCase     ,		__lowerCAmelCase				):
        snake_case__    =					[jsonl_path]
    snake_case__    =					tmp_path / "cache"
    snake_case__    =					{"col_1": "string", "col_2": "int64", "col_3": "float64"}
    snake_case__    =					JsonDatasetReader(__lowerCAmelCase     ,		cache_dir=__lowerCAmelCase				).read()
    _check_json_dataset(__lowerCAmelCase     ,		__lowerCAmelCase				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase=("train",)				):
    assert isinstance(__lowerCAmelCase     ,		__lowerCAmelCase				)
    for split in splits:
        snake_case__    =					dataset_dict[split]
        assert dataset.num_rows == 4
        assert dataset.num_columns == 3
        assert dataset.column_names == ["col_1", "col_2", "col_3"]
        for feature, expected_dtype in expected_features.items():
            assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("keep_in_memory"     ,		[False, True]				)
def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    snake_case__    =					tmp_path / "cache"
    snake_case__    =					{"col_1": "string", "col_2": "int64", "col_3": "float64"}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        snake_case__    =					JsonDatasetReader({"train": jsonl_path}     ,		cache_dir=__lowerCAmelCase     ,		keep_in_memory=__lowerCAmelCase				).read()
    _check_json_datasetdict(__lowerCAmelCase     ,		__lowerCAmelCase				)





@pytest.mark.parametrize(
    "features"     ,		[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]     ,		)
def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    snake_case__    =					tmp_path / "cache"
    snake_case__    =					{"col_1": "string", "col_2": "int64", "col_3": "float64"}
    snake_case__    =					features.copy() if features else default_expected_features
    snake_case__    =					(
        Features({feature: Value(__lowerCAmelCase				) for feature, dtype in features.items()}				) if features is not None else None
    )
    snake_case__    =					JsonDatasetReader({"train": jsonl_path}     ,		features=__lowerCAmelCase     ,		cache_dir=__lowerCAmelCase				).read()
    _check_json_datasetdict(__lowerCAmelCase     ,		__lowerCAmelCase				)





@pytest.mark.parametrize("split"     ,		[None, NamedSplit("train"				), "train", "test"]				)
def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    if split:
        snake_case__    =					{split: jsonl_path}
    else:
        snake_case__    =					"train"
        snake_case__    =					{"train": jsonl_path, "test": jsonl_path}
    snake_case__    =					tmp_path / "cache"
    snake_case__    =					{"col_1": "string", "col_2": "int64", "col_3": "float64"}
    snake_case__    =					JsonDatasetReader(__lowerCAmelCase     ,		cache_dir=__lowerCAmelCase				).read()
    _check_json_datasetdict(__lowerCAmelCase     ,		__lowerCAmelCase     ,		splits=list(path.keys()				)				)
    assert all(dataset[split].split == split for split in path.keys()				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    return json.load(__lowerCAmelCase				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    return [json.loads(__lowerCAmelCase				) for line in buffer]





class        _SCREAMING_SNAKE_CASE	:
      @pytest.mark.parametrize("lines, load_json_function"		,						[(True, load_json_lines), (False, load_json)]			)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
          with io.BytesIO() as buffer:
              JsonDatasetWriter(lowerCamelCase		,						lowerCamelCase		,						lines=lowerCamelCase			).write()
              buffer.seek(0			)
              snake_case__    =					load_json_function(lowerCamelCase			)
          assert isinstance(lowerCamelCase		,						lowerCamelCase			)
          assert isinstance(exported_content[0]		,						lowerCamelCase			)
          assert len(lowerCamelCase			) == 10
      @pytest.mark.parametrize(
          "orient, container, keys, len_at"		,						[
              ("records", list, {"tokens", "labels", "answers", "id"}, None),
              ("split", dict, {"columns", "data"}, "data"),
              ("index", dict, set("0123456789"			), None),
              ("columns", dict, {"tokens", "labels", "answers", "id"}, "tokens"),
              ("values", list, None, None),
              ("table", dict, {"schema", "data"}, "data"),
          ]		,						)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
          with io.BytesIO() as buffer:
              JsonDatasetWriter(lowerCamelCase		,						lowerCamelCase		,						lines=lowerCamelCase		,						orient=lowerCamelCase			).write()
              buffer.seek(0			)
              snake_case__    =					load_json(lowerCamelCase			)
          assert isinstance(lowerCamelCase		,						lowerCamelCase			)
          if keys:
              if container is dict:
                  assert exported_content.keys() == keys
              else:
                  assert exported_content[0].keys() == keys
          else:
              assert not hasattr(lowerCamelCase		,						"keys"			) and not hasattr(exported_content[0]		,						"keys"			)
          if len_at:
              assert len(exported_content[len_at]			) == 10
          else:
              assert len(lowerCamelCase			) == 10
      @pytest.mark.parametrize("lines, load_json_function"		,						[(True, load_json_lines), (False, load_json)]			)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
          with io.BytesIO() as buffer:
              JsonDatasetWriter(lowerCamelCase		,						lowerCamelCase		,						lines=lowerCamelCase		,						num_proc=2			).write()
              buffer.seek(0			)
              snake_case__    =					load_json_function(lowerCamelCase			)
          assert isinstance(lowerCamelCase		,						lowerCamelCase			)
          assert isinstance(exported_content[0]		,						lowerCamelCase			)
          assert len(lowerCamelCase			) == 10
      @pytest.mark.parametrize(
          "orient, container, keys, len_at"		,						[
              ("records", list, {"tokens", "labels", "answers", "id"}, None),
              ("split", dict, {"columns", "data"}, "data"),
              ("index", dict, set("0123456789"			), None),
              ("columns", dict, {"tokens", "labels", "answers", "id"}, "tokens"),
              ("values", list, None, None),
              ("table", dict, {"schema", "data"}, "data"),
          ]		,						)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
          with io.BytesIO() as buffer:
              JsonDatasetWriter(lowerCamelCase		,						lowerCamelCase		,						lines=lowerCamelCase		,						orient=lowerCamelCase		,						num_proc=2			).write()
              buffer.seek(0			)
              snake_case__    =					load_json(lowerCamelCase			)
          assert isinstance(lowerCamelCase		,						lowerCamelCase			)
          if keys:
              if container is dict:
                  assert exported_content.keys() == keys
              else:
                  assert exported_content[0].keys() == keys
          else:
              assert not hasattr(lowerCamelCase		,						"keys"			) and not hasattr(exported_content[0]		,						"keys"			)
          if len_at:
              assert len(exported_content[len_at]			) == 10
          else:
              assert len(lowerCamelCase			) == 10
      def A_      (      self		,						lowerCamelCase			):
          with pytest.raises(lowerCamelCase			):
              with io.BytesIO() as buffer:
                  JsonDatasetWriter(lowerCamelCase		,						lowerCamelCase		,						num_proc=0			)



      @pytest.mark.parametrize("compression, extension"		,						[("gzip", "gz"), ("bz2", "bz2"), ("xz", "xz")]			)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
          snake_case__    =					tmp_path_factory.mktemp("data"			) / F"""test.json.{extension}"""
          snake_case__    =					str(shared_datadir / F"""test_file.json.{extension}"""			)
          JsonDatasetWriter(lowerCamelCase		,						lowerCamelCase		,						compression=lowerCamelCase			).write()

          with fsspec.open(lowerCamelCase		,						"rb"		,						compression="infer"			) as f:
              snake_case__    =					f.read()
          with fsspec.open(lowerCamelCase		,						"rb"		,						compression="infer"			) as f:
              snake_case__    =					f.read()
          assert exported_content == original_content





from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
__magic_name__  							=	299_792_458

# Symbols
__magic_name__							,   __magic_name__							,   __magic_name__							,   __magic_name__  							=	symbols('''ct x y z''')





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    if velocity > c:
        raise ValueError("Speed must not exceed light speed 299,792,458 [m/s]!"				)
    elif velocity < 1:
        # Usually the speed should be much higher than 1 (c order of magnitude)
        raise ValueError("Speed must be greater than or equal to 1!"				)

    return velocity / c





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    return 1 / sqrt(1 - beta(__lowerCAmelCase				) ** 2				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    return np.array(
        [
            [gamma(__lowerCAmelCase				), -gamma(__lowerCAmelCase				) * beta(__lowerCAmelCase				), 0, 0],
            [-gamma(__lowerCAmelCase				) * beta(__lowerCAmelCase				), gamma(__lowerCAmelCase				), 0, 0],
            [0, 0, 1, 0],
            [0, 0, 0, 1],
        ]				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase = None				):
    # Ensure event is not empty
    if event is None:
        snake_case__    =					np.array([ct, x, y, z]				)  # Symbolic four vector
    else:
        event[0] *= c  # x0 is ct (speed of light * time)

    return transformation_matrix(__lowerCAmelCase				) @ event


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # Example of symbolic vector:
     __magic_name__  							=	transform(29_979_245)
     print('''Example of four vector: ''')
     print(F'''ct\' = {four_vector[0]}''')
     print(F'''x\' = {four_vector[1]}''')
     print(F'''y\' = {four_vector[2]}''')
     print(F'''z\' = {four_vector[3]}''')

     # Substitute symbols with numerical values
     __magic_name__  							=	{ct: c, x: 1, y: 1, z: 1}
     __magic_name__  							=	[four_vector[i].subs(sub_dict) for i in range(4)]

     print(F'''\n{numerical_vector}''')



