






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    (			snake_case_ :							Optional[int]					,  snake_case_ :							int       ):
	assert isinstance(snake_case_					,  snake_case_       )
	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    (			snake_case_ :							str					,  snake_case_ :							Tuple					,  snake_case_ :							Tuple       ):
	snake_case__			:				int       =  tmp_path / "cache"
	snake_case__			:				Optional[Any]       =  {"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__			:				int       =  JsonDatasetReader(snake_case_					,  cache_dir=snake_case_					,  keep_in_memory=snake_case_       ).read()
	_check_json_dataset(snake_case_					,  snake_case_       )







@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    (			snake_case_ :							int					,  snake_case_ :							Union[str, Any]					,  snake_case_ :							Optional[Any]       ):
	snake_case__			:				Optional[Any]       =  tmp_path / "cache"
	snake_case__			:				List[Any]       =  {"col_1": "string", "col_2": "int64", "col_3": "float64"}
	snake_case__			:				int       =  features.copy() if features else default_expected_features
	snake_case__			:				Any       =  (
	    Features({feature: Value(snake_case_       ) for feature, dtype in features.items()}       ) if features is not None else None
	)
	snake_case__			:				List[Any]       =  JsonDatasetReader(snake_case_					,  features=snake_case_					,  cache_dir=snake_case_       ).read()
	_check_json_dataset(snake_case_					,  snake_case_       )







@pytest.mark.parametrize(
    "features"					,  [
        None,
        {"col_3": "float64", "col_1": "string", "col_2": "int64"},
    ]					,  )
def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							List[str]					,  snake_case_ :							Optional[Any]					,  snake_case_ :							Union[str, Any]       ):
	snake_case__			:				int       =  tmp_path / "cache"
	snake_case__			:				int       =  {"col_3": "float64", "col_1": "string", "col_2": "int64"}
	snake_case__			:				Optional[int]       =  features.copy() if features else default_expected_features
	snake_case__			:				int       =  (
	    Features({feature: Value(snake_case_       ) for feature, dtype in features.items()}       ) if features is not None else None
	)
	snake_case__			:				Dict       =  JsonDatasetReader(snake_case_					,  features=snake_case_					,  cache_dir=snake_case_       ).read()
	assert isinstance(snake_case_					,  snake_case_       )
	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    (			snake_case_ :							Dict					,  snake_case_ :							List[str]       ):
	# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
	snake_case__			:				str       =  {"col_2": "int64", "col_3": "float64", "col_1": "string"}
	snake_case__			:				int       =  features.copy()
	snake_case__			:				str       =  (
	    Features({feature: Value(snake_case_       ) for feature, dtype in features.items()}       ) if features is not None else None
	)
	snake_case__			:				Optional[Any]       =  tmp_path / "cache"
	snake_case__			:				Tuple       =  JsonDatasetReader(snake_case_					,  features=snake_case_					,  cache_dir=snake_case_       ).read()
	assert isinstance(snake_case_					,  snake_case_       )
	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    (			snake_case_ :							List[Any]					,  snake_case_ :							List[Any]					,  snake_case_ :							str       ):
	snake_case__			:				str       =  tmp_path / "cache"
	snake_case__			:				int       =  {"col_1": "string", "col_2": "int64", "col_3": "float64"}
	snake_case__			:				Dict       =  JsonDatasetReader(snake_case_					,  cache_dir=snake_case_					,  split=snake_case_       ).read()
	_check_json_dataset(snake_case_					,  snake_case_       )
	assert dataset.split == split if split else "train"







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







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Any					,  snake_case_ :							List[Any]					,  snake_case_ :							Union[str, Any]=("train",)       ):
	assert isinstance(snake_case_					,  snake_case_       )
	for split in splits:
		snake_case__			:				List[str]       =  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    (			snake_case_ :							int					,  snake_case_ :							List[Any]					,  snake_case_ :							Union[str, Any]       ):
	snake_case__			:				Tuple       =  tmp_path / "cache"
	snake_case__			:				Optional[int]       =  {"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__			:				Union[str, Any]       =  JsonDatasetReader({"train": jsonl_path}					,  cache_dir=snake_case_					,  keep_in_memory=snake_case_       ).read()
	_check_json_datasetdict(snake_case_					,  snake_case_       )







@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    (			snake_case_ :							Any					,  snake_case_ :							int					,  snake_case_ :							int       ):
	snake_case__			:				Optional[int]       =  tmp_path / "cache"
	snake_case__			:				Union[str, Any]       =  {"col_1": "string", "col_2": "int64", "col_3": "float64"}
	snake_case__			:				Any       =  features.copy() if features else default_expected_features
	snake_case__			:				Union[str, Any]       =  (
	    Features({feature: Value(snake_case_       ) for feature, dtype in features.items()}       ) if features is not None else None
	)
	snake_case__			:				Dict       =  JsonDatasetReader({"train": jsonl_path}					,  features=snake_case_					,  cache_dir=snake_case_       ).read()
	_check_json_datasetdict(snake_case_					,  snake_case_       )







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







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str       ):
	return json.load(snake_case_       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Dict       ):
	return [json.loads(snake_case_       ) for line in buffer]





class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					@pytest.mark.parametrize("lines, load_json_function"							,		[(True, load_json_lines), (False, load_json)]	)
					def 	_lowercase					( self					:  Optional[Any]							,		__A					:  Union[str, Any]							,		__A					:  Optional[Any]							,		__A					:  Any	):
						with io.BytesIO() as buffer:
							JsonDatasetWriter(__A							,		__A							,		lines=__A	).write()
							buffer.seek(0	)
							snake_case__			:				List[str]       =  load_json_function(__A	)
						assert isinstance(__A							,		__A	)
						assert isinstance(exported_content[0]							,		__A	)
						assert len(__A	) == 1_0



					@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 	_lowercase					( self					:  List[Any]							,		__A					:  List[str]							,		__A					:  List[str]							,		__A					:  str							,		__A					:  List[str]							,		__A					:  Optional[Any]	):
						with io.BytesIO() as buffer:
							JsonDatasetWriter(__A							,		__A							,		lines=__A							,		orient=__A	).write()
							buffer.seek(0	)
							snake_case__			:				Union[str, Any]       =  load_json(__A	)
						assert isinstance(__A							,		__A	)
						if keys:
							if container is dict:
								assert exported_content.keys() == keys
							else:
								assert exported_content[0].keys() == keys
						else:
							assert not hasattr(__A							,		"keys"	) and not hasattr(exported_content[0]							,		"keys"	)
						if len_at:
							assert len(exported_content[len_at]	) == 1_0
						else:
							assert len(__A	) == 1_0



					@pytest.mark.parametrize("lines, load_json_function"							,		[(True, load_json_lines), (False, load_json)]	)
					def 	_lowercase					( self					:  Optional[int]							,		__A					:  Optional[Any]							,		__A					:  Union[str, Any]							,		__A					:  List[Any]	):
						with io.BytesIO() as buffer:
							JsonDatasetWriter(__A							,		__A							,		lines=__A							,		num_proc=2	).write()
							buffer.seek(0	)
							snake_case__			:				List[Any]       =  load_json_function(__A	)
						assert isinstance(__A							,		__A	)
						assert isinstance(exported_content[0]							,		__A	)
						assert len(__A	) == 1_0



					@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 	_lowercase					( self					:  List[Any]							,		__A					:  Union[str, Any]							,		__A					:  int							,		__A					:  Tuple							,		__A					:  Optional[Any]							,		__A					:  List[Any]	):
						with io.BytesIO() as buffer:
							JsonDatasetWriter(__A							,		__A							,		lines=__A							,		orient=__A							,		num_proc=2	).write()
							buffer.seek(0	)
							snake_case__			:				Tuple       =  load_json(__A	)
						assert isinstance(__A							,		__A	)
						if keys:
							if container is dict:
								assert exported_content.keys() == keys
							else:
								assert exported_content[0].keys() == keys
						else:
							assert not hasattr(__A							,		"keys"	) and not hasattr(exported_content[0]							,		"keys"	)
						if len_at:
							assert len(exported_content[len_at]	) == 1_0
						else:
							assert len(__A	) == 1_0



					def 	_lowercase					( self					:  Dict							,		__A					:  Tuple	):
						with pytest.raises(__A	):
							with io.BytesIO() as buffer:
								JsonDatasetWriter(__A							,		__A							,		num_proc=0	)






					@pytest.mark.parametrize("compression, extension"							,		[("gzip", "gz"), ("bz2", "bz2"), ("xz", "xz")]	)
					def 	_lowercase					( self					:  Tuple							,		__A					:  List[str]							,		__A					:  int							,		__A					:  Optional[Any]							,		__A					:  Union[str, Any]							,		__A					:  List[Any]	):
						snake_case__			:				Tuple       =  tmp_path_factory.mktemp("data"	) / f'''test.json.{extension}'''
						snake_case__			:				Union[str, Any]       =  str(shared_datadir / f'''test_file.json.{extension}'''	)
						JsonDatasetWriter(__A							,		__A							,		compression=__A	).write()

						with fsspec.open(__A							,		"rb"							,		compression="infer"	) as f:
							snake_case__			:				List[Any]       =  f.read()
						with fsspec.open(__A							,		"rb"							,		compression="infer"	) as f:
							snake_case__			:				List[Any]       =  f.read()
						assert exported_content == original_content











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase :      Union[str, Any]										=				{
    """configuration_time_series_transformer""": [
        """TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TimeSeriesTransformerConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase :      Tuple										=				[
	    """TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TimeSeriesTransformerForPrediction""",
	    """TimeSeriesTransformerModel""",
	    """TimeSeriesTransformerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_time_series_transformer import (
	    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TimeSeriesTransformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimeSeriesTransformerForPrediction,
		    TimeSeriesTransformerModel,
		    TimeSeriesTransformerPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase :      Tuple										=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



