






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











import os

import pytest
from attr import dataclass


__lowerCamelCase :      Any										=				"""us-east-1"""  # defaults region



@dataclass
class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					a_     =		42
					a_     =		"arn:aws:iam::558105141721:role/sagemaker_execution_role"
					a_     =		{
					    "task_name": "mnli",
					    "per_device_train_batch_size": 1_6,
					    "per_device_eval_batch_size": 1_6,
					    "do_train": True,
					    "do_eval": True,
					    "do_predict": True,
					    "output_dir": "/opt/ml/model",
					    "overwrite_output_dir": True,
					    "max_steps": 5_0_0,
					    "save_steps": 5_5_0_0,
					}
					a_     =		{**hyperparameters, "max_steps": 1_0_0_0}



					@property
					def 	_lowercase					( self					:  List[Any]	):
						if self.framework == "pytorch":
							return [
							    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
							    {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
							    {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
							]
						else:
							return [
							    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
							    {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
							    {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
							]



					@property
					def 	_lowercase					( self					:  Any	):
						return f'''{self.framework}-transfromers-test'''



					@property
					def 	_lowercase					( self					:  Optional[Any]	):
						return f'''./tests/sagemaker/scripts/{self.framework}'''






					@property
					def 	_lowercase					( self					:  Union[str, Any]	):
						if self.framework == "pytorch":
							return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
						else:
							return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"







@pytest.fixture(scope="class"       )
def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Optional[Any]       ):
	snake_case__			:				Optional[int]       =  SageMakerTestEnvironment(framework=request.cls.framework       )



