


"""simple docstring"""


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   snake_case		(     UpperCamelCase__       :						List[str]			,				UpperCamelCase__       :						str    )   ->      Union[str, Any]:
      assert isinstance(UpperCamelCase__			,				UpperCamelCase__    )
      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   snake_case		(     UpperCamelCase__       :						List[str]			,				UpperCamelCase__       :						Any			,				UpperCamelCase__       :						Any    )   ->      List[Any]:
      lowerCamelCase					:					Optional[Any]        =	tmp_path / """cache"""
      lowerCamelCase					:					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():
            lowerCamelCase					:					List[str]        =	JsonDatasetReader(UpperCamelCase__			,				cache_dir=UpperCamelCase__			,				keep_in_memory=UpperCamelCase__    ).read()
      _check_json_dataset(UpperCamelCase__			,				UpperCamelCase__    )




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




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




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




def   snake_case		(     UpperCamelCase__       :						str			,				UpperCamelCase__       :						List[str]			,				UpperCamelCase__       :						List[str]=("train",)    )   ->      Optional[int]:
      assert isinstance(UpperCamelCase__			,				UpperCamelCase__    )
      for split in splits:
            lowerCamelCase					:					int        =	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   snake_case		(     UpperCamelCase__       :						List[str]			,				UpperCamelCase__       :						Any			,				UpperCamelCase__       :						int    )   ->      List[str]:
      lowerCamelCase					:					Optional[int]        =	tmp_path / """cache"""
      lowerCamelCase					:					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():
            lowerCamelCase					:					Dict        =	JsonDatasetReader({"""train""": jsonl_path}			,				cache_dir=UpperCamelCase__			,				keep_in_memory=UpperCamelCase__    ).read()
      _check_json_datasetdict(UpperCamelCase__			,				UpperCamelCase__    )




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




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




def   snake_case		(     UpperCamelCase__       :						List[str]    )   ->      str:
      return json.load(UpperCamelCase__    )




def   snake_case		(     UpperCamelCase__       :						List[Any]    )   ->      Optional[Any]:
      return [json.loads(UpperCamelCase__    ) for line in buffer]






class 			A__	:


     """simple docstring"""

     @pytest.mark.parametrize("""lines, load_json_function"""     ,		[(True, load_json_lines), (False, load_json)]	)
     def 		a__						(	self:						List[str]     ,		__a:						str     ,		__a:						List[str]     ,		__a:						str	)->    Dict:
           with io.BytesIO() as buffer:
                 JsonDatasetWriter(__a     ,		__a     ,		lines=__a	).write()
                 buffer.seek(0	)
                 lowerCamelCase					:					Tuple        =	load_json_function(__a	)
           assert isinstance(__a     ,		__a	)
           assert isinstance(exported_content[0]     ,		__a	)
           assert len(__a	) == 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:						Optional[int]     ,		__a:						Tuple     ,		__a:						Any     ,		__a:						List[str]     ,		__a:						Tuple     ,		__a:						Dict	)->    int:
           with io.BytesIO() as buffer:
                 JsonDatasetWriter(__a     ,		__a     ,		lines=__a     ,		orient=__a	).write()
                 buffer.seek(0	)
                 lowerCamelCase					:					Dict        =	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]	) == 10
           else:
                 assert len(__a	) == 10

     @pytest.mark.parametrize("""lines, load_json_function"""     ,		[(True, load_json_lines), (False, load_json)]	)
     def 		a__						(	self:						Union[str, Any]     ,		__a:						List[Any]     ,		__a:						Any     ,		__a:						Dict	)->    int:
           with io.BytesIO() as buffer:
                 JsonDatasetWriter(__a     ,		__a     ,		lines=__a     ,		num_proc=2	).write()
                 buffer.seek(0	)
                 lowerCamelCase					:					str        =	load_json_function(__a	)
           assert isinstance(__a     ,		__a	)
           assert isinstance(exported_content[0]     ,		__a	)
           assert len(__a	) == 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:						str     ,		__a:						Any     ,		__a:						int     ,		__a:						Tuple     ,		__a:						List[Any]     ,		__a:						Union[str, Any]	)->    Dict:
           with io.BytesIO() as buffer:
                 JsonDatasetWriter(__a     ,		__a     ,		lines=__a     ,		orient=__a     ,		num_proc=2	).write()
                 buffer.seek(0	)
                 lowerCamelCase					:					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]	) == 10
           else:
                 assert len(__a	) == 10

     def 		a__						(	self:						Optional[int]     ,		__a:						List[Any]	)->    List[str]:
           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 		a__						(	self:						Tuple     ,		__a:						Tuple     ,		__a:						Union[str, Any]     ,		__a:						Union[str, Any]     ,		__a:						Any     ,		__a:						Optional[int]	)->    Optional[int]:
           lowerCamelCase					:					List[str]        =	tmp_path_factory.mktemp("""data"""	) / f'test.json.{extension}'
           lowerCamelCase					:					Tuple        =	str(shared_datadir / f'test_file.json.{extension}'	)
           JsonDatasetWriter(__a     ,		__a     ,		compression=__a	).write()

           with fsspec.open(__a     ,		"""rb"""     ,		compression="""infer"""	) as f:
                 lowerCamelCase					:					int        =	f.read()
           with fsspec.open(__a     ,		"""rb"""     ,		compression="""infer"""	) as f:
                 lowerCamelCase					:					List[str]        =	f.read()
           assert exported_content == original_content








"""simple docstring"""


from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase      :str	    =		logging.get_logger(__name__)

__lowerCamelCase      :Any	    =		{
    'huggingface/time-series-transformer-tourism-monthly': (
        'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					List[Any]													='''time_series_transformer'''
     snake_case__	:					List[Any]													={
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
         '''num_hidden_layers''': '''encoder_layers''',
     }

     def __init__(	self:						List[str]     ,		__a:						Optional[int] = None     ,		__a:						Optional[int] = None     ,		__a:						str = "student_t"     ,		__a:						str = "nll"     ,		__a:						int = 1     ,		__a:						List[int] = [1, 2, 3, 4, 5, 6, 7]     ,		__a:						Optional[Union[str, bool]] = "mean"     ,		__a:						int = 0     ,		__a:						int = 0     ,		__a:						int = 0     ,		__a:						int = 0     ,		__a:						Optional[List[int]] = None     ,		__a:						Optional[List[int]] = None     ,		__a:						int = 32     ,		__a:						int = 32     ,		__a:						int = 2     ,		__a:						int = 2     ,		__a:						int = 2     ,		__a:						int = 2     ,		__a:						bool = True     ,		__a:						str = "gelu"     ,		__a:						int = 64     ,		__a:						float = 0.1     ,		__a:						float = 0.1     ,		__a:						float = 0.1     ,		__a:						float = 0.1     ,		__a:						float = 0.1     ,		__a:						int = 100     ,		__a:						float = 0.02     ,		__a:						Tuple=True     ,		**__a:						str     ,		)->    Any:
           # time series specific configuration
           lowerCamelCase					:					str        =	prediction_length
           lowerCamelCase					:					Optional[Any]        =	context_length or prediction_length
           lowerCamelCase					:					Tuple        =	distribution_output
           lowerCamelCase					:					Any        =	loss
           lowerCamelCase					:					List[Any]        =	input_size
           lowerCamelCase					:					int        =	num_time_features
           lowerCamelCase					:					Dict        =	lags_sequence
           lowerCamelCase					:					Optional[int]        =	scaling
           lowerCamelCase					:					int        =	num_dynamic_real_features
           lowerCamelCase					:					Tuple        =	num_static_real_features
           lowerCamelCase					:					Any        =	num_static_categorical_features
           if cardinality and num_static_categorical_features > 0:
                 if len(__a	) != num_static_categorical_features:
                       raise ValueError(
                           """The cardinality should be a list of the same length as `num_static_categorical_features`"""	)
                 lowerCamelCase					:					int        =	cardinality
           else:
                 lowerCamelCase					:					Dict        =	[0]
           if embedding_dimension and num_static_categorical_features > 0:
                 if len(__a	) != num_static_categorical_features:
                       raise ValueError(
                           """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""	)
                 lowerCamelCase					:					str        =	embedding_dimension
           else:
                 lowerCamelCase					:					str        =	[min(50     ,		(cat + 1) // 2	) for cat in self.cardinality]
           lowerCamelCase					:					Any        =	num_parallel_samples

           # Transformer architecture configuration
           lowerCamelCase					:					Any        =	input_size * len(__a	) + self._number_of_features
           lowerCamelCase					:					List[str]        =	d_model
           lowerCamelCase					:					Tuple        =	encoder_attention_heads
           lowerCamelCase					:					Optional[int]        =	decoder_attention_heads
           lowerCamelCase					:					Union[str, Any]        =	encoder_ffn_dim
           lowerCamelCase					:					str        =	decoder_ffn_dim
           lowerCamelCase					:					str        =	encoder_layers
           lowerCamelCase					:					Any        =	decoder_layers

           lowerCamelCase					:					Optional[int]        =	dropout
           lowerCamelCase					:					List[str]        =	attention_dropout
           lowerCamelCase					:					Tuple        =	activation_dropout
           lowerCamelCase					:					Optional[int]        =	encoder_layerdrop
           lowerCamelCase					:					int        =	decoder_layerdrop

           lowerCamelCase					:					Optional[int]        =	activation_function
           lowerCamelCase					:					Optional[Any]        =	init_std

           lowerCamelCase					:					Optional[Any]        =	use_cache

           super().__init__(is_encoder_decoder=__a     ,		**__a	)







     @property
     def 		a__						(	self:						int	)->    int:
           return (
               sum(self.embedding_dimension	)
               + self.num_dynamic_real_features
               + self.num_time_features
               + self.num_static_real_features
               + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
           )




