



'''simple docstring'''






import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil

class        SCREAMING_SNAKE_CASE(							SCREAMING_SNAKE_CASE__    ):







 """simple docstring"""






 def   A			(		self    :  List[Any]	)    ->	int:
        UpperCAmelCase				: List[str]											=	pa.array(TypedSequence([1, 2, 3]	)	)
        self.assertEqual(arr.type	,    pa.intaa()	)







 def   A			(		self    :  Optional[Any]	)    ->	Optional[Any]:

        with self.assertRaises(_lowercase	):
               UpperCAmelCase				: Dict											=	pa.array(TypedSequence([1, 2, 3]	)	,    type=pa.intaa()	)







 def   A			(		self    :  List[str]	)    ->	str:

        with self.assertRaises(_lowercase	):
               UpperCAmelCase				: str											=	pa.array(TypedSequence([1, 2, 3]	,    try_type=Value('''bool'''	)	,    type=Value('''int64'''	)	)	)







 def   A			(		self    :  Tuple	)    ->	Optional[Any]:
        UpperCAmelCase				: Dict											=	pa.array(TypedSequence([1, 2, 3]	,    type=Value('''int32'''	)	)	)
        self.assertEqual(arr.type	,    pa.intaa()	)







 def   A			(		self    :  Dict	)    ->	Dict:

        with self.assertRaises((TypeError, pa.lib.ArrowInvalid)	):
               UpperCAmelCase				: Tuple											=	pa.array(TypedSequence(['''foo''', '''bar''']	,    type=Value('''int64'''	)	)	)







 def   A			(		self    :  Any	)    ->	Optional[int]:
        UpperCAmelCase				: Tuple											=	pa.array(TypedSequence([1, 2, 3]	,    try_type=Value('''int32'''	)	)	)
        self.assertEqual(arr.type	,    pa.intaa()	)







 def   A			(		self    :  Tuple	)    ->	Dict:
        UpperCAmelCase				: Optional[int]											=	pa.array(TypedSequence(['''foo''', '''bar''']	,    try_type=Value('''int64'''	)	)	)
        self.assertEqual(arr.type	,    pa.string()	)







 def   A			(		self    :  Optional[Any]	)    ->	int:
        UpperCAmelCase				: List[Any]											=	pa.array(TypedSequence([[[1, 2, 3]]]	,    type=ArrayaD((1, 3)	,    '''int64'''	)	)	)
        self.assertEqual(arr.type	,    ArrayaDExtensionType((1, 3)	,    '''int64'''	)	)







 def   A			(		self    :  Tuple	)    ->	List[Any]:

        with self.assertRaises((TypeError, pa.lib.ArrowInvalid)	):
               UpperCAmelCase				: List[str]											=	pa.array(TypedSequence(['''foo''', '''bar''']	,    type=ArrayaD((1, 3)	,    '''int64'''	)	)	)







 def   A			(		self    :  Optional[Any]	)    ->	Union[str, Any]:
        UpperCAmelCase				: List[Any]											=	pa.array(TypedSequence([[[1, 2, 3]]]	,    try_type=ArrayaD((1, 3)	,    '''int64'''	)	)	)
        self.assertEqual(arr.type	,    ArrayaDExtensionType((1, 3)	,    '''int64'''	)	)







 def   A			(		self    :  Optional[Any]	)    ->	Optional[Any]:
        UpperCAmelCase				: str											=	pa.array(TypedSequence(['''foo''', '''bar''']	,    try_type=ArrayaD((1, 3)	,    '''int64'''	)	)	)
        self.assertEqual(arr.type	,    pa.string()	)







 @require_pil
 def   A			(		self    :  Any	)    ->	List[str]:
        import PIL.Image

        UpperCAmelCase				: int											=	PIL.Image.fromarray(np.arange(10	,    dtype=np.uinta	).reshape(2	,    5	)	)
        with patch(
            '''datasets.arrow_writer.cast_to_python_objects'''	,    side_effect=_lowercase	) as mock_cast_to_python_objects:
               UpperCAmelCase				: Any											=	pa.array(TypedSequence([{'''path''': None, '''bytes''': b'''image_bytes'''}, pil_image]	,    type=Image()	)	)
               UpperCAmelCase				: List[Any]											=	mock_cast_to_python_objects.call_args_list[-1]
               self.assertIn('''optimize_list_casting'''	,    _lowercase	)
               self.assertFalse(kwargs['''optimize_list_casting''']	)


def 				snake_case_  (      _lowerCAmelCase						:		Tuple     ,   _lowerCAmelCase						:		int						)   ->     Any:
       UpperCAmelCase				: List[str]											=	pa.BufferReader(__UpperCamelCase						) if isinstance(__UpperCamelCase     ,   pa.Buffer						) else pa.memory_map(__UpperCamelCase						)
       UpperCAmelCase				: Optional[Any]											=	pa.ipc.open_stream(__UpperCamelCase						)
       UpperCAmelCase				: pa.Table											=	f.read_all()
       assert len(pa_table.to_batches()						) == expected_num_chunks
       assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
       del pa_table


@pytest.mark.parametrize('''writer_batch_size'''     ,   [None, 1, 10]						)
@pytest.mark.parametrize(
    '''fields'''     ,   [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]						)
def 				snake_case_  (      _lowerCAmelCase						:		Tuple     ,   _lowerCAmelCase						:		Union[str, Any]						)   ->     Union[str, Any]:
       UpperCAmelCase				: Optional[int]											=	pa.BufferOutputStream()
       UpperCAmelCase				: List[Any]											=	pa.schema(__UpperCamelCase						) if fields else None
       with ArrowWriter(stream=__UpperCamelCase     ,   schema=__UpperCamelCase     ,   writer_batch_size=__UpperCamelCase						) as writer:
              writer.write({'''col_1''': '''foo''', '''col_2''': 1}						)
              writer.write({'''col_1''': '''bar''', '''col_2''': 2}						)
              UpperCAmelCase				: Optional[Any]											=	writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       if not fields:
              UpperCAmelCase				: Tuple											=	{"""col_1""": pa.string(), """col_2""": pa.intaa()}
       assert writer._schema == pa.schema(__UpperCamelCase     ,   metadata=writer._schema.metadata						)
       _check_output(output.getvalue()     ,   expected_num_chunks=num_examples if writer_batch_size == 1 else 1						)


def 				snake_case_  (      )   ->     Tuple:
       UpperCAmelCase				: Union[str, Any]											=	pa.BufferOutputStream()
       UpperCAmelCase				: Optional[Any]											=	Features({'''labels''': ClassLabel(names=['''neg''', '''pos''']						)}						)
       with ArrowWriter(stream=__UpperCamelCase     ,   features=__UpperCamelCase						) as writer:
              writer.write({'''labels''': 0}						)
              writer.write({'''labels''': 1}						)
              UpperCAmelCase				: int											=	writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       assert writer._schema == features.arrow_schema
       assert writer._schema.metadata == features.arrow_schema.metadata
       UpperCAmelCase				: int											=	pa.BufferReader(output.getvalue()						)
       UpperCAmelCase				: Optional[int]											=	pa.ipc.open_stream(__UpperCamelCase						)
       UpperCAmelCase				: pa.Table											=	f.read_all()
       UpperCAmelCase				: Optional[int]											=	pa_table.schema
       assert pa_table.num_rows == 2
       assert schema == features.arrow_schema
       assert schema.metadata == features.arrow_schema.metadata
       assert features == Features.from_arrow_schema(__UpperCamelCase						)


@pytest.mark.parametrize('''writer_batch_size'''     ,   [None, 1, 10]						)
def 				snake_case_  (      _lowerCAmelCase						:		Tuple						)   ->     Any:
       UpperCAmelCase				: int											=	pa.BufferOutputStream()
       with ArrowWriter(
           stream=__UpperCamelCase     ,   writer_batch_size=__UpperCamelCase     ,   hash_salt='''split_name'''     ,   check_duplicates=__UpperCamelCase     ,   ) as writer:
              with pytest.raises(__UpperCamelCase						):
                     writer.write({'''col_1''': '''foo''', '''col_2''': 1}     ,   key=[1, 2]						)
                     UpperCAmelCase				: List[Any]											=	writer.finalize()


@pytest.mark.parametrize('''writer_batch_size'''     ,   [None, 2, 10]						)
def 				snake_case_  (      _lowerCAmelCase						:		str						)   ->     Optional[int]:
       UpperCAmelCase				: List[str]											=	pa.BufferOutputStream()
       with ArrowWriter(
           stream=__UpperCamelCase     ,   writer_batch_size=__UpperCamelCase     ,   hash_salt='''split_name'''     ,   check_duplicates=__UpperCamelCase     ,   ) as writer:
              with pytest.raises(__UpperCamelCase						):
                     writer.write({'''col_1''': '''foo''', '''col_2''': 1}     ,   key=10						)
                     writer.write({'''col_1''': '''bar''', '''col_2''': 2}     ,   key=10						)
                     UpperCAmelCase				: Dict											=	writer.finalize()


@pytest.mark.parametrize('''writer_batch_size'''     ,   [None, 2, 10]						)
def 				snake_case_  (      _lowerCAmelCase						:		List[str]						)   ->     Union[str, Any]:
       UpperCAmelCase				: List[Any]											=	pa.BufferOutputStream()
       with ArrowWriter(
           stream=__UpperCamelCase     ,   writer_batch_size=__UpperCamelCase     ,   hash_salt='''split_name'''     ,   check_duplicates=__UpperCamelCase     ,   ) as writer:
              writer.write({'''col_1''': '''foo''', '''col_2''': 1}     ,   key=1						)
              writer.write({'''col_1''': '''bar''', '''col_2''': 2}     ,   key=2						)
              UpperCAmelCase				: List[Any]											=	writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       _check_output(output.getvalue()     ,   expected_num_chunks=num_examples if writer_batch_size == 1 else 1						)


@pytest.mark.parametrize('''writer_batch_size'''     ,   [None, 1, 10]						)
@pytest.mark.parametrize(
    '''fields'''     ,   [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]						)
def 				snake_case_  (      _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		Optional[int]						)   ->     Optional[Any]:
       UpperCAmelCase				: Union[str, Any]											=	pa.BufferOutputStream()
       UpperCAmelCase				: int											=	pa.schema(__UpperCamelCase						) if fields else None
       with ArrowWriter(stream=__UpperCamelCase     ,   schema=__UpperCamelCase     ,   writer_batch_size=__UpperCamelCase						) as writer:
              writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}						)
              writer.write_batch({'''col_1''': [], '''col_2''': []}						)
              UpperCAmelCase				: List[Any]											=	writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       if not fields:
              UpperCAmelCase				: Tuple											=	{"""col_1""": pa.string(), """col_2""": pa.intaa()}
       assert writer._schema == pa.schema(__UpperCamelCase     ,   metadata=writer._schema.metadata						)
       _check_output(output.getvalue()     ,   expected_num_chunks=num_examples if writer_batch_size == 1 else 1						)


@pytest.mark.parametrize('''writer_batch_size'''     ,   [None, 1, 10]						)
@pytest.mark.parametrize(
    '''fields'''     ,   [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]						)
def 				snake_case_  (      _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		int						)   ->     List[str]:
       UpperCAmelCase				: Union[str, Any]											=	pa.BufferOutputStream()
       UpperCAmelCase				: str											=	pa.schema(__UpperCamelCase						) if fields else None
       with ArrowWriter(stream=__UpperCamelCase     ,   schema=__UpperCamelCase     ,   writer_batch_size=__UpperCamelCase						) as writer:
              writer.write_table(pa.Table.from_pydict({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}						)						)
              UpperCAmelCase				: Dict											=	writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       if not fields:
              UpperCAmelCase				: List[str]											=	{"""col_1""": pa.string(), """col_2""": pa.intaa()}
       assert writer._schema == pa.schema(__UpperCamelCase     ,   metadata=writer._schema.metadata						)
       _check_output(output.getvalue()     ,   expected_num_chunks=num_examples if writer_batch_size == 1 else 1						)


@pytest.mark.parametrize('''writer_batch_size'''     ,   [None, 1, 10]						)
@pytest.mark.parametrize(
    '''fields'''     ,   [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]						)
def 				snake_case_  (      _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		Dict						)   ->     List[Any]:
       UpperCAmelCase				: str											=	pa.BufferOutputStream()
       UpperCAmelCase				: Optional[int]											=	pa.schema(__UpperCamelCase						) if fields else None
       with ArrowWriter(stream=__UpperCamelCase     ,   schema=__UpperCamelCase     ,   writer_batch_size=__UpperCamelCase						) as writer:
              writer.write_row(pa.Table.from_pydict({'''col_1''': ['''foo'''], '''col_2''': [1]}						)						)
              writer.write_row(pa.Table.from_pydict({'''col_1''': ['''bar'''], '''col_2''': [2]}						)						)
              UpperCAmelCase				: Optional[Any]											=	writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       if not fields:
              UpperCAmelCase				: List[Any]											=	{"""col_1""": pa.string(), """col_2""": pa.intaa()}
       assert writer._schema == pa.schema(__UpperCamelCase     ,   metadata=writer._schema.metadata						)
       _check_output(output.getvalue()     ,   expected_num_chunks=num_examples if writer_batch_size == 1 else 1						)


def 				snake_case_  (      )   ->     Dict:






       with tempfile.TemporaryDirectory() as tmp_dir:
              UpperCAmelCase				: str											=	{"""col_1""": pa.string(), """col_2""": pa.intaa()}
              UpperCAmelCase				: Any											=	os.path.join(__UpperCamelCase     ,   '''test.arrow'''						)
              with ArrowWriter(path=__UpperCamelCase     ,   schema=pa.schema(__UpperCamelCase						)						) as writer:
                     writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}						)
                     UpperCAmelCase				: Dict											=	writer.finalize()
              assert num_examples == 2
              assert num_bytes > 0
              assert writer._schema == pa.schema(__UpperCamelCase     ,   metadata=writer._schema.metadata						)
              _check_output(__UpperCamelCase     ,   1						)


def 				snake_case_  (      _lowerCAmelCase						:		Any						)   ->     Dict:






       if pa.types.is_list(__UpperCamelCase						):
              return get_base_dtype(arr_type.value_type						)
       else:
              return arr_type


def 				snake_case_  (      _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		Dict						)   ->     Any:






       if isinstance(lst[0]     ,   __UpperCamelCase						):
              change_first_primitive_element_in_list(lst[0]     ,   __UpperCamelCase						)
       else:
              UpperCAmelCase				: Tuple											=	value


@pytest.mark.parametrize('''optimized_int_type, expected_dtype'''     ,   [(None, pa.intaa()), (Value('''int32'''						), pa.intaa())]						)
@pytest.mark.parametrize('''sequence'''     ,   [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]						)
def 				snake_case_  (      _lowerCAmelCase						:		Any     ,   _lowerCAmelCase						:		List[str]     ,   _lowerCAmelCase						:		List[str]						)   ->     Optional[Any]:
       UpperCAmelCase				: str											=	pa.array(TypedSequence(__UpperCamelCase     ,   optimized_int_type=__UpperCamelCase						)						)
       assert get_base_dtype(arr.type						) == expected_dtype


@pytest.mark.parametrize(
    '''col, expected_dtype'''     ,   [
        ('''attention_mask''', pa.inta()),
        ('''special_tokens_mask''', pa.inta()),
        ('''token_type_ids''', pa.inta()),
        ('''input_ids''', pa.intaa()),
        ('''other''', pa.intaa()),
    ]     ,   )
@pytest.mark.parametrize('''sequence'''     ,   [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]						)
def 				snake_case_  (      _lowerCAmelCase						:		Dict     ,   _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		List[Any]						)   ->     Tuple:
       UpperCAmelCase				: List[str]											=	pa.array(OptimizedTypedSequence(__UpperCamelCase     ,   col=__UpperCamelCase						)						)
       assert get_base_dtype(arr.type						) == expected_dtype

       # not in range
       if col != "other":
              # avoids errors due to in-place modifications
              UpperCAmelCase				: Any											=	copy.deepcopy(__UpperCamelCase						)
              UpperCAmelCase				: List[str]											=	np.iinfo(expected_dtype.to_pandas_dtype()						).max + 1
              change_first_primitive_element_in_list(__UpperCamelCase     ,   __UpperCamelCase						)
              UpperCAmelCase				: str											=	pa.array(OptimizedTypedSequence(__UpperCamelCase     ,   col=__UpperCamelCase						)						)
              assert get_base_dtype(arr.type						) == pa.intaa()


@pytest.mark.parametrize('''raise_exception'''     ,   [False, True]						)
def 				snake_case_  (      _lowerCAmelCase						:		List[str]     ,   _lowerCAmelCase						:		Dict						)   ->     Optional[Any]:
       UpperCAmelCase				: List[str]											=	str(tmp_path / '''dataset-train.arrow'''						)
       try:
              with ArrowWriter(path=__UpperCamelCase						) as writer:
                     if raise_exception:
                            raise pa.lib.ArrowInvalid()
                     else:
                            writer.stream.close()
       except pa.lib.ArrowInvalid:
              pass
       finally:
              assert writer.stream.closed


def 				snake_case_  (      _lowerCAmelCase						:		Tuple						)   ->     Union[str, Any]:
       UpperCAmelCase				: List[Any]											=	"""mock://dataset-train.arrow"""
       with ArrowWriter(path=__UpperCamelCase     ,   storage_options=mockfs.storage_options						) as writer:
              assert isinstance(writer._fs     ,   type(__UpperCamelCase						)						)
              assert writer._fs.storage_options == mockfs.storage_options
              writer.write({'''col_1''': '''foo''', '''col_2''': 1}						)
              writer.write({'''col_1''': '''bar''', '''col_2''': 2}						)
              UpperCAmelCase				: int											=	writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       assert mockfs.exists(__UpperCamelCase						)


def 				snake_case_  (      )   ->     List[str]:
       UpperCAmelCase				: Union[str, Any]											=	pa.BufferOutputStream()
       with ParquetWriter(stream=__UpperCamelCase						) as writer:
              writer.write({'''col_1''': '''foo''', '''col_2''': 1}						)
              writer.write({'''col_1''': '''bar''', '''col_2''': 2}						)
              UpperCAmelCase				: str											=	writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       UpperCAmelCase				: Optional[int]											=	pa.BufferReader(output.getvalue()						)
       UpperCAmelCase				: pa.Table											=	pq.read_table(__UpperCamelCase						)
       assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}


@require_pil
@pytest.mark.parametrize('''embed_local_files'''     ,   [False, True]						)
def 				snake_case_  (      _lowerCAmelCase						:		Optional[int]     ,   _lowerCAmelCase						:		Optional[int]						)   ->     Optional[Any]:
       import PIL.Image

       UpperCAmelCase				: int											=	str(tmp_path / '''test_image_rgb.jpg'''						)
       PIL.Image.fromarray(np.zeros((5, 5)     ,   dtype=np.uinta						)						).save(__UpperCamelCase     ,   format='''png'''						)
       UpperCAmelCase				: List[str]											=	pa.BufferOutputStream()
       with ParquetWriter(
           stream=__UpperCamelCase     ,   features=Features({'''image''': Image()}						)     ,   embed_local_files=__UpperCamelCase						) as writer:
              writer.write({'''image''': image_path}						)
              writer.finalize()
       UpperCAmelCase				: Tuple											=	pa.BufferReader(output.getvalue()						)
       UpperCAmelCase				: pa.Table											=	pq.read_table(__UpperCamelCase						)
       UpperCAmelCase				: List[Any]											=	pa_table.to_pydict()
       if embed_local_files:
              assert isinstance(out['''image'''][0]['''path''']     ,   __UpperCamelCase						)
              with open(__UpperCamelCase     ,   '''rb'''						) as f:
                     assert out["image"][0]["bytes"] == f.read()
       else:
              assert out["image"][0]["path"] == image_path
              assert out["image"][0]["bytes"] is None




def 				snake_case_  (      )   ->     Any:
       UpperCAmelCase				: Tuple											=	pa.schema([pa.field('''col_1'''     ,   pa.string()     ,   nullable=__UpperCamelCase						)]						)
       UpperCAmelCase				: Optional[Any]											=	pa.BufferOutputStream()
       with ArrowWriter(stream=__UpperCamelCase						) as writer:
              writer._build_writer(inferred_schema=__UpperCamelCase						)
              assert writer._schema == pa.schema([pa.field('''col_1'''     ,   pa.string()						)]						)










'''simple docstring'''






from typing import List, Optional, Union

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


UpperCamelCase__:     Optional[Any]										=    logging.get_logger(__name__)

UpperCamelCase__:     Tuple										=    {
    "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        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= """time_series_transformer"""
	lowerCamelCase__    				= {
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	    """num_hidden_layers""": """encoder_layers""",
	}







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

								# Transformer architecture configuration
								UpperCAmelCase				: int											=	input_size * len(__snake_case	) + self._number_of_features
								UpperCAmelCase				: int											=	d_model
								UpperCAmelCase				: str											=	encoder_attention_heads
								UpperCAmelCase				: str											=	decoder_attention_heads
								UpperCAmelCase				: List[str]											=	encoder_ffn_dim
								UpperCAmelCase				: Any											=	decoder_ffn_dim
								UpperCAmelCase				: Any											=	encoder_layers
								UpperCAmelCase				: str											=	decoder_layers

								UpperCAmelCase				: Optional[int]											=	dropout
								UpperCAmelCase				: Union[str, Any]											=	attention_dropout
								UpperCAmelCase				: List[Any]											=	activation_dropout
								UpperCAmelCase				: Optional[int]											=	encoder_layerdrop
								UpperCAmelCase				: Dict											=	decoder_layerdrop

								UpperCAmelCase				: Tuple											=	activation_function
								UpperCAmelCase				: Dict											=	init_std

								UpperCAmelCase				: Optional[int]											=	use_cache

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







	@property
	def   A			(		self    :  Union[str, Any]	)    ->	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
								)





