



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 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



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

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					pa.array(TypedSequence([1, 2, 3]			)			)
						self.assertEqual(arr.type		,pa.intaa()			)



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						with self.assertRaises(__lowerCamelCase			):
										lowerCAmelCase__					:    Optional[int]												=					pa.array(TypedSequence([1, 2, 3]			)		,type=pa.intaa()			)



		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Union[str, Any]												=					pa.array(TypedSequence([1, 2, 3]		,type=Value('''int32'''			)			)			)
						self.assertEqual(arr.type		,pa.intaa()			)



		def 		lowerCAmelCase__				(self			)					->      Optional[int]:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					pa.array(TypedSequence([1, 2, 3]		,try_type=Value('''int32'''			)			)			)
						self.assertEqual(arr.type		,pa.intaa()			)



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					pa.array(TypedSequence(['''foo''', '''bar''']		,try_type=Value('''int64'''			)			)			)
						self.assertEqual(arr.type		,pa.string()			)



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					pa.array(TypedSequence([[[1, 2, 3]]]		,type=ArrayaD((1, 3)		,'''int64'''			)			)			)
						self.assertEqual(arr.type		,ArrayaDExtensionType((1, 3)		,'''int64'''			)			)



		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					pa.array(TypedSequence([[[1, 2, 3]]]		,try_type=ArrayaD((1, 3)		,'''int64'''			)			)			)
						self.assertEqual(arr.type		,ArrayaDExtensionType((1, 3)		,'''int64'''			)			)



		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					pa.array(TypedSequence(['''foo''', '''bar''']		,try_type=ArrayaD((1, 3)		,'''int64'''			)			)			)
						self.assertEqual(arr.type		,pa.string()			)



		@require_pil
		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						import PIL.Image

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






def      lowerCAmelCase__		(   lowerCamelCase_		:		Any	,lowerCamelCase_		:		int):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[int]												=					pa.BufferReader(lowerCamelCase_) if isinstance(lowerCamelCase_	,pa.Buffer) else pa.memory_map(lowerCamelCase_)
				lowerCAmelCase__					:    int												=					pa.ipc.open_stream(lowerCamelCase_)
				lowerCAmelCase__					:    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      lowerCAmelCase__		(   lowerCamelCase_		:		Any	,lowerCamelCase_		:		List[Any]):





				'''simple docstring'''

				lowerCAmelCase__					:    Union[str, Any]												=					pa.BufferOutputStream()
				lowerCAmelCase__					:    List[Any]												=					pa.schema(lowerCamelCase_) if fields else None
				with ArrowWriter(stream=lowerCamelCase_	,schema=lowerCamelCase_	,writer_batch_size=lowerCamelCase_) as writer:
								writer.write({'''col_1''': '''foo''', '''col_2''': 1})
								writer.write({'''col_1''': '''bar''', '''col_2''': 2})
								lowerCAmelCase__						,		lowerCAmelCase__					:    List[Any]												=					writer.finalize()
				assert num_examples == 2
				assert num_bytes > 0
				if not fields:
								lowerCAmelCase__					:    List[Any]												=					{'''col_1''': pa.string(), '''col_2''': pa.intaa()}
				assert writer._schema == pa.schema(lowerCamelCase_	,metadata=writer._schema.metadata)
				_check_output(output.getvalue()	,expected_num_chunks=num_examples if writer_batch_size == 1 else 1)






def      lowerCAmelCase__		(   ):





				'''simple docstring'''

				lowerCAmelCase__					:    str												=					pa.BufferOutputStream()
				lowerCAmelCase__					:    Union[str, Any]												=					Features({'''labels''': ClassLabel(names=['''neg''', '''pos'''])})
				with ArrowWriter(stream=lowerCamelCase_	,features=lowerCamelCase_) as writer:
								writer.write({'''labels''': 0})
								writer.write({'''labels''': 1})
								lowerCAmelCase__						,		lowerCAmelCase__					:    List[Any]												=					writer.finalize()
				assert num_examples == 2
				assert num_bytes > 0
				assert writer._schema == features.arrow_schema
				assert writer._schema.metadata == features.arrow_schema.metadata
				lowerCAmelCase__					:    int												=					pa.BufferReader(output.getvalue())
				lowerCAmelCase__					:    Dict												=					pa.ipc.open_stream(lowerCamelCase_)
				lowerCAmelCase__					:    pa.Table												=					f.read_all()
				lowerCAmelCase__					:    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(lowerCamelCase_)






@pytest.mark.parametrize('''writer_batch_size'''	,[None, 1, 10])
def      lowerCAmelCase__		(   lowerCamelCase_		:		str):





				'''simple docstring'''

				lowerCAmelCase__					:    Dict												=					pa.BufferOutputStream()
				with ArrowWriter(
				    stream=lowerCamelCase_	,writer_batch_size=lowerCamelCase_	,hash_salt='''split_name'''	,check_duplicates=lowerCamelCase_	,) as writer:
								with pytest.raises(lowerCamelCase_):
												writer.write({'''col_1''': '''foo''', '''col_2''': 1}	,key=[1, 2])
												lowerCAmelCase__						,		lowerCAmelCase__					:    int												=					writer.finalize()






@pytest.mark.parametrize('''writer_batch_size'''	,[None, 2, 10])
def      lowerCAmelCase__		(   lowerCamelCase_		:		List[Any]):





				'''simple docstring'''

				lowerCAmelCase__					:    Dict												=					pa.BufferOutputStream()
				with ArrowWriter(
				    stream=lowerCamelCase_	,writer_batch_size=lowerCamelCase_	,hash_salt='''split_name'''	,check_duplicates=lowerCamelCase_	,) as writer:
								with pytest.raises(lowerCamelCase_):
												writer.write({'''col_1''': '''foo''', '''col_2''': 1}	,key=10)
												writer.write({'''col_1''': '''bar''', '''col_2''': 2}	,key=10)
												lowerCAmelCase__						,		lowerCAmelCase__					:    List[str]												=					writer.finalize()






@pytest.mark.parametrize('''writer_batch_size'''	,[None, 2, 10])
def      lowerCAmelCase__		(   lowerCamelCase_		:		Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__					:    Dict												=					pa.BufferOutputStream()
				with ArrowWriter(
				    stream=lowerCamelCase_	,writer_batch_size=lowerCamelCase_	,hash_salt='''split_name'''	,check_duplicates=lowerCamelCase_	,) as writer:
								writer.write({'''col_1''': '''foo''', '''col_2''': 1}	,key=1)
								writer.write({'''col_1''': '''bar''', '''col_2''': 2}	,key=2)
								lowerCAmelCase__						,		lowerCAmelCase__					:    Optional[int]												=					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      lowerCAmelCase__		(   lowerCamelCase_		:		Union[str, Any]	,lowerCamelCase_		:		Optional[int]):





				'''simple docstring'''

				lowerCAmelCase__					:    str												=					pa.BufferOutputStream()
				lowerCAmelCase__					:    Dict												=					pa.schema(lowerCamelCase_) if fields else None
				with ArrowWriter(stream=lowerCamelCase_	,schema=lowerCamelCase_	,writer_batch_size=lowerCamelCase_) as writer:
								writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]})
								writer.write_batch({'''col_1''': [], '''col_2''': []})
								lowerCAmelCase__						,		lowerCAmelCase__					:    Any												=					writer.finalize()
				assert num_examples == 2
				assert num_bytes > 0
				if not fields:
								lowerCAmelCase__					:    Any												=					{'''col_1''': pa.string(), '''col_2''': pa.intaa()}
				assert writer._schema == pa.schema(lowerCamelCase_	,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      lowerCAmelCase__		(   lowerCamelCase_		:		Any	,lowerCamelCase_		:		List[str]):





				'''simple docstring'''

				lowerCAmelCase__					:    int												=					pa.BufferOutputStream()
				lowerCAmelCase__					:    List[str]												=					pa.schema(lowerCamelCase_) if fields else None
				with ArrowWriter(stream=lowerCamelCase_	,schema=lowerCamelCase_	,writer_batch_size=lowerCamelCase_) as writer:
								writer.write_table(pa.Table.from_pydict({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}))
								lowerCAmelCase__						,		lowerCAmelCase__					:    Any												=					writer.finalize()
				assert num_examples == 2
				assert num_bytes > 0
				if not fields:
								lowerCAmelCase__					:    Union[str, Any]												=					{'''col_1''': pa.string(), '''col_2''': pa.intaa()}
				assert writer._schema == pa.schema(lowerCamelCase_	,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      lowerCAmelCase__		(   lowerCamelCase_		:		Tuple	,lowerCamelCase_		:		int):





				'''simple docstring'''

				lowerCAmelCase__					:    List[Any]												=					pa.BufferOutputStream()
				lowerCAmelCase__					:    Union[str, Any]												=					pa.schema(lowerCamelCase_) if fields else None
				with ArrowWriter(stream=lowerCamelCase_	,schema=lowerCamelCase_	,writer_batch_size=lowerCamelCase_) 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]}))
								lowerCAmelCase__						,		lowerCAmelCase__					:    int												=					writer.finalize()
				assert num_examples == 2
				assert num_bytes > 0
				if not fields:
								lowerCAmelCase__					:    List[Any]												=					{'''col_1''': pa.string(), '''col_2''': pa.intaa()}
				assert writer._schema == pa.schema(lowerCamelCase_	,metadata=writer._schema.metadata)
				_check_output(output.getvalue()	,expected_num_chunks=num_examples if writer_batch_size == 1 else 1)






def      lowerCAmelCase__		(   ):





				'''simple docstring'''

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






def      lowerCAmelCase__		(   lowerCamelCase_		:		List[str]):





				'''simple docstring'''

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






def      lowerCAmelCase__		(   lowerCamelCase_		:		int	,lowerCamelCase_		:		Any):





				'''simple docstring'''

				if isinstance(lst[0]	,lowerCamelCase_):
								change_first_primitive_element_in_list(lst[0]	,lowerCamelCase_)
				else:
								lowerCAmelCase__					:    int												=					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      lowerCAmelCase__		(   lowerCamelCase_		:		List[str]	,lowerCamelCase_		:		Tuple	,lowerCamelCase_		:		int):





				'''simple docstring'''

				lowerCAmelCase__					:    Union[str, Any]												=					pa.array(TypedSequence(lowerCamelCase_	,optimized_int_type=lowerCamelCase_))
				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      lowerCAmelCase__		(   lowerCamelCase_		:		List[str]	,lowerCamelCase_		:		Optional[Any]	,lowerCamelCase_		:		Any):





				'''simple docstring'''

				lowerCAmelCase__					:    str												=					pa.array(OptimizedTypedSequence(lowerCamelCase_	,col=lowerCamelCase_))
				assert get_base_dtype(arr.type) == expected_dtype

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






@pytest.mark.parametrize('''raise_exception'''	,[False, True])
def      lowerCAmelCase__		(   lowerCamelCase_		:		List[str]	,lowerCamelCase_		:		int):





				'''simple docstring'''

				lowerCAmelCase__					:    Any												=					str(tmp_path / '''dataset-train.arrow''')
				try:
								with ArrowWriter(path=lowerCamelCase_) as writer:
												if raise_exception:
																raise pa.lib.ArrowInvalid()
												else:
																writer.stream.close()
				except pa.lib.ArrowInvalid:
								pass
				finally:
								assert writer.stream.closed






def      lowerCAmelCase__		(   lowerCamelCase_		:		Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__					:    int												=					'''mock://dataset-train.arrow'''
				with ArrowWriter(path=lowerCamelCase_	,storage_options=mockfs.storage_options) as writer:
								assert isinstance(writer._fs	,type(lowerCamelCase_))
								assert writer._fs.storage_options == mockfs.storage_options
								writer.write({'''col_1''': '''foo''', '''col_2''': 1})
								writer.write({'''col_1''': '''bar''', '''col_2''': 2})
								lowerCAmelCase__						,		lowerCAmelCase__					:    int												=					writer.finalize()
				assert num_examples == 2
				assert num_bytes > 0
				assert mockfs.exists(lowerCamelCase_)






def      lowerCAmelCase__		(   ):





				'''simple docstring'''

				lowerCAmelCase__					:    int												=					pa.BufferOutputStream()
				with ParquetWriter(stream=lowerCamelCase_) as writer:
								writer.write({'''col_1''': '''foo''', '''col_2''': 1})
								writer.write({'''col_1''': '''bar''', '''col_2''': 2})
								lowerCAmelCase__						,		lowerCAmelCase__					:    Dict												=					writer.finalize()
				assert num_examples == 2
				assert num_bytes > 0
				lowerCAmelCase__					:    Optional[int]												=					pa.BufferReader(output.getvalue())
				lowerCAmelCase__					:    pa.Table												=					pq.read_table(lowerCamelCase_)
				assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}






@require_pil
@pytest.mark.parametrize('''embed_local_files'''	,[False, True])
def      lowerCAmelCase__		(   lowerCamelCase_		:		List[Any]	,lowerCamelCase_		:		List[Any]):





				'''simple docstring'''

				import PIL.Image

				lowerCAmelCase__					:    Optional[int]												=					str(tmp_path / '''test_image_rgb.jpg''')
				PIL.Image.fromarray(np.zeros((5, 5)	,dtype=np.uinta)).save(lowerCamelCase_	,format='''png''')
				lowerCAmelCase__					:    Tuple												=					pa.BufferOutputStream()
				with ParquetWriter(
				    stream=lowerCamelCase_	,features=Features({'''image''': Image()})	,embed_local_files=lowerCamelCase_) as writer:
								writer.write({'''image''': image_path})
								writer.finalize()
				lowerCAmelCase__					:    int												=					pa.BufferReader(output.getvalue())
				lowerCAmelCase__					:    pa.Table												=					pq.read_table(lowerCamelCase_)
				lowerCAmelCase__					:    Tuple												=					pa_table.to_pydict()
				if embed_local_files:
								assert isinstance(out['''image'''][0]['''path''']	,lowerCamelCase_)
								with open(lowerCamelCase_	,'''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      lowerCAmelCase__		(   ):





				'''simple docstring'''

				lowerCAmelCase__					:    Tuple												=					pa.schema([pa.field('''col_1'''	,pa.string()	,nullable=lowerCamelCase_)])
				lowerCAmelCase__					:    Optional[Any]												=					pa.BufferOutputStream()
				with ArrowWriter(stream=lowerCamelCase_) as writer:
								writer._build_writer(inferred_schema=lowerCamelCase_)
								assert writer._schema == pa.schema([pa.field('''col_1'''	,pa.string())])






import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize






def      lowerCAmelCase__		(   lowerCamelCase_		:		ndarray):





				'''simple docstring'''

				return np.dot(lowerCamelCase_	,lowerCamelCase_)



class 	lowerCamelCase__      :




		'''simple docstring'''



		def __init__(self		,*,
		    __lowerCamelCase = np.inf		,__lowerCamelCase = "linear"		,__lowerCamelCase = 0.0		,)					->      None:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					regularization
						lowerCAmelCase__					:    str												=					gamma
						if kernel == "linear":
										lowerCAmelCase__					:    Dict												=					self.__linear
						elif kernel == "rbf":
										if self.gamma == 0:
														raise ValueError('''rbf kernel requires gamma'''			)
										if not isinstance(self.gamma		,(float, int)			):
														raise ValueError('''gamma must be float or int'''			)
										if not self.gamma > 0:
														raise ValueError('''gamma must be > 0'''			)
										lowerCAmelCase__					:    Optional[Any]												=					self.__rbf
										# in the future, there could be a default value like in sklearn
										# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
										# previously it was 1/(n_features)
						else:
										lowerCAmelCase__					:    List[str]												=					f"""Unknown kernel: {kernel}"""
										raise ValueError(__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      float:

						"""simple docstring"""






						return np.dot(__lowerCamelCase		,__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      float:

						"""simple docstring"""






						return np.exp(-(self.gamma * norm_squared(vectora - vectora			))			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      None:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					observations
						lowerCAmelCase__					:    Optional[int]												=					classes

						# using Wolfe's Dual to calculate w.
						# Primal problem: minimize 1/2*norm_squared(w)
						#   constraint: yn(w . xn + b) >= 1
						#
						# With l a vector
						# Dual problem: maximize sum_n(ln) -
						#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
						#   constraint: self.C >= ln >= 0
						#           and sum_n(ln*yn) = 0
						# Then we get w using w = sum_n(ln*yn*xn)
						# At the end we can get b ~= mean(yn - w . xn)
						#
						# Since we use kernels, we only need l_star to calculate b
						# and to classify observations

						((lowerCAmelCase__)						,		)					:    List[str]												=					np.shape(__lowerCamelCase			)

						def to_minimize(__lowerCamelCase			) -> float:
										lowerCAmelCase__					:    List[str]												=					0
										((lowerCAmelCase__)						,		)					:    str												=					np.shape(__lowerCamelCase			)
										for i in range(__lowerCamelCase			):
														for j in range(__lowerCamelCase			):
																		s += (
																		    candidate[i]
																		    * candidate[j]
																		    * classes[i]
																		    * classes[j]
																		    * self.kernel(observations[i]		,observations[j]			)
																		)
										return 1 / 2 * s - sum(__lowerCamelCase			)

						lowerCAmelCase__					:    List[str]												=					LinearConstraint(__lowerCamelCase		,0		,0			)
						lowerCAmelCase__					:    List[str]												=					Bounds(0		,self.regularization			)

						lowerCAmelCase__					:    int												=					minimize(
						    __lowerCamelCase		,np.ones(__lowerCamelCase			)		,bounds=__lowerCamelCase		,constraints=[ly_contraint]			).x
						lowerCAmelCase__					:    List[Any]												=					l_star

						# calculating mean offset of separation plane to points
						lowerCAmelCase__					:    Optional[Any]												=					0
						for i in range(__lowerCamelCase			):
										for j in range(__lowerCamelCase			):
														s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
														    observations[i]		,observations[j]			)
						lowerCAmelCase__					:    Dict												=					s / n



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					sum(
						    self.optimum[n]
						    * self.classes[n]
						    * self.kernel(self.observations[n]		,__lowerCamelCase			)
						    for n in range(len(self.classes			)			)			)
						return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
							import doctest

							doctest.testmod()

