






'''simple docstring'''




import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
			import PIL.Image

			from .features import FeatureType


lowerCAmelCase__          =						None
lowerCAmelCase__          =						'<' if sys.byteorder == 'little' else '>'
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
lowerCAmelCase__          =						[
    np.dtype('|b1'),
    np.dtype('|u1'),
    np.dtype('<u2'),
    np.dtype('>u2'),
    np.dtype('<i2'),
    np.dtype('>i2'),
    np.dtype('<u4'),
    np.dtype('>u4'),
    np.dtype('<i4'),
    np.dtype('>i4'),
    np.dtype('<f4'),
    np.dtype('>f4'),
    np.dtype('<f8'),
    np.dtype('>f8'),
]







@dataclass
class       __lowercase					:
	_lowerCamelCase              =  True
	_lowerCamelCase              =  None
	# Automatically constructed
	_lowerCamelCase              =  '''PIL.Image.Image'''
	_lowerCamelCase              =  pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}    )
	_lowerCamelCase              =  field(default='''Image'''   ,				init=__lowerCamelCase   ,				repr=__lowerCamelCase    )

	def __call__(      self   :						Union[str, Any]):
			return self.pa_type

	def      __UpperCamelCase							(      self   :						Any  ,					UpperCAmelCase_   :						Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]):
			if config.PIL_AVAILABLE:
					import PIL.Image
			else:
					raise ImportError('To support encoding images, please install \'Pillow\'.')

			if isinstance(UpperCAmelCase_  ,					UpperCAmelCase_):
					UpperCamelCase__				:					int					=					np.array(UpperCAmelCase_)

			if isinstance(UpperCAmelCase_  ,					UpperCAmelCase_):
					return {"path": value, "bytes": None}
			elif isinstance(UpperCAmelCase_  ,					UpperCAmelCase_):
					return {"path": None, "bytes": value}
			elif isinstance(UpperCAmelCase_  ,					np.ndarray):
					# convert the image array to PNG/TIFF bytes
					return encode_np_array(UpperCAmelCase_)
			elif isinstance(UpperCAmelCase_  ,					PIL.Image.Image):
					# convert the PIL image to bytes (default format is PNG/TIFF)
					return encode_pil_image(UpperCAmelCase_)
			elif value.get('path') is not None and os.path.isfile(value['path']):
					# we set "bytes": None to not duplicate the data if they're already available locally
					return {"bytes": None, "path": value.get('path')}
			elif value.get('bytes') is not None or value.get('path') is not None:
					# store the image bytes, and path is used to infer the image format using the file extension
					return {"bytes": value.get('bytes'), "path": value.get('path')}
			else:
					raise ValueError(
					    F'An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.')

	def      __UpperCamelCase							(      self   :						Optional[Any]  ,					UpperCAmelCase_   :						dict  ,					UpperCAmelCase_   :						int=None):
			if not self.decode:
					raise RuntimeError('Decoding is disabled for this feature. Please use Image(decode=True) instead.')

			if config.PIL_AVAILABLE:
					import PIL.Image
			else:
					raise ImportError('To support decoding images, please install \'Pillow\'.')

			if token_per_repo_id is None:
					UpperCamelCase__				:					int					=					{}

			UpperCamelCase__				:					int					=					value['path'], value['bytes']
			if bytes_ is None:
					if path is None:
							raise ValueError(F'An image should have one of \'path\' or \'bytes\' but both are None in {value}.')
					else:
							if is_local_path(UpperCAmelCase_):
									UpperCamelCase__				:					int					=					PIL.Image.open(UpperCAmelCase_)
							else:
									UpperCamelCase__				:					str					=					path.split('::')[-1]
									try:
											UpperCamelCase__				:					Union[str, Any]					=					string_to_dict(UpperCAmelCase_  ,					config.HUB_DATASETS_URL)['repo_id']
											UpperCamelCase__				:					Optional[Any]					=					token_per_repo_id.get(UpperCAmelCase_)
									except ValueError:
											UpperCamelCase__				:					Any					=					None
									with xopen(UpperCAmelCase_  ,					'rb'  ,					use_auth_token=UpperCAmelCase_) as f:
											UpperCamelCase__				:					Optional[int]					=					BytesIO(f.read())
									UpperCamelCase__				:					Tuple					=					PIL.Image.open(bytes_)
			else:
					UpperCamelCase__				:					List[Any]					=					PIL.Image.open(BytesIO(bytes_))
			image.load()  # to avoid "Too many open files" errors
			return image

	def      __UpperCamelCase							(      self   :						List[str]):
			from .features import Value

			return (
			    self
			    if self.decode
			    else {
			        "bytes": Value('binary'),
			        "path": Value('string'),
			    }
			)

	def      __UpperCamelCase							(      self   :						Tuple  ,					UpperCAmelCase_   :						Union[pa.StringArray, pa.StructArray, pa.ListArray]):
			if pa.types.is_string(storage.type):
					UpperCamelCase__				:					Dict					=					pa.array([None] * len(UpperCAmelCase_)  ,					type=pa.binary())
					UpperCamelCase__				:					Union[str, Any]					=					pa.StructArray.from_arrays([bytes_array, storage]  ,					['bytes', 'path']  ,					mask=storage.is_null())
			elif pa.types.is_binary(storage.type):
					UpperCamelCase__				:					Tuple					=					pa.array([None] * len(UpperCAmelCase_)  ,					type=pa.string())
					UpperCamelCase__				:					Optional[int]					=					pa.StructArray.from_arrays([storage, path_array]  ,					['bytes', 'path']  ,					mask=storage.is_null())
			elif pa.types.is_struct(storage.type):
					if storage.type.get_field_index('bytes') >= 0:
							UpperCamelCase__				:					Tuple					=					storage.field('bytes')
					else:
							UpperCamelCase__				:					Tuple					=					pa.array([None] * len(UpperCAmelCase_)  ,					type=pa.binary())
					if storage.type.get_field_index('path') >= 0:
							UpperCamelCase__				:					int					=					storage.field('path')
					else:
							UpperCamelCase__				:					List[str]					=					pa.array([None] * len(UpperCAmelCase_)  ,					type=pa.string())
					UpperCamelCase__				:					Dict					=					pa.StructArray.from_arrays([bytes_array, path_array]  ,					['bytes', 'path']  ,					mask=storage.is_null())
			elif pa.types.is_list(storage.type):
					UpperCamelCase__				:					Optional[Any]					=					pa.array(
					    [encode_np_array(np.array(UpperCAmelCase_))['bytes'] if arr is not None else None for arr in storage.to_pylist()]  ,					type=pa.binary()  ,					)
					UpperCamelCase__				:					Optional[Any]					=					pa.array([None] * len(UpperCAmelCase_)  ,					type=pa.string())
					UpperCamelCase__				:					Optional[int]					=					pa.StructArray.from_arrays(
					    [bytes_array, path_array]  ,					['bytes', 'path']  ,					mask=bytes_array.is_null())
			return array_cast(UpperCAmelCase_  ,					self.pa_type)





	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						pa.StructArray):

			@no_op_if_value_is_null
			def path_to_bytes(UpperCAmelCase_   :						int):
					with xopen(UpperCAmelCase_  ,					'rb') as f:
							UpperCamelCase__				:					List[str]					=					f.read()
					return bytes_

			UpperCamelCase__				:					Optional[Any]					=					pa.array(
			    [
			        (path_to_bytes(x['path']) if x['bytes'] is None else x['bytes']) if x is not None else None
			        for x in storage.to_pylist()
			    ]  ,					type=pa.binary()  ,					)
			UpperCamelCase__				:					List[Any]					=					pa.array(
			    [os.path.basename(UpperCAmelCase_) if path is not None else None for path in storage.field('path').to_pylist()]  ,					type=pa.string()  ,					)
			UpperCamelCase__				:					str					=					pa.StructArray.from_arrays([bytes_array, path_array]  ,					['bytes', 'path']  ,					mask=bytes_array.is_null())
			return array_cast(UpperCAmelCase_  ,					self.pa_type)







def       __UpperCAmelCase						(  )							->     List[str]:
		if config.PIL_AVAILABLE:
				import PIL.Image
		else:
				raise ImportError('To support encoding images, please install \'Pillow\'.')

		global _IMAGE_COMPRESSION_FORMATS
		if _IMAGE_COMPRESSION_FORMATS is None:
				PIL.Image.init()
				UpperCamelCase__				:					Tuple					=					list(set(PIL.Image.OPEN.keys()) & set(PIL.Image.SAVE.keys()))
		return _IMAGE_COMPRESSION_FORMATS







def       __UpperCAmelCase						(  lowerCamelCase_)							->     bytes:
		UpperCamelCase__				:					List[Any]					=					BytesIO()
		if image.format in list_image_compression_formats():
				UpperCamelCase__				:					Dict					=					image.format
		else:
				UpperCamelCase__				:					List[str]					=					'PNG' if image.mode in ['1', 'L', 'LA', 'RGB', 'RGBA'] else 'TIFF'
		image.save(lowerCamelCase_     ,      format=lowerCamelCase_)
		return buffer.getvalue()







def       __UpperCAmelCase						(  lowerCamelCase_)							->     dict:
		if hasattr(lowerCamelCase_     ,      'filename') and image.filename != "":
				return {"path": image.filename, "bytes": None}
		else:
				return {"path": None, "bytes": image_to_bytes(lowerCamelCase_)}







def       __UpperCAmelCase						(  lowerCamelCase_)							->     dict:
		if config.PIL_AVAILABLE:
				import PIL.Image
		else:
				raise ImportError('To support encoding images, please install \'Pillow\'.')

		UpperCamelCase__				:					List[Any]					=					array.dtype
		UpperCamelCase__				:					Union[str, Any]					=					dtype.byteorder if dtype.byteorder != '=' else _NATIVE_BYTEORDER
		UpperCamelCase__				:					Optional[int]					=					dtype.kind
		UpperCamelCase__				:					int					=					dtype.itemsize

		UpperCamelCase__				:					List[str]					=					None

		# Multi-channel array case (only np.dtype("|u1") is allowed)
		if array.shape[2:]:
				UpperCamelCase__				:					Union[str, Any]					=					np.dtype('|u1')
				if dtype_kind not in ["u", "i"]:
						raise TypeError(
						    f'Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.')
				if dtype is not dest_dtype:
						warnings.warn(f'Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'')
    # Exact match
		elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
				UpperCamelCase__				:					Union[str, Any]					=					dtype
		else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
				while dtype_itemsize >= 1:
						UpperCamelCase__				:					List[str]					=					dtype_byteorder + dtype_kind + str(lowerCamelCase_)
						UpperCamelCase__				:					Any					=					np.dtype(lowerCamelCase_)
						if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
								warnings.warn(f'Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'')
								break
						else:
								dtype_itemsize //= 2

		if dest_dtype is None:
				raise TypeError(
				    f'Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}')
		UpperCamelCase__				:					Optional[int]					=					PIL.Image.fromarray(array.astype(lowerCamelCase_))
		return {"path": None, "bytes": image_to_bytes(lowerCamelCase_)}







def       __UpperCAmelCase						(  lowerCamelCase_)							->     List[dict]:
		if config.PIL_AVAILABLE:
				import PIL.Image
		else:
				raise ImportError('To support encoding images, please install \'Pillow\'.')

		if objs:
				UpperCamelCase__				:					str					=					first_non_null_value(lowerCamelCase_)
				if isinstance(lowerCamelCase_     ,      lowerCamelCase_):
						return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
				if isinstance(lowerCamelCase_     ,      np.ndarray):
						UpperCamelCase__				:					List[str]					=					no_op_if_value_is_null(lowerCamelCase_)
						return [obj_to_image_dict_func(lowerCamelCase_) for obj in objs]
				elif isinstance(lowerCamelCase_     ,      PIL.Image.Image):
						UpperCamelCase__				:					Union[str, Any]					=					no_op_if_value_is_null(lowerCamelCase_)
						return [obj_to_image_dict_func(lowerCamelCase_) for obj in objs]
				else:
						return objs
		else:
				return objs












'''simple docstring'''




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







def       __UpperCAmelCase						(  lowerCamelCase_)							->     float:
		return np.dot(lowerCamelCase_     ,      lowerCamelCase_)







class       __lowercase					:

	def __init__(      self   :						Tuple  ,					*,
	    UpperCAmelCase_   :						float = np.inf  ,					UpperCAmelCase_   :						str = "linear"  ,					UpperCAmelCase_   :						float = 0.0  ,					):
			UpperCamelCase__				:					Union[str, Any]					=					regularization
			UpperCamelCase__				:					Optional[int]					=					gamma
			if kernel == "linear":
					UpperCamelCase__				:					List[str]					=					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')
					UpperCamelCase__				:					Union[str, 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:
					UpperCamelCase__				:					Optional[int]					=					F'Unknown kernel: {kernel}'
					raise ValueError(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Any  ,					UpperCAmelCase_   :						ndarray  ,					UpperCAmelCase_   :						ndarray):
			return np.dot(UpperCAmelCase_  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						ndarray  ,					UpperCAmelCase_   :						ndarray):
			return np.exp(-(self.gamma * norm_squared(vectora - vectora)))

	def      __UpperCamelCase							(      self   :						Any  ,					UpperCAmelCase_   :						list[ndarray]  ,					UpperCAmelCase_   :						ndarray):
			UpperCamelCase__				:					Any					=					observations
			UpperCamelCase__				:					Tuple					=					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

			((UpperCamelCase__),	)				:					Optional[Any]					=					np.shape(UpperCAmelCase_)

			def to_minimize(UpperCAmelCase_   :						ndarray) -> float:
					UpperCamelCase__				:					Union[str, Any]					=					0
					((UpperCamelCase__),	)				:					int					=					np.shape(UpperCAmelCase_)
					for i in range(UpperCAmelCase_):
							for j in range(UpperCAmelCase_):
									s += (
									    candidate[i]
									    * candidate[j]
									    * classes[i]
									    * classes[j]
									    * self.kernel(observations[i]  ,					observations[j])
									)
					return 1 / 2 * s - sum(UpperCAmelCase_)

			UpperCamelCase__				:					List[str]					=					LinearConstraint(UpperCAmelCase_  ,					0  ,					0)
			UpperCamelCase__				:					Dict					=					Bounds(0  ,					self.regularization)

			UpperCamelCase__				:					Any					=					minimize(
			    UpperCAmelCase_  ,					np.ones(UpperCAmelCase_)  ,					bounds=UpperCAmelCase_  ,					constraints=[ly_contraint]).x
			UpperCamelCase__				:					str					=					l_star

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





	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						ndarray):
			UpperCamelCase__				:					Optional[int]					=					sum(
			    self.optimum[n]
			    * self.classes[n]
			    * self.kernel(self.observations[n]  ,					UpperCAmelCase_)
			    for n in range(len(self.classes)))
			return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()




