






'''simple docstring'''







from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload





class 						SCREAMING_SNAKE_CASE       :






			def __init__(			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      Collection[float] | None = None       )      ->      None:
							if components is None:
											a_		:	List[str]									=   []
							a_		:	Dict									=   list(__SCREAMING_SNAKE_CASE       )






			def __len__(			self   :      Any       )      ->      int:
							return len(self.__components       )






			def __str__(			self   :      List[Any]       )      ->      str:
							return "(" + ",".join(map(__SCREAMING_SNAKE_CASE	,    self.__components       )       ) + ")"






			def __add__(			self   :      int	,    __SCREAMING_SNAKE_CASE   :      Vector       )      ->      Vector:
							a_		:	Union[str, Any]									=   len(self       )
							if size == len(__SCREAMING_SNAKE_CASE       ):
											a_		:	Optional[Any]									=   [self.__components[i] + other.component(__SCREAMING_SNAKE_CASE       ) for i in range(__SCREAMING_SNAKE_CASE       )]
											return Vector(__SCREAMING_SNAKE_CASE       )
							else:
											raise Exception('''must have the same size'''       )






			def __sub__(			self   :      int	,    __SCREAMING_SNAKE_CASE   :      Vector       )      ->      Vector:
							a_		:	List[str]									=   len(self       )
							if size == len(__SCREAMING_SNAKE_CASE       ):
											a_		:	Tuple									=   [self.__components[i] - other.component(__SCREAMING_SNAKE_CASE       ) for i in range(__SCREAMING_SNAKE_CASE       )]
											return Vector(__SCREAMING_SNAKE_CASE       )
							else:  # error case
											raise Exception('''must have the same size'''       )






			@overload
			def __mul__(			self   :      Any	,    __SCREAMING_SNAKE_CASE   :      float       )      ->      Vector:
							...






			@overload
			def __mul__(			self   :      Any	,    __SCREAMING_SNAKE_CASE   :      Vector       )      ->      float:
							...






			def __mul__(			self   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      float | Vector       )      ->      float | Vector:
							if isinstance(__SCREAMING_SNAKE_CASE	,    (float, int)       ):
											a_		:	Tuple									=   [c * other for c in self.__components]
											return Vector(__SCREAMING_SNAKE_CASE       )
							elif isinstance(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ) and len(self       ) == len(__SCREAMING_SNAKE_CASE       ):
											a_		:	Dict									=   len(self       )
											a_		:	str									=   [self.__components[i] * other.component(__SCREAMING_SNAKE_CASE       ) for i in range(__SCREAMING_SNAKE_CASE       )]
											return sum(__SCREAMING_SNAKE_CASE       )
							else:  # error case
											raise Exception('''invalid operand!'''       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      Vector:
							return Vector(self.__components       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      float:
							if isinstance(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ) and -len(self.__components       ) <= i < len(self.__components       ):
											return self.__components[i]
							else:
											raise Exception('''index out of range'''       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str	,    __SCREAMING_SNAKE_CASE   :      int	,    __SCREAMING_SNAKE_CASE   :      float       )      ->      None:
							assert -len(self.__components       ) <= pos < len(self.__components       )
							a_		:	Optional[int]									=   value






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      float:
							if len(self.__components       ) == 0:
											raise Exception('''Vector is empty'''       )
							a_		:	Dict									=   [c**2 for c in self.__components]
							return math.sqrt(sum(__SCREAMING_SNAKE_CASE       )       )







			def       SCREAMING_SNAKE_CASE   (			self   :      str	,    __SCREAMING_SNAKE_CASE   :      Vector	,    __SCREAMING_SNAKE_CASE   :      bool = False       )      ->      float:
							a_		:	Any									=   self * other
							a_		:	Any									=   self.euclidean_length() * other.euclidean_length()
							if deg:
											return math.degrees(math.acos(num / den       )       )
							else:
											return math.acos(num / den       )





def 		_UpperCAmelCase (				__A				: int   ):
				assert isinstance(__A    ,		__A   )
				return Vector([0] * dimension   )





def 		_UpperCAmelCase (				__A				: int    ,		__A				: int   ):
				assert isinstance(__A    ,		__A   ) and (isinstance(__A    ,		__A   ))
				a_		:	str									=   [0] * dimension
				a_		:	int									=   1
				return Vector(__A   )





def 		_UpperCAmelCase (				__A				: float    ,		__A				: Vector    ,		__A				: Vector   ):
				assert (
				    isinstance(__A    ,		__A   )
				    and isinstance(__A    ,		__A   )
				    and (isinstance(__A    ,		(int, float)   ))
				)
				return x * scalar + y





def 		_UpperCAmelCase (				__A				: int    ,		__A				: int    ,		__A				: int   ):
				random.seed(__A   )
				a_		:	Any									=   [random.randint(__A    ,		__A   ) for _ in range(__A   )]
				return Vector(__A   )





class 						SCREAMING_SNAKE_CASE       :






			def __init__(			self   :      int	,    __SCREAMING_SNAKE_CASE   :      list[list[float]]	,    __SCREAMING_SNAKE_CASE   :      int	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      None:
							a_		:	Union[str, Any]									=   matrix
							a_		:	List[str]									=   w
							a_		:	Optional[Any]									=   h






			def __str__(			self   :      Tuple       )      ->      str:
							a_		:	List[Any]									=   ''''''
							for i in range(self.__height       ):
											ans += "|"
											for j in range(self.__width       ):
															if j < self.__width - 1:
																			ans += str(self.__matrix[i][j]       ) + ","
															else:
																			ans += str(self.__matrix[i][j]       ) + "|\n"
							return ans






			def __add__(			self   :      int	,    __SCREAMING_SNAKE_CASE   :      Matrix       )      ->      Matrix:
							if self.__width == other.width() and self.__height == other.height():
											a_		:	Optional[Any]									=   []
											for i in range(self.__height       ):
															a_		:	Optional[Any]									=   [
															    self.__matrix[i][j] + other.component(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
															    for j in range(self.__width       )
															]
															matrix.append(__SCREAMING_SNAKE_CASE       )
											return Matrix(__SCREAMING_SNAKE_CASE	,    self.__width	,    self.__height       )
							else:
											raise Exception('''matrix must have the same dimension!'''       )






			def __sub__(			self   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      Matrix       )      ->      Matrix:
							if self.__width == other.width() and self.__height == other.height():
											a_		:	Optional[int]									=   []
											for i in range(self.__height       ):
															a_		:	Dict									=   [
															    self.__matrix[i][j] - other.component(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
															    for j in range(self.__width       )
															]
															matrix.append(__SCREAMING_SNAKE_CASE       )
											return Matrix(__SCREAMING_SNAKE_CASE	,    self.__width	,    self.__height       )
							else:
											raise Exception('''matrices must have the same dimension!'''       )






			@overload
			def __mul__(			self   :      int	,    __SCREAMING_SNAKE_CASE   :      float       )      ->      Matrix:
							...






			@overload
			def __mul__(			self   :      Any	,    __SCREAMING_SNAKE_CASE   :      Vector       )      ->      Vector:
							...






			def __mul__(			self   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      float | Vector       )      ->      Vector | Matrix:
							if isinstance(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ):  # matrix-vector
											if len(__SCREAMING_SNAKE_CASE       ) == self.__width:
															a_		:	int									=   zero_vector(self.__height       )
															for i in range(self.__height       ):
																			a_		:	str									=   [
																			    self.__matrix[i][j] * other.component(__SCREAMING_SNAKE_CASE       )
																			    for j in range(self.__width       )
																			]
																			ans.change_component(__SCREAMING_SNAKE_CASE	,    sum(__SCREAMING_SNAKE_CASE       )       )
															return ans
											else:
															raise Exception(
															    '''vector must have the same size as the '''
															    '''number of columns of the matrix!'''       )
							elif isinstance(__SCREAMING_SNAKE_CASE	,    (int, float)       ):  # matrix-scalar
											a_		:	Tuple									=   [
											    [self.__matrix[i][j] * other for j in range(self.__width       )]
											    for i in range(self.__height       )
											]
											return Matrix(__SCREAMING_SNAKE_CASE	,    self.__width	,    self.__height       )
							return None






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      int:
							return self.__height






			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      int:
							return self.__width






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      int	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      float:
							if 0 <= x < self.__height and 0 <= y < self.__width:
											return self.__matrix[x][y]
							else:
											raise Exception('''change_component: indices out of bounds'''       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      int	,    __SCREAMING_SNAKE_CASE   :      int	,    __SCREAMING_SNAKE_CASE   :      float       )      ->      None:
							if 0 <= x < self.__height and 0 <= y < self.__width:
											a_		:	Dict									=   value
							else:
											raise Exception('''change_component: indices out of bounds'''       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      int	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      float:
							if self.__height != self.__width:
											raise Exception('''Matrix is not square'''       )
							a_		:	int									=   self.__matrix[:x] + self.__matrix[x + 1 :]
							for i in range(len(__SCREAMING_SNAKE_CASE       )       ):
											a_		:	Tuple									=   minor[i][:y] + minor[i][y + 1 :]
							return Matrix(__SCREAMING_SNAKE_CASE	,    self.__width - 1	,    self.__height - 1       ).determinant()






			def       SCREAMING_SNAKE_CASE   (			self   :      str	,    __SCREAMING_SNAKE_CASE   :      int	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      float:
							if self.__height != self.__width:
											raise Exception('''Matrix is not square'''       )
							if 0 <= x < self.__height and 0 <= y < self.__width:
											return (-1) ** (x + y) * self.minor(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
							else:
											raise Exception('''Indices out of bounds'''       )







			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      float:
							if self.__height != self.__width:
											raise Exception('''Matrix is not square'''       )
							if self.__height < 1:
											raise Exception('''Matrix has no element'''       )
							elif self.__height == 1:
											return self.__matrix[0][0]
							elif self.__height == 2:
											return (
											    self.__matrix[0][0] * self.__matrix[1][1]
											    - self.__matrix[0][1] * self.__matrix[1][0]
											)
							else:
											a_		:	str									=   [
											    self.__matrix[0][y] * self.cofactor(0	,    __SCREAMING_SNAKE_CASE       ) for y in range(self.__width       )
											]
											return sum(__SCREAMING_SNAKE_CASE       )





def 		_UpperCAmelCase (				__A				: int   ):
				a_		:	list[list[float]]									=   [[0] * n for _ in range(__A   )]
				return Matrix(__A    ,		__A    ,		__A   )





def 		_UpperCAmelCase (				__A				: int    ,		__A				: int    ,		__A				: int    ,		__A				: int   ):
				random.seed(__A   )
				a_		:	list[list[float]]									=   [
				    [random.randint(__A    ,		__A   ) for _ in range(__A   )] for _ in range(__A   )
				]
				return Matrix(__A    ,		__A    ,		__A   )












'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowerCAmelCase									=   {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   ['DeiTFeatureExtractor']
		__lowerCAmelCase									=   ['DeiTImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   [
		    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'DeiTForImageClassification',
		    'DeiTForImageClassificationWithTeacher',
		    'DeiTForMaskedImageModeling',
		    'DeiTModel',
		    'DeiTPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   [
		    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFDeiTForImageClassification',
		    'TFDeiTForImageClassificationWithTeacher',
		    'TFDeiTForMaskedImageModeling',
		    'TFDeiTModel',
		    'TFDeiTPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

		__lowerCAmelCase									=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




