



from __future__ import annotations

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



class 	_lowercase							:





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_ = None		):
						"""simple docstring"""

						if components is None:
											a					     =   []
						a					     =   list(__a		)




	def __len__(self		):
						"""simple docstring"""

						return len(self.__components		)




	def __str__(self		):
						"""simple docstring"""

						return "(" + ",".join(map(__a		,       self.__components		)		) + ")"




	def __add__(self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   len(self		)
						if size == len(__a		):
											a					     =   [self.__components[i] + other.component(__a		) for i in range(__a		)]
											return Vector(__a		)
						else:
											raise Exception("must have the same size"		)




	def __sub__(self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   len(self		)
						if size == len(__a		):
											a					     =   [self.__components[i] - other.component(__a		) for i in range(__a		)]
											return Vector(__a		)
						else:  # error case
											raise Exception("must have the same size"		)




	@overload
	def __mul__(self		,       lowerCamelCase_		):
						"""simple docstring"""

						...




	@overload
	def __mul__(self		,       lowerCamelCase_		):
						"""simple docstring"""

						...




	def __mul__(self		,       lowerCamelCase_		):
						"""simple docstring"""

						if isinstance(__a		,       (float, int)		):
											a					     =   [c * other for c in self.__components]
											return Vector(__a		)
						elif isinstance(__a		,       __a		) and len(self		) == len(__a		):
											a					     =   len(self		)
											a					     =   [self.__components[i] * other.component(__a		) for i in range(__a		)]
											return sum(__a		)
						else:  # error case
											raise Exception("invalid operand!"		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return Vector(self.__components		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						if isinstance(__a		,       __a		) and -len(self.__components		) <= i < len(self.__components		):
											return self.__components[i]
						else:
											raise Exception("index out of range"		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						assert -len(self.__components		) <= pos < len(self.__components		)
						a					     =   value




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						if len(self.__components		) == 0:
											raise Exception("Vector is empty"		)
						a					     =   [c**2 for c in self.__components]
						return math.sqrt(sum(__a		)		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = False		):
						"""simple docstring"""

						a					     =   self * other
						a					     =   self.euclidean_length() * other.euclidean_length()
						if deg:
											return math.degrees(math.acos(num / den		)		)
						else:
											return math.acos(num / den		)






def     a(      A       :    int       )		->       Vector:
					"""simple docstring"""





					assert isinstance(A ,     A       )
					return Vector([0] * dimension       )






def     a(      A       :    int ,     A       :    int       )		->       Vector:
					"""simple docstring"""





					assert isinstance(A ,     A       ) and (isinstance(A ,     A       ))
					a					     =   [0] * dimension
					a					     =   1
					return Vector(A       )






def     a(      A       :    float ,     A       :    Vector ,     A       :    Vector       )		->       Vector:
					"""simple docstring"""





					assert (
					    isinstance(A ,     A       )
					    and isinstance(A ,     A       )
					    and (isinstance(A ,     (int, float)       ))
					)
					return x * scalar + y






def     a(      A       :    int ,     A       :    int ,     A       :    int       )		->       Vector:
					"""simple docstring"""





					random.seed(A       )
					a					     =   [random.randint(A ,     A       ) for _ in range(A       )]
					return Vector(A       )



class 	_lowercase							:





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   matrix
						a					     =   w
						a					     =   h




	def __str__(self		):
						"""simple docstring"""

						a					     =   ""
						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		,       lowerCamelCase_		):
						"""simple docstring"""

						if self.__width == other.width() and self.__height == other.height():
											a					     =   []
											for i in range(self.__height		):
																a					     =   [
																    self.__matrix[i][j] + other.component(__a		,       __a		)
																    for j in range(self.__width		)
																]
																matrix.append(__a		)
											return Matrix(__a		,       self.__width		,       self.__height		)
						else:
											raise Exception("matrix must have the same dimension!"		)




	def __sub__(self		,       lowerCamelCase_		):
						"""simple docstring"""

						if self.__width == other.width() and self.__height == other.height():
											a					     =   []
											for i in range(self.__height		):
																a					     =   [
																    self.__matrix[i][j] - other.component(__a		,       __a		)
																    for j in range(self.__width		)
																]
																matrix.append(__a		)
											return Matrix(__a		,       self.__width		,       self.__height		)
						else:
											raise Exception("matrices must have the same dimension!"		)




	@overload
	def __mul__(self		,       lowerCamelCase_		):
						"""simple docstring"""

						...




	@overload
	def __mul__(self		,       lowerCamelCase_		):
						"""simple docstring"""

						...




	def __mul__(self		,       lowerCamelCase_		):
						"""simple docstring"""

						if isinstance(__a		,       __a		):  # matrix-vector
											if len(__a		) == self.__width:
																a					     =   zero_vector(self.__height		)
																for i in range(self.__height		):
																					a					     =   [
																					    self.__matrix[i][j] * other.component(__a		)
																					    for j in range(self.__width		)
																					]
																					ans.change_component(__a		,       sum(__a		)		)
																return ans
											else:
																raise Exception(
																    "vector must have the same size as the "
																    "number of columns of the matrix!"		)
						elif isinstance(__a		,       (int, float)		):  # matrix-scalar
											a					     =   [
											    [self.__matrix[i][j] * other for j in range(self.__width		)]
											    for i in range(self.__height		)
											]
											return Matrix(__a		,       self.__width		,       self.__height		)
						return None




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return self.__height




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return self.__width




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						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      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						if 0 <= x < self.__height and 0 <= y < self.__width:
											a					     =   value
						else:
											raise Exception("change_component: indices out of bounds"		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						if self.__height != self.__width:
											raise Exception("Matrix is not square"		)
						a					     =   self.__matrix[:x] + self.__matrix[x + 1 :]
						for i in range(len(__a		)		):
											a					     =   minor[i][:y] + minor[i][y + 1 :]
						return Matrix(__a		,       self.__width - 1		,       self.__height - 1		).determinant()




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						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(__a		,       __a		)
						else:
											raise Exception("Indices out of bounds"		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						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					     =   [
											    self.__matrix[0][y] * self.cofactor(0		,       __a		) for y in range(self.__width		)
											]
											return sum(__a		)






def     a(      A       :    int       )		->       Matrix:
					"""simple docstring"""





					a					     =   [[0] * n for _ in range(A       )]
					return Matrix(A ,     A ,     A       )







def     a(      A       :    int ,     A       :    int ,     A       :    int ,     A       :    int       )		->       Matrix:
					"""simple docstring"""





					random.seed(A       )
					a					     =   [
					    [random.randint(A ,     A       ) for _ in range(A       )] for _ in range(A       )
					]
					return Matrix(A ,     A ,     A       )







'''simple docstring'''







import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            =['image_processor', 'tokenizer']
				__a            ='OwlViTImageProcessor'
				__a            =('CLIPTokenizer', 'CLIPTokenizerFast')




				def __init__(  self     :					List[Any]					,				__a     :					str=None					,				__a     :					List[str]=None					,				**__a     :					List[Any]					):
								_a								= None
								if "feature_extractor" in kwargs:
												warnings.warn(
												    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
												    " instead."					,				__a					,				)
												_a								= kwargs.pop("feature_extractor"					)

								_a								= image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
												raise ValueError("You need to specify an `image_processor`."					)
								if tokenizer is None:
												raise ValueError("You need to specify a `tokenizer`."					)

								super().__init__(__a					,				__a					)




				def __call__(  self     :					Union[str, Any]					,				__a     :					Any=None					,				__a     :					List[str]=None					,				__a     :					int=None					,				__a     :					Optional[int]="max_length"					,				__a     :					List[str]="np"					,				**__a     :					Any					):

								if text is None and query_images is None and images is None:
												raise ValueError(
												    "You have to specify at least one text or query image or image. All three cannot be none."					)

								if text is not None:
												if isinstance(__a					,				__a					) or (isinstance(__a					,				__a					) and not isinstance(text[0]					,				__a					)):
																_a								= [self.tokenizer(__a					,				padding=__a					,				return_tensors=__a					,				**__a					)]

												elif isinstance(__a					,				__a					) and isinstance(text[0]					,				__a					):
																_a								= []

																# Maximum number of queries across batch
																_a								= max([len(__a					) for t in text]					)

																# Pad all batch samples to max number of text queries
																for t in text:
																				if len(__a					) != max_num_queries:
																								_a								= t + [" "] * (max_num_queries - len(__a					))

																				_a								= self.tokenizer(__a					,				padding=__a					,				return_tensors=__a					,				**__a					)
																				encodings.append(__a					)
												else:
																raise TypeError("Input text should be a string, a list of strings or a nested list of strings"					)

												if return_tensors == "np":
																_a								= np.concatenate([encoding["input_ids"] for encoding in encodings]					,				axis=0					)
																_a								= np.concatenate([encoding["attention_mask"] for encoding in encodings]					,				axis=0					)

												elif return_tensors == "jax" and is_flax_available():
																import jax.numpy as jnp

																_a								= jnp.concatenate([encoding["input_ids"] for encoding in encodings]					,				axis=0					)
																_a								= jnp.concatenate([encoding["attention_mask"] for encoding in encodings]					,				axis=0					)

												elif return_tensors == "pt" and is_torch_available():
																import torch

																_a								= torch.cat([encoding["input_ids"] for encoding in encodings]					,				dim=0					)
																_a								= torch.cat([encoding["attention_mask"] for encoding in encodings]					,				dim=0					)

												elif return_tensors == "tf" and is_tf_available():
																import tensorflow as tf

																_a								= tf.stack([encoding["input_ids"] for encoding in encodings]					,				axis=0					)
																_a								= tf.stack([encoding["attention_mask"] for encoding in encodings]					,				axis=0					)

												else:
																raise ValueError("Target return tensor type could not be returned"					)

												_a								= BatchEncoding()
												_a								= input_ids
												_a								= attention_mask

								if query_images is not None:
												_a								= BatchEncoding()
												_a								= self.image_processor(
												    __a					,				return_tensors=__a					,				**__a					).pixel_values
												_a								= query_pixel_values

								if images is not None:
												_a								= self.image_processor(__a					,				return_tensors=__a					,				**__a					)

								if text is not None and images is not None:
												_a								= image_features.pixel_values
												return encoding
								elif query_images is not None and images is not None:
												_a								= image_features.pixel_values
												return encoding
								elif text is not None or query_images is not None:
												return encoding
								else:
												return BatchEncoding(data=dict(**__a					)					,				tensor_type=__a					)




				def   UpperCamelCase__   (  self     :					List[str]					,				*__a     :					Union[str, Any]					,				**__a     :					int					):
								return self.image_processor.post_process(*__a					,				**__a					)




				def   UpperCamelCase__   (  self     :					Optional[int]					,				*__a     :					Optional[Any]					,				**__a     :					List[str]					):
								return self.image_processor.post_process_object_detection(*__a					,				**__a					)




				def   UpperCamelCase__   (  self     :					Optional[Any]					,				*__a     :					Dict					,				**__a     :					Union[str, Any]					):
								return self.image_processor.post_process_image_guided_detection(*__a					,				**__a					)




				def   UpperCamelCase__   (  self     :					str					,				*__a     :					Tuple					,				**__a     :					Tuple					):
								return self.tokenizer.batch_decode(*__a					,				**__a					)




				def   UpperCamelCase__   (  self     :					List[str]					,				*__a     :					List[Any]					,				**__a     :					Optional[int]					):
								return self.tokenizer.decode(*__a					,				**__a					)




				@property
				def   UpperCamelCase__   (  self     :					List[str]					):
								warnings.warn(
								    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."					,				__a					,				)
								return self.image_processor_class




				@property
				def   UpperCamelCase__   (  self     :					str					):
								warnings.warn(
								    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."					,				__a					,				)
								return self.image_processor
