

import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      ["image_processor", "tokenizer"]
   lowerCamelCase__				=      "BlipImageProcessor"
   lowerCamelCase__				=      "AutoTokenizer"





   def __init__(       self					:			Tuple     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			Any			):
       super().__init__(__lowerCamelCase     , __lowerCamelCase			)

       # add QFormer tokenizer
       SCREAMING_SNAKE_CASE										=   qformer_tokenizer





   def __call__(       self					:			List[str]     , __lowerCamelCase					:			ImageInput = None     , __lowerCamelCase					:			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     , __lowerCamelCase					:			bool = True     , __lowerCamelCase					:			Union[bool, str, PaddingStrategy] = False     , __lowerCamelCase					:			Union[bool, str, TruncationStrategy] = None     , __lowerCamelCase					:			Optional[int] = None     , __lowerCamelCase					:			int = 0     , __lowerCamelCase					:			Optional[int] = None     , __lowerCamelCase					:			Optional[bool] = None     , __lowerCamelCase					:			bool = False     , __lowerCamelCase					:			bool = False     , __lowerCamelCase					:			bool = False     , __lowerCamelCase					:			bool = False     , __lowerCamelCase					:			bool = False     , __lowerCamelCase					:			bool = True     , __lowerCamelCase					:			Optional[Union[str, TensorType]] = None     , **__lowerCamelCase					:			Dict     , ):
       if images is None and text is None:
           raise ValueError("You have to specify at least images or text."			)

       SCREAMING_SNAKE_CASE										=   BatchFeature()

       if text is not None:
           SCREAMING_SNAKE_CASE										=   self.tokenizer(
               text=__lowerCamelCase     , add_special_tokens=__lowerCamelCase     , padding=__lowerCamelCase     , truncation=__lowerCamelCase     , max_length=__lowerCamelCase     , stride=__lowerCamelCase     , pad_to_multiple_of=__lowerCamelCase     , return_attention_mask=__lowerCamelCase     , return_overflowing_tokens=__lowerCamelCase     , return_special_tokens_mask=__lowerCamelCase     , return_offsets_mapping=__lowerCamelCase     , return_token_type_ids=__lowerCamelCase     , return_length=__lowerCamelCase     , verbose=__lowerCamelCase     , return_tensors=__lowerCamelCase     , **__lowerCamelCase     , )
           encoding.update(__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   self.qformer_tokenizer(
               text=__lowerCamelCase     , add_special_tokens=__lowerCamelCase     , padding=__lowerCamelCase     , truncation=__lowerCamelCase     , max_length=__lowerCamelCase     , stride=__lowerCamelCase     , pad_to_multiple_of=__lowerCamelCase     , return_attention_mask=__lowerCamelCase     , return_overflowing_tokens=__lowerCamelCase     , return_special_tokens_mask=__lowerCamelCase     , return_offsets_mapping=__lowerCamelCase     , return_token_type_ids=__lowerCamelCase     , return_length=__lowerCamelCase     , verbose=__lowerCamelCase     , return_tensors=__lowerCamelCase     , **__lowerCamelCase     , )
           SCREAMING_SNAKE_CASE										=   qformer_text_encoding.pop("input_ids"			)
           SCREAMING_SNAKE_CASE										=   qformer_text_encoding.pop("attention_mask"			)

       if images is not None:
           SCREAMING_SNAKE_CASE										=   self.image_processor(__lowerCamelCase     , return_tensors=__lowerCamelCase			)
           encoding.update(__lowerCamelCase			)

       return encoding





   def      _snake_case		(       self					:			List[str]     , *__lowerCamelCase					:			Union[str, Any]     , **__lowerCamelCase					:			Union[str, Any]			):
       return self.tokenizer.batch_decode(*__lowerCamelCase     , **__lowerCamelCase			)





   def      _snake_case		(       self					:			Union[str, Any]     , *__lowerCamelCase					:			Union[str, Any]     , **__lowerCamelCase					:			Any			):
       return self.tokenizer.decode(*__lowerCamelCase     , **__lowerCamelCase			)





   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   self.tokenizer.model_input_names
       SCREAMING_SNAKE_CASE										=   self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)





   def      _snake_case		(       self					:			str     , __lowerCamelCase					:			Union[str, Any]     , **__lowerCamelCase					:			Optional[int]			):
       if os.path.isfile(__lowerCamelCase			):
           raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file"			)
       os.makedirs(__lowerCamelCase     , exist_ok=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   os.path.join(__lowerCamelCase     , "qformer_tokenizer"			)
       self.qformer_tokenizer.save_pretrained(__lowerCamelCase			)
       return super().save_pretrained(__lowerCamelCase     , **__lowerCamelCase			)





   @classmethod
   def      _snake_case		(       cls					:			Union[str, Any]     , __lowerCamelCase					:			Tuple     , **__lowerCamelCase					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained(__lowerCamelCase     , subfolder="qformer_tokenizer"			)
       SCREAMING_SNAKE_CASE										=   cls._get_arguments_from_pretrained(__lowerCamelCase     , **__lowerCamelCase			)
       args.append(__lowerCamelCase			)
       return cls(*__lowerCamelCase			)





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


def 					_a							(					lowerCAmelCase )->	float:
		return np.dot(lowerCAmelCase    ,						lowerCAmelCase )



class    lowercase_		:



						def __init__(			self	:   int					,							*,
						    snake_case__	:   float = np.inf					,							snake_case__	:   str = "linear"					,							snake_case__	:   float = 0.0					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							regularization
								SCREAMING_SNAKE_CASE_					      =							gamma
								if kernel == "linear":
										SCREAMING_SNAKE_CASE_					      =							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'  )
										SCREAMING_SNAKE_CASE_					      =							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:
										SCREAMING_SNAKE_CASE_					      =							f'''Unknown kernel: {kernel}'''
										raise ValueError(snake_case__  )



						def 	__a					(			self	:   Tuple					,							snake_case__	:   ndarray					,							snake_case__	:   ndarray  ):

								"""simple docstring"""

								return np.dot(snake_case__					,							snake_case__  )



						def 	__a					(			self	:   int					,							snake_case__	:   ndarray					,							snake_case__	:   ndarray  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   int					,							snake_case__	:   list[ndarray]					,							snake_case__	:   ndarray  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							observations
								SCREAMING_SNAKE_CASE_					      =							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

								((SCREAMING_SNAKE_CASE_)      ,				)					      =							np.shape(snake_case__  )

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

								SCREAMING_SNAKE_CASE_					      =							LinearConstraint(snake_case__					,							0					,							0  )
								SCREAMING_SNAKE_CASE_					      =							Bounds(0					,							self.regularization  )

								SCREAMING_SNAKE_CASE_					      =							minimize(
								    snake_case__					,							np.ones(snake_case__  )					,							bounds=snake_case__					,							constraints=[ly_contraint]  ).x
								SCREAMING_SNAKE_CASE_					      =							l_star

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






						def 	__a					(			self	:   Any					,							snake_case__	:   ndarray  ):

								"""simple docstring"""

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


if __name__ == "__main__":
						import doctest

						doctest.testmod()