





import mpmath  # for roots of unity
import numpy as np



class  __lowerCAmelCase	:






			def __init__(self ,							__magic_name__=None ,							__magic_name__=None   )					->       Any:


							'''simple docstring'''



							snake_case_						:   List[str]           =  list(poly_a or [0]   )[:]
							snake_case_						:   Optional[Any]           =  list(poly_b or [0]   )[:]

							# Remove leading zero coefficients
							while self.polyA[-1] == 0:
											self.polyA.pop()
							snake_case_						:   int           =  len(self.polyA   )

							while self.polyB[-1] == 0:
											self.polyB.pop()
							snake_case_						:   str           =  len(self.polyB   )

							# Add 0 to make lengths equal a power of 2
							snake_case_						:   List[str]           =  int(
							    2 ** np.ceil(np.loga(len(self.polyA   ) + len(self.polyB   ) - 1   )   )   )

							while len(self.polyA   ) < self.c_max_length:
											self.polyA.append(0   )
							while len(self.polyB   ) < self.c_max_length:
											self.polyB.append(0   )
							# A complex root used for the fourier transform
							snake_case_						:   List[Any]           =  complex(mpmath.root(x=1 ,							n=self.c_max_length ,							k=1   )   )

							# The product
							snake_case_						:   Any           =  self.__multiply()






			def       lowerCamelCase  (self ,							__magic_name__   )					->       int:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  [[x] for x in self.polyA] if which == '''A''' else [[x] for x in self.polyB]
							# Corner case
							if len(__lowerCAmelCase   ) <= 1:
											return dft[0]
							#
							snake_case_						:   Optional[int]           =  self.c_max_length // 2
							while next_ncol > 0:
											snake_case_						:   Optional[int]           =  [[] for i in range(__lowerCAmelCase   )]
											snake_case_						:   str           =  self.root**next_ncol

											# First half of next step
											snake_case_						:   Union[str, Any]           =  1
											for j in range(self.c_max_length // (next_ncol * 2)   ):
															for i in range(__lowerCAmelCase   ):
																			new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]   )
															current_root *= root
											# Second half of next step
											snake_case_						:   Union[str, Any]           =  1
											for j in range(self.c_max_length // (next_ncol * 2)   ):
															for i in range(__lowerCAmelCase   ):
																			new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]   )
															current_root *= root
											# Update
											snake_case_						:   Optional[Any]           =  new_dft
											snake_case_						:   List[Any]           =  next_ncol // 2
							return dft[0]






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Tuple           =  self.__dft('''A'''   )
							snake_case_						:   Optional[Any]           =  self.__dft('''B'''   )
							snake_case_						:   Dict           =  [[dft_a[i] * dft_b[i] for i in range(self.c_max_length   )]]
							del dft_a
							del dft_b

							# Corner Case
							if len(inverce_c[0]   ) <= 1:
											return inverce_c[0]
							# Inverse DFT
							snake_case_						:   str           =  2
							while next_ncol <= self.c_max_length:
											snake_case_						:   Tuple           =  [[] for i in range(__lowerCAmelCase   )]
											snake_case_						:   int           =  self.root ** (next_ncol // 2)
											snake_case_						:   Optional[Any]           =  1
											# First half of next step
											for j in range(self.c_max_length // next_ncol   ):
															for i in range(next_ncol // 2   ):
																			# Even positions
																			new_inverse_c[i].append(
																			    (
																			        inverce_c[i][j]
																			        + inverce_c[i][j + self.c_max_length // next_ncol]
																			    )
																			    / 2   )
																			# Odd positions
																			new_inverse_c[i + next_ncol // 2].append(
																			    (
																			        inverce_c[i][j]
																			        - inverce_c[i][j + self.c_max_length // next_ncol]
																			    )
																			    / (2 * current_root)   )
															current_root *= root
											# Update
											snake_case_						:   Union[str, Any]           =  new_inverse_c
											next_ncol *= 2
							# Unpack
							snake_case_						:   List[Any]           =  [round(x[0].real ,							8   ) + round(x[0].imag ,							8   ) * 1J for x in inverce_c]

							# Remove leading 0's
							while inverce_c[-1] == 0:
											inverce_c.pop()
							return inverce_c






			def __str__(self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  '''A = ''' + ''' + '''.join(
							    F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyA[: self.len_A]   )   )
							snake_case_						:   Union[str, Any]           =  '''B = ''' + ''' + '''.join(
							    F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyB[: self.len_B]   )   )
							snake_case_						:   List[Any]           =  '''A*B = ''' + ''' + '''.join(
							    F'''{coef}*x^{i}''' for coef, i in enumerate(self.product   )   )

							return F'''{a}\n{b}\n{c}'''


# Unit tests
if __name__ == "__main__":
	import doctest

	doctest.testmod()







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  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					Optional[Any]	      =				['''image_processor''', '''tokenizer''']
			lowerCamelCase_					:					List[Any]	      =				'''BlipImageProcessor'''
			lowerCamelCase_					:					Union[str, Any]	      =				'''AutoTokenizer'''






			def __init__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



							super().__init__(__magic_name__ ,							__magic_name__   )

							# add QFormer tokenizer
							snake_case_						:   Optional[Any]           =  qformer_tokenizer






			def __call__(self ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = True ,							__magic_name__ = False ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = 0 ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = False ,							__magic_name__ = True ,							__magic_name__ = None ,							**__magic_name__ ,							)					->       BatchFeature:


							'''simple docstring'''



							if images is None and text is None:
											raise ValueError('''You have to specify at least images or text.'''   )

							snake_case_						:   Tuple           =  BatchFeature()

							if text is not None:
											snake_case_						:   Tuple           =  self.tokenizer(
											    text=__magic_name__ ,							add_special_tokens=__magic_name__ ,							padding=__magic_name__ ,							truncation=__magic_name__ ,							max_length=__magic_name__ ,							stride=__magic_name__ ,							pad_to_multiple_of=__magic_name__ ,							return_attention_mask=__magic_name__ ,							return_overflowing_tokens=__magic_name__ ,							return_special_tokens_mask=__magic_name__ ,							return_offsets_mapping=__magic_name__ ,							return_token_type_ids=__magic_name__ ,							return_length=__magic_name__ ,							verbose=__magic_name__ ,							return_tensors=__magic_name__ ,							**__magic_name__ ,							)
											encoding.update(__magic_name__   )
											snake_case_						:   Optional[Any]           =  self.qformer_tokenizer(
											    text=__magic_name__ ,							add_special_tokens=__magic_name__ ,							padding=__magic_name__ ,							truncation=__magic_name__ ,							max_length=__magic_name__ ,							stride=__magic_name__ ,							pad_to_multiple_of=__magic_name__ ,							return_attention_mask=__magic_name__ ,							return_overflowing_tokens=__magic_name__ ,							return_special_tokens_mask=__magic_name__ ,							return_offsets_mapping=__magic_name__ ,							return_token_type_ids=__magic_name__ ,							return_length=__magic_name__ ,							verbose=__magic_name__ ,							return_tensors=__magic_name__ ,							**__magic_name__ ,							)
											snake_case_						:   Optional[int]           =  qformer_text_encoding.pop('''input_ids'''   )
											snake_case_						:   Tuple           =  qformer_text_encoding.pop('''attention_mask'''   )

							if images is not None:
											snake_case_						:   Any           =  self.image_processor(__magic_name__ ,							return_tensors=__magic_name__   )
											encoding.update(__magic_name__   )

							return encoding






			def       lowerCamelCase  (self ,							*__magic_name__ ,							**__magic_name__   )					->       List[Any]:


							'''simple docstring'''



							return self.tokenizer.batch_decode(*__magic_name__ ,							**__magic_name__   )






			def       lowerCamelCase  (self ,							*__magic_name__ ,							**__magic_name__   )					->       Union[str, Any]:


							'''simple docstring'''



							return self.tokenizer.decode(*__magic_name__ ,							**__magic_name__   )






			@property
			# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  self.tokenizer.model_input_names
							snake_case_						:   List[Any]           =  self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							**__magic_name__   )					->       List[Any]:


							'''simple docstring'''



							if os.path.isfile(__magic_name__   ):
											raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''   )
							os.makedirs(__magic_name__ ,							exist_ok=__magic_name__   )
							snake_case_						:   Any           =  os.path.join(__magic_name__ ,							'''qformer_tokenizer'''   )
							self.qformer_tokenizer.save_pretrained(__magic_name__   )
							return super().save_pretrained(__magic_name__ ,							**__magic_name__   )






			@classmethod
			def       lowerCamelCase  (cls ,							__magic_name__ ,							**__magic_name__   )					->       Union[str, Any]:


							'''simple docstring'''



							snake_case_						:   Any           =  AutoTokenizer.from_pretrained(__magic_name__ ,							subfolder='''qformer_tokenizer'''   )
							snake_case_						:   str           =  cls._get_arguments_from_pretrained(__magic_name__ ,							**__magic_name__   )
							args.append(__magic_name__   )
							return cls(*__magic_name__   )
