



"""simple docstring"""







import mpmath  # for roots of unity
import numpy as np






class      UpperCAmelCase__		:



		def __init__( self  :    Dict      ,							snake_case  :    Optional[int]=None      ,							snake_case  :    Union[str, Any]=None				)			->						Optional[Any]:






									'''simple docstring'''

									A         =					list(poly_a or [0]				)[:]
									A         =					list(poly_b or [0]				)[:]

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

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

									# Add 0 to make lengths equal a power of 2
									A         =					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
									A         =					complex(mpmath.root(x=1      ,							n=self.c_max_length      ,							k=1				)				)

									# The product
									A         =					self.__multiply()



		def 					A_		( self  :    int      ,							snake_case  :    Dict				)			->						List[str]:






									'''simple docstring'''

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

																# First half of next step
																A         =					1
																for j in range(self.c_max_length // (next_ncol * 2)				):
																							for i in range(snake_case				):
																														new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]				)
																							current_root *= root
																# Second half of next step
																A         =					1
																for j in range(self.c_max_length // (next_ncol * 2)				):
																							for i in range(snake_case				):
																														new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]				)
																							current_root *= root
																# Update
																A         =					new_dft
																A         =					next_ncol // 2
									return dft[0]



		def 					A_		( self  :    Optional[int]				)			->						str:






									'''simple docstring'''

									A         =					self.__dft('A'				)
									A         =					self.__dft('B'				)
									A         =					[[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
									A         =					2
									while next_ncol <= self.c_max_length:
																A         =					[[] for i in range(snake_case				)]
																A         =					self.root ** (next_ncol // 2)
																A         =					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
																A         =					new_inverse_c
																next_ncol *= 2
									# Unpack
									A         =					[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  :    Optional[int]				)			->						Tuple:






									'''simple docstring'''

									A         =					'A = ' + ' + '.join(
									    f"""{coef}*x^{i}""" for coef, i in enumerate(self.polyA[: self.len_A]				)				)
									A         =					'B = ' + ' + '.join(
									    f"""{coef}*x^{i}""" for coef, i in enumerate(self.polyB[: self.len_B]				)				)
									A         =					'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()








"""simple docstring"""







import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import (
							    AutoProcessor,
							    BertTokenizerFast,
							    BlipImageProcessor,
							    GPTaTokenizer,
							    InstructBlipProcessor,
							    PreTrainedTokenizerFast,
							)






@require_vision
class      UpperCAmelCase__		(      unittest.TestCase     ):



		def 					A_		( self  :    Dict				)			->						Dict:






									'''simple docstring'''

									A         =					tempfile.mkdtemp()

									A         =					BlipImageProcessor()
									A         =					GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'				)
									A         =					BertTokenizerFast.from_pretrained('hf-internal-testing/tiny-random-bert'				)

									A         =					InstructBlipProcessor(snake_case      ,							snake_case      ,							snake_case				)

									processor.save_pretrained(self.tmpdirname				)



		def 					A_		( self  :    List[str]      ,							**snake_case  :    str				)			->						Dict:






									'''simple docstring'''

									return AutoProcessor.from_pretrained(self.tmpdirname      ,							**snake_case				).tokenizer



		def 					A_		( self  :    int      ,							**snake_case  :    Optional[Any]				)			->						Any:






									'''simple docstring'''

									return AutoProcessor.from_pretrained(self.tmpdirname      ,							**snake_case				).image_processor



		def 					A_		( self  :    Any      ,							**snake_case  :    Union[str, Any]				)			->						Any:






									'''simple docstring'''

									return AutoProcessor.from_pretrained(self.tmpdirname      ,							**snake_case				).qformer_tokenizer



		def 					A_		( self  :    int				)			->						Union[str, Any]:






									'''simple docstring'''

									shutil.rmtree(self.tmpdirname				)



		def 					A_		( self  :    List[Any]				)			->						Tuple:






									'''simple docstring'''

									A         =					[np.random.randint(255      ,							size=(3, 30, 400)      ,							dtype=np.uinta				)]

									A         =					[Image.fromarray(np.moveaxis(snake_case      ,							0      ,							-1				)				) for x in image_inputs]

									return image_inputs



		def 					A_		( self  :    List[Any]				)			->						Optional[int]:






									'''simple docstring'''

									A         =					InstructBlipProcessor(
									    tokenizer=self.get_tokenizer()      ,							image_processor=self.get_image_processor()      ,							qformer_tokenizer=self.get_qformer_tokenizer()      ,							)
									processor.save_pretrained(self.tmpdirname				)

									A         =					self.get_tokenizer(bos_token='(BOS)'      ,							eos_token='(EOS)'				)
									A         =					self.get_image_processor(do_normalize=snake_case      ,							padding_value=1.0				)

									A         =					InstructBlipProcessor.from_pretrained(
									    self.tmpdirname      ,							bos_token='(BOS)'      ,							eos_token='(EOS)'      ,							do_normalize=snake_case      ,							padding_value=1.0				)

									self.assertEqual(processor.tokenizer.get_vocab()      ,							tokenizer_add_kwargs.get_vocab()				)
									self.assertIsInstance(processor.tokenizer      ,							snake_case				)

									self.assertEqual(processor.image_processor.to_json_string()      ,							image_processor_add_kwargs.to_json_string()				)
									self.assertIsInstance(processor.image_processor      ,							snake_case				)
									self.assertIsInstance(processor.qformer_tokenizer      ,							snake_case				)



		def 					A_		( self  :    Optional[Any]				)			->						Dict:






									'''simple docstring'''

									A         =					self.get_image_processor()
									A         =					self.get_tokenizer()
									A         =					self.get_qformer_tokenizer()

									A         =					InstructBlipProcessor(
									    tokenizer=snake_case      ,							image_processor=snake_case      ,							qformer_tokenizer=snake_case				)

									A         =					self.prepare_image_inputs()

									A         =					image_processor(snake_case      ,							return_tensors='np'				)
									A         =					processor(images=snake_case      ,							return_tensors='np'				)

									for key in input_feat_extract.keys():
																self.assertAlmostEqual(input_feat_extract[key].sum()      ,							input_processor[key].sum()      ,							delta=1E-2				)



		def 					A_		( self  :    Tuple				)			->						List[str]:






									'''simple docstring'''

									A         =					self.get_image_processor()
									A         =					self.get_tokenizer()
									A         =					self.get_qformer_tokenizer()

									A         =					InstructBlipProcessor(
									    tokenizer=snake_case      ,							image_processor=snake_case      ,							qformer_tokenizer=snake_case				)

									A         =					'lower newer'

									A         =					processor(text=snake_case				)

									A         =					tokenizer(snake_case      ,							return_token_type_ids=snake_case				)
									A         =					qformer_tokenizer(snake_case      ,							return_token_type_ids=snake_case				)

									for key in encoded_tokens.keys():
																self.assertListEqual(encoded_tokens[key]      ,							encoded_processor[key]				)

									for key in encoded_tokens_qformer.keys():
																self.assertListEqual(encoded_tokens_qformer[key]      ,							encoded_processor['qformer_' + key]				)



		def 					A_		( self  :    List[Any]				)			->						Optional[int]:






									'''simple docstring'''

									A         =					self.get_image_processor()
									A         =					self.get_tokenizer()
									A         =					self.get_qformer_tokenizer()

									A         =					InstructBlipProcessor(
									    tokenizer=snake_case      ,							image_processor=snake_case      ,							qformer_tokenizer=snake_case				)

									A         =					'lower newer'
									A         =					self.prepare_image_inputs()

									A         =					processor(text=snake_case      ,							images=snake_case				)

									self.assertListEqual(
									    list(inputs.keys()				)      ,							['input_ids', 'attention_mask', 'qformer_input_ids', 'qformer_attention_mask', 'pixel_values']      ,							)

									# test if it raises when no input is passed
									with pytest.raises(snake_case				):
																processor()



		def 					A_		( self  :    Optional[Any]				)			->						List[str]:






									'''simple docstring'''

									A         =					self.get_image_processor()
									A         =					self.get_tokenizer()
									A         =					self.get_qformer_tokenizer()

									A         =					InstructBlipProcessor(
									    tokenizer=snake_case      ,							image_processor=snake_case      ,							qformer_tokenizer=snake_case				)

									A         =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									A         =					processor.batch_decode(snake_case				)
									A         =					tokenizer.batch_decode(snake_case				)

									self.assertListEqual(snake_case      ,							snake_case				)



		def 					A_		( self  :    Tuple				)			->						List[Any]:






									'''simple docstring'''

									A         =					self.get_image_processor()
									A         =					self.get_tokenizer()
									A         =					self.get_qformer_tokenizer()

									A         =					InstructBlipProcessor(
									    tokenizer=snake_case      ,							image_processor=snake_case      ,							qformer_tokenizer=snake_case				)

									A         =					'lower newer'
									A         =					self.prepare_image_inputs()

									A         =					processor(text=snake_case      ,							images=snake_case				)

									self.assertListEqual(
									    list(inputs.keys()				)      ,							['input_ids', 'attention_mask', 'qformer_input_ids', 'qformer_attention_mask', 'pixel_values']      ,							)



