






'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class UpperCAmelCase_					(    unittest.TestCase				):




      """simple docstring"""




      def 				A__      ( self			:		List[str]		)  ->  List[str]:


       '''simple docstring'''

       lowercase					:   Any				  =tempfile.mkdtemp()

       # fmt: off
       lowercase					:   Optional[int]				  =['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
       # fmt: on
       lowercase					:   List[Any]				  =os.path.join(self.tmpdirname     ,      VOCAB_FILES_NAMES['''vocab_file''']		)
       with open(self.vocab_file     ,      '''w'''     ,      encoding='''utf-8'''		) as vocab_writer:
        vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]		)		)

       lowercase					:   List[Any]				  ={
           '''do_resize''': True,
           '''size''': {'''height''': 18, '''width''': 18},
           '''do_normalize''': True,
           '''image_mean''': [0.5, 0.5, 0.5],
           '''image_std''': [0.5, 0.5, 0.5],
       }
       lowercase					:   Dict				  =os.path.join(self.tmpdirname     ,      UpperCAmelCase		)
       with open(self.image_processor_file     ,      '''w'''     ,      encoding='''utf-8'''		) as fp:
        json.dump(UpperCAmelCase     ,      UpperCAmelCase		)






      def 				A__      ( self			:		Any     ,      **UpperCAmelCase			:		Any		)  ->  List[Any]:


       '''simple docstring'''

       return BertTokenizer.from_pretrained(self.tmpdirname     ,      **UpperCAmelCase		)






      def 				A__      ( self			:		Tuple     ,      **UpperCAmelCase			:		Any		)  ->  Tuple:


       '''simple docstring'''

       return ViTImageProcessor.from_pretrained(self.tmpdirname     ,      **UpperCAmelCase		)






      def 				A__      ( self			:		Optional[Any]		)  ->  Tuple:


       '''simple docstring'''

       shutil.rmtree(self.tmpdirname		)






      def 				A__      ( self			:		List[str]		)  ->  int:


       '''simple docstring'''

       lowercase					:   Optional[int]				  =[np.random.randint(255     ,      size=(3, 30, 400)     ,      dtype=np.uinta		)]

       lowercase					:   Any				  =[Image.fromarray(np.moveaxis(UpperCAmelCase     ,      0     ,      -1		)		) for x in image_inputs]

       return image_inputs






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


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =self.get_tokenizer()
       lowercase					:   Optional[int]				  =self.get_image_processor()

       lowercase					:   int				  =VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,      image_processor=UpperCAmelCase		)

       processor.save_pretrained(self.tmpdirname		)
       lowercase					:   Optional[int]				  =VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname		)

       self.assertEqual(processor.tokenizer.get_vocab()     ,      tokenizer.get_vocab()		)
       self.assertIsInstance(processor.tokenizer     ,      (BertTokenizer, BertTokenizerFast)		)

       self.assertEqual(processor.image_processor.to_json_string()     ,      image_processor.to_json_string()		)
       self.assertIsInstance(processor.image_processor     ,      UpperCAmelCase		)






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


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =VisionTextDualEncoderProcessor(
           tokenizer=self.get_tokenizer()     ,      image_processor=self.get_image_processor()		)
       processor.save_pretrained(self.tmpdirname		)

       lowercase					:   Optional[int]				  =self.get_tokenizer(bos_token='''(BOS)'''     ,      eos_token='''(EOS)'''		)
       lowercase					:   str				  =self.get_image_processor(do_normalize=UpperCAmelCase     ,      padding_value=1.0		)

       lowercase					:   str				  =VisionTextDualEncoderProcessor.from_pretrained(
           self.tmpdirname     ,      bos_token='''(BOS)'''     ,      eos_token='''(EOS)'''     ,      do_normalize=UpperCAmelCase     ,      padding_value=1.0		)

       self.assertEqual(processor.tokenizer.get_vocab()     ,      tokenizer_add_kwargs.get_vocab()		)
       self.assertIsInstance(processor.tokenizer     ,      (BertTokenizer, BertTokenizerFast)		)

       self.assertEqual(processor.image_processor.to_json_string()     ,      image_processor_add_kwargs.to_json_string()		)
       self.assertIsInstance(processor.image_processor     ,      UpperCAmelCase		)






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


       '''simple docstring'''

       lowercase					:   Any				  =self.get_image_processor()
       lowercase					:   Optional[int]				  =self.get_tokenizer()

       lowercase					:   Union[str, Any]				  =VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,      image_processor=UpperCAmelCase		)

       lowercase					:   str				  =self.prepare_image_inputs()

       lowercase					:   Any				  =image_processor(UpperCAmelCase     ,      return_tensors='''np'''		)
       lowercase					:   Union[str, Any]				  =processor(images=UpperCAmelCase     ,      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			:		str		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   str				  =self.get_image_processor()
       lowercase					:   Optional[Any]				  =self.get_tokenizer()

       lowercase					:   Dict				  =VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,      image_processor=UpperCAmelCase		)

       lowercase					:   int				  ='''lower newer'''

       lowercase					:   List[str]				  =processor(text=UpperCAmelCase		)

       lowercase					:   Tuple				  =tokenizer(UpperCAmelCase		)

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






      def 				A__      ( self			:		int		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   Optional[int]				  =self.get_image_processor()
       lowercase					:   Dict				  =self.get_tokenizer()

       lowercase					:   Dict				  =VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,      image_processor=UpperCAmelCase		)

       lowercase					:   Dict				  ='''lower newer'''
       lowercase					:   List[Any]				  =self.prepare_image_inputs()

       lowercase					:   Dict				  =processor(text=UpperCAmelCase     ,      images=UpperCAmelCase		)

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

       # test if it raises when no input is passed
       with self.assertRaises(UpperCAmelCase		):
        processor()






      def 				A__      ( self			:		List[str]		)  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   List[str]				  =self.get_image_processor()
       lowercase					:   str				  =self.get_tokenizer()

       lowercase					:   str				  =VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,      image_processor=UpperCAmelCase		)

       lowercase					:   Optional[Any]				  =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       lowercase					:   Tuple				  =processor.batch_decode(UpperCAmelCase		)
       lowercase					:   Optional[int]				  =tokenizer.batch_decode(UpperCAmelCase		)

       self.assertListEqual(UpperCAmelCase     ,      UpperCAmelCase		)






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


       '''simple docstring'''

       lowercase					:   Union[str, Any]				  =self.get_image_processor()
       lowercase					:   List[str]				  =self.get_tokenizer()

       lowercase					:   Tuple				  =VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,      image_processor=UpperCAmelCase		)

       lowercase					:   Optional[int]				  ='''lower newer'''
       lowercase					:   Dict				  =self.prepare_image_inputs()

       lowercase					:   Any				  =processor(text=UpperCAmelCase     ,      images=UpperCAmelCase		)

       self.assertListEqual(list(inputs.keys()		)     ,      processor.model_input_names		)











'''simple docstring'''



import mpmath  # for roots of unity
import numpy as np
class UpperCAmelCase_					:




      """simple docstring"""




      def __init__( self			:		List[Any]     ,      UpperCAmelCase			:		Dict=None     ,      UpperCAmelCase			:		Optional[int]=None		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Any				  =list(poly_a or [0]		)[:]
       lowercase					:   Dict				  =list(poly_b or [0]		)[:]

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

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

       # Add 0 to make lengths equal a power of 2
       lowercase					:   Tuple				  =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
       lowercase					:   Optional[int]				  =complex(mpmath.root(x=1     ,      n=self.c_max_length     ,      k=1		)		)

       # The product
       lowercase					:   str				  =self.__multiply()






      def 				A__      ( self			:		Optional[Any]     ,      UpperCAmelCase			:		Union[str, Any]		)  ->  Tuple:


       '''simple docstring'''

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

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






      def 				A__      ( self			:		int		)  ->  str:


       '''simple docstring'''

       lowercase					:   List[Any]				  =self.__dft('''A'''		)
       lowercase					:   Union[str, Any]				  =self.__dft('''B'''		)
       lowercase					:   Any				  =[[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
       lowercase					:   Tuple				  =2
       while next_ncol <= self.c_max_length:
        lowercase					:   Tuple				  =[[] for i in range(UpperCAmelCase		)]
        lowercase					:   Tuple				  =self.root ** (next_ncol // 2)
        lowercase					:   Optional[int]				  =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
        lowercase					:   List[Any]				  =new_inverse_c
        next_ncol *= 2
       # Unpack
       lowercase					:   List[str]				  =[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			:		Dict		)  ->  Optional[Any]:


       '''simple docstring'''

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



