


"""simple docstring"""







import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

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


if is_vision_available():
       from PIL import Image

       from transformers import FlavaImageProcessor, FlavaProcessor
       from transformers.models.flava.image_processing_flava import (
           FLAVA_CODEBOOK_MEAN,
           FLAVA_CODEBOOK_STD,
           FLAVA_IMAGE_MEAN,
           FLAVA_IMAGE_STD,
       )






@require_vision
class  __A							(			unittest.TestCase     ):






 """simple docstring"""




 def  SCREAMING_SNAKE_CASE				(						self		)   ->	List[str]:
     a											=tempfile.mkdtemp()

     # fmt: off
     a											=['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
     # fmt: on
     a											=os.path.join(self.tmpdirname					,       VOCAB_FILES_NAMES['''vocab_file''']		)

     with open(self.vocab_file					,       '''w'''					,       encoding='''utf-8'''		) as fp:
         fp.write(''''''.join([x + '''\n''' for x in vocab_tokens]		)		)

     a											={
         '''image_mean''': FLAVA_IMAGE_MEAN,
         '''image_std''': FLAVA_IMAGE_STD,
         '''do_normalize''': True,
         '''do_resize''': True,
         '''size''': 224,
         '''do_center_crop''': True,
         '''crop_size''': 224,
         '''input_size_patches''': 14,
         '''total_mask_patches''': 75,
         '''mask_group_max_patches''': None,
         '''mask_group_min_patches''': 16,
         '''mask_group_min_aspect_ratio''': 0.3,
         '''mask_group_max_aspect_ratio''': None,
         '''codebook_do_resize''': True,
         '''codebook_size''': 112,
         '''codebook_do_center_crop''': True,
         '''codebook_crop_size''': 112,
         '''codebook_do_map_pixels''': True,
         '''codebook_do_normalize''': True,
         '''codebook_image_mean''': FLAVA_CODEBOOK_MEAN,
         '''codebook_image_std''': FLAVA_CODEBOOK_STD,
     }

     a											=os.path.join(self.tmpdirname					,       __A		)
     with open(self.image_processor_file					,       '''w'''					,       encoding='''utf-8'''		) as fp:
         json.dump(__A					,       __A		)


 def  SCREAMING_SNAKE_CASE				(						self					,       **__A		)   ->	Optional[int]:
     return BertTokenizer.from_pretrained(self.tmpdirname					,       **__A		)


 def  SCREAMING_SNAKE_CASE				(						self					,       **__A		)   ->	List[str]:
     return BertTokenizerFast.from_pretrained(self.tmpdirname					,       **__A		)


 def  SCREAMING_SNAKE_CASE				(						self					,       **__A		)   ->	Optional[int]:
     return FlavaImageProcessor.from_pretrained(self.tmpdirname					,       **__A		)


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[Any]:
     shutil.rmtree(self.tmpdirname		)


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	int:
     a											=[np.random.randint(255					,       size=(3, 30, 400)					,       dtype=np.uinta		)]

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

     return image_inputs


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	List[Any]:
     a											=self.get_tokenizer()
     a											=self.get_rust_tokenizer()
     a											=self.get_image_processor()

     a											=FlavaProcessor(tokenizer=__A					,       image_processor=__A		)
     processor_slow.save_pretrained(self.tmpdirname		)
     a											=FlavaProcessor.from_pretrained(self.tmpdirname					,       use_fast=__A		)

     a											=FlavaProcessor(tokenizer=__A					,       image_processor=__A		)
     processor_fast.save_pretrained(self.tmpdirname		)
     a											=FlavaProcessor.from_pretrained(self.tmpdirname		)

     self.assertEqual(processor_slow.tokenizer.get_vocab()					,       tokenizer_slow.get_vocab()		)
     self.assertEqual(processor_fast.tokenizer.get_vocab()					,       tokenizer_fast.get_vocab()		)
     self.assertEqual(tokenizer_slow.get_vocab()					,       tokenizer_fast.get_vocab()		)
     self.assertIsInstance(processor_slow.tokenizer					,       __A		)
     self.assertIsInstance(processor_fast.tokenizer					,       __A		)

     self.assertEqual(processor_slow.image_processor.to_json_string()					,       image_processor.to_json_string()		)
     self.assertEqual(processor_fast.image_processor.to_json_string()					,       image_processor.to_json_string()		)
     self.assertIsInstance(processor_slow.image_processor					,       __A		)
     self.assertIsInstance(processor_fast.image_processor					,       __A		)


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	str:
     a											=FlavaProcessor(tokenizer=self.get_tokenizer()					,       image_processor=self.get_image_processor()		)
     processor.save_pretrained(self.tmpdirname		)

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

     a											=FlavaProcessor.from_pretrained(
         self.tmpdirname					,       bos_token='''(BOS)'''					,       eos_token='''(EOS)'''					,       do_normalize=__A					,       padding_value=1.0		)

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

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


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	int:
     a											=self.get_image_processor()
     a											=self.get_tokenizer()

     a											=FlavaProcessor(tokenizer=__A					,       image_processor=__A		)

     a											=self.prepare_image_inputs()

     a											=image_processor(__A					,       return_tensors='''np'''		)
     a											=processor(images=__A					,       return_tensors='''np'''		)

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

     # With rest of the args
     random.seed(1234		)
     a											=image_processor(
         __A					,       return_image_mask=__A					,       return_codebook_pixels=__A					,       return_tensors='''np'''		)
     random.seed(1234		)
     a											=processor(
         images=__A					,       return_image_mask=__A					,       return_codebook_pixels=__A					,       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  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[Any]:
     a											=self.get_image_processor()
     a											=self.get_tokenizer()

     a											=FlavaProcessor(tokenizer=__A					,       image_processor=__A		)

     a											='''lower newer'''

     a											=processor(text=__A		)

     a											=tokenizer(__A		)

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


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	List[Any]:
     a											=self.get_image_processor()
     a											=self.get_tokenizer()

     a											=FlavaProcessor(tokenizer=__A					,       image_processor=__A		)

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

     a											=processor(text=__A					,       images=__A		)

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

     # add extra args
     a											=processor(text=__A					,       images=__A					,       return_codebook_pixels=__A					,       return_image_mask=__A		)

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

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


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Tuple:
     a											=self.get_image_processor()
     a											=self.get_tokenizer()

     a											=FlavaProcessor(tokenizer=__A					,       image_processor=__A		)

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

     a											=processor.batch_decode(__A		)
     a											=tokenizer.batch_decode(__A		)

     self.assertListEqual(__A					,       __A		)





 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[Any]:
     a											=self.get_image_processor()
     a											=self.get_tokenizer()

     a											=FlavaProcessor(tokenizer=__A					,       image_processor=__A		)

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

     a											=processor(text=__A					,       images=__A		)

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



"""simple docstring"""







from __future__ import annotations

import random
import string






class  __A							:






 """simple docstring"""




 def __init__(						self					,       __A = None		)   ->	None:
     a											=passcode or self.__passcode_creator()
     a											=self.__make_key_list()
     a											=self.__make_shift_key()


 def __str__(						self		)   ->	str:
     return "".join(self.__passcode		)


 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	list[int]:
     for i in range(1					,       len(__A		)					,       2		):
         iterlist[i] *= -1
     return iterlist


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	list[str]:
     a											=string.ascii_letters + string.digits
     a											=[random.choice(__A		) for _ in range(random.randint(10					,       20		)		)]
     return password


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	list[str]:
     a											=(
         string.ascii_letters + string.digits + string.punctuation + ''' \t\n'''
     )

     a											=[]

     # creates points known as breakpoints to break the key_list_options at those
     # points and pivot each substring
     a											=sorted(set(self.__passcode		)		)
     a											=[]

     # algorithm for creating a new shuffled list, keys_l, out of key_list_options
     for i in key_list_options:
         temp_list.extend(__A		)

         # checking breakpoints at which to pivot temporary sublist and add it into
         # keys_l
         if i in breakpoints or i == key_list_options[-1]:
             keys_l.extend(temp_list[::-1]		)
             temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
     return keys_l


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	int:
     a											=sum(self.__neg_pos([ord(__A		) for x in self.__passcode]		)		)
     return num if num > 0 else len(self.__passcode		)


 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	str:
     a											=''''''

     # decoding shift like Caesar cipher algorithm implementing negative shift or
     # reverse shift or left shift
     for i in encoded_message:
         a											=self.__key_list.index(__A		)
         decoded_message += self.__key_list[
             (position - self.__shift_key) % -len(self.__key_list		)
         ]

     return decoded_message





 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	str:
     a											=''''''

     # encoding shift like Caesar cipher algorithm implementing positive shift or
     # forward shift or right shift
     for i in plaintext:
         a											=self.__key_list.index(__A		)
         encoded_message += self.__key_list[
             (position + self.__shift_key) % len(self.__key_list		)
         ]

     return encoded_message

def _A		(		lowercase = "Hello, this is a modified Caesar cipher"						):







    """simple docstring"""
    a											=ShuffledShiftCipher()
    return cipa.decrypt(cipa.encrypt(lowercase						)						)


if __name__ == "__main__":
       import doctest

       doctest.testmod()