






"""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   _lowercase			(  unittest.TestCase    ):


      def _UpperCamelCase    (     self					:						List[Any]    ):





            """simple docstring"""







            __snake_case   :     List[Any]	      =tempfile.mkdtemp()

            # fmt: off
            __snake_case   :     Optional[int]	      =['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
            # fmt: on
            __snake_case   :     Optional[int]	      =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]    )    )

            __snake_case   :     Tuple	      ={
                'image_mean': FLAVA_IMAGE_MEAN,
                'image_std': FLAVA_IMAGE_STD,
                'do_normalize': True,
                'do_resize': True,
                'size': 2_2_4,
                'do_center_crop': True,
                'crop_size': 2_2_4,
                'input_size_patches': 1_4,
                'total_mask_patches': 7_5,
                'mask_group_max_patches': None,
                'mask_group_min_patches': 1_6,
                'mask_group_min_aspect_ratio': 0.3,
                'mask_group_max_aspect_ratio': None,
                'codebook_do_resize': True,
                'codebook_size': 1_1_2,
                'codebook_do_center_crop': True,
                'codebook_crop_size': 1_1_2,
                'codebook_do_map_pixels': True,
                'codebook_do_normalize': True,
                'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
                'codebook_image_std': FLAVA_CODEBOOK_STD,
            }

            __snake_case   :     Optional[int]	      =os.path.join(self.tmpdirname				, UpperCamelCase_    )
            with open(self.image_processor_file				, '''w'''				, encoding='''utf-8'''    ) as fp:
                  json.dump(UpperCamelCase_				, UpperCamelCase_    )


      def _UpperCamelCase    (     self					:						List[Any]				, **a					:						List[Any]    ):





            """simple docstring"""







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


      def _UpperCamelCase    (     self					:						Optional[Any]				, **a					:						str    ):





            """simple docstring"""







            return BertTokenizerFast.from_pretrained(self.tmpdirname				, **UpperCamelCase_    )


      def _UpperCamelCase    (     self					:						List[Any]				, **a					:						Union[str, Any]    ):





            """simple docstring"""







            return FlavaImageProcessor.from_pretrained(self.tmpdirname				, **UpperCamelCase_    )


      def _UpperCamelCase    (     self					:						List[str]    ):





            """simple docstring"""







            shutil.rmtree(self.tmpdirname    )


      def _UpperCamelCase    (     self					:						Tuple    ):





            """simple docstring"""







            __snake_case   :     Tuple	      =[np.random.randint(2_5_5				, size=(3, 3_0, 4_0_0)				, dtype=np.uinta    )]

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

            return image_inputs


      def _UpperCamelCase    (     self					:						Union[str, Any]    ):





            """simple docstring"""







            __snake_case   :     str	      =self.get_tokenizer()
            __snake_case   :     List[Any]	      =self.get_rust_tokenizer()
            __snake_case   :     Optional[Any]	      =self.get_image_processor()

            __snake_case   :     int	      =FlavaProcessor(tokenizer=UpperCamelCase_				, image_processor=UpperCamelCase_    )
            processor_slow.save_pretrained(self.tmpdirname    )
            __snake_case   :     str	      =FlavaProcessor.from_pretrained(self.tmpdirname				, use_fast=UpperCamelCase_    )

            __snake_case   :     int	      =FlavaProcessor(tokenizer=UpperCamelCase_				, image_processor=UpperCamelCase_    )
            processor_fast.save_pretrained(self.tmpdirname    )
            __snake_case   :     List[str]	      =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				, UpperCamelCase_    )
            self.assertIsInstance(processor_fast.tokenizer				, UpperCamelCase_    )

            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				, UpperCamelCase_    )
            self.assertIsInstance(processor_fast.image_processor				, UpperCamelCase_    )


      def _UpperCamelCase    (     self					:						Union[str, Any]    ):





            """simple docstring"""







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

            __snake_case   :     Any	      =self.get_tokenizer(bos_token='''(BOS)'''				, eos_token='''(EOS)'''    )
            __snake_case   :     Any	      =self.get_image_processor(do_normalize=UpperCamelCase_				, padding_value=1.0    )

            __snake_case   :     Any	      =FlavaProcessor.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				, UpperCamelCase_    )

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


      def _UpperCamelCase    (     self					:						Union[str, Any]    ):





            """simple docstring"""







            __snake_case   :     List[Any]	      =self.get_image_processor()
            __snake_case   :     int	      =self.get_tokenizer()

            __snake_case   :     Optional[int]	      =FlavaProcessor(tokenizer=UpperCamelCase_				, image_processor=UpperCamelCase_    )

            __snake_case   :     Optional[Any]	      =self.prepare_image_inputs()

            __snake_case   :     str	      =image_processor(UpperCamelCase_				, return_tensors='''np'''    )
            __snake_case   :     str	      =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    )

            # With rest of the args
            random.seed(1_2_3_4    )
            __snake_case   :     Optional[int]	      =image_processor(
                UpperCamelCase_				, return_image_mask=UpperCamelCase_				, return_codebook_pixels=UpperCamelCase_				, return_tensors='''np'''    )
            random.seed(1_2_3_4    )
            __snake_case   :     int	      =processor(
                images=UpperCamelCase_				, return_image_mask=UpperCamelCase_				, return_codebook_pixels=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 _UpperCamelCase    (     self					:						str    ):





            """simple docstring"""







            __snake_case   :     Tuple	      =self.get_image_processor()
            __snake_case   :     Dict	      =self.get_tokenizer()

            __snake_case   :     List[str]	      =FlavaProcessor(tokenizer=UpperCamelCase_				, image_processor=UpperCamelCase_    )

            __snake_case   :     Optional[int]	      ='lower newer'

            __snake_case   :     str	      =processor(text=UpperCamelCase_    )

            __snake_case   :     Tuple	      =tokenizer(UpperCamelCase_    )

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


      def _UpperCamelCase    (     self					:						Tuple    ):





            """simple docstring"""







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

            __snake_case   :     str	      =FlavaProcessor(tokenizer=UpperCamelCase_				, image_processor=UpperCamelCase_    )

            __snake_case   :     int	      ='lower newer'
            __snake_case   :     int	      =self.prepare_image_inputs()

            __snake_case   :     Optional[int]	      =processor(text=UpperCamelCase_				, images=UpperCamelCase_    )

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

            # add extra args
            __snake_case   :     Union[str, Any]	      =processor(text=UpperCamelCase_				, images=UpperCamelCase_				, return_codebook_pixels=UpperCamelCase_				, return_image_mask=UpperCamelCase_    )

            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(UpperCamelCase_    ):
                  processor()


      def _UpperCamelCase    (     self					:						List[Any]    ):





            """simple docstring"""







            __snake_case   :     int	      =self.get_image_processor()
            __snake_case   :     Tuple	      =self.get_tokenizer()

            __snake_case   :     Any	      =FlavaProcessor(tokenizer=UpperCamelCase_				, image_processor=UpperCamelCase_    )

            __snake_case   :     str	      =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            __snake_case   :     Tuple	      =processor.batch_decode(UpperCamelCase_    )
            __snake_case   :     Optional[int]	      =tokenizer.batch_decode(UpperCamelCase_    )

            self.assertListEqual(UpperCamelCase_				, UpperCamelCase_    )







      def _UpperCamelCase    (     self					:						Optional[int]    ):





            """simple docstring"""







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

            __snake_case   :     Any	      =FlavaProcessor(tokenizer=UpperCamelCase_				, image_processor=UpperCamelCase_    )

            __snake_case   :     str	      ='lower newer'
            __snake_case   :     Tuple	      =self.prepare_image_inputs()

            __snake_case   :     List[str]	      =processor(text=UpperCamelCase_				, images=UpperCamelCase_    )

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








import random
import sys

UpperCAmelCase__    :							str       		=     'ABCDEFGHIJKLMNOPQRSTUVWXYZ'


def 		_lowerCamelCase					(       )      ->		None:





  """simple docstring"""





  _lowercase:			Any      =     input('Enter message: '				)
  _lowercase:			List[str]      =     'LFWOAYUISVKMNXPBDCRJTQEGHZ'
  _lowercase:			Tuple      =     input('Encrypt/Decrypt [e/d]: '				)

  check_valid_key(UpperCamelCase				)

  if resp.lower().startswith('e'				):
    _lowercase:			int      =     'encrypt'
    _lowercase:			Optional[int]      =     encrypt_message(UpperCamelCase       ,  UpperCamelCase				)
  elif resp.lower().startswith('d'				):
    _lowercase:			str      =     'decrypt'
    _lowercase:			Dict      =     decrypt_message(UpperCamelCase       ,  UpperCamelCase				)

  print(F'\n{mode.title()}ion: \n{translated}'				)


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		None:





  """simple docstring"""





  _lowercase:			List[Any]      =     list(UpperCamelCase				)
  _lowercase:			Union[str, Any]      =     list(UpperCamelCase				)
  key_list.sort()
  letters_list.sort()

  if key_list != letters_list:
    sys.exit('Error in the key or symbol set.'				)


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase				)      ->		str:





  """simple docstring"""





  return translate_message(UpperCamelCase       ,  UpperCamelCase       ,  'encrypt'				)


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase				)      ->		str:





  """simple docstring"""





  return translate_message(UpperCamelCase       ,  UpperCamelCase       ,  'decrypt'				)


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)      ->		str:





  """simple docstring"""





  _lowercase:			Optional[int]      =     ''
  _lowercase:			Any      =     LETTERS
  _lowercase:			Tuple      =     key

  if mode == "decrypt":
    _lowercase  ,       _lowercase:			Tuple      =     chars_b, chars_a

  for symbol in message:
    if symbol.upper() in chars_a:
      _lowercase:			int      =     chars_a.find(symbol.upper()				)
      if symbol.isupper():
        translated += chars_b[sym_index].upper()
      else:
        translated += chars_b[sym_index].lower()
    else:
      translated += symbol

  return translated


def 		_lowerCamelCase					(       )      ->		str:





  """simple docstring"""





  _lowercase:			Union[str, Any]      =     list(UpperCamelCase				)
  random.shuffle(UpperCamelCase				)
  return "".join(UpperCamelCase				)


if __name__ == "__main__":
   main()
