





def 	lowerCAmelCase_					(			__a      ,       __a						)	->						Optional[Any]:







  """simple docstring"""





  lowerCamelCase__: Any         =''
  for i in table:
    res += inp[i - 1]
  return res





def 	lowerCAmelCase_					(			__a						)	->						int:







  """simple docstring"""





  return data[1:] + data[0]





def 	lowerCAmelCase_					(			__a      ,       __a						)	->						int:







  """simple docstring"""





  lowerCamelCase__: Union[str, Any]         =''
  for i in range(len(__UpperCAmelCase						)						):
    if a[i] == b[i]:
      res += "0"
    else:
      res += "1"
  return res





def 	lowerCAmelCase_					(			__a      ,       __a						)	->						str:







  """simple docstring"""





  lowerCamelCase__: Optional[Any]         =int("0b" + data[0] + data[-1]      ,       2						)
  lowerCamelCase__: Optional[int]         =int("0b" + data[1:3]      ,       2						)
  return bin(s[row][col]						)[2:]





def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a      ,       __a      ,       __a						)	->						Union[str, Any]:







  """simple docstring"""





  lowerCamelCase__: List[str]         =message[:4]
  lowerCamelCase__: Optional[Any]         =message[4:]
  lowerCamelCase__: Dict         =apply_table(__UpperCAmelCase      ,       __UpperCAmelCase						)
  lowerCamelCase__: Optional[Any]         =xor(__UpperCAmelCase      ,       __UpperCAmelCase						)
  lowerCamelCase__: Optional[int]         =apply_sbox(__UpperCAmelCase      ,       temp[:4]						)  # noqa: E741
  lowerCamelCase__: Tuple         =apply_sbox(__UpperCAmelCase      ,       temp[4:]						)
  lowerCamelCase__: int         ='0' * (2 - len(__UpperCAmelCase						)) + l  # noqa: E741
  lowerCamelCase__: List[Any]         ='0' * (2 - len(__UpperCAmelCase						)) + r
  lowerCamelCase__: List[Any]         =apply_table(l + r      ,       __UpperCAmelCase						)
  lowerCamelCase__: Union[str, Any]         =xor(__UpperCAmelCase      ,       __UpperCAmelCase						)
  return temp + right


if __name__ == "__main__":
     __A							   =							input("Enter 10 bit key: ")
     __A							   =							input("Enter 8 bit message: ")

     __A							   =							[6, 3, 7, 4, 8, 5, 10, 9]
     __A							   =							[3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
     __A							   =							[2, 4, 3, 1]
     __A							   =							[2, 6, 3, 1, 4, 8, 5, 7]
     __A							   =							[4, 1, 3, 5, 7, 2, 8, 6]
     __A							   =							[4, 1, 2, 3, 2, 3, 4, 1]
     __A							   =							[[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
     __A							   =							[[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

     # key generation
     __A							   =							apply_table(key, paa_table)
     __A							   =							temp[:5]
     __A							   =							temp[5:]
     __A							   =							left_shift(left)
     __A							   =							left_shift(right)
     __A							   =							apply_table(left + right, pa_table)
     __A							   =							left_shift(left)
     __A							   =							left_shift(right)
     __A							   =							left_shift(left)
     __A							   =							left_shift(right)
     __A							   =							apply_table(left + right, pa_table)

     # encryption
     __A							   =							apply_table(message, IP)
     __A							   =							function(expansion, sa, sa, keya, temp)
     __A							   =							temp[4:] + temp[:4]
     __A							   =							function(expansion, sa, sa, keya, temp)
     __A							   =							apply_table(temp, IP_inv)
     print("Cipher text is:", CT)

     # decryption
     __A							   =							apply_table(CT, IP)
     __A							   =							function(expansion, sa, sa, keya, temp)
     __A							   =							temp[4:] + temp[:4]
     __A							   =							function(expansion, sa, sa, keya, temp)
     __A							   =							apply_table(temp, IP_inv)
     print("Plain text after decypting is:", PT)






'''simple docstring'''

import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)







def a_     (    __UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    List[str]      							=checkpoints.load_tax_checkpoint(__UpperCAmelCase					)
			snake_case:    str      							=flatten_dict(__UpperCAmelCase					)
			return flax_params







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    int      							={}

			snake_case:    Any      							={
			    'token_embedder': 'embeddings',
			    'encoder_norm': 'layernorm',
			    'kernel': 'weight',
			    '.out': '.output',
			    'scale': 'weight',
			    'embedders_0.pos_embedding': 'row_embedder.weight',
			    'embedders_1.pos_embedding': 'column_embedder.weight',
			}

			snake_case:    Union[str, Any]      							={
			    'query': 'attention.query',
			    'key': 'attention.key',
			    'value': 'attention.value',
			    'output.dense': 'output',
			    'encoder_decoder_attention.o': 'encoder_decoder_attention.attention.o',
			    'pre_self_attention_layer_norm': 'self_attention.layer_norm',
			    'pre_cross_attention_layer_norm': 'encoder_decoder_attention.layer_norm',
			    'mlp.': 'mlp.DenseReluDense.',
			    'pre_mlp_layer_norm': 'mlp.layer_norm',
			    'self_attention.o': 'self_attention.attention.o',
			    'decoder.embeddings.embedding': 'decoder.embed_tokens.weight',
			    'decoder.relpos_bias.rel_embedding': 'decoder.layer.0.self_attention.attention.relative_attention_bias.weight',
			    'decoder.decoder_norm.weight': 'decoder.final_layer_norm.weight',
			    'decoder.logits_dense.weight': 'decoder.lm_head.weight',
			}

			for key in flax_dict.keys():
						if "target" in key:
									# remove the first prefix from the key
									snake_case:    Dict      							='.'.join(key[1:]					)

									# rename the key
									for old, new in CONVERSION_MAPPING.items():
												snake_case:    Union[str, Any]      							=new_key.replace(__UpperCAmelCase				,							__UpperCAmelCase					)

									if "decoder" in new_key:
												for old, new in DECODER_CONVERSION_MAPPING.items():
															snake_case:    Optional[Any]      							=new_key.replace(__UpperCAmelCase				,							__UpperCAmelCase					)

									if "layers" in new_key and "decoder" not in new_key:
												# use regex to replace the layer number
												snake_case:    Any      							=re.sub(R'layers_(\d+)'				,							R'layer.\1'				,							__UpperCAmelCase					)
												snake_case:    Optional[int]      							=new_key.replace('encoder'				,							'encoder.encoder'					)

									elif "layers" in new_key and "decoder" in new_key:
												# use regex to replace the layer number
												snake_case:    Optional[Any]      							=re.sub(R'layers_(\d+)'				,							R'layer.\1'				,							__UpperCAmelCase					)

									snake_case:    Optional[int]      							=flax_dict[key]

			snake_case:    Optional[int]      							={}
			# convert converted_dict into torch format
			for key in converted_dict.keys():
						if ("embed_tokens" not in key) and ("embedder" not in key):
									snake_case:    Union[str, Any]      							=torch.from_numpy(converted_dict[key].T					)
						else:
									snake_case:    Union[str, Any]      							=torch.from_numpy(converted_dict[key]					)

			return converted_torch_dict







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase=False				,							__UpperCAmelCase=False					)				->					Tuple:





			"""simple docstring"""







			snake_case:    Any      							=get_flax_param(__UpperCAmelCase					)

			if not use_large:
						snake_case:    Optional[int]      							=PixaStructVisionConfig()
						snake_case:    int      							=PixaStructTextConfig()
			else:
						snake_case:    Tuple      							=PixaStructVisionConfig(
						    hidden_size=15_36				,							d_ff=39_68				,							num_attention_heads=24				,							num_hidden_layers=18					)
						snake_case:    List[Any]      							=PixaStructTextConfig(hidden_size=15_36				,							d_ff=39_68				,							num_heads=24				,							num_layers=18					)
			snake_case:    Any      							=PixaStructConfig(
			    vision_config=encoder_config.to_dict()				,							text_config=decoder_config.to_dict()				,							is_vqa=__UpperCAmelCase					)

			snake_case:    List[Any]      							=PixaStructForConditionalGeneration(__UpperCAmelCase					)

			snake_case:    str      							=rename_and_convert_flax_params(__UpperCAmelCase					)
			model.load_state_dict(__UpperCAmelCase					)

			snake_case:    List[Any]      							=AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'					)
			snake_case:    Optional[Any]      							=PixaStructImageProcessor()
			snake_case:    Optional[int]      							=PixaStructProcessor(image_processor=__UpperCAmelCase				,							tokenizer=__UpperCAmelCase					)

			if use_large:
						snake_case:    Optional[Any]      							=40_96

			snake_case:    str      							=True

			# mkdir if needed
			os.makedirs(__UpperCAmelCase				,							exist_ok=__UpperCAmelCase					)

			model.save_pretrained(__UpperCAmelCase					)
			processor.save_pretrained(__UpperCAmelCase					)

			print('Model saved in {}'.format(__UpperCAmelCase					)					)


if __name__ == "__main__":
	a							   =				argparse.ArgumentParser()
	parser.add_argument('--t5x_checkpoint_path', default=None, type=str, help='Path to the original T5x checkpoint.')
	parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument('--use_large', action='store_true', help='Use large model.')
	parser.add_argument('--is_vqa', action='store_true', help='Use large model.')
	a							   =				parser.parse_args()

	convert_pixastruct_original_pytorch_checkpoint_to_hf(
	    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
	)




