



'''simple docstring'''


from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
						from PIL import Image

						from ..image_utils import load_image

if is_tf_available():
						from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
						import torch

						from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

lowercase_      :				Union[str, Any]   					=    logging.get_logger(__name__)




@add_end_docstrings(_UpperCAmelCase			)
class      __UpperCamelCase       (_UpperCAmelCase			):







	def __init__(    self ,				*_lowerCAmelCase ,				**_lowerCAmelCase						)    ->    Dict:
							'''simple docstring'''






							super().__init__(*_lowerCAmelCase ,				**_lowerCAmelCase						)
							requires_backends(self ,				"""vision"""						)
							self.check_model_type(
							    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING						)







	def   _a   (    self ,				_lowerCAmelCase=None ,				_lowerCAmelCase=None ,				_lowerCAmelCase=None						)    ->    str:
							'''simple docstring'''






							lowercase        =							{}
							lowercase        =							{}

							if prompt is not None:
													lowercase        =							prompt

							if generate_kwargs is not None:
													lowercase        =							generate_kwargs
							if max_new_tokens is not None:
													if "generate_kwargs" not in forward_kwargs:
																			lowercase        =							{}
													if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
																			raise ValueError(
																			    """'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
																			    """ please use only one"""						)
													lowercase        =							max_new_tokens
							return preprocess_params, forward_kwargs, {}







	def __call__(    self ,				_lowerCAmelCase ,				**_lowerCAmelCase						)    ->    Any:
							'''simple docstring'''






							return super().__call__(_lowerCAmelCase ,				**_lowerCAmelCase						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase=None						)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							load_image(_lowerCAmelCase						)

							if prompt is not None:
													if not isinstance(_lowerCAmelCase ,				_lowerCAmelCase						):
																			raise ValueError(
																			    F"""Received an invalid text input, got - {type(_lowerCAmelCase						)} - but expected a single string. """
																			    """Note also that one single text can be provided for conditional image to text generation."""						)

													lowercase        =							self.model.config.model_type

													if model_type == "git":
																			lowercase        =							self.image_processor(images=_lowerCAmelCase ,				return_tensors=self.framework						)
																			lowercase        =							self.tokenizer(text=_lowerCAmelCase ,				add_special_tokens=_lowerCAmelCase						).input_ids
																			lowercase        =							[self.tokenizer.cls_token_id] + input_ids
																			lowercase        =							torch.tensor(_lowerCAmelCase						).unsqueeze(0						)
																			model_inputs.update({"""input_ids""": input_ids}						)

													elif model_type == "pix2struct":
																			lowercase        =							self.image_processor(images=_lowerCAmelCase ,				header_text=_lowerCAmelCase ,				return_tensors=self.framework						)

													elif model_type != "vision-encoder-decoder":
																			# vision-encoder-decoder does not support conditional generation
																			lowercase        =							self.image_processor(images=_lowerCAmelCase ,				return_tensors=self.framework						)
																			lowercase        =							self.tokenizer(_lowerCAmelCase ,				return_tensors=self.framework						)
																			model_inputs.update(_lowerCAmelCase						)

													else:
																			raise ValueError(F"""Model type {model_type} does not support conditional text generation"""						)

							else:
													lowercase        =							self.image_processor(images=_lowerCAmelCase ,				return_tensors=self.framework						)

							if self.model.config.model_type == "git" and prompt is None:
													lowercase        =							None

							return model_inputs







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase=None						)    ->    Union[str, Any]:
							'''simple docstring'''






							if (
							    "input_ids" in model_inputs
							    and isinstance(model_inputs["""input_ids"""] ,				_lowerCAmelCase						)
							    and all(x is None for x in model_inputs["""input_ids"""]						)
							):
													lowercase        =							None

							if generate_kwargs is None:
													lowercase        =							{}
							# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
							#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
							#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
							#  in the `_prepare_model_inputs` method.
							lowercase        =							model_inputs.pop(self.model.main_input_name						)
							lowercase        =							self.model.generate(_lowerCAmelCase ,				**_lowerCAmelCase ,				**_lowerCAmelCase						)
							return model_outputs







	def   _a   (    self ,				_lowerCAmelCase						)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							[]
							for output_ids in model_outputs:
													lowercase        =							{
													    """generated_text""": self.tokenizer.decode(
													        _lowerCAmelCase ,				skip_special_tokens=_lowerCAmelCase ,				)
													}
													records.append(_lowerCAmelCase						)
							return records







'''simple docstring'''


def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Union[str, Any] ,  lowercase_    :      str						):
						lowercase        =							""""""
						for i in table:
												res += inp[i - 1]
						return res







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[Any]						):
						return data[1:] + data[0]







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Tuple ,  lowercase_    :      Dict						):
						lowercase        =							""""""
						for i in range(len(lowercase_						)						):
												if a[i] == b[i]:
																		res += "0"
												else:
																		res += "1"
						return res







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str ,  lowercase_    :      str						):
						lowercase        =							int("""0b""" + data[0] + data[-1] ,  2						)
						lowercase        =							int("""0b""" + data[1:3] ,  2						)
						return bin(s[row][col]						)[2:]







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Dict ,  lowercase_    :      Tuple ,  lowercase_    :      Tuple ,  lowercase_    :      str ,  lowercase_    :      Any						):
						lowercase        =							message[:4]
						lowercase        =							message[4:]
						lowercase        =							apply_table(lowercase_ ,  lowercase_						)
						lowercase        =							xor(lowercase_ ,  lowercase_						)
						lowercase        =							apply_sbox(lowercase_ ,  temp[:4]						)  # noqa: E741
						lowercase        =							apply_sbox(lowercase_ ,  temp[4:]						)
						lowercase        =							"""0""" * (2 - len(lowercase_						)) + l  # noqa: E741
						lowercase        =							"""0""" * (2 - len(lowercase_						)) + r
						lowercase        =							apply_table(l + r ,  lowercase_						)
						lowercase        =							xor(lowercase_ ,  lowercase_						)
						return temp + right


if __name__ == "__main__":
						lowercase_      :				Tuple   					=    input('''Enter 10 bit key: ''')
						lowercase_      :				Any   					=    input('''Enter 8 bit message: ''')

						lowercase_      :				Dict   					=    [6, 3, 7, 4, 8, 5, 10, 9]
						lowercase_      :				str   					=    [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
						lowercase_      :				List[Any]   					=    [2, 4, 3, 1]
						lowercase_      :				List[str]   					=    [2, 6, 3, 1, 4, 8, 5, 7]
						lowercase_      :				Tuple   					=    [4, 1, 3, 5, 7, 2, 8, 6]
						lowercase_      :				Optional[Any]   					=    [4, 1, 2, 3, 2, 3, 4, 1]
						lowercase_      :				List[str]   					=    [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
						lowercase_      :				List[Any]   					=    [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

						# key generation
						lowercase_      :				Union[str, Any]   					=    apply_table(key, paa_table)
						lowercase_      :				Optional[Any]   					=    temp[:5]
						lowercase_      :				int   					=    temp[5:]
						lowercase_      :				List[str]   					=    left_shift(left)
						lowercase_      :				int   					=    left_shift(right)
						lowercase_      :				Tuple   					=    apply_table(left + right, pa_table)
						lowercase_      :				List[str]   					=    left_shift(left)
						lowercase_      :				Optional[Any]   					=    left_shift(right)
						lowercase_      :				Union[str, Any]   					=    left_shift(left)
						lowercase_      :				Union[str, Any]   					=    left_shift(right)
						lowercase_      :				Optional[int]   					=    apply_table(left + right, pa_table)

						# encryption
						lowercase_      :				int   					=    apply_table(message, IP)
						lowercase_      :				Dict   					=    function(expansion, sa, sa, keya, temp)
						lowercase_      :				Any   					=    temp[4:] + temp[:4]
						lowercase_      :				List[Any]   					=    function(expansion, sa, sa, keya, temp)
						lowercase_      :				Tuple   					=    apply_table(temp, IP_inv)
						print('''Cipher text is:''', CT)

						# decryption
						lowercase_      :				List[str]   					=    apply_table(CT, IP)
						lowercase_      :				Optional[int]   					=    function(expansion, sa, sa, keya, temp)
						lowercase_      :				Optional[Any]   					=    temp[4:] + temp[:4]
						lowercase_      :				Optional[int]   					=    function(expansion, sa, sa, keya, temp)
						lowercase_      :				Optional[Any]   					=    apply_table(temp, IP_inv)
						print('''Plain text after decypting is:''', PT)


