





"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor



class      __SCREAMING_SNAKE_CASE    (						unittest.TestCase   ):







  '''simple docstring'''


  def __init__(		self   :       Optional[int]					,   __a   :       Optional[int]					,   __a   :       List[str]=13					,   __a   :       Optional[Any]=3					,   __a   :       Optional[Any]=224					,   __a   :       str=30					,   __a   :       Optional[Any]=400					,   __a   :       List[Any]=True					,   __a   :       Any=None					,   __a   :       Union[str, Any]=True					,   __a   :       Dict=[0.5, 0.5, 0.5]					,   __a   :       str=[0.5, 0.5, 0.5]					,   )		->				Tuple:
        _UpperCamelCase      :	Any						=       size if size is not None else {"height": 18, "width": 18}
        _UpperCamelCase      :	Dict						=       parent
        _UpperCamelCase      :	Optional[Any]						=       batch_size
        _UpperCamelCase      :	List[Any]						=       num_channels
        _UpperCamelCase      :	Union[str, Any]						=       image_size
        _UpperCamelCase      :	Optional[int]						=       min_resolution
        _UpperCamelCase      :	int						=       max_resolution
        _UpperCamelCase      :	str						=       do_resize
        _UpperCamelCase      :	Tuple						=       size
        _UpperCamelCase      :	int						=       do_normalize
        _UpperCamelCase      :	int						=       image_mean
        _UpperCamelCase      :	Tuple						=       image_std


  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				str:
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
        }





@require_torch
@require_vision
class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase		,  unittest.TestCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:str	     =					ViTImageProcessor if is_vision_available() else None

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				Dict:
        _UpperCamelCase      :	str						=       EfficientFormerImageProcessorTester(self	)

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				List[str]:
        return self.image_proc_tester.prepare_image_processor_dict()

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				str:
        _UpperCamelCase      :	List[Any]						=       self.image_processing_class(**self.image_processor_dict	)
        self.assertTrue(hasattr(__a					,   "image_mean"	)	)
        self.assertTrue(hasattr(__a					,   "image_std"	)	)
        self.assertTrue(hasattr(__a					,   "do_normalize"	)	)
        self.assertTrue(hasattr(__a					,   "do_resize"	)	)
        self.assertTrue(hasattr(__a					,   "size"	)	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				Optional[Any]:
        pass

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Dict:
        # Initialize image_processor
        _UpperCamelCase      :	Tuple						=       self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        _UpperCamelCase      :	List[str]						=       prepare_image_inputs(self.image_proc_tester					,   equal_resolution=__a	)
        for image in image_inputs:
              self.assertIsInstance(__a					,   Image.Image	)

        # Test not batched input
        _UpperCamelCase      :	Union[str, Any]						=       image_processor(image_inputs[0]					,   return_tensors="pt"	).pixel_values
        self.assertEqual(
            encoded_images.shape					,   (
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["height"],
                self.image_proc_tester.size["width"],
            )					,   )

        # Test batched
        _UpperCamelCase      :	Any						=       image_processor(__a					,   return_tensors="pt"	).pixel_values
        self.assertEqual(
            encoded_images.shape					,   (
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["height"],
                self.image_proc_tester.size["width"],
            )					,   )

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				Dict:
        # Initialize image_processor
        _UpperCamelCase      :	Optional[Any]						=       self.image_processing_class(**self.image_processor_dict	)
        # create random numpy tensors
        _UpperCamelCase      :	Union[str, Any]						=       prepare_image_inputs(self.image_proc_tester					,   equal_resolution=__a					,   numpify=__a	)
        for image in image_inputs:
              self.assertIsInstance(__a					,   np.ndarray	)

        # Test not batched input
        _UpperCamelCase      :	Optional[Any]						=       image_processor(image_inputs[0]					,   return_tensors="pt"	).pixel_values
        self.assertEqual(
            encoded_images.shape					,   (
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["height"],
                self.image_proc_tester.size["width"],
            )					,   )

        # Test batched
        _UpperCamelCase      :	Optional[Any]						=       image_processor(__a					,   return_tensors="pt"	).pixel_values
        self.assertEqual(
            encoded_images.shape					,   (
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["height"],
                self.image_proc_tester.size["width"],
            )					,   )


  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				Any:
        # Initialize image_processor
        _UpperCamelCase      :	Optional[Any]						=       self.image_processing_class(**self.image_processor_dict	)
        # create random PyTorch tensors
        _UpperCamelCase      :	Optional[int]						=       prepare_image_inputs(self.image_proc_tester					,   equal_resolution=__a					,   torchify=__a	)
        for image in image_inputs:
              self.assertIsInstance(__a					,   torch.Tensor	)

        # Test not batched input
        _UpperCamelCase      :	List[Any]						=       image_processor(image_inputs[0]					,   return_tensors="pt"	).pixel_values
        self.assertEqual(
            encoded_images.shape					,   (
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["height"],
                self.image_proc_tester.size["width"],
            )					,   )

        # Test batched
        _UpperCamelCase      :	Tuple						=       image_processor(__a					,   return_tensors="pt"	).pixel_values
        self.assertEqual(
            encoded_images.shape					,   (
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["height"],
                self.image_proc_tester.size["width"],
            )					,   )







"""simple docstring"""


from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

lowerCamelCase__				  =   (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowerCamelCase__				  =   [ord(letter) for letter in string.ascii_lowercase]
lowerCamelCase__				  =   {ord(char) for char in VALID_CHARS}

lowerCamelCase__				  =   ["the", "be", "to", "of", "and", "in", "that", "have"]




def 					lowercase__    (					lowercase_		,lowercase_			)					->		str | None:




      """simple docstring"""

      _UpperCamelCase      :	str						=       ""
      _UpperCamelCase      :	int
      _UpperCamelCase      :	int
      _UpperCamelCase      :	int

      for keychar, cipherchar in zip(cycle(lowercase_			)		,lowercase_			):
            _UpperCamelCase      :	Dict						=       cipherchar ^ keychar
            if decodedchar not in VALID_INTS:
                  return None
            decoded += chr(lowercase_			)

      return decoded




def 					lowercase__    (					lowercase_			)					->		list[str]:




      """simple docstring"""

      _UpperCamelCase      :	list[str]						=       []
      for key in product(lowercase_		,repeat=3			):
            _UpperCamelCase      :	int						=       try_key(lowercase_		,lowercase_			)
            if encoded is not None:
                  possibles.append(lowercase_			)
      return possibles




def 					lowercase__    (					lowercase_		,lowercase_			)					->		list[str]:




      """simple docstring"""

      return [possible for possible in possibles if common_word in possible.lower()]




def 					lowercase__    (					lowercase_ = "p059_cipher.txt"			)					->		int:




      """simple docstring"""

      _UpperCamelCase      :	list[int]
      _UpperCamelCase      :	list[str]
      _UpperCamelCase      :	str
      _UpperCamelCase      :	str
      _UpperCamelCase      :	str						=       Path(lowercase_			).parent.joinpath(lowercase_			).read_text(encoding="utf-8"			)

      _UpperCamelCase      :	Optional[Any]						=       [int(lowercase_			) for number in data.strip().split(","			)]

      _UpperCamelCase      :	List[str]						=       filter_valid_chars(lowercase_			)
      for common_word in COMMON_WORDS:
            _UpperCamelCase      :	Union[str, Any]						=       filter_common_word(lowercase_		,lowercase_			)
            if len(lowercase_			) == 1:
                  break

      _UpperCamelCase      :	Union[str, Any]						=       possibles[0]
      return sum(ord(lowercase_			) for char in decoded_text			)


if __name__ == "__main__":
  print(f"""{solution() = }""")
