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 PoolFormerImageProcessor




class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   def __init__(						self       :	Dict		,	_lowercase       :	List[str]		,	_lowercase       :	List[Any]=7		,	_lowercase       :	Optional[int]=3		,	_lowercase       :	Optional[int]=30		,	_lowercase       :	Tuple=400		,	_lowercase       :	Any=True		,	_lowercase       :	Any=None		,	_lowercase       :	Union[str, Any]=0.9		,	_lowercase       :	Optional[int]=None		,	_lowercase       :	int=True		,	_lowercase       :	Optional[Any]=[0.5, 0.5, 0.5]		,	_lowercase       :	Union[str, Any]=[0.5, 0.5, 0.5]		,	):



    """simple docstring"""



    _UpperCamelCase:  str    						=			size if size is not None else {'''shortest_edge''': 30}
    _UpperCamelCase:  int    						=			crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
    _UpperCamelCase:  Optional[Any]    						=			parent
    _UpperCamelCase:  Optional[Any]    						=			batch_size
    _UpperCamelCase:  Optional[Any]    						=			num_channels
    _UpperCamelCase:  int    						=			min_resolution
    _UpperCamelCase:  List[str]    						=			max_resolution
    _UpperCamelCase:  int    						=			do_resize_and_center_crop
    _UpperCamelCase:  List[str]    						=			size
    _UpperCamelCase:  Optional[int]    						=			crop_pct
    _UpperCamelCase:  Tuple    						=			crop_size
    _UpperCamelCase:  Dict    						=			do_normalize
    _UpperCamelCase:  Tuple    						=			image_mean
    _UpperCamelCase:  Optional[Any]    						=			image_std



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    return {
        "size": self.size,
        "do_resize_and_center_crop": self.do_resize_and_center_crop,
        "crop_pct": self.crop_pct,
        "crop_size": self.crop_size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
    }




@require_torch
@require_vision
class 			__magic_name__	(		__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     Any	      = PoolFormerImageProcessor if is_vision_available() else None



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  Tuple    						=			PoolFormerImageProcessingTester(self				)



   @property
   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    return self.image_processor_tester.prepare_image_processor_dict()



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			self.image_processing_class(**self.image_processor_dict				)
    self.assertTrue(hasattr(_lowercase		,	'''do_resize_and_center_crop'''				)				)
    self.assertTrue(hasattr(_lowercase		,	'''size'''				)				)
    self.assertTrue(hasattr(_lowercase		,	'''crop_pct'''				)				)
    self.assertTrue(hasattr(_lowercase		,	'''do_normalize'''				)				)
    self.assertTrue(hasattr(_lowercase		,	'''image_mean'''				)				)
    self.assertTrue(hasattr(_lowercase		,	'''image_std'''				)				)



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    _UpperCamelCase:  Tuple    						=			self.image_processing_class.from_dict(self.image_processor_dict				)
    self.assertEqual(image_processor.size		,	{'''shortest_edge''': 30}				)
    self.assertEqual(image_processor.crop_size		,	{'''height''': 30, '''width''': 30}				)

    _UpperCamelCase:  Tuple    						=			self.image_processing_class.from_dict(self.image_processor_dict		,	size=42		,	crop_size=84				)
    self.assertEqual(image_processor.size		,	{'''shortest_edge''': 42}				)
    self.assertEqual(image_processor.crop_size		,	{'''height''': 84, '''width''': 84}				)



   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    pass



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			self.image_processing_class(**self.image_processor_dict				)
    # create random PIL images
    _UpperCamelCase:  Dict    						=			prepare_image_inputs(self.image_processor_tester		,	equal_resolution=_lowercase				)
    for image in image_inputs:
     self.assertIsInstance(_lowercase		,	Image.Image				)

    # Test not batched input
    _UpperCamelCase:  Optional[int]    						=			image_processing(image_inputs[0]		,	return_tensors='''pt'''				).pixel_values

    self.assertEqual(
        encoded_images.shape		,	(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )		,	)

    # Test batched
    _UpperCamelCase:  int    						=			image_processing(_lowercase		,	return_tensors='''pt'''				).pixel_values
    self.assertEqual(
        encoded_images.shape		,	(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )		,	)



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



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			self.image_processing_class(**self.image_processor_dict				)
    # create random numpy tensors
    _UpperCamelCase:  str    						=			prepare_image_inputs(self.image_processor_tester		,	equal_resolution=_lowercase		,	numpify=_lowercase				)
    for image in image_inputs:
     self.assertIsInstance(_lowercase		,	np.ndarray				)

    # Test not batched input
    _UpperCamelCase:  Dict    						=			image_processing(image_inputs[0]		,	return_tensors='''pt'''				).pixel_values
    self.assertEqual(
        encoded_images.shape		,	(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )		,	)

    # Test batched
    _UpperCamelCase:  Dict    						=			image_processing(_lowercase		,	return_tensors='''pt'''				).pixel_values
    self.assertEqual(
        encoded_images.shape		,	(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )		,	)



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  Union[str, Any]    						=			self.image_processing_class(**self.image_processor_dict				)
    # create random PyTorch tensors
    _UpperCamelCase:  int    						=			prepare_image_inputs(self.image_processor_tester		,	equal_resolution=_lowercase		,	torchify=_lowercase				)
    for image in image_inputs:
     self.assertIsInstance(_lowercase		,	torch.Tensor				)

    # Test not batched input
    _UpperCamelCase:  Any    						=			image_processing(image_inputs[0]		,	return_tensors='''pt'''				).pixel_values
    self.assertEqual(
        encoded_images.shape		,	(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )		,	)

    # Test batched
    _UpperCamelCase:  List[str]    						=			image_processing(_lowercase		,	return_tensors='''pt'''				).pixel_values
    self.assertEqual(
        encoded_images.shape		,	(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )		,	)
from __future__ import annotations




def    lowerCAmelCase_  (		lowercase:				str	,       lowercase:				list[str] | None = None	,       lowercase:				dict[str, float] | None = None	,       lowercase:				bool = False	,       )     ->					tuple[int, float, str]:







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			cipher_alphabet or [chr(lowercase		) for i in range(97	,       123		)]

 # If the argument is None or the user provided an empty dictionary
 if not frequencies_dict:
  # Frequencies of letters in the english language (how much they show up)
  _UpperCamelCase:  Any    						=			{
      '''a''': 0.08497,
      '''b''': 0.01492,
      '''c''': 0.02202,
      '''d''': 0.04253,
      '''e''': 0.11162,
      '''f''': 0.02228,
      '''g''': 0.02015,
      '''h''': 0.06094,
      '''i''': 0.07546,
      '''j''': 0.00153,
      '''k''': 0.01292,
      '''l''': 0.04025,
      '''m''': 0.02406,
      '''n''': 0.06749,
      '''o''': 0.07507,
      '''p''': 0.01929,
      '''q''': 0.00095,
      '''r''': 0.07587,
      '''s''': 0.06327,
      '''t''': 0.09356,
      '''u''': 0.02758,
      '''v''': 0.00978,
      '''w''': 0.02560,
      '''x''': 0.00150,
      '''y''': 0.01994,
      '''z''': 0.00077,
  }
 else:
  # Custom frequencies dictionary
  _UpperCamelCase:  str    						=			frequencies_dict

 if not case_sensitive:
  _UpperCamelCase:  Union[str, Any]    						=			ciphertext.lower()

 # Chi squared statistic values
 _UpperCamelCase:  dict[int, tuple[float, str]]    						=			{}

 # cycle through all of the shifts
 for shift in range(len(lowercase		)		):
  _UpperCamelCase:  Tuple    						=			''''''

  # decrypt the message with the shift
  for letter in ciphertext:
   try:
    # Try to index the letter in the alphabet
    _UpperCamelCase:  Optional[int]    						=			(alphabet_letters.index(letter.lower()		) - shift) % len(
        lowercase		)
    decrypted_with_shift += (
        alphabet_letters[new_key].upper()
        if case_sensitive and letter.isupper()
        else alphabet_letters[new_key]
    )
   except ValueError:
    # Append the character if it isn't in the alphabet
    decrypted_with_shift += letter

  _UpperCamelCase:  Optional[int]    						=			0.0

  # Loop through each letter in the decoded message with the shift
  for letter in decrypted_with_shift:
   if case_sensitive:
    _UpperCamelCase:  Optional[int]    						=			letter.lower()
    if letter in frequencies:
     # Get the amount of times the letter occurs in the message
     _UpperCamelCase:  Any    						=			decrypted_with_shift.lower().count(lowercase		)

     # Get the excepcted amount of times the letter should appear based
     # on letter frequencies
     _UpperCamelCase:  int    						=			frequencies[letter] * occurrences

     # Complete the chi squared statistic formula
     _UpperCamelCase:  int    						=			((occurrences - expected) ** 2) / expected

     # Add the margin of error to the total chi squared statistic
     chi_squared_statistic += chi_letter_value
   else:
    if letter.lower() in frequencies:
     # Get the amount of times the letter occurs in the message
     _UpperCamelCase:  List[Any]    						=			decrypted_with_shift.count(lowercase		)

     # Get the excepcted amount of times the letter should appear based
     # on letter frequencies
     _UpperCamelCase:  Union[str, Any]    						=			frequencies[letter] * occurrences

     # Complete the chi squared statistic formula
     _UpperCamelCase:  List[str]    						=			((occurrences - expected) ** 2) / expected

     # Add the margin of error to the total chi squared statistic
     chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
  _UpperCamelCase:  List[Any]    						=			(
      chi_squared_statistic,
      decrypted_with_shift,
  )

 # Get the most likely cipher by finding the cipher with the smallest chi squared
 # statistic
 def chi_squared_statistic_values_sorting_key(lowercase:				int		) -> tuple[float, str]:
  return chi_squared_statistic_values[key]

 _UpperCamelCase:  int    						=			min(
     lowercase	,       key=lowercase	,       )

 # Get all the data from the most likely cipher (key, decoded message)
 (
     (
     _UpperCamelCase
 )						,				(
     _UpperCamelCase
 )						,				
 ):  str    						=			chi_squared_statistic_values[most_likely_cipher]

 # Return the data on the most likely shift
 return (
     most_likely_cipher,
     most_likely_cipher_chi_squared_value,
     decoded_most_likely_cipher,
 )