





from __future__ import annotations




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = False	,		)       ->     tuple[int, float, str]:
  lowercase__			    =						cipher_alphabet or [chr(_SCREAMING_SNAKE_CASE	) 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)
    lowercase__			    =						{
        'a': 0.0_8_4_9_7,
        'b': 0.0_1_4_9_2,
        'c': 0.0_2_2_0_2,
        'd': 0.0_4_2_5_3,
        'e': 0.1_1_1_6_2,
        'f': 0.0_2_2_2_8,
        'g': 0.0_2_0_1_5,
        'h': 0.0_6_0_9_4,
        'i': 0.0_7_5_4_6,
        'j': 0.0_0_1_5_3,
        'k': 0.0_1_2_9_2,
        'l': 0.0_4_0_2_5,
        'm': 0.0_2_4_0_6,
        'n': 0.0_6_7_4_9,
        'o': 0.0_7_5_0_7,
        'p': 0.0_1_9_2_9,
        'q': 0.0_0_0_9_5,
        'r': 0.0_7_5_8_7,
        's': 0.0_6_3_2_7,
        't': 0.0_9_3_5_6,
        'u': 0.0_2_7_5_8,
        'v': 0.0_0_9_7_8,
        'w': 0.0_2_5_6_0,
        'x': 0.0_0_1_5_0,
        'y': 0.0_1_9_9_4,
        'z': 0.0_0_0_7_7,
    }
  else:
    # Custom frequencies dictionary
    lowercase__			    =						frequencies_dict

  if not case_sensitive:
    lowercase__			    =						ciphertext.lower()

  # Chi squared statistic values
  lowercase__			    =						{}

  # cycle through all of the shifts
  for shift in range(len(_SCREAMING_SNAKE_CASE	)	):
    lowercase__			    =						''

    # decrypt the message with the shift
    for letter in ciphertext:
      try:
        # Try to index the letter in the alphabet
        lowercase__			    =						(alphabet_letters.index(letter.lower()	) - shift) % len(
            _SCREAMING_SNAKE_CASE	)
        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

    lowercase__			    =						0.0

    # Loop through each letter in the decoded message with the shift
    for letter in decrypted_with_shift:
      if case_sensitive:
        lowercase__			    =						letter.lower()
        if letter in frequencies:
          # Get the amount of times the letter occurs in the message
          lowercase__			    =						decrypted_with_shift.lower().count(_SCREAMING_SNAKE_CASE	)

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

          # Complete the chi squared statistic formula
          lowercase__			    =						((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
          lowercase__			    =						decrypted_with_shift.count(_SCREAMING_SNAKE_CASE	)

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

          # Complete the chi squared statistic formula
          lowercase__			    =						((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
    lowercase__			    =						(
        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(_SCREAMING_SNAKE_CASE	) -> tuple[float, str]:
    return chi_squared_statistic_values[key]

  lowercase__			    =						min(
      _SCREAMING_SNAKE_CASE	,		key=_SCREAMING_SNAKE_CASE	,		)

  # Get all the data from the most likely cipher (key, decoded message)
  (
      (
      lowercase__					
  )    ,					(
      lowercase__					
  )    ,										
  )			    =						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,
  )









import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
      from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import ViTImageProcessor
class        SCREAMING_SNAKE_CASE     :


   def __init__(					self    :       List[Any]    ,  a    :       Any    ,  a    :       Optional[int]=13    ,  a    :       Tuple=30    ,  a    :       Union[str, Any]=2    ,  a    :       List[str]=3    ,  a    :       Dict=True    ,  a    :       List[str]=True    ,  a    :       List[Any]=32    ,  a    :       List[str]=5    ,  a    :       Optional[int]=4    ,  a    :       List[str]=37    ,  a    :       Dict="gelu"    ,  a    :       Dict=0.1    ,  a    :       List[str]=0.1    ,  a    :       int=10    ,  a    :       List[str]=0.02    ,  a    :       int=None    ,  a    :       List[str]=2    ,  )->     Dict:





     """simple docstring"""







     lowercase__			    =						parent
     lowercase__			    =						batch_size
     lowercase__			    =						image_size
     lowercase__			    =						patch_size
     lowercase__			    =						num_channels
     lowercase__			    =						is_training
     lowercase__			    =						use_labels
     lowercase__			    =						hidden_size
     lowercase__			    =						num_hidden_layers
     lowercase__			    =						num_attention_heads
     lowercase__			    =						intermediate_size
     lowercase__			    =						hidden_act
     lowercase__			    =						hidden_dropout_prob
     lowercase__			    =						attention_probs_dropout_prob
     lowercase__			    =						type_sequence_label_size
     lowercase__			    =						initializer_range
     lowercase__			    =						scope
     lowercase__			    =						encoder_stride

     # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
     lowercase__			    =						(image_size // patch_size) ** 2
     lowercase__			    =						num_patches + 1


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Any:





     """simple docstring"""







     lowercase__			    =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

     lowercase__			    =						None
     if self.use_labels:
       lowercase__			    =						ids_tensor([self.batch_size]    ,  self.type_sequence_label_size )

     lowercase__			    =						self.get_config()

     return config, pixel_values, labels


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     List[Any]:





     """simple docstring"""







     return ViTConfig(
         image_size=self.image_size    ,  patch_size=self.patch_size    ,  num_channels=self.num_channels    ,  hidden_size=self.hidden_size    ,  num_hidden_layers=self.num_hidden_layers    ,  num_attention_heads=self.num_attention_heads    ,  intermediate_size=self.intermediate_size    ,  hidden_act=self.hidden_act    ,  hidden_dropout_prob=self.hidden_dropout_prob    ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,  is_decoder=a    ,  initializer_range=self.initializer_range    ,  encoder_stride=self.encoder_stride    ,  )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any]    ,  a    :       List[Any]    ,  a    :       List[str]    ,  a    :       Dict )->     Optional[Any]:





     """simple docstring"""







     lowercase__			    =						ViTModel(config=a )
     model.to(a )
     model.eval()
     lowercase__			    =						model(a )
     self.parent.assertEqual(result.last_hidden_state.shape    ,  (self.batch_size, self.seq_length, self.hidden_size) )


   def  SCREAMING_SNAKE_CASE_							(					self    :       int    ,  a    :       Optional[Any]    ,  a    :       int    ,  a    :       Tuple )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						ViTForMaskedImageModeling(config=a )
     model.to(a )
     model.eval()
     lowercase__			    =						model(a )
     self.parent.assertEqual(
         result.reconstruction.shape    ,  (self.batch_size, self.num_channels, self.image_size, self.image_size) )

     # test greyscale images
     lowercase__			    =						1
     lowercase__			    =						ViTForMaskedImageModeling(a )
     model.to(a )
     model.eval()

     lowercase__			    =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
     lowercase__			    =						model(a )
     self.parent.assertEqual(result.reconstruction.shape    ,  (self.batch_size, 1, self.image_size, self.image_size) )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str]    ,  a    :       List[str]    ,  a    :       int    ,  a    :       List[Any] )->     str:





     """simple docstring"""







     lowercase__			    =						self.type_sequence_label_size
     lowercase__			    =						ViTForImageClassification(a )
     model.to(a )
     model.eval()
     lowercase__			    =						model(a    ,  labels=a )
     self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.type_sequence_label_size) )

     # test greyscale images
     lowercase__			    =						1
     lowercase__			    =						ViTForImageClassification(a )
     model.to(a )
     model.eval()

     lowercase__			    =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
     lowercase__			    =						model(a )
     self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.type_sequence_label_size) )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     List[str]:





     """simple docstring"""







     lowercase__			    =						self.prepare_config_and_inputs()
     (
         (
         lowercase__
     )    ,					(
         lowercase__
     )    ,					(
         lowercase__
     )    ,					
     )			    =						config_and_inputs
     lowercase__			    =						{'pixel_values': pixel_values}
     return config, inputs_dict






@require_torch
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase    ,			UpperCAmelCase    ,			unittest.TestCase   ):
   _UpperCamelCase					:	Any				      = (
       (
           ViTModel,
           ViTForImageClassification,
           ViTForMaskedImageModeling,
       )
       if is_torch_available()
       else ()
   )
   _UpperCamelCase					:	Union[str, Any]				      = (
       {'feature-extraction': ViTModel, 'image-classification': ViTForImageClassification}
       if is_torch_available()
       else {}
   )
   _UpperCamelCase					:	int				      = True

   _UpperCamelCase					:	int				      = False
   _UpperCamelCase					:	Union[str, Any]				      = False
   _UpperCamelCase					:	Dict				      = False


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     List[str]:





     """simple docstring"""







     lowercase__			    =						ViTModelTester(self )
     lowercase__			    =						ConfigTester(self    ,  config_class=a    ,  has_text_modality=a    ,  hidden_size=37 )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Union[str, Any]:





     """simple docstring"""







     self.config_tester.run_common_tests()


   @unittest.skip(reason='ViT does not use inputs_embeds' )
   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Optional[Any]:





     """simple docstring"""







     pass


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     Union[str, Any]:





     """simple docstring"""







     lowercase__    ,					lowercase__			    =						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       lowercase__			    =						model_class(a )
       self.assertIsInstance(model.get_input_embeddings()    ,  (nn.Module) )
       lowercase__			    =						model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(a    ,  nn.Linear ) )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str] )->     Optional[int]:





     """simple docstring"""







     lowercase__    ,					lowercase__			    =						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       lowercase__			    =						model_class(a )
       lowercase__			    =						inspect.signature(model.forward )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       lowercase__			    =						[*signature.parameters.keys()]

       lowercase__			    =						['pixel_values']
       self.assertListEqual(arg_names[:1]    ,  a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str] )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Tuple:





     """simple docstring"""







     lowercase__			    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     int:





     """simple docstring"""







     lowercase__			    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*a )


   @slow
   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict )->     List[Any]:





     """simple docstring"""







     for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       lowercase__			    =						ViTModel.from_pretrained(a )
       self.assertIsNotNone(a )






def     __UpperCamelCase						()       ->     str:
  lowercase__			    =						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
  return image


@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):


   @cached_property
   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     List[Any]:





     """simple docstring"""







     return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224' ) if is_vision_available() else None


   @slow
   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						ViTForImageClassification.from_pretrained('google/vit-base-patch16-224' ).to(a )

     lowercase__			    =						self.default_image_processor
     lowercase__			    =						prepare_img()
     lowercase__			    =						image_processor(images=a    ,  return_tensors='pt' ).to(a )

     # forward pass
     with torch.no_grad():
       lowercase__			    =						model(**a )

     # verify the logits
     lowercase__			    =						torch.Size((1, 1_000) )
     self.assertEqual(outputs.logits.shape    ,  a )

     lowercase__			    =						torch.tensor([-0.2744, 0.8215, -0.0836] ).to(a )

     self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,  a    ,  atol=1E-4 ) )


   @slow
   def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     List[str]:





     """simple docstring"""







     lowercase__			    =						ViTModel.from_pretrained('facebook/dino-vits8' ).to(a )

     lowercase__			    =						ViTImageProcessor.from_pretrained('facebook/dino-vits8'    ,  size=480 )
     lowercase__			    =						prepare_img()
     lowercase__			    =						image_processor(images=a    ,  return_tensors='pt' )
     lowercase__			    =						inputs.pixel_values.to(a )

     # forward pass
     with torch.no_grad():
       lowercase__			    =						model(a    ,  interpolate_pos_encoding=a )

     # verify the logits
     lowercase__			    =						torch.Size((1, 3_601, 384) )
     self.assertEqual(outputs.last_hidden_state.shape    ,  a )

     lowercase__			    =						torch.tensor(
         [[4.2340, 4.3906, -6.6692], [4.5463, 1.8928, -6.7257], [4.4429, 0.8496, -5.8585]] ).to(a )

     self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]    ,  a    ,  atol=1E-4 ) )


   @slow
   @require_accelerate
   @require_torch_gpu
   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     str:





     """simple docstring"""







     lowercase__			    =						ViTModel.from_pretrained('facebook/dino-vits8'    ,  torch_dtype=torch.floataa    ,  device_map='auto' )
     lowercase__			    =						self.default_image_processor

     lowercase__			    =						prepare_img()
     lowercase__			    =						image_processor(images=a    ,  return_tensors='pt' )
     lowercase__			    =						inputs.pixel_values.to(a )

     # forward pass to make sure inference works in fp16
     with torch.no_grad():
       lowercase__			    =						model(a )


