
'''simple docstring'''

import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
snake_case									=    {
    """E""": 12.70,
    """T""": 9.06,
    """A""": 8.17,
    """O""": 7.51,
    """I""": 6.97,
    """N""": 6.75,
    """S""": 6.33,
    """H""": 6.09,
    """R""": 5.99,
    """D""": 4.25,
    """L""": 4.03,
    """C""": 2.78,
    """U""": 2.76,
    """M""": 2.41,
    """W""": 2.36,
    """F""": 2.23,
    """G""": 2.02,
    """Y""": 1.97,
    """P""": 1.93,
    """B""": 1.29,
    """V""": 0.98,
    """K""": 0.77,
    """J""": 0.15,
    """X""": 0.15,
    """Q""": 0.10,
    """Z""": 0.07,
}
snake_case									=    """ETAOINSHRDLCUMWFGYPBVKJXQZ"""
snake_case									=    """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   int     		=	{letter: 0 for letter in string.ascii_uppercase}
 for letter in message.upper():
  if letter in LETTERS:
   letter_count[letter] += 1

 return letter_count


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 return x[0]


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Optional[Any]     		=	get_letter_count(lowerCamelCase_			)
 lowerCAmelCase__     :   dict[int, list[str]]     		=	{
     freq: [] for letter, freq in letter_to_freq.items()
 }
 for letter in LETTERS:
  freq_to_letter[letter_to_freq[letter]].append(lowerCamelCase_			)

 lowerCAmelCase__     :   dict[int, str]     		=	{}

 for freq in freq_to_letter:
  freq_to_letter[freq].sort(key=ETAOIN.find      ,			reverse=lowerCamelCase_			)
  lowerCAmelCase__     :   List[Any]     		=	"".join(freq_to_letter[freq]			)

 lowerCAmelCase__     :   int     		=	list(freq_to_letter_str.items()			)
 freq_pairs.sort(key=lowerCamelCase_      ,			reverse=lowerCamelCase_			)

 lowerCAmelCase__     :   list[str]     		=	[freq_pair[1] for freq_pair in freq_pairs]

 return "".join(lowerCamelCase_			)


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[Any]     		=	get_frequency_order(lowerCamelCase_			)
 lowerCAmelCase__     :   str     		=	0
 for common_letter in ETAOIN[:6]:
  if common_letter in freq_order[:6]:
   match_score += 1

 for uncommon_letter in ETAOIN[-6:]:
  if uncommon_letter in freq_order[-6:]:
   match_score += 1

 return match_score


if __name__ == "__main__":
     import doctest

     doctest.testmod()







'''simple docstring'''

import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
     import jax

     from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel




class    lowerCAmelCase       (     unittest.TestCase  ):


 def __init__(    self  : Optional[Any]			,							a__  : List[Any]			,							a__  : Any=13			,							a__  : Optional[int]=30			,							a__  : List[Any]=2			,							a__  : Union[str, Any]=3			,							a__  : Tuple=True			,							a__  : Tuple=True			,							a__  : List[str]=32			,							a__  : Tuple=5			,							a__  : List[str]=4			,							a__  : str=37			,							a__  : Dict="gelu"			,							a__  : Union[str, Any]=0.1			,							a__  : Union[str, Any]=0.1			,							a__  : List[Any]=10			,							a__  : List[Any]=0.02			,							):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	parent
  lowerCAmelCase__     :   Union[str, Any]     		=	batch_size
  lowerCAmelCase__     :   List[Any]     		=	image_size
  lowerCAmelCase__     :   int     		=	patch_size
  lowerCAmelCase__     :   Tuple     		=	num_channels
  lowerCAmelCase__     :   List[str]     		=	is_training
  lowerCAmelCase__     :   str     		=	use_labels
  lowerCAmelCase__     :   Dict     		=	hidden_size
  lowerCAmelCase__     :   Any     		=	num_hidden_layers
  lowerCAmelCase__     :   Any     		=	num_attention_heads
  lowerCAmelCase__     :   Any     		=	intermediate_size
  lowerCAmelCase__     :   Union[str, Any]     		=	hidden_act
  lowerCAmelCase__     :   Optional[int]     		=	hidden_dropout_prob
  lowerCAmelCase__     :   str     		=	attention_probs_dropout_prob
  lowerCAmelCase__     :   Optional[int]     		=	type_sequence_label_size
  lowerCAmelCase__     :   List[str]     		=	initializer_range

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


 def    _A	(    self  : int    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Dict     		=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

  lowerCAmelCase__     :   Any     		=	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			,							)

  return config, pixel_values


 def    _A	(    self  : Optional[Any]			,							a__  : Optional[int]			,							a__  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	FlaxViTModel(config=a__    )
  lowerCAmelCase__     :   Any     		=	model(a__    )
  # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
  lowerCAmelCase__     :   List[str]     		=	(self.image_size, self.image_size)
  lowerCAmelCase__     :   List[str]     		=	(self.patch_size, self.patch_size)
  lowerCAmelCase__     :   int     		=	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
  self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, num_patches + 1, self.hidden_size)    )


 def    _A	(    self  : str			,							a__  : Optional[int]			,							a__  : Dict    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[Any]     		=	self.type_sequence_label_size
  lowerCAmelCase__     :   Any     		=	FlaxViTForImageClassification(config=a__    )
  lowerCAmelCase__     :   Dict     		=	model(a__    )
  self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.type_sequence_label_size)    )

  # test greyscale images
  lowerCAmelCase__     :   Tuple     		=	1
  lowerCAmelCase__     :   List[Any]     		=	FlaxViTForImageClassification(a__    )

  lowerCAmelCase__     :   Union[str, Any]     		=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
  lowerCAmelCase__     :   int     		=	model(a__    )


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	self.prepare_config_and_inputs()
  (
      (
      lowerCAmelCase__
  )		,     (
      lowerCAmelCase__
  )		,     
  )     :   Tuple     		=	config_and_inputs
  lowerCAmelCase__     :   Optional[Any]     		=	{"pixel_values": pixel_values}
  return config, inputs_dict




@require_flax
class    lowerCAmelCase       (     UpperCamelCase_    ,  unittest.TestCase  ):
 A_				:				Union[str, Any]	=			(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()


 def    _A	(    self  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[Any]     		=	FlaxViTModelTester(self    )
  lowerCAmelCase__     :   Union[str, Any]     		=	ConfigTester(self			,							config_class=a__			,							has_text_modality=a__			,							hidden_size=37    )


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  self.config_tester.run_common_tests()


 def    _A	(    self  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*a__    )


 def    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_image_classification(*a__    )


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

  lowerCAmelCase__		,     lowerCAmelCase__     :   List[Any]     		=	self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   lowerCAmelCase__     :   Dict     		=	model_class(a__    )
   lowerCAmelCase__     :   str     		=	inspect.signature(model.__call__    )
   # signature.parameters is an OrderedDict => so arg_names order is deterministic
   lowerCAmelCase__     :   List[Any]     		=	[*signature.parameters.keys()]

   lowerCAmelCase__     :   List[str]     		=	["pixel_values"]
   self.assertListEqual(arg_names[:1]			,							a__    )


 def    _A	(    self  : str    ):
  '''simple docstring'''

  lowerCAmelCase__		,     lowerCAmelCase__     :   int     		=	self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   with self.subTest(model_class.__name__    ):
    lowerCAmelCase__     :   List[Any]     		=	self._prepare_for_class(a__			,							a__    )
    lowerCAmelCase__     :   Optional[int]     		=	model_class(a__    )

    @jax.jit
    def model_jitted(a__  : Optional[int]			,							**a__  : Optional[Any]    ):
     return model(pixel_values=a__			,							**a__    )

    with self.subTest("JIT Enabled"    ):
     lowerCAmelCase__     :   Any     		=	model_jitted(**a__    ).to_tuple()

    with self.subTest("JIT Disabled"    ):
     with jax.disable_jit():
      lowerCAmelCase__     :   str     		=	model_jitted(**a__    ).to_tuple()

    self.assertEqual(len(a__    )			,							len(a__    )    )
    for jitted_output, output in zip(a__			,							a__    ):
     self.assertEqual(jitted_output.shape			,							output.shape    )


 @slow
 def    _A	(    self  : Any    ):
  '''simple docstring'''

  for model_class_name in self.all_model_classes:
   lowerCAmelCase__     :   Optional[Any]     		=	model_class_name.from_pretrained("google/vit-base-patch16-224"    )
   lowerCAmelCase__     :   Tuple     		=	model(np.ones((1, 3, 224, 224)    )    )
   self.assertIsNotNone(a__    )





