



from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Tuple       =	analyze_text(__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	list(" " + ascii_lowercase     )
 # what is our total sum of probabilities.
 __SCREAMING_SNAKE_CASE       :    int       =	sum(single_char_strings.values()     )

 # one length string
 __SCREAMING_SNAKE_CASE       :    str       =	0
 # for each alpha we go in our dict and if it is in it we calculate entropy
 for ch in my_alphas:
  if ch in single_char_strings:
   __SCREAMING_SNAKE_CASE       :    List[Any]       =	single_char_strings[ch]
   __SCREAMING_SNAKE_CASE       :    str       =	my_str / all_sum
   my_fir_sum += prob * math.loga(__lowerCamelCase     )  # entropy formula.

    # print entropy
 print(F"""{round(-1 * my_fir_sum     ):.1f}"""     )

 # two len string
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	sum(two_char_strings.values()     )
 __SCREAMING_SNAKE_CASE       :    int       =	0
 # for each alpha (two in size) calculate entropy.
 for cha in my_alphas:
  for cha in my_alphas:
   __SCREAMING_SNAKE_CASE       :    int       =	cha + cha
   if sequence in two_char_strings:
    __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	two_char_strings[sequence]
    __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	int(__lowerCamelCase     ) / all_sum
    my_sec_sum += prob * math.loga(__lowerCamelCase     )

    # print second entropy
 print(F"""{round(-1 * my_sec_sum     ):.1f}"""     )

 # print the difference between them
 print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum)     ):.1f}"""     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Any       =	Counter()  # type: ignore
 __SCREAMING_SNAKE_CASE       :    int       =	Counter()  # type: ignore
 single_char_strings[text[-1]] += 1

 # first case when we have space at start.
 two_char_strings[" " + text[0]] += 1
 for i in range(0		,	len(__lowerCamelCase     ) - 1     ):
  single_char_strings[text[i]] += 1
  two_char_strings[text[i : i + 2]] += 1
 return single_char_strings, two_char_strings






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 import doctest

 doctest.testmod()
 # text = (
 #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
 #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
 #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
 #     "beloved evident through no service elderly is. Blind there if every no so "
 #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
 #     "of message cottage windows do besides against uncivil.  Delightful "
 #     "unreserved impossible few estimating men favourable see entreaties. She "
 #     "propriety immediate was improving. He or entrance humoured likewise "
 #     "moderate. Much nor game son say feel. Fat make met can must form into "
 #     "gate. Me we offending prevailed discovery. "
 # )

 # calculate_prob(text)


if __name__ == "__main__":
      main()











import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor

class       _SCREAMING_SNAKE_CASE	(unittest.TestCase ):





     def __init__(       self :							Union[str, Any]     ,  UpperCamelCase :							List[Any]     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							Dict[str, int] = None     ,  UpperCamelCase :							int = 3_2     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							Union[int, float] = 1 / 2_5_5     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							Optional[Union[float, List[float]]] = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]     ,  UpperCamelCase :							Optional[Union[float, List[float]]] = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							Dict=7     ,  UpperCamelCase :							Dict=3_0     ,  UpperCamelCase :							Tuple=4_0_0     ,  UpperCamelCase :							Union[str, Any]=3     ,  )->Optional[int]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	parent
      __SCREAMING_SNAKE_CASE       :    str       =	do_resize
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	size if size is not None else {"shortest_edge": 2_8_8}
      __SCREAMING_SNAKE_CASE       :    List[str]       =	size_divisor
      __SCREAMING_SNAKE_CASE       :    str       =	do_rescale
      __SCREAMING_SNAKE_CASE       :    int       =	rescale_factor
      __SCREAMING_SNAKE_CASE       :    Tuple       =	do_normalize
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	do_center_crop
      __SCREAMING_SNAKE_CASE       :    List[str]       =	image_mean
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	image_std
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	do_pad
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	batch_size
      __SCREAMING_SNAKE_CASE       :    str       =	num_channels
      __SCREAMING_SNAKE_CASE       :    List[str]       =	min_resolution
      __SCREAMING_SNAKE_CASE       :    Tuple       =	max_resolution





     def 	__snake_case    (       self :							Optional[Any]				)->List[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,
          "size_divisor": self.size_divisor,
      }






     def 	__snake_case    (       self :							Optional[Any]     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							Dict=False				)->Optional[Any]:
      if not batched:
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.size["shortest_edge"]
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	image_inputs[0]
       if isinstance(UpperCamelCase     ,  Image.Image				):
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[Any]       =	image.size
       else:
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	image.shape[1], image.shape[2]
       __SCREAMING_SNAKE_CASE       :    Tuple       =	size / min(UpperCamelCase     ,  UpperCamelCase				)
       if h < w:
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    int       =	size, scale * w
       else:
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[str]       =	scale * h, size

       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	int((1_3_3_3 / 8_0_0) * size				)
       if max(UpperCamelCase     ,  UpperCamelCase				) > max_size:
        __SCREAMING_SNAKE_CASE       :    Any       =	max_size / max(UpperCamelCase     ,  UpperCamelCase				)
        __SCREAMING_SNAKE_CASE       :    List[Any]       =	newh * scale
        __SCREAMING_SNAKE_CASE       :    Optional[int]       =	neww * scale

       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[Any]       =	int(newh + 0.5				), int(neww + 0.5				)
       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[Any]       =	(
           newh // self.size_divisor * self.size_divisor,
           neww // self.size_divisor * self.size_divisor,
       )

      else:
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	[]
       for image in image_inputs:
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.get_expected_values([image]				)
        expected_values.append((expected_height, expected_width)				)
       __SCREAMING_SNAKE_CASE       :    int       =	max(UpperCamelCase     ,  key=lambda UpperCamelCase				: item[0]				)[0]
       __SCREAMING_SNAKE_CASE       :    List[str]       =	max(UpperCamelCase     ,  key=lambda UpperCamelCase				: item[1]				)[1]

      return expected_height, expected_width






@require_torch
@require_vision
class       _SCREAMING_SNAKE_CASE	(UpperCamelCase			,    unittest.TestCase ):
     lowerCAmelCase					=			BridgeTowerImageProcessor if is_vision_available() else None





     def 	__snake_case    (       self :							str				)->int:
      __SCREAMING_SNAKE_CASE       :    str       =	BridgeTowerImageProcessingTester(self				)





     @property
     def 	__snake_case    (       self :							str				)->Any:
      return self.image_processor_tester.prepare_image_processor_dict()





     def 	__snake_case    (       self :							Optional[int]				)->Dict:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.image_processing_class(**self.image_processor_dict				)
      self.assertTrue(hasattr(UpperCamelCase     ,  "image_mean"				)				)
      self.assertTrue(hasattr(UpperCamelCase     ,  "image_std"				)				)
      self.assertTrue(hasattr(UpperCamelCase     ,  "do_normalize"				)				)
      self.assertTrue(hasattr(UpperCamelCase     ,  "do_resize"				)				)
      self.assertTrue(hasattr(UpperCamelCase     ,  "size"				)				)
      self.assertTrue(hasattr(UpperCamelCase     ,  "size_divisor"				)				)





     def 	__snake_case    (       self :							Optional[int]				)->int:
      pass





     def 	__snake_case    (       self :							str				)->Union[str, Any]:
      # Initialize image processor
      __SCREAMING_SNAKE_CASE       :    Any       =	self.image_processing_class(**self.image_processor_dict				)
      # create random PIL images
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=UpperCamelCase				)
      for image in image_inputs:
       self.assertIsInstance(UpperCamelCase     ,  Image.Image				)

      # Test not batched input
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	image_processing(image_inputs[0]     ,  return_tensors="pt"				).pixel_values

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Tuple       =	self.image_processor_tester.get_expected_values(UpperCamelCase				)
      self.assertEqual(
          encoded_images.shape     ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,  )

      # Test batched
      __SCREAMING_SNAKE_CASE       :    Any       =	image_processing(UpperCamelCase     ,  return_tensors="pt"				).pixel_values

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.image_processor_tester.get_expected_values(UpperCamelCase     ,  batched=UpperCamelCase				)
      self.assertEqual(
          encoded_images.shape     ,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )     ,  )





     def 	__snake_case    (       self :							List[str]				)->Optional[int]:
      # Initialize image processor
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.image_processing_class(**self.image_processor_dict				)
      # create random numpy tensors
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=UpperCamelCase     ,  numpify=UpperCamelCase				)
      for image in image_inputs:
       self.assertIsInstance(UpperCamelCase     ,  np.ndarray				)

      # Test not batched input
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	image_processing(image_inputs[0]     ,  return_tensors="pt"				).pixel_values

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.image_processor_tester.get_expected_values(UpperCamelCase				)
      self.assertEqual(
          encoded_images.shape     ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,  )

      # Test batched
      __SCREAMING_SNAKE_CASE       :    str       =	image_processing(UpperCamelCase     ,  return_tensors="pt"				).pixel_values

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[Any]       =	self.image_processor_tester.get_expected_values(UpperCamelCase     ,  batched=UpperCamelCase				)
      self.assertEqual(
          encoded_images.shape     ,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )     ,  )






     def 	__snake_case    (       self :							Optional[Any]				)->Union[str, Any]:
      # Initialize image processor
      __SCREAMING_SNAKE_CASE       :    Dict       =	self.image_processing_class(**self.image_processor_dict				)
      # create random PyTorch tensors
      __SCREAMING_SNAKE_CASE       :    List[str]       =	prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=UpperCamelCase     ,  torchify=UpperCamelCase				)
      for image in image_inputs:
       self.assertIsInstance(UpperCamelCase     ,  torch.Tensor				)

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

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Dict       =	self.image_processor_tester.get_expected_values(UpperCamelCase				)
      self.assertEqual(
          encoded_images.shape     ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,  )

      # Test batched
      __SCREAMING_SNAKE_CASE       :    str       =	image_processing(UpperCamelCase     ,  return_tensors="pt"				).pixel_values

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Any       =	self.image_processor_tester.get_expected_values(UpperCamelCase     ,  batched=UpperCamelCase				)
      self.assertEqual(
          encoded_images.shape     ,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )     ,  )






