import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import AlignProcessor, EfficientNetImageProcessor



@require_vision
class     _A   (    unittest.TestCase							):






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      tempfile.mkdtemp()

       UpperCamelCase__										=      [
           '''[UNK]''',
           '''[CLS]''',
           '''[SEP]''',
           '''[PAD]''',
           '''[MASK]''',
           '''want''',
           '''##want''',
           '''##ed''',
           '''wa''',
           '''un''',
           '''runn''',
           '''##ing''',
           ''',''',
           '''low''',
           '''lowest''',
       ]
       UpperCamelCase__										=      os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''vocab_file''']	)
       with open(self.vocab_file   ,				'''w'''   ,				encoding='''utf-8'''	) as vocab_writer:
            vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]	)	)

       UpperCamelCase__										=      {
           '''do_resize''': True,
           '''size''': 20,
           '''do_center_crop''': True,
           '''crop_size''': 18,
           '''do_normalize''': True,
           '''image_mean''': [0.48145466, 0.4578275, 0.40821073],
           '''image_std''': [0.26862954, 0.26130258, 0.27577711],
       }
       UpperCamelCase__										=      os.path.join(self.tmpdirname   ,				SCREAMING_SNAKE_CASE_	)
       with open(self.image_processor_file   ,				'''w'''   ,				encoding='''utf-8'''	) as fp:
            json.dump(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)






  def 		_a					(self   ,				**SCREAMING_SNAKE_CASE_	)		->      List[Any]:





       '''simple docstring'''





       return BertTokenizer.from_pretrained(self.tmpdirname   ,				**SCREAMING_SNAKE_CASE_	)






  def 		_a					(self   ,				**SCREAMING_SNAKE_CASE_	)		->      Tuple:





       '''simple docstring'''





       return BertTokenizerFast.from_pretrained(self.tmpdirname   ,				**SCREAMING_SNAKE_CASE_	)






  def 		_a					(self   ,				**SCREAMING_SNAKE_CASE_	)		->      Tuple:





       '''simple docstring'''





       return EfficientNetImageProcessor.from_pretrained(self.tmpdirname   ,				**SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       shutil.rmtree(self.tmpdirname	)






  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      [np.random.randint(255   ,				size=(3, 30, 400)   ,				dtype=np.uinta	)]
       UpperCamelCase__										=      [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_   ,				0   ,				-1	)	) for x in image_inputs]
       return image_inputs






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





       '''simple docstring'''





       UpperCamelCase__										=      self.get_tokenizer()
       UpperCamelCase__										=      self.get_rust_tokenizer()
       UpperCamelCase__										=      self.get_image_processor()

       UpperCamelCase__										=      AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,				image_processor=SCREAMING_SNAKE_CASE_	)
       processor_slow.save_pretrained(self.tmpdirname	)
       UpperCamelCase__										=      AlignProcessor.from_pretrained(self.tmpdirname   ,				use_fast=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,				image_processor=SCREAMING_SNAKE_CASE_	)
       processor_fast.save_pretrained(self.tmpdirname	)
       UpperCamelCase__										=      AlignProcessor.from_pretrained(self.tmpdirname	)

       self.assertEqual(processor_slow.tokenizer.get_vocab()   ,				tokenizer_slow.get_vocab()	)
       self.assertEqual(processor_fast.tokenizer.get_vocab()   ,				tokenizer_fast.get_vocab()	)
       self.assertEqual(tokenizer_slow.get_vocab()   ,				tokenizer_fast.get_vocab()	)
       self.assertIsInstance(processor_slow.tokenizer   ,				SCREAMING_SNAKE_CASE_	)
       self.assertIsInstance(processor_fast.tokenizer   ,				SCREAMING_SNAKE_CASE_	)

       self.assertEqual(processor_slow.image_processor.to_json_string()   ,				image_processor.to_json_string()	)
       self.assertEqual(processor_fast.image_processor.to_json_string()   ,				image_processor.to_json_string()	)
       self.assertIsInstance(processor_slow.image_processor   ,				SCREAMING_SNAKE_CASE_	)
       self.assertIsInstance(processor_fast.image_processor   ,				SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       UpperCamelCase__										=      AlignProcessor(tokenizer=self.get_tokenizer()   ,				image_processor=self.get_image_processor()	)
       processor.save_pretrained(self.tmpdirname	)

       UpperCamelCase__										=      self.get_tokenizer(bos_token='''(BOS)'''   ,				eos_token='''(EOS)'''	)
       UpperCamelCase__										=      self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_   ,				padding_value=1.0	)

       UpperCamelCase__										=      AlignProcessor.from_pretrained(
           self.tmpdirname   ,				bos_token='''(BOS)'''   ,				eos_token='''(EOS)'''   ,				do_normalize=SCREAMING_SNAKE_CASE_   ,				padding_value=1.0	)

       self.assertEqual(processor.tokenizer.get_vocab()   ,				tokenizer_add_kwargs.get_vocab()	)
       self.assertIsInstance(processor.tokenizer   ,				SCREAMING_SNAKE_CASE_	)

       self.assertEqual(processor.image_processor.to_json_string()   ,				image_processor_add_kwargs.to_json_string()	)
       self.assertIsInstance(processor.image_processor   ,				SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      self.get_image_processor()
       UpperCamelCase__										=      self.get_tokenizer()

       UpperCamelCase__										=      AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,				image_processor=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      self.prepare_image_inputs()

       UpperCamelCase__										=      image_processor(SCREAMING_SNAKE_CASE_   ,				return_tensors='''np'''	)
       UpperCamelCase__										=      processor(images=SCREAMING_SNAKE_CASE_   ,				return_tensors='''np'''	)

       for key in input_image_proc.keys():
            self.assertAlmostEqual(input_image_proc[key].sum()   ,				input_processor[key].sum()   ,				delta=1E-2	)






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      self.get_image_processor()
       UpperCamelCase__										=      self.get_tokenizer()

       UpperCamelCase__										=      AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,				image_processor=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      '''lower newer'''

       UpperCamelCase__										=      processor(text=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      tokenizer(SCREAMING_SNAKE_CASE_   ,				padding='''max_length'''   ,				max_length=64	)

       for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key]   ,				encoded_processor[key]	)






  def 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       UpperCamelCase__										=      self.get_image_processor()
       UpperCamelCase__										=      self.get_tokenizer()

       UpperCamelCase__										=      AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,				image_processor=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      '''lower newer'''
       UpperCamelCase__										=      self.prepare_image_inputs()

       UpperCamelCase__										=      processor(text=SCREAMING_SNAKE_CASE_   ,				images=SCREAMING_SNAKE_CASE_	)

       self.assertListEqual(list(inputs.keys()	)   ,				['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']	)

       # test if it raises when no input is passed
       with pytest.raises(SCREAMING_SNAKE_CASE_	):
            processor()






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      self.get_image_processor()
       UpperCamelCase__										=      self.get_tokenizer()

       UpperCamelCase__										=      AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,				image_processor=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       UpperCamelCase__										=      processor.batch_decode(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      tokenizer.batch_decode(SCREAMING_SNAKE_CASE_	)

       self.assertListEqual(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      self.get_image_processor()
       UpperCamelCase__										=      self.get_tokenizer()

       UpperCamelCase__										=      AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,				image_processor=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      '''lower newer'''
       UpperCamelCase__										=      self.prepare_image_inputs()

       UpperCamelCase__										=      processor(text=SCREAMING_SNAKE_CASE_   ,				images=SCREAMING_SNAKE_CASE_	)

       self.assertListEqual(list(inputs.keys()	)   ,				processor.model_input_names	)




from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def  __UpperCamelCase    (						A							):
     UpperCamelCase__						,       UpperCamelCase__										=      analyze_text(A							)
     UpperCamelCase__										=      list(''' ''' + ascii_lowercase							)
     # what is our total sum of probabilities.
     UpperCamelCase__										=      sum(single_char_strings.values()							)

     # one length string
     UpperCamelCase__										=      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:
               UpperCamelCase__										=      single_char_strings[ch]
               UpperCamelCase__										=      my_str / all_sum
               my_fir_sum += prob * math.loga(A							)  # entropy formula.

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

     # two len string
     UpperCamelCase__										=      sum(two_char_strings.values()							)
     UpperCamelCase__										=      0
     # for each alpha (two in size) calculate entropy.
     for cha in my_alphas:
          for cha in my_alphas:
               UpperCamelCase__										=      cha + cha
               if sequence in two_char_strings:
                    UpperCamelCase__										=      two_char_strings[sequence]
                    UpperCamelCase__										=      int(A							) / all_sum
                    my_sec_sum += prob * math.loga(A							)

    # 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  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      Counter()  # type: ignore
     UpperCamelCase__										=      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(A							) - 1							):
          single_char_strings[text[i]] += 1
          two_char_strings[text[i : i + 2]] += 1
     return single_char_strings, two_char_strings







def  __UpperCamelCase    (						):
     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()



