




"""simple docstring"""


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
  import torch


if is_vision_available():
  from PIL import Image

  from transformers import MgpstrProcessor, ViTImageProcessor


@require_torch
@require_vision
class _lowerCamelCase    (					unittest.TestCase						):
   _lowerCamelCase    :Union[str, Any]     				=     ViTImageProcessor if is_vision_available() else None






   @property
   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						str:

         """simple docstring"""
         return self.image_processor_tester.prepare_image_processor_dict()






   def 	_lowerCAmelCase   (		self       :		Dict   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			Tuple   =	(3, 32, 1_28)
         lowerCAmelCase__      :			List[Any]   =	tempfile.mkdtemp()

         # fmt: off
         lowerCAmelCase__      :			Union[str, Any]   =	["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
         # fmt: on
         lowerCAmelCase__      :			Dict   =	dict(zip(UpperCamelCase   ,     range(len(UpperCamelCase   )   )   )   )

         lowerCAmelCase__      :			int   =	os.path.join(self.tmpdirname   ,     VOCAB_FILES_NAMES["""vocab_file"""]   )
         with open(self.vocab_file   ,     """w"""   ,     encoding="""utf-8"""   ) as fp:
               fp.write(json.dumps(UpperCamelCase   ) + """\n"""   )

         lowerCAmelCase__      :			Any   =	{
             """do_normalize""": False,
             """do_resize""": True,
             """image_processor_type""": """ViTImageProcessor""",
             """resample""": 3,
             """size""": {"""height""": 32, """width""": 1_28},
         }
         lowerCAmelCase__      :			List[str]   =	os.path.join(self.tmpdirname   ,     UpperCamelCase   )
         with open(self.image_processor_file   ,     """w"""   ,     encoding="""utf-8"""   ) as fp:
               json.dump(UpperCamelCase   ,     UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   ,     **UpperCamelCase       :		Dict   )			->						Optional[Any]:

         """simple docstring"""
         return MgpstrTokenizer.from_pretrained(self.tmpdirname   ,     **UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		List[Any]   ,     **UpperCamelCase       :		str   )			->						str:

         """simple docstring"""
         return ViTImageProcessor.from_pretrained(self.tmpdirname   ,     **UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						Tuple:

         """simple docstring"""
         shutil.rmtree(self.tmpdirname   )






   def 	_lowerCAmelCase   (		self       :		Dict   )			->						Optional[int]:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	np.random.randint(2_55   ,     size=(3, 30, 4_00)   ,     dtype=np.uinta   )

         lowerCAmelCase__      :			Union[str, Any]   =	Image.fromarray(np.moveaxis(UpperCamelCase   ,     0   ,     -1   )   )

         return image_input






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

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	self.get_tokenizer()
         lowerCAmelCase__      :			List[Any]   =	self.get_image_processor()

         lowerCAmelCase__      :			List[str]   =	MgpstrProcessor(tokenizer=UpperCamelCase   ,     image_processor=UpperCamelCase   )
         processor.save_pretrained(self.tmpdirname   )
         lowerCAmelCase__      :			List[str]   =	MgpstrProcessor.from_pretrained(self.tmpdirname   ,     use_fast=UpperCamelCase   )

         self.assertEqual(processor.char_tokenizer.get_vocab()   ,     tokenizer.get_vocab()   )
         self.assertIsInstance(processor.char_tokenizer   ,     UpperCamelCase   )

         self.assertEqual(processor.image_processor.to_json_string()   ,     image_processor.to_json_string()   )
         self.assertIsInstance(processor.image_processor   ,     UpperCamelCase   )






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

         """simple docstring"""
         lowerCAmelCase__      :			int   =	self.get_tokenizer()
         lowerCAmelCase__      :			Any   =	self.get_image_processor()

         lowerCAmelCase__      :			int   =	MgpstrProcessor(tokenizer=UpperCamelCase   ,     image_processor=UpperCamelCase   )
         processor.save_pretrained(self.tmpdirname   )

         lowerCAmelCase__      :			str   =	self.get_tokenizer(bos_token="""(BOS)"""   ,     eos_token="""(EOS)"""   )
         lowerCAmelCase__      :			List[str]   =	self.get_image_processor(do_normalize=UpperCamelCase   ,     padding_value=1.0   )

         lowerCAmelCase__      :			str   =	MgpstrProcessor.from_pretrained(
             self.tmpdirname   ,     bos_token="""(BOS)"""   ,     eos_token="""(EOS)"""   ,     do_normalize=UpperCamelCase   ,     padding_value=1.0   )

         self.assertEqual(processor.char_tokenizer.get_vocab()   ,     tokenizer_add_kwargs.get_vocab()   )
         self.assertIsInstance(processor.char_tokenizer   ,     UpperCamelCase   )

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






   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			int   =	self.get_image_processor()
         lowerCAmelCase__      :			str   =	self.get_tokenizer()

         lowerCAmelCase__      :			str   =	MgpstrProcessor(tokenizer=UpperCamelCase   ,     image_processor=UpperCamelCase   )

         lowerCAmelCase__      :			Dict   =	self.prepare_image_inputs()

         lowerCAmelCase__      :			List[Any]   =	image_processor(UpperCamelCase   ,     return_tensors="""np"""   )
         lowerCAmelCase__      :			Optional[Any]   =	processor(images=UpperCamelCase   ,     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 	_lowerCAmelCase   (		self       :		List[Any]   )			->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	self.get_image_processor()
         lowerCAmelCase__      :			List[Any]   =	self.get_tokenizer()

         lowerCAmelCase__      :			Any   =	MgpstrProcessor(tokenizer=UpperCamelCase   ,     image_processor=UpperCamelCase   )

         lowerCAmelCase__      :			Tuple   =	"""test"""

         lowerCAmelCase__      :			int   =	processor(text=UpperCamelCase   )

         lowerCAmelCase__      :			List[str]   =	tokenizer(UpperCamelCase   )
         for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]   ,     encoded_processor[key]   )






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

         """simple docstring"""
         lowerCAmelCase__      :			Tuple   =	self.get_image_processor()
         lowerCAmelCase__      :			Union[str, Any]   =	self.get_tokenizer()

         lowerCAmelCase__      :			List[Any]   =	MgpstrProcessor(tokenizer=UpperCamelCase   ,     image_processor=UpperCamelCase   )

         lowerCAmelCase__      :			str   =	"""test"""
         lowerCAmelCase__      :			List[str]   =	self.prepare_image_inputs()

         lowerCAmelCase__      :			Union[str, Any]   =	processor(text=UpperCamelCase   ,     images=UpperCamelCase   )

         self.assertListEqual(list(inputs.keys()   )   ,     ["""pixel_values""", """labels"""]   )

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






   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	self.get_image_processor()
         lowerCAmelCase__      :			Tuple   =	self.get_tokenizer()

         lowerCAmelCase__      :			Union[str, Any]   =	MgpstrProcessor(tokenizer=UpperCamelCase   ,     image_processor=UpperCamelCase   )

         lowerCAmelCase__      :			Dict   =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

         lowerCAmelCase__      :			List[str]   =	processor.char_decode(UpperCamelCase   )
         lowerCAmelCase__      :			Optional[int]   =	tokenizer.batch_decode(UpperCamelCase   )
         lowerCAmelCase__      :			Tuple   =	[seq.replace(""" """   ,     """"""   ) for seq in decoded_tok]

         self.assertListEqual(UpperCamelCase   ,     UpperCamelCase   )






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

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	self.get_image_processor()
         lowerCAmelCase__      :			Tuple   =	self.get_tokenizer()

         lowerCAmelCase__      :			Optional[Any]   =	MgpstrProcessor(tokenizer=UpperCamelCase   ,     image_processor=UpperCamelCase   )

         lowerCAmelCase__      :			Tuple   =	None
         lowerCAmelCase__      :			Union[str, Any]   =	self.prepare_image_inputs()

         lowerCAmelCase__      :			int   =	processor(text=UpperCamelCase   ,     images=UpperCamelCase   )

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






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

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	self.get_image_processor()
         lowerCAmelCase__      :			Any   =	self.get_tokenizer()

         lowerCAmelCase__      :			int   =	MgpstrProcessor(tokenizer=UpperCamelCase   ,     image_processor=UpperCamelCase   )

         lowerCAmelCase__      :			Union[str, Any]   =	torch.randn(1   ,     27   ,     38   )
         lowerCAmelCase__      :			Any   =	torch.randn(1   ,     27   ,     5_02_57   )
         lowerCAmelCase__      :			Any   =	torch.randn(1   ,     27   ,     3_05_22   )

         lowerCAmelCase__      :			Optional[Any]   =	processor.batch_decode([char_input, bpe_input, wp_input]   )

         self.assertListEqual(list(results.keys()   )   ,     ["""generated_text""", """scores""", """char_preds""", """bpe_preds""", """wp_preds"""]   )











"""simple docstring"""


from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def 	lowercase_				(			__UpperCAmelCase				)						->    None:
      lowerCAmelCase__ ,   lowerCAmelCase__      :			int   =	analyze_text(__UpperCAmelCase				)
      lowerCAmelCase__      :			Optional[Any]   =	list(""" """ + ascii_lowercase				)
      # what is our total sum of probabilities.
      lowerCAmelCase__      :			List[str]   =	sum(single_char_strings.values()				)

      # one length string
      lowerCAmelCase__      :			List[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:
                  lowerCAmelCase__      :			List[Any]   =	single_char_strings[ch]
                  lowerCAmelCase__      :			int   =	my_str / all_sum
                  my_fir_sum += prob * math.loga(__UpperCAmelCase				)  # entropy formula.

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

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

    # 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 	lowercase_				(			__UpperCAmelCase				)						->    tuple[dict, dict]:
      lowerCAmelCase__      :			Any   =	Counter()  # type: ignore
      lowerCAmelCase__      :			Tuple   =	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(__UpperCAmelCase				) - 1				):
            single_char_strings[text[i]] += 1
            two_char_strings[text[i : i + 2]] += 1
      return single_char_strings, two_char_strings



def 	lowercase_				(			)						->    Any:
      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()





