






"""simple docstring"""





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetaImageProcessor


class      _lowercase (     unittest.TestCase						):


      def __init__(    self				:	Any							,   a				:	List[str]							,   a				:	Optional[int]=7							,   a				:	str=3							,   a				:	int=3_0							,   a				:	Any=4_0_0							,   a				:	Any=True							,   a				:	Any=None							,   a				:	int=True							,   a				:	str=[0.5, 0.5, 0.5]							,   a				:	List[Any]=[0.5, 0.5, 0.5]							,   a				:	Dict=True							,   a				:	Dict=1 / 2_5_5							,   a				:	Any=True							,   ):





            """simple docstring"""







            __snake_case      :    Optional[int]  			=size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
            __snake_case      :    Any  			=parent
            __snake_case      :    List[Any]  			=batch_size
            __snake_case      :    Optional[int]  			=num_channels
            __snake_case      :    Dict  			=min_resolution
            __snake_case      :    str  			=max_resolution
            __snake_case      :    Optional[Any]  			=do_resize
            __snake_case      :    List[str]  			=size
            __snake_case      :    str  			=do_normalize
            __snake_case      :    Dict  			=image_mean
            __snake_case      :    List[Any]  			=image_std
            __snake_case      :    Dict  			=do_rescale
            __snake_case      :    Tuple  			=rescale_factor
            __snake_case      :    Any  			=do_pad


      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_rescale": self.do_rescale,
                "rescale_factor": self.rescale_factor,
                "do_pad": self.do_pad,
            }







      def _UpperCamelCase						(    self				:	Tuple							,   a				:	List[str]							,   a				:	List[Any]=False						):





            """simple docstring"""







            if not batched:
                  __snake_case      :    List[str]  			=image_inputs[0]
                  if isinstance(a							,   Image.Image						):
                        __snake_case			,				__snake_case      :    int  			=image.size
                  else:
                        __snake_case			,				__snake_case      :    List[str]  			=image.shape[1], image.shape[2]
                  if w < h:
                        __snake_case      :    Optional[Any]  			=int(self.size['''shortest_edge'''] * h / w						)
                        __snake_case      :    Optional[int]  			=self.size['''shortest_edge''']
                  elif w > h:
                        __snake_case      :    int  			=self.size['''shortest_edge''']
                        __snake_case      :    int  			=int(self.size['''shortest_edge'''] * w / h						)
                  else:
                        __snake_case      :    Optional[Any]  			=self.size['''shortest_edge''']
                        __snake_case      :    Tuple  			=self.size['''shortest_edge''']

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

            return expected_height, expected_width



@require_torch
@require_vision
class      _lowercase (     lowerCAmelCase		,  unittest.TestCase						):
      _a   :     Any								=   DetaImageProcessor if is_vision_available() else None


      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            __snake_case      :    Union[str, Any]  			=DetaImageProcessingTester(self						)


      @property
      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            return self.image_processor_tester.prepare_image_processor_dict()


      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







            __snake_case      :    Optional[Any]  			=self.image_processing_class(**self.image_processor_dict						)
            self.assertTrue(hasattr(a							,   '''image_mean'''						)						)
            self.assertTrue(hasattr(a							,   '''image_std'''						)						)
            self.assertTrue(hasattr(a							,   '''do_normalize'''						)						)
            self.assertTrue(hasattr(a							,   '''do_resize'''						)						)
            self.assertTrue(hasattr(a							,   '''do_rescale'''						)						)
            self.assertTrue(hasattr(a							,   '''do_pad'''						)						)
            self.assertTrue(hasattr(a							,   '''size'''						)						)


      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            __snake_case      :    str  			=self.image_processing_class.from_dict(self.image_processor_dict						)
            self.assertEqual(image_processor.size							,   {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}						)
            self.assertEqual(image_processor.do_pad							,   a						)


      def _UpperCamelCase						(    self				:	Optional[Any]						):





            """simple docstring"""







            pass


      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            __snake_case      :    Any  			=self.image_processing_class(**self.image_processor_dict						)
            # create random PIL images
            __snake_case      :    int  			=prepare_image_inputs(self.image_processor_tester							,   equal_resolution=a						)
            for image in image_inputs:
                  self.assertIsInstance(a							,   Image.Image						)

            # Test not batched input
            __snake_case      :    List[str]  			=image_processing(image_inputs[0]							,   return_tensors='''pt'''						).pixel_values

            __snake_case			,				__snake_case      :    Tuple  			=self.image_processor_tester.get_expected_values(a						)

            self.assertEqual(
                encoded_images.shape							,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,   )

            # Test batched
            __snake_case			,				__snake_case      :    Any  			=self.image_processor_tester.get_expected_values(a							,   batched=a						)

            __snake_case      :    str  			=image_processing(a							,   return_tensors='''pt'''						).pixel_values
            self.assertEqual(
                encoded_images.shape							,   (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )							,   )


      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            __snake_case      :    Optional[Any]  			=self.image_processing_class(**self.image_processor_dict						)
            # create random numpy tensors
            __snake_case      :    List[str]  			=prepare_image_inputs(self.image_processor_tester							,   equal_resolution=a							,   numpify=a						)
            for image in image_inputs:
                  self.assertIsInstance(a							,   np.ndarray						)

            # Test not batched input
            __snake_case      :    List[str]  			=image_processing(image_inputs[0]							,   return_tensors='''pt'''						).pixel_values

            __snake_case			,				__snake_case      :    List[str]  			=self.image_processor_tester.get_expected_values(a						)

            self.assertEqual(
                encoded_images.shape							,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,   )

            # Test batched
            __snake_case      :    Dict  			=image_processing(a							,   return_tensors='''pt'''						).pixel_values

            __snake_case			,				__snake_case      :    Optional[Any]  			=self.image_processor_tester.get_expected_values(a							,   batched=a						)

            self.assertEqual(
                encoded_images.shape							,   (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )							,   )


      def _UpperCamelCase						(    self				:	Optional[Any]						):





            """simple docstring"""







            __snake_case      :    Any  			=self.image_processing_class(**self.image_processor_dict						)
            # create random PyTorch tensors
            __snake_case      :    Dict  			=prepare_image_inputs(self.image_processor_tester							,   equal_resolution=a							,   torchify=a						)
            for image in image_inputs:
                  self.assertIsInstance(a							,   torch.Tensor						)

            # Test not batched input
            __snake_case      :    List[Any]  			=image_processing(image_inputs[0]							,   return_tensors='''pt'''						).pixel_values

            __snake_case			,				__snake_case      :    Any  			=self.image_processor_tester.get_expected_values(a						)

            self.assertEqual(
                encoded_images.shape							,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,   )

            # Test batched
            __snake_case      :    Optional[int]  			=image_processing(a							,   return_tensors='''pt'''						).pixel_values

            __snake_case			,				__snake_case      :    List[Any]  			=self.image_processor_tester.get_expected_values(a							,   batched=a						)

            self.assertEqual(
                encoded_images.shape							,   (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )							,   )


      @slow
      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            __snake_case      :    int  			=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
            with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''							,   '''r'''						) as f:
                  __snake_case      :    Optional[int]  			=json.loads(f.read()						)

            __snake_case      :    Dict  			={'''image_id''': 3_9_7_6_9, '''annotations''': target}

            # encode them
            __snake_case      :    Any  			=DetaImageProcessor()
            __snake_case      :    Optional[Any]  			=image_processing(images=a							,   annotations=a							,   return_tensors='''pt'''						)

            # verify pixel values
            __snake_case      :    int  			=torch.Size([1, 3, 8_0_0, 1_0_6_6]						)
            self.assertEqual(encoding['''pixel_values'''].shape							,   a						)

            __snake_case      :    int  			=torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]						)
            self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]							,   a							,   atol=1e-4						)						)

            # verify area
            __snake_case      :    Tuple  			=torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]						)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']							,   a						)						)
            # verify boxes
            __snake_case      :    Dict  			=torch.Size([6, 4]						)
            self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape							,   a						)
            __snake_case      :    Tuple  			=torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]						)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]							,   a							,   atol=1e-3						)						)
            # verify image_id
            __snake_case      :    Dict  			=torch.tensor([3_9_7_6_9]						)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']							,   a						)						)
            # verify is_crowd
            __snake_case      :    Optional[Any]  			=torch.tensor([0, 0, 0, 0, 0, 0]						)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']							,   a						)						)
            # verify class_labels
            __snake_case      :    Any  			=torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]						)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']							,   a						)						)
            # verify orig_size
            __snake_case      :    Tuple  			=torch.tensor([4_8_0, 6_4_0]						)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']							,   a						)						)
            # verify size
            __snake_case      :    List[str]  			=torch.tensor([8_0_0, 1_0_6_6]						)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							,   a						)						)







      @slow
      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            __snake_case      :    Any  			=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
            with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''							,   '''r'''						) as f:
                  __snake_case      :    int  			=json.loads(f.read()						)

            __snake_case      :    str  			={'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

            __snake_case      :    int  			=pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''						)

            # encode them
            __snake_case      :    List[str]  			=DetaImageProcessor(format='''coco_panoptic'''						)
            __snake_case      :    Union[str, Any]  			=image_processing(images=a							,   annotations=a							,   masks_path=a							,   return_tensors='''pt'''						)

            # verify pixel values
            __snake_case      :    Optional[Any]  			=torch.Size([1, 3, 8_0_0, 1_0_6_6]						)
            self.assertEqual(encoding['''pixel_values'''].shape							,   a						)

            __snake_case      :    List[str]  			=torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]						)
            self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]							,   a							,   atol=1e-4						)						)

            # verify area
            __snake_case      :    Any  			=torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]						)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']							,   a						)						)
            # verify boxes
            __snake_case      :    List[Any]  			=torch.Size([6, 4]						)
            self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape							,   a						)
            __snake_case      :    Dict  			=torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]						)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]							,   a							,   atol=1e-3						)						)
            # verify image_id
            __snake_case      :    List[Any]  			=torch.tensor([3_9_7_6_9]						)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']							,   a						)						)
            # verify is_crowd
            __snake_case      :    Optional[int]  			=torch.tensor([0, 0, 0, 0, 0, 0]						)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']							,   a						)						)
            # verify class_labels
            __snake_case      :    Any  			=torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]						)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']							,   a						)						)
            # verify masks
            __snake_case      :    Dict  			=8_2_2_8_7_3
            self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()							,   a						)
            # verify orig_size
            __snake_case      :    str  			=torch.tensor([4_8_0, 6_4_0]						)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']							,   a						)						)
            # verify size
            __snake_case      :    int  			=torch.tensor([8_0_0, 1_0_6_6]						)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							,   a						)						)










"""simple docstring"""





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def     __lowercase					(    a :				str )					->					None:
      __snake_case			,				__snake_case      :    List[Any]  			=analyze_text(a )
      __snake_case      :    Union[str, Any]  			=list(''' ''' + ascii_lowercase )
      # what is our total sum of probabilities.
      __snake_case      :    List[Any]  			=sum(single_char_strings.values() )

      # one length string
      __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:
                  __snake_case      :    Union[str, Any]  			=single_char_strings[ch]
                  __snake_case      :    Any  			=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
      __snake_case      :    Optional[Any]  			=sum(two_char_strings.values() )
      __snake_case      :    Dict  			=0
      # for each alpha (two in size) calculate entropy.
      for cha in my_alphas:
            for cha in my_alphas:
                  __snake_case      :    Tuple  			=cha + cha
                  if sequence in two_char_strings:
                        __snake_case      :    List[Any]  			=two_char_strings[sequence]
                        __snake_case      :    Tuple  			=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     __lowercase					(    a :				str )					->					tuple[dict, dict]:
      __snake_case      :    List[str]  			=Counter()  # type: ignore
      __snake_case      :    Union[str, Any]  			=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     __lowercase					(    )					->					int:
      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()


