

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 DeformableDetrImageProcessor






class  UpperCAmelCase__	( unittest.TestCase ):






  """simple docstring"""


  def __init__(							self		:	List[str]			,     __lowerCamelCase		:	Any			,     __lowerCamelCase		:	Dict=7			,     __lowerCamelCase		:	Optional[int]=3			,     __lowerCamelCase		:	int=30			,     __lowerCamelCase		:	Tuple=400			,     __lowerCamelCase		:	Dict=True			,     __lowerCamelCase		:	List[str]=None			,     __lowerCamelCase		:	Tuple=True			,     __lowerCamelCase		:	Optional[Any]=[0.5, 0.5, 0.5]			,     __lowerCamelCase		:	int=[0.5, 0.5, 0.5]			,     __lowerCamelCase		:	str=True			,     __lowerCamelCase		:	Any=1 / 255			,     __lowerCamelCase		:	Union[str, Any]=True			,     )	->							List[Any]:
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       SCREAMING_SNAKE_CASE__					=					size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
       SCREAMING_SNAKE_CASE__					=					parent
       SCREAMING_SNAKE_CASE__					=					batch_size
       SCREAMING_SNAKE_CASE__					=					num_channels
       SCREAMING_SNAKE_CASE__					=					min_resolution
       SCREAMING_SNAKE_CASE__					=					max_resolution
       SCREAMING_SNAKE_CASE__					=					do_resize
       SCREAMING_SNAKE_CASE__					=					size
       SCREAMING_SNAKE_CASE__					=					do_normalize
       SCREAMING_SNAKE_CASE__					=					image_mean
       SCREAMING_SNAKE_CASE__					=					image_std
       SCREAMING_SNAKE_CASE__					=					do_rescale
       SCREAMING_SNAKE_CASE__					=					rescale_factor
       SCREAMING_SNAKE_CASE__					=					do_pad






  def    lowercase_	(							self		:	Any      )	->							Union[str, Any]:
       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    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	List[Any]			,     __lowerCamelCase		:	Any=False      )	->							str:
       if not batched:
            SCREAMING_SNAKE_CASE__					=					image_inputs[0]
            if isinstance(__lowerCamelCase			,     Image.Image      ):
                 SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					image.size
            else:
                 SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					image.shape[1], image.shape[2]
            if w < h:
                 SCREAMING_SNAKE_CASE__					=					int(self.size['''shortest_edge'''] * h / w      )
                 SCREAMING_SNAKE_CASE__					=					self.size['''shortest_edge''']
            elif w > h:
                 SCREAMING_SNAKE_CASE__					=					self.size['''shortest_edge''']
                 SCREAMING_SNAKE_CASE__					=					int(self.size['''shortest_edge'''] * w / h      )
            else:
                 SCREAMING_SNAKE_CASE__					=					self.size['''shortest_edge''']
                 SCREAMING_SNAKE_CASE__					=					self.size['''shortest_edge''']

       else:
            SCREAMING_SNAKE_CASE__					=					[]
            for image in image_inputs:
                 SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.get_expected_values([image]      )
                 expected_values.append((expected_height, expected_width)      )
            SCREAMING_SNAKE_CASE__					=					max(__lowerCamelCase			,     key=lambda __lowerCamelCase      : item[0]      )[0]
            SCREAMING_SNAKE_CASE__					=					max(__lowerCamelCase			,     key=lambda __lowerCamelCase      : item[1]      )[1]

       return expected_height, expected_width






@require_torch
@require_vision
class  UpperCAmelCase__	( A__		,  unittest.TestCase ):






  """simple docstring"""


  a     =      DeformableDetrImageProcessor if is_vision_available() else None






  def    lowercase_	(							self		:	Optional[int]      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					DeformableDetrImageProcessingTester(self      )






  @property
  def    lowercase_	(							self		:	Dict      )	->							int:
       return self.image_processor_tester.prepare_image_processor_dict()






  def    lowercase_	(							self		:	Optional[Any]      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					self.image_processing_class(**self.image_processor_dict      )
       self.assertTrue(hasattr(__lowerCamelCase			,     '''image_mean'''      )      )
       self.assertTrue(hasattr(__lowerCamelCase			,     '''image_std'''      )      )
       self.assertTrue(hasattr(__lowerCamelCase			,     '''do_normalize'''      )      )
       self.assertTrue(hasattr(__lowerCamelCase			,     '''do_resize'''      )      )
       self.assertTrue(hasattr(__lowerCamelCase			,     '''do_rescale'''      )      )
       self.assertTrue(hasattr(__lowerCamelCase			,     '''do_pad'''      )      )
       self.assertTrue(hasattr(__lowerCamelCase			,     '''size'''      )      )






  def    lowercase_	(							self		:	int      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					self.image_processing_class.from_dict(self.image_processor_dict      )
       self.assertEqual(image_processor.size			,     {'''shortest_edge''': 18, '''longest_edge''': 1333}      )
       self.assertEqual(image_processor.do_pad			,     __lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.image_processing_class.from_dict(
           self.image_processor_dict			,     size=42			,     max_size=84			,     pad_and_return_pixel_mask=__lowerCamelCase      )
       self.assertEqual(image_processor.size			,     {'''shortest_edge''': 42, '''longest_edge''': 84}      )
       self.assertEqual(image_processor.do_pad			,     __lowerCamelCase      )






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






  def    lowercase_	(							self		:	Dict      )	->							List[str]:
       # Initialize image_processing
       SCREAMING_SNAKE_CASE__					=					self.image_processing_class(**self.image_processor_dict      )
       # create random PIL images
       SCREAMING_SNAKE_CASE__					=					prepare_image_inputs(self.image_processor_tester			,     equal_resolution=__lowerCamelCase      )
       for image in image_inputs:
            self.assertIsInstance(__lowerCamelCase			,     Image.Image      )

       # Test not batched input
       SCREAMING_SNAKE_CASE__					=					image_processing(image_inputs[0]			,     return_tensors='''pt'''      ).pixel_values

       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.image_processor_tester.get_expected_values(__lowerCamelCase      )

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

       # Test batched
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.image_processor_tester.get_expected_values(__lowerCamelCase			,     batched=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					image_processing(__lowerCamelCase			,     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    lowercase_	(							self		:	Tuple      )	->							Optional[Any]:
       # Initialize image_processing
       SCREAMING_SNAKE_CASE__					=					self.image_processing_class(**self.image_processor_dict      )
       # create random numpy tensors
       SCREAMING_SNAKE_CASE__					=					prepare_image_inputs(self.image_processor_tester			,     equal_resolution=__lowerCamelCase			,     numpify=__lowerCamelCase      )
       for image in image_inputs:
            self.assertIsInstance(__lowerCamelCase			,     np.ndarray      )

       # Test not batched input
       SCREAMING_SNAKE_CASE__					=					image_processing(image_inputs[0]			,     return_tensors='''pt'''      ).pixel_values

       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.image_processor_tester.get_expected_values(__lowerCamelCase      )

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

       # Test batched
       SCREAMING_SNAKE_CASE__					=					image_processing(__lowerCamelCase			,     return_tensors='''pt'''      ).pixel_values

       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.image_processor_tester.get_expected_values(__lowerCamelCase			,     batched=__lowerCamelCase      )

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






  def    lowercase_	(							self		:	Optional[Any]      )	->							str:
       # Initialize image_processing
       SCREAMING_SNAKE_CASE__					=					self.image_processing_class(**self.image_processor_dict      )
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE__					=					prepare_image_inputs(self.image_processor_tester			,     equal_resolution=__lowerCamelCase			,     torchify=__lowerCamelCase      )
       for image in image_inputs:
            self.assertIsInstance(__lowerCamelCase			,     torch.Tensor      )

       # Test not batched input
       SCREAMING_SNAKE_CASE__					=					image_processing(image_inputs[0]			,     return_tensors='''pt'''      ).pixel_values

       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.image_processor_tester.get_expected_values(__lowerCamelCase      )

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

       # Test batched
       SCREAMING_SNAKE_CASE__					=					image_processing(__lowerCamelCase			,     return_tensors='''pt'''      ).pixel_values

       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.image_processor_tester.get_expected_values(__lowerCamelCase			,     batched=__lowerCamelCase      )

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






  @slow
  def    lowercase_	(							self		:	Optional[int]      )	->							int:
       # prepare image and target
       SCREAMING_SNAKE_CASE__					=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''			,     '''r'''      ) as f:
            SCREAMING_SNAKE_CASE__					=					json.loads(f.read()      )

       SCREAMING_SNAKE_CASE__					=					{'''image_id''': 3_9769, '''annotations''': target}

       # encode them
       SCREAMING_SNAKE_CASE__					=					DeformableDetrImageProcessor()
       SCREAMING_SNAKE_CASE__					=					image_processing(images=__lowerCamelCase			,     annotations=__lowerCamelCase			,     return_tensors='''pt'''      )

       # verify pixel values
       SCREAMING_SNAKE_CASE__					=					torch.Size([1, 3, 800, 1066]      )
       self.assertEqual(encoding['''pixel_values'''].shape			,     __lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					torch.tensor([0.2796, 0.3138, 0.3481]      )
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]			,     __lowerCamelCase			,     atol=1e-4      )      )

       # verify area
       SCREAMING_SNAKE_CASE__					=					torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']			,     __lowerCamelCase      )      )
       # verify boxes
       SCREAMING_SNAKE_CASE__					=					torch.Size([6, 4]      )
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape			,     __lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]			,     __lowerCamelCase			,     atol=1e-3      )      )
       # verify image_id
       SCREAMING_SNAKE_CASE__					=					torch.tensor([3_9769]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']			,     __lowerCamelCase      )      )
       # verify is_crowd
       SCREAMING_SNAKE_CASE__					=					torch.tensor([0, 0, 0, 0, 0, 0]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']			,     __lowerCamelCase      )      )
       # verify class_labels
       SCREAMING_SNAKE_CASE__					=					torch.tensor([75, 75, 63, 65, 17, 17]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']			,     __lowerCamelCase      )      )
       # verify orig_size
       SCREAMING_SNAKE_CASE__					=					torch.tensor([480, 640]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']			,     __lowerCamelCase      )      )
       # verify size
       SCREAMING_SNAKE_CASE__					=					torch.tensor([800, 1066]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']			,     __lowerCamelCase      )      )







  @slow
  def    lowercase_	(							self		:	Optional[Any]      )	->							Optional[int]:
       # prepare image, target and masks_path
       SCREAMING_SNAKE_CASE__					=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''			,     '''r'''      ) as f:
            SCREAMING_SNAKE_CASE__					=					json.loads(f.read()      )

       SCREAMING_SNAKE_CASE__					=					{'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

       SCREAMING_SNAKE_CASE__					=					pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''      )

       # encode them
       SCREAMING_SNAKE_CASE__					=					DeformableDetrImageProcessor(format='''coco_panoptic'''      )
       SCREAMING_SNAKE_CASE__					=					image_processing(images=__lowerCamelCase			,     annotations=__lowerCamelCase			,     masks_path=__lowerCamelCase			,     return_tensors='''pt'''      )

       # verify pixel values
       SCREAMING_SNAKE_CASE__					=					torch.Size([1, 3, 800, 1066]      )
       self.assertEqual(encoding['''pixel_values'''].shape			,     __lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					torch.tensor([0.2796, 0.3138, 0.3481]      )
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]			,     __lowerCamelCase			,     atol=1e-4      )      )

       # verify area
       SCREAMING_SNAKE_CASE__					=					torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']			,     __lowerCamelCase      )      )
       # verify boxes
       SCREAMING_SNAKE_CASE__					=					torch.Size([6, 4]      )
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape			,     __lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]			,     __lowerCamelCase			,     atol=1e-3      )      )
       # verify image_id
       SCREAMING_SNAKE_CASE__					=					torch.tensor([3_9769]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']			,     __lowerCamelCase      )      )
       # verify is_crowd
       SCREAMING_SNAKE_CASE__					=					torch.tensor([0, 0, 0, 0, 0, 0]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']			,     __lowerCamelCase      )      )
       # verify class_labels
       SCREAMING_SNAKE_CASE__					=					torch.tensor([17, 17, 63, 75, 75, 93]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']			,     __lowerCamelCase      )      )
       # verify masks
       SCREAMING_SNAKE_CASE__					=					82_2873
       self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()			,     __lowerCamelCase      )
       # verify orig_size
       SCREAMING_SNAKE_CASE__					=					torch.tensor([480, 640]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']			,     __lowerCamelCase      )      )
       # verify size
       SCREAMING_SNAKE_CASE__					=					torch.tensor([800, 1066]      )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']			,     __lowerCamelCase      )      )





from __future__ import annotations

import collections
import pprint
from pathlib import Path
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     return "".join(sorted(_A       )       )
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     return word_by_signature[signature(_A       )]


_SCREAMING_SNAKE_CASE  :						str         =    Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
_SCREAMING_SNAKE_CASE  :						str         =    sorted({word.strip().lower() for word in data.splitlines()})

_SCREAMING_SNAKE_CASE  :						List[Any]         =    collections.defaultdict(list)
for word in word_list:
       word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE  :						Tuple         =    {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

       with open('''anagrams.txt''', '''w''') as file:
              file.write('''all_anagrams = \n ''')
              file.write(pprint.pformat(all_anagrams))


