





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    lowerCAmelCase				(  unittest.TestCase   ):







  def __init__(     self    :Union[str, Any]       ,				_lowercase    :int       ,				_lowercase    :List[Any]=7       ,				_lowercase    :Union[str, Any]=3       ,				_lowercase    :Optional[Any]=30       ,				_lowercase    :Dict=4_00       ,				_lowercase    :Optional[Any]=True       ,				_lowercase    :Union[str, Any]=None       ,				_lowercase    :Any=True       ,				_lowercase    :int=[0.5, 0.5, 0.5]       ,				_lowercase    :List[str]=[0.5, 0.5, 0.5]       ,				_lowercase    :List[str]=True       ,				_lowercase    :Tuple=1 / 2_55       ,				_lowercase    :List[str]=True       ,				):






     '''simple docstring'''





     lowercase__					  =    size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
     lowercase__					  =    parent
     lowercase__					  =    batch_size
     lowercase__					  =    num_channels
     lowercase__					  =    min_resolution
     lowercase__					  =    max_resolution
     lowercase__					  =    do_resize
     lowercase__					  =    size
     lowercase__					  =    do_normalize
     lowercase__					  =    image_mean
     lowercase__					  =    image_std
     lowercase__					  =    do_rescale
     lowercase__					  =    rescale_factor
     lowercase__					  =    do_pad







  def     UpperCAmelCase	(     self    :Optional[Any]   ):






     '''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    :Any       ,				_lowercase    :Optional[int]       ,				_lowercase    :Dict=False   ):






     '''simple docstring'''





     if not batched:
        lowercase__					  =    image_inputs[0]
        if isinstance(SCREAMING_SNAKE_CASE_       ,				Image.Image   ):
           lowercase__  ,				lowercase__					  =    image.size
        else:
           lowercase__  ,				lowercase__					  =    image.shape[1], image.shape[2]
        if w < h:
           lowercase__					  =    int(self.size["shortest_edge"] * h / w   )
           lowercase__					  =    self.size["shortest_edge"]
        elif w > h:
           lowercase__					  =    self.size["shortest_edge"]
           lowercase__					  =    int(self.size["shortest_edge"] * w / h   )
        else:
           lowercase__					  =    self.size["shortest_edge"]
           lowercase__					  =    self.size["shortest_edge"]

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

     return expected_height, expected_width





@require_torch
@require_vision
class    lowerCAmelCase				(  __lowercase				,				unittest.TestCase   ):
  __lowerCamelCase						=     DetaImageProcessor if is_vision_available() else None







  def     UpperCAmelCase	(     self    :int   ):






     '''simple docstring'''





     lowercase__					  =    DetaImageProcessingTester(self   )







  @property
  def     UpperCAmelCase	(     self    :Tuple   ):






     '''simple docstring'''





     return self.image_processor_tester.prepare_image_processor_dict()







  def     UpperCAmelCase	(     self    :Tuple   ):






     '''simple docstring'''





     lowercase__					  =    self.image_processing_class(**self.image_processor_dict   )
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,				"image_mean"   )   )
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,				"image_std"   )   )
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,				"do_normalize"   )   )
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,				"do_resize"   )   )
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,				"do_rescale"   )   )
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,				"do_pad"   )   )
     self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,				"size"   )   )







  def     UpperCAmelCase	(     self    :str   ):






     '''simple docstring'''





     lowercase__					  =    self.image_processing_class.from_dict(self.image_processor_dict   )
     self.assertEqual(image_processor.size       ,				{"shortest_edge": 18, "longest_edge": 13_33}   )
     self.assertEqual(image_processor.do_pad       ,				SCREAMING_SNAKE_CASE_   )







  def     UpperCAmelCase	(     self    :int   ):






     '''simple docstring'''





     pass







  def     UpperCAmelCase	(     self    :List[str]   ):






     '''simple docstring'''





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

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

     lowercase__  ,				lowercase__					  =    self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_   )

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

     # Test batched
     lowercase__  ,				lowercase__					  =    self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_       ,				batched=SCREAMING_SNAKE_CASE_   )

     lowercase__					  =    image_processing(SCREAMING_SNAKE_CASE_       ,				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    :int   ):






     '''simple docstring'''





     lowercase__					  =    self.image_processing_class(**self.image_processor_dict   )
     # create random numpy tensors
     lowercase__					  =    prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=SCREAMING_SNAKE_CASE_       ,				numpify=SCREAMING_SNAKE_CASE_   )
     for image in image_inputs:
        self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,				np.ndarray   )

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

     lowercase__  ,				lowercase__					  =    self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_   )

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

     # Test batched
     lowercase__					  =    image_processing(SCREAMING_SNAKE_CASE_       ,				return_tensors="pt"   ).pixel_values

     lowercase__  ,				lowercase__					  =    self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_       ,				batched=SCREAMING_SNAKE_CASE_   )

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







  def     UpperCAmelCase	(     self    :Any   ):






     '''simple docstring'''





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

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

     lowercase__  ,				lowercase__					  =    self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_   )

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

     # Test batched
     lowercase__					  =    image_processing(SCREAMING_SNAKE_CASE_       ,				return_tensors="pt"   ).pixel_values

     lowercase__  ,				lowercase__					  =    self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_       ,				batched=SCREAMING_SNAKE_CASE_   )

     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    :str   ):






     '''simple docstring'''





     lowercase__					  =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
     with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"       ,				"r"   ) as f:
        lowercase__					  =    json.loads(f.read()   )

     lowercase__					  =    {"image_id": 3_97_69, "annotations": target}

     # encode them
     lowercase__					  =    DetaImageProcessor()
     lowercase__					  =    image_processing(images=SCREAMING_SNAKE_CASE_       ,				annotations=SCREAMING_SNAKE_CASE_       ,				return_tensors="pt"   )

     # verify pixel values
     lowercase__					  =    torch.Size([1, 3, 8_00, 10_66]   )
     self.assertEqual(encoding["pixel_values"].shape       ,				SCREAMING_SNAKE_CASE_   )

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

     # verify area
     lowercase__					  =    torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]   )
     self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,				SCREAMING_SNAKE_CASE_   )   )
     # verify boxes
     lowercase__					  =    torch.Size([6, 4]   )
     self.assertEqual(encoding["labels"][0]["boxes"].shape       ,				SCREAMING_SNAKE_CASE_   )
     lowercase__					  =    torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]   )
     self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,				SCREAMING_SNAKE_CASE_       ,				atol=1e-3   )   )
     # verify image_id
     lowercase__					  =    torch.tensor([3_97_69]   )
     self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,				SCREAMING_SNAKE_CASE_   )   )
     # verify is_crowd
     lowercase__					  =    torch.tensor([0, 0, 0, 0, 0, 0]   )
     self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,				SCREAMING_SNAKE_CASE_   )   )
     # verify class_labels
     lowercase__					  =    torch.tensor([75, 75, 63, 65, 17, 17]   )
     self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,				SCREAMING_SNAKE_CASE_   )   )
     # verify orig_size
     lowercase__					  =    torch.tensor([4_80, 6_40]   )
     self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,				SCREAMING_SNAKE_CASE_   )   )
     # verify size
     lowercase__					  =    torch.tensor([8_00, 10_66]   )
     self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,				SCREAMING_SNAKE_CASE_   )   )







  @slow
  def     UpperCAmelCase	(     self    :Any   ):






     '''simple docstring'''





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

     lowercase__					  =    {"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

     lowercase__					  =    pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"   )

     # encode them
     lowercase__					  =    DetaImageProcessor(format="coco_panoptic"   )
     lowercase__					  =    image_processing(images=SCREAMING_SNAKE_CASE_       ,				annotations=SCREAMING_SNAKE_CASE_       ,				masks_path=SCREAMING_SNAKE_CASE_       ,				return_tensors="pt"   )

     # verify pixel values
     lowercase__					  =    torch.Size([1, 3, 8_00, 10_66]   )
     self.assertEqual(encoding["pixel_values"].shape       ,				SCREAMING_SNAKE_CASE_   )

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

     # verify area
     lowercase__					  =    torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]   )
     self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,				SCREAMING_SNAKE_CASE_   )   )
     # verify boxes
     lowercase__					  =    torch.Size([6, 4]   )
     self.assertEqual(encoding["labels"][0]["boxes"].shape       ,				SCREAMING_SNAKE_CASE_   )
     lowercase__					  =    torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]   )
     self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,				SCREAMING_SNAKE_CASE_       ,				atol=1e-3   )   )
     # verify image_id
     lowercase__					  =    torch.tensor([3_97_69]   )
     self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,				SCREAMING_SNAKE_CASE_   )   )
     # verify is_crowd
     lowercase__					  =    torch.tensor([0, 0, 0, 0, 0, 0]   )
     self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,				SCREAMING_SNAKE_CASE_   )   )
     # verify class_labels
     lowercase__					  =    torch.tensor([17, 17, 63, 75, 75, 93]   )
     self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,				SCREAMING_SNAKE_CASE_   )   )
     # verify masks
     lowercase__					  =    82_28_73
     self.assertEqual(encoding["labels"][0]["masks"].sum().item()       ,				SCREAMING_SNAKE_CASE_   )
     # verify orig_size
     lowercase__					  =    torch.tensor([4_80, 6_40]   )
     self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,				SCREAMING_SNAKE_CASE_   )   )
     # verify size
     lowercase__					  =    torch.tensor([8_00, 10_66]   )
     self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,				SCREAMING_SNAKE_CASE_   )   )








"""simple docstring"""




from __future__ import annotations



def 							_lowerCamelCase			(			__a,				__a = None     ):
		SCREAMING_SNAKE_CASE_    			=			word_bank or []
		# create a table
		SCREAMING_SNAKE_CASE_    			=			len(__a     ) + 1

		SCREAMING_SNAKE_CASE_    			=			[]
		for _ in range(__a     ):
				table.append([]     )
		# seed value
		SCREAMING_SNAKE_CASE_    			=			[[]]  # because empty string has empty combination

		# iterate through the indices
		for i in range(__a     ):
				# condition
				if table[i] != []:
						for word in word_bank:
								# slice condition
								if target[i : i + len(__a     )] == word:
										SCREAMING_SNAKE_CASE_    			=			[
										    [word, *way] for way in table[i]
										]
										# adds the word to every combination the current position holds
										# now,push that combination to the table[i+len(word)]
										table[i + len(__a     )] += new_combinations

    # combinations are in reverse order so reverse for better output
		for combination in table[len(__a     )]:
				combination.reverse()

		return table[len(__a     )]


if __name__ == "__main__":
						print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
						print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
						print(
						    all_construct(
						        'hexagonosaurus',
						        ['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
						    )
						)