



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    ,						_lowercase    ,						_lowercase=7    ,						_lowercase=3    ,						_lowercase=30    ,						_lowercase=400    ,						_lowercase=True    ,						_lowercase=None    ,						_lowercase=True    ,						_lowercase=[0.5, 0.5, 0.5]    ,						_lowercase=[0.5, 0.5, 0.5]    ,						_lowercase=True    ,						_lowercase=1 / 255    ,						_lowercase=True    ,						):
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       lowerCAmelCase_					:       Optional[Any]    =		size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1_333}
       lowerCAmelCase_					:       Dict    =		parent
       lowerCAmelCase_					:       Optional[Any]    =		batch_size
       lowerCAmelCase_					:       Optional[Any]    =		num_channels
       lowerCAmelCase_					:       Union[str, Any]    =		min_resolution
       lowerCAmelCase_					:       List[str]    =		max_resolution
       lowerCAmelCase_					:       Optional[Any]    =		do_resize
       lowerCAmelCase_					:       Optional[Any]    =		size
       lowerCAmelCase_					:       Optional[int]    =		do_normalize
       lowerCAmelCase_					:       str    =		image_mean
       lowerCAmelCase_					:       Optional[int]    =		image_std
       lowerCAmelCase_					:       Optional[int]    =		do_rescale
       lowerCAmelCase_					:       Optional[int]    =		rescale_factor
       lowerCAmelCase_					:       List[str]    =		do_pad







  def 	UpperCAmelCase__		(	self						):
       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    ,						_lowercase    ,						_lowercase=False						):
       if not batched:
            lowerCAmelCase_					:       List[Any]    =		image_inputs[0]
            if isinstance(_lowercase    ,						Image.Image						):
                 lowerCAmelCase_					:       Dict    =		image.size
            else:
                 lowerCAmelCase_					:       List[Any]    =		image.shape[1], image.shape[2]
            if w < h:
                 lowerCAmelCase_					:       Optional[Any]    =		int(self.size["""shortest_edge"""] * h / w						)
                 lowerCAmelCase_					:       Tuple    =		self.size["""shortest_edge"""]
            elif w > h:
                 lowerCAmelCase_					:       Union[str, Any]    =		self.size["""shortest_edge"""]
                 lowerCAmelCase_					:       Tuple    =		int(self.size["""shortest_edge"""] * w / h						)
            else:
                 lowerCAmelCase_					:       Tuple    =		self.size["""shortest_edge"""]
                 lowerCAmelCase_					:       str    =		self.size["""shortest_edge"""]

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

       return expected_height, expected_width





@require_torch
@require_vision
class      lowercase__    (							__A     ,					unittest.TestCase  ):
  __UpperCamelCase	     =  DetaImageProcessor if is_vision_available() else None







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Union[str, Any]    =		DetaImageProcessingTester(self						)







  @property
  def 	UpperCAmelCase__		(	self						):
       return self.image_processor_tester.prepare_image_processor_dict()







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Optional[int]    =		self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(_lowercase    ,						"""image_mean"""						)						)
       self.assertTrue(hasattr(_lowercase    ,						"""image_std"""						)						)
       self.assertTrue(hasattr(_lowercase    ,						"""do_normalize"""						)						)
       self.assertTrue(hasattr(_lowercase    ,						"""do_resize"""						)						)
       self.assertTrue(hasattr(_lowercase    ,						"""do_rescale"""						)						)
       self.assertTrue(hasattr(_lowercase    ,						"""do_pad"""						)						)
       self.assertTrue(hasattr(_lowercase    ,						"""size"""						)						)







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       str    =		self.image_processing_class.from_dict(self.image_processor_dict						)
       self.assertEqual(image_processor.size    ,						{"""shortest_edge""": 18, """longest_edge""": 1_333}						)
       self.assertEqual(image_processor.do_pad    ,						_lowercase						)







  def 	UpperCAmelCase__		(	self						):
       pass







  def 	UpperCAmelCase__		(	self						):
       # Initialize image_processing
       lowerCAmelCase_					:       Dict    =		self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       lowerCAmelCase_					:       Dict    =		prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=_lowercase						)
       for image in image_inputs:
            self.assertIsInstance(_lowercase    ,						Image.Image						)

       # Test not batched input
       lowerCAmelCase_					:       Optional[Any]    =		image_processing(image_inputs[0]    ,						return_tensors="""pt"""						).pixel_values

       lowerCAmelCase_					:       List[Any]    =		self.image_processor_tester.get_expected_values(_lowercase						)

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

       # Test batched
       lowerCAmelCase_					:       Any    =		self.image_processor_tester.get_expected_values(_lowercase    ,						batched=_lowercase						)

       lowerCAmelCase_					:       Optional[Any]    =		image_processing(_lowercase    ,						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						):
       # Initialize image_processing
       lowerCAmelCase_					:       Any    =		self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       lowerCAmelCase_					:       Dict    =		prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=_lowercase    ,						numpify=_lowercase						)
       for image in image_inputs:
            self.assertIsInstance(_lowercase    ,						np.ndarray						)

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

       lowerCAmelCase_					:       Optional[Any]    =		self.image_processor_tester.get_expected_values(_lowercase						)

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

       # Test batched
       lowerCAmelCase_					:       List[Any]    =		image_processing(_lowercase    ,						return_tensors="""pt"""						).pixel_values

       lowerCAmelCase_					:       Tuple    =		self.image_processor_tester.get_expected_values(_lowercase    ,						batched=_lowercase						)

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







  def 	UpperCAmelCase__		(	self						):
       # Initialize image_processing
       lowerCAmelCase_					:       int    =		self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       lowerCAmelCase_					:       Optional[int]    =		prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=_lowercase    ,						torchify=_lowercase						)
       for image in image_inputs:
            self.assertIsInstance(_lowercase    ,						torch.Tensor						)

       # Test not batched input
       lowerCAmelCase_					:       Union[str, Any]    =		image_processing(image_inputs[0]    ,						return_tensors="""pt"""						).pixel_values

       lowerCAmelCase_					:       Union[str, Any]    =		self.image_processor_tester.get_expected_values(_lowercase						)

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

       # Test batched
       lowerCAmelCase_					:       Optional[Any]    =		image_processing(_lowercase    ,						return_tensors="""pt"""						).pixel_values

       lowerCAmelCase_					:       Optional[int]    =		self.image_processor_tester.get_expected_values(_lowercase    ,						batched=_lowercase						)

       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						):
       # prepare image and target
       lowerCAmelCase_					:       str    =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
       with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""    ,						"""r"""						) as f:
            lowerCAmelCase_					:       str    =		json.loads(f.read()						)

       lowerCAmelCase_					:       Tuple    =		{"""image_id""": 39_769, """annotations""": target}

       # encode them
       lowerCAmelCase_					:       Optional[Any]    =		DetaImageProcessor()
       lowerCAmelCase_					:       List[Any]    =		image_processing(images=_lowercase    ,						annotations=_lowercase    ,						return_tensors="""pt"""						)

       # verify pixel values
       lowerCAmelCase_					:       Optional[Any]    =		torch.Size([1, 3, 800, 1_066]						)
       self.assertEqual(encoding["""pixel_values"""].shape    ,						_lowercase						)

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

       # verify area
       lowerCAmelCase_					:       Any    =		torch.tensor([5887.9600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]						)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    ,						_lowercase						)						)
       # verify boxes
       lowerCAmelCase_					:       Dict    =		torch.Size([6, 4]						)
       self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    ,						_lowercase						)
       lowerCAmelCase_					:       Optional[int]    =		torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]						)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    ,						_lowercase    ,						atol=1e-3						)						)
       # verify image_id
       lowerCAmelCase_					:       Optional[int]    =		torch.tensor([39_769]						)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    ,						_lowercase						)						)
       # verify is_crowd
       lowerCAmelCase_					:       Optional[Any]    =		torch.tensor([0, 0, 0, 0, 0, 0]						)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    ,						_lowercase						)						)
       # verify class_labels
       lowerCAmelCase_					:       Optional[int]    =		torch.tensor([75, 75, 63, 65, 17, 17]						)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    ,						_lowercase						)						)
       # verify orig_size
       lowerCAmelCase_					:       Tuple    =		torch.tensor([480, 640]						)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    ,						_lowercase						)						)
       # verify size
       lowerCAmelCase_					:       int    =		torch.tensor([800, 1_066]						)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    ,						_lowercase						)						)







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

       lowerCAmelCase_					:       Any    =		{"""file_name""": """000000039769.png""", """image_id""": 39_769, """segments_info""": target}

       lowerCAmelCase_					:       Optional[Any]    =		pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""						)

       # encode them
       lowerCAmelCase_					:       Optional[Any]    =		DetaImageProcessor(format="""coco_panoptic"""						)
       lowerCAmelCase_					:       List[str]    =		image_processing(images=_lowercase    ,						annotations=_lowercase    ,						masks_path=_lowercase    ,						return_tensors="""pt"""						)

       # verify pixel values
       lowerCAmelCase_					:       Union[str, Any]    =		torch.Size([1, 3, 800, 1_066]						)
       self.assertEqual(encoding["""pixel_values"""].shape    ,						_lowercase						)

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

       # verify area
       lowerCAmelCase_					:       List[str]    =		torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5879.6562, 7634.1147]						)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    ,						_lowercase						)						)
       # verify boxes
       lowerCAmelCase_					:       List[Any]    =		torch.Size([6, 4]						)
       self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    ,						_lowercase						)
       lowerCAmelCase_					:       Tuple    =		torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]						)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    ,						_lowercase    ,						atol=1e-3						)						)
       # verify image_id
       lowerCAmelCase_					:       List[str]    =		torch.tensor([39_769]						)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    ,						_lowercase						)						)
       # verify is_crowd
       lowerCAmelCase_					:       Union[str, Any]    =		torch.tensor([0, 0, 0, 0, 0, 0]						)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    ,						_lowercase						)						)
       # verify class_labels
       lowerCAmelCase_					:       Optional[int]    =		torch.tensor([17, 17, 63, 75, 75, 93]						)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    ,						_lowercase						)						)
       # verify masks
       lowerCAmelCase_					:       Dict    =		822_873
       self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()    ,						_lowercase						)
       # verify orig_size
       lowerCAmelCase_					:       List[Any]    =		torch.tensor([480, 640]						)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    ,						_lowercase						)						)
       # verify size
       lowerCAmelCase_					:       List[Any]    =		torch.tensor([800, 1_066]						)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    ,						_lowercase						)						)







from collections.abc import Generator
from math import sin






def 							_lowerCAmelCase (  _a   :					bytes			)	->			bytes:
     if len(_a			) != 32:
          raise ValueError("""Input must be of length 32"""			)

     lowerCAmelCase_					:       Any    =		B""""""
     for i in [3, 2, 1, 0]:
          little_endian += string_aa[8 * i : 8 * i + 8]
     return little_endian






def 							_lowerCAmelCase (  _a   :					int			)	->			bytes:
     if i < 0:
          raise ValueError("""Input must be non-negative"""			)

     lowerCAmelCase_					:       Tuple    =		format(_a					,   """08x"""			)[-8:]
     lowerCAmelCase_					:       Any    =		B""""""
     for i in [3, 2, 1, 0]:
          little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("""utf-8"""			)
     return little_endian_hex






def 							_lowerCAmelCase (  _a   :					bytes			)	->			bytes:
     lowerCAmelCase_					:       Tuple    =		B""""""
     for char in message:
          bit_string += format(_a					,   """08b"""			).encode("""utf-8"""			)
     lowerCAmelCase_					:       Dict    =		format(len(_a			)					,   """064b"""			).encode("""utf-8"""			)

     # Pad bit_string to a multiple of 512 chars
     bit_string += b"1"
     while len(_a			) % 5_12 != 4_48:
          bit_string += b"0"
     bit_string += to_little_endian(start_len[32:]			) + to_little_endian(start_len[:32]			)

     return bit_string






def 							_lowerCAmelCase (  _a   :					bytes			)	->			Generator[list[int], None, None]:
     if len(_a			) % 5_12 != 0:
          raise ValueError("""Input must have length that's a multiple of 512"""			)

     for pos in range(0					,   len(_a			)					,   5_12			):
          lowerCAmelCase_					:       int    =		bit_string[pos : pos + 5_12]
          lowerCAmelCase_					:       Any    =		[]
          for i in range(0					,   5_12					,   32			):
               block_words.append(int(to_little_endian(block[i : i + 32]			)					,   2			)			)
          yield block_words






def 							_lowerCAmelCase (  _a   :					int			)	->			int:
     if i < 0:
          raise ValueError("""Input must be non-negative"""			)

     lowerCAmelCase_					:       List[str]    =		format(_a					,   """032b"""			)
     lowerCAmelCase_					:       Optional[int]    =		""""""
     for c in i_str:
          new_str += "1" if c == "0" else "0"
     return int(_a					,   2			)






def 							_lowerCAmelCase (  _a   :					int					,   _a   :					int			)	->			int:
     return (a + b) % 2**32






def 							_lowerCAmelCase (  _a   :					int					,   _a   :					int			)	->			int:
     if i < 0:
          raise ValueError("""Input must be non-negative"""			)
     if shift < 0:
          raise ValueError("""Shift must be non-negative"""			)
     return ((i << shift) ^ (i >> (32 - shift))) % 2**32






def 							_lowerCAmelCase (  _a   :					bytes			)	->			bytes:
     lowerCAmelCase_					:       Union[str, Any]    =		preprocess(_a			)

     lowerCAmelCase_					:       Optional[int]    =		[int(2**32 * abs(sin(i + 1			)			)			) for i in range(64			)]

     # Starting states
     lowerCAmelCase_					:       Tuple    =		0X67452301
     lowerCAmelCase_					:       Optional[int]    =		0XEFCDAB89
     lowerCAmelCase_					:       Tuple    =		0X98BADCFE
     lowerCAmelCase_					:       Tuple    =		0X10325476

     lowerCAmelCase_					:       Dict    =		[
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
     ]

     # Process bit string in chunks, each with 16 32-char words
     for block_words in get_block_words(_a			):
          lowerCAmelCase_					:       int    =		aa
          lowerCAmelCase_					:       Any    =		ba
          lowerCAmelCase_					:       List[str]    =		ca
          lowerCAmelCase_					:       Optional[Any]    =		da

          # Hash current chunk
          for i in range(64			):
               if i <= 15:
                    # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
                    lowerCAmelCase_					:       int    =		d ^ (b & (c ^ d))
                    lowerCAmelCase_					:       List[Any]    =		i
               elif i <= 31:
                    # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
                    lowerCAmelCase_					:       List[Any]    =		c ^ (d & (b ^ c))
                    lowerCAmelCase_					:       Optional[Any]    =		(5 * i + 1) % 16
               elif i <= 47:
                    lowerCAmelCase_					:       Union[str, Any]    =		b ^ c ^ d
                    lowerCAmelCase_					:       Optional[Any]    =		(3 * i + 5) % 16
               else:
                    lowerCAmelCase_					:       Any    =		c ^ (b | not_aa(_a			))
                    lowerCAmelCase_					:       List[str]    =		(7 * i) % 16
               lowerCAmelCase_					:       Tuple    =		(f + a + added_consts[i] + block_words[g]) % 2**32
               lowerCAmelCase_					:       Tuple    =		d
               lowerCAmelCase_					:       Optional[Any]    =		c
               lowerCAmelCase_					:       Dict    =		b
               lowerCAmelCase_					:       List[Any]    =		sum_aa(_a					,   left_rotate_aa(_a					,   shift_amounts[i]			)			)

          # Add hashed chunk to running total
          lowerCAmelCase_					:       Optional[int]    =		sum_aa(_a					,   _a			)
          lowerCAmelCase_					:       Optional[int]    =		sum_aa(_a					,   _a			)
          lowerCAmelCase_					:       Dict    =		sum_aa(_a					,   _a			)
          lowerCAmelCase_					:       Tuple    =		sum_aa(_a					,   _a			)

     lowerCAmelCase_					:       int    =		reformat_hex(_a			) + reformat_hex(_a			) + reformat_hex(_a			) + reformat_hex(_a			)
     return digest


if __name__ == "__main__":
      import doctest

      doctest.testmod()
