



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 ConditionalDetrImageProcessor



class 		_snake_case   (      unittest.TestCase					):





 """simple docstring"""



 def __init__(    self						,     a						,     a=7						,     a=3						,     a=3_0						,     a=4_0_0						,     a=True						,     a=None						,     a=True						,     a=[0.5, 0.5, 0.5]						,     a=[0.5, 0.5, 0.5]						,     a=True						,     a=1 / 2_5_5						,     a=True						,     )						->				Union[str, Any]:




   """simple docstring"""







   _A							     =     size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
   _A							     =     parent
   _A							     =     batch_size
   _A							     =     num_channels
   _A							     =     min_resolution
   _A							     =     max_resolution
   _A							     =     do_resize
   _A							     =     size
   _A							     =     do_normalize
   _A							     =     image_mean
   _A							     =     image_std
   _A							     =     do_rescale
   _A							     =     rescale_factor
   _A							     =     do_pad
 def lowercase_		(    self       )						->				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 lowercase_		(    self						,     a						,     a=False       )						->				str:




   """simple docstring"""







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

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

   return expected_height, expected_width







@require_torch
@require_vision
class 		_snake_case   (      lowerCamelCase       ,unittest.TestCase					):





 """simple docstring"""



 lowerCamelCase_        =      ConditionalDetrImageProcessor if is_vision_available() else None
 def lowercase_		(    self       )						->				Any:




   """simple docstring"""







   _A							     =     ConditionalDetrImageProcessingTester(self       )
 @property
 def lowercase_		(    self       )						->				Union[str, Any]:




   """simple docstring"""







   return self.image_processor_tester.prepare_image_processor_dict()
 def lowercase_		(    self       )						->				List[str]:




   """simple docstring"""







   _A							     =     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_						,     '''size'''       )       )
 def lowercase_		(    self       )						->				Dict:




   """simple docstring"""







   _A							     =     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_       )

   _A							     =     self.image_processing_class.from_dict(
       self.image_processor_dict						,     size=4_2						,     max_size=8_4						,     pad_and_return_pixel_mask=A_       )
   self.assertEqual(image_processor.size						,     {'''shortest_edge''': 4_2, '''longest_edge''': 8_4}       )
   self.assertEqual(image_processor.do_pad						,     A_       )
 def lowercase_		(    self       )						->				List[Any]:




   """simple docstring"""







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




   """simple docstring"""







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

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

   _A     , _A							     =     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
   _A     , _A							     =     self.image_processor_tester.get_expected_values(A_						,     batched=A_       )

   _A							     =     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 lowercase_		(    self       )						->				Tuple:




   """simple docstring"""







   _A							     =     self.image_processing_class(**self.image_processor_dict       )
   # create random numpy tensors
   _A							     =     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
   _A							     =     image_processing(image_inputs[0]						,     return_tensors='''pt'''       ).pixel_values

   _A     , _A							     =     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
   _A							     =     image_processing(A_						,     return_tensors='''pt'''       ).pixel_values

   _A     , _A							     =     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 lowercase_		(    self       )						->				str:




   """simple docstring"""







   _A							     =     self.image_processing_class(**self.image_processor_dict       )
   # create random PyTorch tensors
   _A							     =     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
   _A							     =     image_processing(image_inputs[0]						,     return_tensors='''pt'''       ).pixel_values

   _A     , _A							     =     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
   _A							     =     image_processing(A_						,     return_tensors='''pt'''       ).pixel_values

   _A     , _A							     =     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 lowercase_		(    self       )						->				Union[str, Any]:




   """simple docstring"""







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

   _A							     =     {'''image_id''': 3_9_7_6_9, '''annotations''': target}

   # encode them
   _A							     =     ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''       )
   _A							     =     image_processing(images=A_						,     annotations=A_						,     return_tensors='''pt'''       )

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

   _A							     =     torch.tensor([0.2_796, 0.3_138, 0.3_481]       )
   self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]						,     A_						,     atol=1e-4       )       )

   # verify area
   _A							     =     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
   _A							     =     torch.Size([6, 4]       )
   self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape						,     A_       )
   _A							     =     torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]       )
   self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]						,     A_						,     atol=1e-3       )       )
   # verify image_id
   _A							     =     torch.tensor([3_9_7_6_9]       )
   self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']						,     A_       )       )
   # verify is_crowd
   _A							     =     torch.tensor([0, 0, 0, 0, 0, 0]       )
   self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']						,     A_       )       )
   # verify class_labels
   _A							     =     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
   _A							     =     torch.tensor([4_8_0, 6_4_0]       )
   self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']						,     A_       )       )
   # verify size
   _A							     =     torch.tensor([8_0_0, 1_0_6_6]       )
   self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']						,     A_       )       )



 @slow
 def lowercase_		(    self       )						->				str:




   """simple docstring"""







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

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

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

   # encode them
   _A							     =     ConditionalDetrImageProcessor(format='''coco_panoptic'''       )
   _A							     =     image_processing(images=A_						,     annotations=A_						,     masks_path=A_						,     return_tensors='''pt'''       )

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

   _A							     =     torch.tensor([0.2_796, 0.3_138, 0.3_481]       )
   self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]						,     A_						,     atol=1e-4       )       )

   # verify area
   _A							     =     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
   _A							     =     torch.Size([6, 4]       )
   self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape						,     A_       )
   _A							     =     torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]       )
   self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]						,     A_						,     atol=1e-3       )       )
   # verify image_id
   _A							     =     torch.tensor([3_9_7_6_9]       )
   self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']						,     A_       )       )
   # verify is_crowd
   _A							     =     torch.tensor([0, 0, 0, 0, 0, 0]       )
   self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']						,     A_       )       )
   # verify class_labels
   _A							     =     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
   _A							     =     8_2_2_8_7_3
   self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()						,     A_       )
   # verify orig_size
   _A							     =     torch.tensor([4_8_0, 6_4_0]       )
   self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']						,     A_       )       )
   # verify size
   _A							     =     torch.tensor([8_0_0, 1_0_6_6]       )
   self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']						,     A_       )       )






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	{
    'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json',
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'donut-swin'

 __UpperCamelCase       =      {
     'num_attention_heads': 'num_heads',
     'num_hidden_layers': 'num_layers',
 }

 def __init__(  self,		A_=224,		A_=4,		A_=3,		A_=96,		A_=[2, 2, 6, 2],		A_=[3, 6, 12, 24],		A_=7,		A_=4.0,		A_=True,		A_=0.0,		A_=0.0,		A_=0.1,		A_="gelu",		A_=False,		A_=0.02,		A_=1E-5,		**A_,		)	->       List[str]:
    super().__init__(**A_		)

    UpperCAmelCase__						=image_size
    UpperCAmelCase__						=patch_size
    UpperCAmelCase__						=num_channels
    UpperCAmelCase__						=embed_dim
    UpperCAmelCase__						=depths
    UpperCAmelCase__						=len(A_		)
    UpperCAmelCase__						=num_heads
    UpperCAmelCase__						=window_size
    UpperCAmelCase__						=mlp_ratio
    UpperCAmelCase__						=qkv_bias
    UpperCAmelCase__						=hidden_dropout_prob
    UpperCAmelCase__						=attention_probs_dropout_prob
    UpperCAmelCase__						=drop_path_rate
    UpperCAmelCase__						=hidden_act
    UpperCAmelCase__						=use_absolute_embeddings
    UpperCAmelCase__						=layer_norm_eps
    UpperCAmelCase__						=initializer_range
    # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
    # this indicates the channel dimension after the last stage of the model
    UpperCAmelCase__						=int(embed_dim * 2 ** (len(A_		) - 1)		)



