
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 YolosImageProcessor




class 				_lowerCamelCase (      unittest.TestCase ):





     """simple docstring"""







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




           """simple docstring"""







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






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """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    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=False  )  ->   List[str]:




           """simple docstring"""







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

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

           return expected_height, expected_width




@require_torch
@require_vision
class 				_lowerCamelCase (      UpperCamelCase_	,							unittest.TestCase ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  YolosImageProcessor if is_vision_available() else None






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				YolosImageProcessingTester(self  )






     @property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           return self.image_processor_tester.prepare_image_processor_dict()






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   int					=				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						,						'''size'''  )  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				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						,						__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Tuple					=				self.image_processing_class.from_dict(
               self.image_processor_dict						,						size=4_2						,						max_size=8_4						,						pad_and_return_pixel_mask=__SCREAMING_SNAKE_CASE  )
           self.assertEqual(image_processor.size						,						{'''shortest_edge''': 4_2, '''longest_edge''': 8_4}  )
           self.assertEqual(image_processor.do_pad						,						__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[str]:




           """simple docstring"""







           pass






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				self.image_processing_class(**self.image_processor_dict  )
           # create random PIL images
           UpperCamelCase__    :   Any					=				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
           UpperCamelCase__    :   Union[str, Any]					=				image_processing(image_inputs[0]						,						return_tensors='''pt'''  ).pixel_values

           UpperCamelCase__				,UpperCamelCase__    :   Any					=				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
           UpperCamelCase__				,UpperCamelCase__    :   List[str]					=				self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE						,						batched=__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Any					=				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    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				self.image_processing_class(**self.image_processor_dict  )
           # create random numpy tensors
           UpperCamelCase__    :   List[str]					=				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
           UpperCamelCase__    :   Optional[Any]					=				image_processing(image_inputs[0]						,						return_tensors='''pt'''  ).pixel_values

           UpperCamelCase__				,UpperCamelCase__    :   List[str]					=				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
           UpperCamelCase__    :   Union[str, Any]					=				image_processing(__SCREAMING_SNAKE_CASE						,						return_tensors='''pt'''  ).pixel_values

           UpperCamelCase__				,UpperCamelCase__    :   Union[str, Any]					=				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    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				self.image_processing_class(**self.image_processor_dict  )
           # create random PyTorch tensors
           UpperCamelCase__    :   List[Any]					=				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
           UpperCamelCase__    :   int					=				image_processing(image_inputs[0]						,						return_tensors='''pt'''  ).pixel_values

           UpperCamelCase__				,UpperCamelCase__    :   Dict					=				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
           UpperCamelCase__    :   Dict					=				image_processing(__SCREAMING_SNAKE_CASE						,						return_tensors='''pt'''  ).pixel_values

           UpperCamelCase__				,UpperCamelCase__    :   Any					=				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    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				self.image_processing_class(**self.image_processor_dict  )
           UpperCamelCase__    :   Optional[int]					=				self.image_processing_class(do_resize=__SCREAMING_SNAKE_CASE						,						do_normalize=__SCREAMING_SNAKE_CASE						,						do_rescale=__SCREAMING_SNAKE_CASE  )
           # create random PyTorch tensors
           UpperCamelCase__    :   Tuple					=				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 whether the method "pad" and calling the image processor return the same tensors
           UpperCamelCase__    :   Union[str, Any]					=				image_processing_a.pad(__SCREAMING_SNAKE_CASE						,						return_tensors='''pt'''  )
           UpperCamelCase__    :   List[str]					=				image_processing_a(__SCREAMING_SNAKE_CASE						,						return_tensors='''pt'''  )

           self.assertTrue(
               torch.allclose(encoded_images_with_method['''pixel_values''']						,						encoded_images['''pixel_values''']						,						atol=1e-4  )  )






     @slow
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




           """simple docstring"""







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

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

           # encode them
           UpperCamelCase__    :   int					=				YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''  )
           UpperCamelCase__    :   int					=				image_processing(images=__SCREAMING_SNAKE_CASE						,						annotations=__SCREAMING_SNAKE_CASE						,						return_tensors='''pt'''  )

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

           UpperCamelCase__    :   Dict					=				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
           UpperCamelCase__    :   Optional[Any]					=				torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]  )
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']						,						__SCREAMING_SNAKE_CASE  )  )
           # verify boxes
           UpperCamelCase__    :   Dict					=				torch.Size([6, 4]  )
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape						,						__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[str]					=				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
           UpperCamelCase__    :   List[Any]					=				torch.tensor([3_9_7_6_9]  )
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']						,						__SCREAMING_SNAKE_CASE  )  )
           # verify is_crowd
           UpperCamelCase__    :   Optional[Any]					=				torch.tensor([0, 0, 0, 0, 0, 0]  )
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']						,						__SCREAMING_SNAKE_CASE  )  )
           # verify class_labels
           UpperCamelCase__    :   Optional[Any]					=				torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]  )
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']						,						__SCREAMING_SNAKE_CASE  )  )
           # verify orig_size
           UpperCamelCase__    :   str					=				torch.tensor([4_8_0, 6_4_0]  )
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']						,						__SCREAMING_SNAKE_CASE  )  )
           # verify size
           UpperCamelCase__    :   Dict					=				torch.tensor([8_0_0, 1_0_6_6]  )
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']						,						__SCREAMING_SNAKE_CASE  )  )






     @slow
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[str]:




           """simple docstring"""







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

           UpperCamelCase__    :   Optional[Any]					=				{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

           UpperCamelCase__    :   str					=				pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''  )

           # encode them
           UpperCamelCase__    :   Optional[int]					=				YolosImageProcessor(format='''coco_panoptic'''  )
           UpperCamelCase__    :   Any					=				image_processing(images=__SCREAMING_SNAKE_CASE						,						annotations=__SCREAMING_SNAKE_CASE						,						masks_path=__SCREAMING_SNAKE_CASE						,						return_tensors='''pt'''  )

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

           UpperCamelCase__    :   Any					=				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
           UpperCamelCase__    :   Union[str, Any]					=				torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]  )
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']						,						__SCREAMING_SNAKE_CASE  )  )
           # verify boxes
           UpperCamelCase__    :   Dict					=				torch.Size([6, 4]  )
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape						,						__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Dict					=				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
           UpperCamelCase__    :   str					=				torch.tensor([3_9_7_6_9]  )
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']						,						__SCREAMING_SNAKE_CASE  )  )
           # verify is_crowd
           UpperCamelCase__    :   Optional[Any]					=				torch.tensor([0, 0, 0, 0, 0, 0]  )
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']						,						__SCREAMING_SNAKE_CASE  )  )
           # verify class_labels
           UpperCamelCase__    :   Tuple					=				torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]  )
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']						,						__SCREAMING_SNAKE_CASE  )  )
           # verify masks
           UpperCamelCase__    :   int					=				8_2_2_8_7_3
           self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()						,						__SCREAMING_SNAKE_CASE  )
           # verify orig_size
           UpperCamelCase__    :   int					=				torch.tensor([4_8_0, 6_4_0]  )
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']						,						__SCREAMING_SNAKE_CASE  )  )
           # verify size
           UpperCamelCase__    :   str					=				torch.tensor([8_0_0, 1_0_6_6]  )
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']						,						__SCREAMING_SNAKE_CASE  )  )




import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  '''M-CLIP'''






     def __init__(							self						,						__SCREAMING_SNAKE_CASE=1_0_2_4						,						__SCREAMING_SNAKE_CASE=7_6_8						,						**__SCREAMING_SNAKE_CASE  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				transformerDimSize
           UpperCamelCase__    :   Any					=				imageDimSize
           super().__init__(**__SCREAMING_SNAKE_CASE  )




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  MCLIPConfig






     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )  ->   Optional[int]:




           """simple docstring"""







           super().__init__(__SCREAMING_SNAKE_CASE						,						*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, Any]					=				XLMRobertaModel(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Tuple					=				torch.nn.Linear(
               in_features=config.transformerDimensions						,						out_features=config.numDims  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				self.transformer(input_ids=__SCREAMING_SNAKE_CASE						,						attention_mask=__SCREAMING_SNAKE_CASE  )[0]
           UpperCamelCase__    :   Tuple					=				(embs * attention_mask.unsqueeze(2  )).sum(dim=1  ) / attention_mask.sum(dim=1  )[:, None]
           return self.LinearTransformation(__SCREAMING_SNAKE_CASE  ), embs


