


"""simple docstring"""







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 				__A    (	unittest.TestCase      ):






 """simple docstring"""




 def __init__(		self			,   __A			,   __A=7			,   __A=3			,   __A=30			,   __A=400			,   __A=True			,   __A=None			,   __A=True			,   __A=[0.5, 0.5, 0.5]			,   __A=[0.5, 0.5, 0.5]			,   __A=True			,   __A=1 / 255			,   __A=True			,   ) ->					List[str]:
     # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
     a					=size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
     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 				SCREAMING_SNAKE_CASE							(		self						) ->					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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A=False						) ->					Optional[Any]:
     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 				__A    (	_SCREAMING_SNAKE_CASE,   unittest.TestCase      ):






 """simple docstring"""




 __lowerCAmelCase			 =  YolosImageProcessor if is_vision_available() else None


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     a					=YolosImageProcessingTester(self						)


 @property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     return self.image_processor_tester.prepare_image_processor_dict()


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     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 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     a					=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			,   __A						)

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


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     pass


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     # Initialize image_processing
     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 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     # Initialize image_processing
     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 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
     # Initialize image_processing
     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,
         )			,   )


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     # Initialize image_processings
     a					=self.image_processing_class(**self.image_processor_dict						)
     a					=self.image_processing_class(do_resize=__A			,   do_normalize=__A			,   do_rescale=__A						)
     # 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 whether the method "pad" and calling the image processor return the same tensors
     a					=image_processing_a.pad(__A			,   return_tensors='''pt'''						)
     a					=image_processing_a(__A			,   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						) ->					Tuple:
     # prepare image and target
     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_9769, '''annotations''': target}

     # encode them
     a					=YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''						)
     a					=image_processing(images=__A			,   annotations=__A			,   return_tensors='''pt'''						)

     # verify pixel values
     a					=torch.Size([1, 3, 800, 1066]						)
     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_887.9_600, 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''']			,   __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_9769]						)
     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([75, 75, 63, 65, 17, 17]						)
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']			,   __A						)						)
     # verify orig_size
     a					=torch.tensor([480, 640]						)
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']			,   __A						)						)
     # verify size
     a					=torch.tensor([800, 1066]						)
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']			,   __A						)						)





 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     # prepare image, target and masks_path
     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_9769, '''segments_info''': target}

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

     # encode them
     a					=YolosImageProcessor(format='''coco_panoptic'''						)
     a					=image_processing(images=__A			,   annotations=__A			,   masks_path=__A			,   return_tensors='''pt'''						)

     # verify pixel values
     a					=torch.Size([1, 3, 800, 1066]						)
     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([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]						)
     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_9769]						)
     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([17, 17, 63, 75, 75, 93]						)
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']			,   __A						)						)
     # verify masks
     a					=82_2873
     self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()			,   __A						)
     # verify orig_size
     a					=torch.tensor([480, 640]						)
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']			,   __A						)						)
     # verify size
     a					=torch.tensor([800, 1066]						)
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']			,   __A						)						)



"""simple docstring"""







import os
import sys
import unittest


lowerCamelCase_       :       Tuple      						=  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


lowerCamelCase_       :       Dict      						=  os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
lowerCamelCase_       :       Dict      						=  os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")






class 				__A    (	unittest.TestCase      ):






 """simple docstring"""




 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     a					=get_test_to_tester_mapping(__A						)
     a					=get_test_to_tester_mapping(__A						)

     a					={'''BertModelTest''': '''BertModelTester'''}

     a					={
         '''BlipModelTest''': '''BlipModelTester''',
         '''BlipTextImageModelTest''': '''BlipTextImageModelsModelTester''',
         '''BlipTextModelTest''': '''BlipTextModelTester''',
         '''BlipTextRetrievalModelTest''': '''BlipTextRetrievalModelTester''',
         '''BlipVQAModelTest''': '''BlipVQAModelTester''',
         '''BlipVisionModelTest''': '''BlipVisionModelTester''',
     }

     self.assertEqual(get_test_info.to_json(__A						)			,   __A						)
     self.assertEqual(get_test_info.to_json(__A						)			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     a					=get_model_to_test_mapping(__A						)
     a					=get_model_to_test_mapping(__A						)

     a					={
         '''BertForMaskedLM''': ['''BertModelTest'''],
         '''BertForMultipleChoice''': ['''BertModelTest'''],
         '''BertForNextSentencePrediction''': ['''BertModelTest'''],
         '''BertForPreTraining''': ['''BertModelTest'''],
         '''BertForQuestionAnswering''': ['''BertModelTest'''],
         '''BertForSequenceClassification''': ['''BertModelTest'''],
         '''BertForTokenClassification''': ['''BertModelTest'''],
         '''BertLMHeadModel''': ['''BertModelTest'''],
         '''BertModel''': ['''BertModelTest'''],
     }

     a					={
         '''BlipForConditionalGeneration''': ['''BlipTextImageModelTest'''],
         '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTest'''],
         '''BlipForQuestionAnswering''': ['''BlipVQAModelTest'''],
         '''BlipModel''': ['''BlipModelTest'''],
         '''BlipTextModel''': ['''BlipTextModelTest'''],
         '''BlipVisionModel''': ['''BlipVisionModelTest'''],
     }

     self.assertEqual(get_test_info.to_json(__A						)			,   __A						)
     self.assertEqual(get_test_info.to_json(__A						)			,   __A						)





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     a					=get_model_to_tester_mapping(__A						)
     a					=get_model_to_tester_mapping(__A						)

     a					={
         '''BertForMaskedLM''': ['''BertModelTester'''],
         '''BertForMultipleChoice''': ['''BertModelTester'''],
         '''BertForNextSentencePrediction''': ['''BertModelTester'''],
         '''BertForPreTraining''': ['''BertModelTester'''],
         '''BertForQuestionAnswering''': ['''BertModelTester'''],
         '''BertForSequenceClassification''': ['''BertModelTester'''],
         '''BertForTokenClassification''': ['''BertModelTester'''],
         '''BertLMHeadModel''': ['''BertModelTester'''],
         '''BertModel''': ['''BertModelTester'''],
     }

     a					={
         '''BlipForConditionalGeneration''': ['''BlipTextImageModelsModelTester'''],
         '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTester'''],
         '''BlipForQuestionAnswering''': ['''BlipVQAModelTester'''],
         '''BlipModel''': ['''BlipModelTester'''],
         '''BlipTextModel''': ['''BlipTextModelTester'''],
         '''BlipVisionModel''': ['''BlipVisionModelTester'''],
     }

     self.assertEqual(get_test_info.to_json(__A						)			,   __A						)
     self.assertEqual(get_test_info.to_json(__A						)			,   __A						)