






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 DetrImageProcessor




class     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







       def __init__( self  ,      _lowerCamelCase  ,      _lowerCamelCase=7  ,      _lowerCamelCase=3  ,      _lowerCamelCase=3_0  ,      _lowerCamelCase=4_0_0  ,      _lowerCamelCase=True  ,      _lowerCamelCase=None  ,      _lowerCamelCase=True  ,      _lowerCamelCase=1 / 2_5_5  ,      _lowerCamelCase=True  ,      _lowerCamelCase=[0.5, 0.5, 0.5]  ,      _lowerCamelCase=[0.5, 0.5, 0.5]  ,      _lowerCamelCase=True  ,      ):
         UpperCamelCase_: Optional[Any]   				=    size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
         UpperCamelCase_: int   				=    parent
         UpperCamelCase_: int   				=    batch_size
         UpperCamelCase_: Optional[int]   				=    num_channels
         UpperCamelCase_: List[str]   				=    min_resolution
         UpperCamelCase_: Optional[Any]   				=    max_resolution
         UpperCamelCase_: Any   				=    do_resize
         UpperCamelCase_: List[Any]   				=    size
         UpperCamelCase_: Union[str, Any]   				=    do_rescale
         UpperCamelCase_: Optional[Any]   				=    rescale_factor
         UpperCamelCase_: List[Any]   				=    do_normalize
         UpperCamelCase_: Optional[int]   				=    image_mean
         UpperCamelCase_: List[Any]   				=    image_std
         UpperCamelCase_: Union[str, Any]   				=    do_pad





       def       _a   ( self							):
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_pad": self.do_pad,
         }





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase=False							):






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

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

         return expected_height, expected_width







@require_torch
@require_vision
class     _lowerCAmelCase(	lowerCamelCase__							,     unittest.TestCase ):





       """simple docstring"""







       a    :		Union[str, Any]									=DetrImageProcessor if is_vision_available() else None





       def       _a   ( self							):
         UpperCamelCase_: Dict   				=    DetrImageProcessingTester(self							)





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





       def       _a   ( self							):
         UpperCamelCase_: Union[str, Any]   				=    self.image_processing_class(**self.image_processor_dict							)
         self.assertTrue(hasattr(__lowerCamelCase  ,      'image_mean'							)							)
         self.assertTrue(hasattr(__lowerCamelCase  ,      'image_std'							)							)
         self.assertTrue(hasattr(__lowerCamelCase  ,      'do_normalize'							)							)
         self.assertTrue(hasattr(__lowerCamelCase  ,      'do_rescale'							)							)
         self.assertTrue(hasattr(__lowerCamelCase  ,      'rescale_factor'							)							)
         self.assertTrue(hasattr(__lowerCamelCase  ,      'do_resize'							)							)
         self.assertTrue(hasattr(__lowerCamelCase  ,      'size'							)							)
         self.assertTrue(hasattr(__lowerCamelCase  ,      'do_pad'							)							)





       def       _a   ( self							):
         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  ,      __lowerCamelCase							)

         UpperCamelCase_: Optional[int]   				=    self.image_processing_class.from_dict(
             self.image_processor_dict  ,      size=4_2  ,      max_size=8_4  ,      pad_and_return_pixel_mask=__lowerCamelCase							)
         self.assertEqual(image_processor.size  ,      {'shortest_edge': 4_2, 'longest_edge': 8_4}							)
         self.assertEqual(image_processor.do_pad  ,      __lowerCamelCase							)





       def       _a   ( self							):
         pass





       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    self.image_processing_class(**self.image_processor_dict							)
         # create random PIL images
         UpperCamelCase_: List[str]   				=    prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=__lowerCamelCase							)
         for image in image_inputs:
           self.assertIsInstance(__lowerCamelCase  ,      Image.Image							)

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

         UpperCamelCase_: Tuple   				=    self.image_processor_tester.get_expected_values(__lowerCamelCase							)

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

         # Test batched
         UpperCamelCase_: Union[str, Any]   				=    self.image_processor_tester.get_expected_values(__lowerCamelCase  ,      batched=__lowerCamelCase							)

         UpperCamelCase_: List[Any]   				=    image_processing(__lowerCamelCase  ,      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       _a   ( self							):
         UpperCamelCase_: int   				=    self.image_processing_class(**self.image_processor_dict							)
         # create random numpy tensors
         UpperCamelCase_: int   				=    prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=__lowerCamelCase  ,      numpify=__lowerCamelCase							)
         for image in image_inputs:
           self.assertIsInstance(__lowerCamelCase  ,      np.ndarray							)

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

         UpperCamelCase_: Any   				=    self.image_processor_tester.get_expected_values(__lowerCamelCase							)

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

         # Test batched
         UpperCamelCase_: Optional[int]   				=    image_processing(__lowerCamelCase  ,      return_tensors='pt'							).pixel_values

         UpperCamelCase_: Union[str, Any]   				=    self.image_processor_tester.get_expected_values(__lowerCamelCase  ,      batched=__lowerCamelCase							)

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





       def       _a   ( self							):
         UpperCamelCase_: Dict   				=    self.image_processing_class(**self.image_processor_dict							)
         # create random PyTorch tensors
         UpperCamelCase_: Tuple   				=    prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=__lowerCamelCase  ,      torchify=__lowerCamelCase							)
         for image in image_inputs:
           self.assertIsInstance(__lowerCamelCase  ,      torch.Tensor							)

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

         UpperCamelCase_: Optional[Any]   				=    self.image_processor_tester.get_expected_values(__lowerCamelCase							)

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

         # Test batched
         UpperCamelCase_: int   				=    image_processing(__lowerCamelCase  ,      return_tensors='pt'							).pixel_values

         UpperCamelCase_: int   				=    self.image_processor_tester.get_expected_values(__lowerCamelCase  ,      batched=__lowerCamelCase							)

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





       @slow
       def       _a   ( self							):
         UpperCamelCase_: Dict   				=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
         with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'  ,      'r'							) as f:
           UpperCamelCase_: Tuple   				=    json.loads(f.read()							)

         UpperCamelCase_: List[str]   				=    {'''image_id''': 3_9_7_6_9, '''annotations''': target}

         # encode them
         UpperCamelCase_: List[Any]   				=    DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'							)
         UpperCamelCase_: Dict   				=    image_processing(images=__lowerCamelCase  ,      annotations=__lowerCamelCase  ,      return_tensors='pt'							)

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

         UpperCamelCase_: str   				=    torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]							)
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]  ,      __lowerCamelCase  ,      atol=1e-4							)							)

         # verify area
         UpperCamelCase_: List[Any]   				=    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']  ,      __lowerCamelCase							)							)
         # verify boxes
         UpperCamelCase_: Optional[int]   				=    torch.Size([6, 4]							)
         self.assertEqual(encoding['labels'][0]['boxes'].shape  ,      __lowerCamelCase							)
         UpperCamelCase_: Dict   				=    torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]							)
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]  ,      __lowerCamelCase  ,      atol=1e-3							)							)
         # verify image_id
         UpperCamelCase_: Any   				=    torch.tensor([3_9_7_6_9]							)
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']  ,      __lowerCamelCase							)							)
         # verify is_crowd
         UpperCamelCase_: Tuple   				=    torch.tensor([0, 0, 0, 0, 0, 0]							)
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']  ,      __lowerCamelCase							)							)
         # verify class_labels
         UpperCamelCase_: Tuple   				=    torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]							)
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']  ,      __lowerCamelCase							)							)
         # verify orig_size
         UpperCamelCase_: List[Any]   				=    torch.tensor([4_8_0, 6_4_0]							)
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']  ,      __lowerCamelCase							)							)
         # verify size
         UpperCamelCase_: Any   				=    torch.tensor([8_0_0, 1_0_6_6]							)
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']  ,      __lowerCamelCase							)							)





       @slow
       def       _a   ( self							):
         UpperCamelCase_: Optional[Any]   				=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
         with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'  ,      'r'							) as f:
           UpperCamelCase_: List[Any]   				=    json.loads(f.read()							)

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

         UpperCamelCase_: Union[str, Any]   				=    pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'							)

         # encode them
         UpperCamelCase_: str   				=    DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'							)
         UpperCamelCase_: Union[str, Any]   				=    image_processing(images=__lowerCamelCase  ,      annotations=__lowerCamelCase  ,      masks_path=__lowerCamelCase  ,      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  ,      __lowerCamelCase							)

         UpperCamelCase_: List[Any]   				=    torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]							)
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]  ,      __lowerCamelCase  ,      atol=1e-4							)							)

         # verify area
         UpperCamelCase_: List[str]   				=    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']  ,      __lowerCamelCase							)							)
         # verify boxes
         UpperCamelCase_: List[str]   				=    torch.Size([6, 4]							)
         self.assertEqual(encoding['labels'][0]['boxes'].shape  ,      __lowerCamelCase							)
         UpperCamelCase_: Union[str, Any]   				=    torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]							)
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]  ,      __lowerCamelCase  ,      atol=1e-3							)							)
         # verify image_id
         UpperCamelCase_: Dict   				=    torch.tensor([3_9_7_6_9]							)
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']  ,      __lowerCamelCase							)							)
         # verify is_crowd
         UpperCamelCase_: List[str]   				=    torch.tensor([0, 0, 0, 0, 0, 0]							)
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']  ,      __lowerCamelCase							)							)
         # verify class_labels
         UpperCamelCase_: str   				=    torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]							)
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']  ,      __lowerCamelCase							)							)
         # verify masks
         UpperCamelCase_: Optional[Any]   				=    8_2_2_8_7_3
         self.assertEqual(encoding['labels'][0]['masks'].sum().item()  ,      __lowerCamelCase							)
         # verify orig_size
         UpperCamelCase_: List[str]   				=    torch.tensor([4_8_0, 6_4_0]							)
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']  ,      __lowerCamelCase							)							)
         # verify size
         UpperCamelCase_: Dict   				=    torch.tensor([8_0_0, 1_0_6_6]							)
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']  ,      __lowerCamelCase							)							)









import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
   import torch

   from transformers.activations import gelu_new, gelu_python, get_activation




@require_torch
class     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







       def       _a   ( self							):
         UpperCamelCase_: Any   				=    torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0]							)
         UpperCamelCase_: List[str]   				=    get_activation('gelu'							)
         self.assertTrue(torch.allclose(gelu_python(_lowerCamelCase							)  ,      torch_builtin(_lowerCamelCase							)							)							)
         self.assertFalse(torch.allclose(gelu_python(_lowerCamelCase							)  ,      gelu_new(_lowerCamelCase							)							)							)





       def       _a   ( self							):
         UpperCamelCase_: Optional[Any]   				=    torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0]							)
         UpperCamelCase_: Union[str, Any]   				=    get_activation('gelu'							)
         UpperCamelCase_: int   				=    get_activation('gelu_10'							)

         UpperCamelCase_: Union[str, Any]   				=    torch_builtin(_lowerCamelCase							)
         UpperCamelCase_: List[str]   				=    geluaa(_lowerCamelCase							)

         UpperCamelCase_: Dict   				=    torch.where(y_gelu_aa < 1_0.0  ,      1  ,      0							)

         self.assertTrue(torch.max(_lowerCamelCase							).item() == 1_0.0							)
         self.assertTrue(torch.allclose(y_gelu * clipped_mask  ,      y_gelu_aa * clipped_mask							)							)





       def       _a   ( self							):
         get_activation('gelu'							)
         get_activation('gelu_10'							)
         get_activation('gelu_fast'							)
         get_activation('gelu_new'							)
         get_activation('gelu_python'							)
         get_activation('gelu_pytorch_tanh'							)
         get_activation('linear'							)
         get_activation('mish'							)
         get_activation('quick_gelu'							)
         get_activation('relu'							)
         get_activation('sigmoid'							)
         get_activation('silu'							)
         get_activation('swish'							)
         get_activation('tanh'							)
         with self.assertRaises(_lowerCamelCase							):
           get_activation('bogus'							)
         with self.assertRaises(_lowerCamelCase							):
           get_activation(_lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: str   				=    get_activation('gelu'							)
         UpperCamelCase_: str   				=    1
         UpperCamelCase_: int   				=    get_activation('gelu'							)
         self.assertEqual(acta.a  ,      1							)
         with self.assertRaises(_lowerCamelCase							):
           UpperCamelCase_: Tuple   				=    acta.a