




"""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 ConditionalDetrImageProcessor


class _lowerCamelCase    (					unittest.TestCase						):






   def __init__(		self       :		List[str]   ,     UpperCamelCase       :		int   ,     UpperCamelCase       :		Optional[int]=7   ,     UpperCamelCase       :		str=3   ,     UpperCamelCase       :		List[str]=30   ,     UpperCamelCase       :		Any=4_00   ,     UpperCamelCase       :		List[Any]=True   ,     UpperCamelCase       :		Union[str, Any]=None   ,     UpperCamelCase       :		List[Any]=True   ,     UpperCamelCase       :		List[str]=[0.5, 0.5, 0.5]   ,     UpperCamelCase       :		List[Any]=[0.5, 0.5, 0.5]   ,     UpperCamelCase       :		Dict=True   ,     UpperCamelCase       :		str=1 / 2_55   ,     UpperCamelCase       :		Dict=True   ,     )			->						Union[str, Any]:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
         lowerCAmelCase__      :			int   =	parent
         lowerCAmelCase__      :			Optional[int]   =	batch_size
         lowerCAmelCase__      :			Tuple   =	num_channels
         lowerCAmelCase__      :			str   =	min_resolution
         lowerCAmelCase__      :			Dict   =	max_resolution
         lowerCAmelCase__      :			Tuple   =	do_resize
         lowerCAmelCase__      :			Optional[Any]   =	size
         lowerCAmelCase__      :			int   =	do_normalize
         lowerCAmelCase__      :			List[Any]   =	image_mean
         lowerCAmelCase__      :			Union[str, Any]   =	image_std
         lowerCAmelCase__      :			Optional[int]   =	do_rescale
         lowerCAmelCase__      :			Union[str, Any]   =	rescale_factor
         lowerCAmelCase__      :			List[str]   =	do_pad






   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						List[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 	_lowerCAmelCase   (		self       :		Optional[Any]   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		List[Any]=False   )			->						Optional[Any]:

         """simple docstring"""
         if not batched:
               lowerCAmelCase__      :			Dict   =	image_inputs[0]
               if isinstance(UpperCamelCase   ,     Image.Image   ):
                     lowerCAmelCase__      :			Dict   =	image.size
               else:
                     lowerCAmelCase__      :			Dict   =	image.shape[1], image.shape[2]
               if w < h:
                     lowerCAmelCase__      :			Any   =	int(self.size["""shortest_edge"""] * h / w   )
                     lowerCAmelCase__      :			Dict   =	self.size["""shortest_edge"""]
               elif w > h:
                     lowerCAmelCase__      :			Union[str, Any]   =	self.size["""shortest_edge"""]
                     lowerCAmelCase__      :			Union[str, Any]   =	int(self.size["""shortest_edge"""] * w / h   )
               else:
                     lowerCAmelCase__      :			Any   =	self.size["""shortest_edge"""]
                     lowerCAmelCase__      :			List[Any]   =	self.size["""shortest_edge"""]

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

         return expected_height, expected_width






@require_torch
@require_vision
class _lowerCamelCase    (					a_       ,	unittest.TestCase						):
   _lowerCamelCase    :Tuple     				=     ConditionalDetrImageProcessor if is_vision_available() else None






   def 	_lowerCAmelCase   (		self       :		Dict   )			->						Optional[int]:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[Any]   =	ConditionalDetrImageProcessingTester(self   )






   @property
   def 	_lowerCAmelCase   (		self       :		Dict   )			->						List[Any]:

         """simple docstring"""
         return self.image_processor_tester.prepare_image_processor_dict()






   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			Dict   =	self.image_processing_class(**self.image_processor_dict   )
         self.assertTrue(hasattr(UpperCamelCase   ,     """image_mean"""   )   )
         self.assertTrue(hasattr(UpperCamelCase   ,     """image_std"""   )   )
         self.assertTrue(hasattr(UpperCamelCase   ,     """do_normalize"""   )   )
         self.assertTrue(hasattr(UpperCamelCase   ,     """do_resize"""   )   )
         self.assertTrue(hasattr(UpperCamelCase   ,     """size"""   )   )






   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	self.image_processing_class.from_dict(self.image_processor_dict   )
         self.assertEqual(image_processor.size   ,     {"""shortest_edge""": 18, """longest_edge""": 13_33}   )
         self.assertEqual(image_processor.do_pad   ,     UpperCamelCase   )

         lowerCAmelCase__      :			Tuple   =	self.image_processing_class.from_dict(
             self.image_processor_dict   ,     size=42   ,     max_size=84   ,     pad_and_return_pixel_mask=UpperCamelCase   )
         self.assertEqual(image_processor.size   ,     {"""shortest_edge""": 42, """longest_edge""": 84}   )
         self.assertEqual(image_processor.do_pad   ,     UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						int:

         """simple docstring"""
         pass






   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						int:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	self.image_processing_class(**self.image_processor_dict   )
         # create random PIL images
         lowerCAmelCase__      :			Union[str, Any]   =	prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=UpperCamelCase   )
         for image in image_inputs:
               self.assertIsInstance(UpperCamelCase   ,     Image.Image   )

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

         lowerCAmelCase__      :			List[Any]   =	self.image_processor_tester.get_expected_values(UpperCamelCase   )

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

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

         lowerCAmelCase__      :			str   =	image_processing(UpperCamelCase   ,     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 	_lowerCAmelCase   (		self       :		Dict   )			->						Optional[int]:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	self.image_processing_class(**self.image_processor_dict   )
         # create random numpy tensors
         lowerCAmelCase__      :			Optional[Any]   =	prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=UpperCamelCase   ,     numpify=UpperCamelCase   )
         for image in image_inputs:
               self.assertIsInstance(UpperCamelCase   ,     np.ndarray   )

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

         lowerCAmelCase__      :			Tuple   =	self.image_processor_tester.get_expected_values(UpperCamelCase   )

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

         # Test batched
         lowerCAmelCase__      :			Dict   =	image_processing(UpperCamelCase   ,     return_tensors="""pt"""   ).pixel_values

         lowerCAmelCase__      :			Tuple   =	self.image_processor_tester.get_expected_values(UpperCamelCase   ,     batched=UpperCamelCase   )

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






   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	self.image_processing_class(**self.image_processor_dict   )
         # create random PyTorch tensors
         lowerCAmelCase__      :			List[Any]   =	prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=UpperCamelCase   ,     torchify=UpperCamelCase   )
         for image in image_inputs:
               self.assertIsInstance(UpperCamelCase   ,     torch.Tensor   )

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

         lowerCAmelCase__      :			Any   =	self.image_processor_tester.get_expected_values(UpperCamelCase   )

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

         # Test batched
         lowerCAmelCase__      :			Optional[int]   =	image_processing(UpperCamelCase   ,     return_tensors="""pt"""   ).pixel_values

         lowerCAmelCase__      :			Optional[int]   =	self.image_processor_tester.get_expected_values(UpperCamelCase   ,     batched=UpperCamelCase   )

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






   @slow
   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
         with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""   ,     """r"""   ) as f:
               lowerCAmelCase__      :			List[str]   =	json.loads(f.read()   )

         lowerCAmelCase__      :			Tuple   =	{"""image_id""": 3_97_69, """annotations""": target}

         # encode them
         lowerCAmelCase__      :			int   =	ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50"""   )
         lowerCAmelCase__      :			List[Any]   =	image_processing(images=UpperCamelCase   ,     annotations=UpperCamelCase   ,     return_tensors="""pt"""   )

         # verify pixel values
         lowerCAmelCase__      :			Dict   =	torch.Size([1, 3, 8_00, 10_66]   )
         self.assertEqual(encoding["""pixel_values"""].shape   ,     UpperCamelCase   )

         lowerCAmelCase__      :			List[str]   =	torch.tensor([0.2796, 0.3138, 0.3481]   )
         self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]   ,     UpperCamelCase   ,     atol=1E-4   )   )

         # verify area
         lowerCAmelCase__      :			Any   =	torch.tensor([5887.9600, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]   )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]   ,     UpperCamelCase   )   )
         # verify boxes
         lowerCAmelCase__      :			List[str]   =	torch.Size([6, 4]   )
         self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape   ,     UpperCamelCase   )
         lowerCAmelCase__      :			Optional[int]   =	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]   )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]   ,     UpperCamelCase   ,     atol=1E-3   )   )
         # verify image_id
         lowerCAmelCase__      :			Tuple   =	torch.tensor([3_97_69]   )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]   ,     UpperCamelCase   )   )
         # verify is_crowd
         lowerCAmelCase__      :			Tuple   =	torch.tensor([0, 0, 0, 0, 0, 0]   )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]   ,     UpperCamelCase   )   )
         # verify class_labels
         lowerCAmelCase__      :			List[str]   =	torch.tensor([75, 75, 63, 65, 17, 17]   )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]   ,     UpperCamelCase   )   )
         # verify orig_size
         lowerCAmelCase__      :			List[Any]   =	torch.tensor([4_80, 6_40]   )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]   ,     UpperCamelCase   )   )
         # verify size
         lowerCAmelCase__      :			Any   =	torch.tensor([8_00, 10_66]   )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]   ,     UpperCamelCase   )   )






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

         """simple docstring"""
         lowerCAmelCase__      :			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__      :			Optional[Any]   =	{"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

         lowerCAmelCase__      :			Any   =	pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""   )

         # encode them
         lowerCAmelCase__      :			Optional[int]   =	ConditionalDetrImageProcessor(format="""coco_panoptic"""   )
         lowerCAmelCase__      :			Optional[int]   =	image_processing(images=UpperCamelCase   ,     annotations=UpperCamelCase   ,     masks_path=UpperCamelCase   ,     return_tensors="""pt"""   )

         # verify pixel values
         lowerCAmelCase__      :			Optional[int]   =	torch.Size([1, 3, 8_00, 10_66]   )
         self.assertEqual(encoding["""pixel_values"""].shape   ,     UpperCamelCase   )

         lowerCAmelCase__      :			List[str]   =	torch.tensor([0.2796, 0.3138, 0.3481]   )
         self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]   ,     UpperCamelCase   ,     atol=1E-4   )   )

         # verify area
         lowerCAmelCase__      :			Union[str, Any]   =	torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 5879.6562, 7634.1147]   )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]   ,     UpperCamelCase   )   )
         # verify boxes
         lowerCAmelCase__      :			Optional[Any]   =	torch.Size([6, 4]   )
         self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape   ,     UpperCamelCase   )
         lowerCAmelCase__      :			List[str]   =	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]   )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]   ,     UpperCamelCase   ,     atol=1E-3   )   )
         # verify image_id
         lowerCAmelCase__      :			List[str]   =	torch.tensor([3_97_69]   )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]   ,     UpperCamelCase   )   )
         # verify is_crowd
         lowerCAmelCase__      :			Tuple   =	torch.tensor([0, 0, 0, 0, 0, 0]   )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]   ,     UpperCamelCase   )   )
         # verify class_labels
         lowerCAmelCase__      :			Optional[int]   =	torch.tensor([17, 17, 63, 75, 75, 93]   )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]   ,     UpperCamelCase   )   )
         # verify masks
         lowerCAmelCase__      :			Tuple   =	82_28_73
         self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()   ,     UpperCamelCase   )
         # verify orig_size
         lowerCAmelCase__      :			List[str]   =	torch.tensor([4_80, 6_40]   )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]   ,     UpperCamelCase   )   )
         # verify size
         lowerCAmelCase__      :			Dict   =	torch.tensor([8_00, 10_66]   )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]   ,     UpperCamelCase   )   )











"""simple docstring"""


from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *





