


"""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 DetrImageProcessor
class 					__lowerCAmelCase					(   unittest.TestCase       ):


		'''simple docstring'''



		def __init__(  self							, _a							, _a=7							, _a=3							, _a=30							, _a=400							, _a=True							, _a=None							, _a=True							, _a=1 / 255							, _a=True							, _a=[0.5, 0.5, 0.5]							, _a=[0.5, 0.5, 0.5]							, _a=True							, ):
				# 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''': 1_333}
				__a           =			parent
				__a           =			batch_size
				__a           =			num_channels
				__a           =			min_resolution
				__a           =			max_resolution
				__a           =			do_resize
				__a           =			size
				__a           =			do_rescale
				__a           =			rescale_factor
				__a           =			do_normalize
				__a           =			image_mean
				__a           =			image_std
				__a           =			do_pad

		def 			__UpperCAmelCase       (  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 			__UpperCAmelCase       (  self							, _a							, _a=False      ):
				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 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE     ,			unittest.TestCase       ):


		'''simple docstring'''



		__UpperCAmelCase		:			str										=				DetrImageProcessor if is_vision_available() else None

		def 			__UpperCAmelCase       (  self      ):
				__a           =			DetrImageProcessingTester(self      )

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

		def 			__UpperCAmelCase       (  self      ):
				__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_rescale'''      )      )
				self.assertTrue(hasattr(_a							, '''rescale_factor'''      )      )
				self.assertTrue(hasattr(_a							, '''do_resize'''      )      )
				self.assertTrue(hasattr(_a							, '''size'''      )      )
				self.assertTrue(hasattr(_a							, '''do_pad'''      )      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.image_processing_class.from_dict(self.image_processor_dict      )
				self.assertEqual(image_processor.size							, {'''shortest_edge''': 18, '''longest_edge''': 1_333}      )
				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 			__UpperCAmelCase       (  self      ):
				pass

		def 			__UpperCAmelCase       (  self      ):
				# 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 			__UpperCAmelCase       (  self      ):
				# 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 			__UpperCAmelCase       (  self      ):
				# 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,
				    )							, )

		@slow
		def 			__UpperCAmelCase       (  self      ):
				# 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''': 39_769, '''annotations''': target}

				# encode them
				__a           =			DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''      )
				__a           =			image_processing(images=_a							, annotations=_a							, return_tensors='''pt'''      )

				# verify pixel values
				__a           =			torch.Size([1, 3, 800, 1_066]      )
				self.assertEqual(encoding['''pixel_values'''].shape							, _a      )

				__a           =			torch.tensor([0.2796, 0.3138, 0.3481]      )
				self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]							, _a							, atol=1E-4      )      )

				# verify area
				__a           =			torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]      )
				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([0.5503, 0.2765, 0.0604, 0.2215]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]							, _a							, atol=1E-3      )      )
				# verify image_id
				__a           =			torch.tensor([39_769]      )
				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, 1_066]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							, _a      )      )





		@slow
		def 			__UpperCAmelCase       (  self      ):
				# 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''': 39_769, '''segments_info''': target}

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

				# encode them
				__a           =			DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''      )
				__a           =			image_processing(images=_a							, annotations=_a							, masks_path=_a							, return_tensors='''pt'''      )

				# verify pixel values
				__a           =			torch.Size([1, 3, 800, 1_066]      )
				self.assertEqual(encoding['''pixel_values'''].shape							, _a      )

				__a           =			torch.tensor([0.2796, 0.3138, 0.3481]      )
				self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]							, _a							, atol=1E-4      )      )

				# verify area
				__a           =			torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]      )
				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([0.2625, 0.5437, 0.4688, 0.8625]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]							, _a							, atol=1E-3      )      )
				# verify image_id
				__a           =			torch.tensor([39_769]      )
				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           =			822_873
				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, 1_066]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							, _a      )      )













"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_								=		logging.get_logger(__name__)

lowerCamelCase_								=		{
    "google/vivit-b-16x2-kinetics400": (
        "https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}






class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      List[str]							=  '''vivit'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=2_24		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=[2, 16, 16]		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=7_68		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=30_72		,SCREAMING_SNAKE_CASE__="gelu_fast"		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=1E-06		,SCREAMING_SNAKE_CASE__=True		,**SCREAMING_SNAKE_CASE__		,)			->      Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :int						=  hidden_size
             __SCREAMING_SNAKE_CASE   :List[Any]						=  num_hidden_layers
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  num_attention_heads
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  intermediate_size
             __SCREAMING_SNAKE_CASE   :Any						=  hidden_act
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  hidden_dropout_prob
             __SCREAMING_SNAKE_CASE   :str						=  attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE   :Any						=  initializer_range
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  layer_norm_eps

             __SCREAMING_SNAKE_CASE   :Optional[int]						=  image_size
             __SCREAMING_SNAKE_CASE   :List[str]						=  num_frames
             __SCREAMING_SNAKE_CASE   :Any						=  tubelet_size
             __SCREAMING_SNAKE_CASE   :str						=  num_channels
             __SCREAMING_SNAKE_CASE   :Any						=  qkv_bias

             super().__init__(**SCREAMING_SNAKE_CASE__							)