
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 DeformableDetrImageProcessor





class  _snake_case   (	unittest.TestCase							):







			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		,  ):
				__magic_name__						:						Optional[int]        =	size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
				__magic_name__						:						Any        =	parent
				__magic_name__						:						List[str]        =	batch_size
				__magic_name__						:						Tuple        =	num_channels
				__magic_name__						:						Union[str, Any]        =	min_resolution
				__magic_name__						:						Any        =	max_resolution
				__magic_name__						:						List[Any]        =	do_resize
				__magic_name__						:						Tuple        =	size
				__magic_name__						:						Tuple        =	do_normalize
				__magic_name__						:						int        =	image_mean
				__magic_name__						:						Tuple        =	image_std
				__magic_name__						:						str        =	do_rescale
				__magic_name__						:						Tuple        =	rescale_factor
				__magic_name__						:						Optional[Any]        =	do_pad







			def      SCREAMING_SNAKE_CASE       (							self						):
				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						):







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

				else:
					__magic_name__						:						Dict        =	[]
					for image in image_inputs:
						__magic_name__						:						Dict        =	self.get_expected_values([image]						)
						expected_values.append((expected_height, expected_width)						)
					__magic_name__						:						Optional[int]        =	max(__SCREAMING_SNAKE_CASE		,  key=lambda _a						: item[0]						)[0]
					__magic_name__						:						Any        =	max(__SCREAMING_SNAKE_CASE		,  key=lambda _a						: item[1]						)[1]

				return expected_height, expected_width







@require_torch
@require_vision
class  _snake_case   (	UpperCamelCase_							,		unittest.TestCase							):
			UpperCamelCase__        =       DeformableDetrImageProcessor if is_vision_available() else None







			def      SCREAMING_SNAKE_CASE       (							self						):
				__magic_name__						:						Optional[Any]        =	DeformableDetrImageProcessingTester(self						)







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







			def      SCREAMING_SNAKE_CASE       (							self						):
				__magic_name__						:						List[Any]        =	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		,  "do_rescale"						)						)
				self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE		,  "do_pad"						)						)
				self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE		,  "size"						)						)







			def      SCREAMING_SNAKE_CASE       (							self						):
				__magic_name__						:						Dict        =	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		,  __SCREAMING_SNAKE_CASE						)

				__magic_name__						:						List[Any]        =	self.image_processing_class.from_dict(
				    self.image_processor_dict		,  size=42		,  max_size=84		,  pad_and_return_pixel_mask=__SCREAMING_SNAKE_CASE						)
				self.assertEqual(image_processor.size		,  {"shortest_edge": 42, "longest_edge": 84}						)
				self.assertEqual(image_processor.do_pad		,  __SCREAMING_SNAKE_CASE						)







			def      SCREAMING_SNAKE_CASE       (							self						):
				pass







			def      SCREAMING_SNAKE_CASE       (							self						):
				__magic_name__						:						Optional[int]        =	self.image_processing_class(**self.image_processor_dict						)
				# create random PIL images
				__magic_name__						:						Tuple        =	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
				__magic_name__						:						int        =	image_processing(image_inputs[0]		,  return_tensors="pt"						).pixel_values

				__magic_name__						:						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
				__magic_name__						:						Tuple        =	self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE		,  batched=__SCREAMING_SNAKE_CASE						)

				__magic_name__						:						int        =	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						):
				__magic_name__						:						List[str]        =	self.image_processing_class(**self.image_processor_dict						)
				# create random numpy tensors
				__magic_name__						:						Dict        =	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
				__magic_name__						:						Tuple        =	image_processing(image_inputs[0]		,  return_tensors="pt"						).pixel_values

				__magic_name__						:						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
				__magic_name__						:						Tuple        =	image_processing(__SCREAMING_SNAKE_CASE		,  return_tensors="pt"						).pixel_values

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

				__magic_name__						:						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
				__magic_name__						:						Union[str, Any]        =	image_processing(__SCREAMING_SNAKE_CASE		,  return_tensors="pt"						).pixel_values

				__magic_name__						:						List[str]        =	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,
				    )		,  )







			@slow
			def      SCREAMING_SNAKE_CASE       (							self						):
				__magic_name__						:						Optional[Any]        =	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
				with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"		,  "r"						) as f:
					__magic_name__						:						Any        =	json.loads(f.read()						)

				__magic_name__						:						Optional[Any]        =	{'''image_id''': 39_769, '''annotations''': target}

				# encode them
				__magic_name__						:						Union[str, Any]        =	DeformableDetrImageProcessor()
				__magic_name__						:						Dict        =	image_processing(images=__SCREAMING_SNAKE_CASE		,  annotations=__SCREAMING_SNAKE_CASE		,  return_tensors="pt"						)

				# verify pixel values
				__magic_name__						:						Any        =	torch.Size([1, 3, 800, 1_066]						)
				self.assertEqual(encoding["pixel_values"].shape		,  __SCREAMING_SNAKE_CASE						)

				__magic_name__						:						Any        =	torch.tensor([0.27_96, 0.31_38, 0.34_81]						)
				self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]		,  __SCREAMING_SNAKE_CASE		,  atol=1e-4						)						)

				# verify area
				__magic_name__						:						List[str]        =	torch.tensor([58_87.96_00, 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"]		,  __SCREAMING_SNAKE_CASE						)						)
				# verify boxes
				__magic_name__						:						List[Any]        =	torch.Size([6, 4]						)
				self.assertEqual(encoding["labels"][0]["boxes"].shape		,  __SCREAMING_SNAKE_CASE						)
				__magic_name__						:						Tuple        =	torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]		,  __SCREAMING_SNAKE_CASE		,  atol=1e-3						)						)
				# verify image_id
				__magic_name__						:						Tuple        =	torch.tensor([39_769]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,  __SCREAMING_SNAKE_CASE						)						)
				# verify is_crowd
				__magic_name__						:						int        =	torch.tensor([0, 0, 0, 0, 0, 0]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,  __SCREAMING_SNAKE_CASE						)						)
				# verify class_labels
				__magic_name__						:						Optional[int]        =	torch.tensor([75, 75, 63, 65, 17, 17]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,  __SCREAMING_SNAKE_CASE						)						)
				# verify orig_size
				__magic_name__						:						Tuple        =	torch.tensor([480, 640]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,  __SCREAMING_SNAKE_CASE						)						)
				# verify size
				__magic_name__						:						Optional[int]        =	torch.tensor([800, 1_066]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,  __SCREAMING_SNAKE_CASE						)						)







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

				__magic_name__						:						List[Any]        =	{'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}

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

				# encode them
				__magic_name__						:						Dict        =	DeformableDetrImageProcessor(format="coco_panoptic"						)
				__magic_name__						:						Optional[int]        =	image_processing(images=__SCREAMING_SNAKE_CASE		,  annotations=__SCREAMING_SNAKE_CASE		,  masks_path=__SCREAMING_SNAKE_CASE		,  return_tensors="pt"						)

				# verify pixel values
				__magic_name__						:						Tuple        =	torch.Size([1, 3, 800, 1_066]						)
				self.assertEqual(encoding["pixel_values"].shape		,  __SCREAMING_SNAKE_CASE						)

				__magic_name__						:						List[Any]        =	torch.tensor([0.27_96, 0.31_38, 0.34_81]						)
				self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]		,  __SCREAMING_SNAKE_CASE		,  atol=1e-4						)						)

				# verify area
				__magic_name__						:						List[Any]        =	torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["area"]		,  __SCREAMING_SNAKE_CASE						)						)
				# verify boxes
				__magic_name__						:						Dict        =	torch.Size([6, 4]						)
				self.assertEqual(encoding["labels"][0]["boxes"].shape		,  __SCREAMING_SNAKE_CASE						)
				__magic_name__						:						Any        =	torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]		,  __SCREAMING_SNAKE_CASE		,  atol=1e-3						)						)
				# verify image_id
				__magic_name__						:						List[str]        =	torch.tensor([39_769]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,  __SCREAMING_SNAKE_CASE						)						)
				# verify is_crowd
				__magic_name__						:						int        =	torch.tensor([0, 0, 0, 0, 0, 0]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,  __SCREAMING_SNAKE_CASE						)						)
				# verify class_labels
				__magic_name__						:						List[Any]        =	torch.tensor([17, 17, 63, 75, 75, 93]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,  __SCREAMING_SNAKE_CASE						)						)
				# verify masks
				__magic_name__						:						List[str]        =	822_873
				self.assertEqual(encoding["labels"][0]["masks"].sum().item()		,  __SCREAMING_SNAKE_CASE						)
				# verify orig_size
				__magic_name__						:						List[Any]        =	torch.tensor([480, 640]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,  __SCREAMING_SNAKE_CASE						)						)
				# verify size
				__magic_name__						:						Tuple        =	torch.tensor([800, 1_066]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,  __SCREAMING_SNAKE_CASE						)						)


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
    from transformers import TvltImageProcessor

if is_speech_available():
    from transformers import TvltFeatureExtractor

from transformers import TvltProcessor




@require_torch
class 					_lowerCamelCase			(   unittest.TestCase			):





     """simple docstring"""







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




           """simple docstring"""







           UpperCamelCase__     : int											=					'''ZinengTang/tvlt-base'''
           UpperCamelCase__     : Optional[Any]											=					tempfile.mkdtemp()






     def  __SCREAMING_SNAKE_CASE      (				self    ,    **__SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







           return TvltImageProcessor.from_pretrained(self.checkpoint    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    **__SCREAMING_SNAKE_CASE	)	-> str:




           """simple docstring"""







           return TvltFeatureExtractor.from_pretrained(self.checkpoint    ,    **__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           shutil.rmtree(self.tmpdirname	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					self.get_image_processor()
           UpperCamelCase__     : Tuple											=					self.get_feature_extractor()

           UpperCamelCase__     : Dict											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)
           processor.save_pretrained(self.tmpdirname	)
           UpperCamelCase__     : Optional[Any]											=					TvltProcessor.from_pretrained(self.tmpdirname	)

           self.assertIsInstance(processor.feature_extractor    ,    __SCREAMING_SNAKE_CASE	)
           self.assertIsInstance(processor.image_processor    ,    __SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					self.get_image_processor()
           UpperCamelCase__     : int											=					self.get_feature_extractor()

           UpperCamelCase__     : Union[str, Any]											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : str											=					np.ones([1_2_0_0_0]	)

           UpperCamelCase__     : Dict											=					feature_extractor(__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)
           UpperCamelCase__     : Dict											=					processor(audio=__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)

           for key in audio_dict.keys():
                 self.assertAlmostEqual(audio_dict[key].sum()    ,    input_processor[key].sum()    ,    delta=1e-2	)






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




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					self.get_image_processor()
           UpperCamelCase__     : Tuple											=					self.get_feature_extractor()

           UpperCamelCase__     : str											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					np.ones([3, 2_2_4, 2_2_4]	)

           UpperCamelCase__     : Optional[int]											=					image_processor(__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)
           UpperCamelCase__     : Tuple											=					processor(images=__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)

           for key in image_dict.keys():
                 self.assertAlmostEqual(image_dict[key].sum()    ,    input_processor[key].sum()    ,    delta=1e-2	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					self.get_image_processor()
           UpperCamelCase__     : Optional[Any]											=					self.get_feature_extractor()

           UpperCamelCase__     : Union[str, Any]											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Dict											=					np.ones([1_2_0_0_0]	)
           UpperCamelCase__     : str											=					np.ones([3, 2_2_4, 2_2_4]	)

           UpperCamelCase__     : List[Any]											=					processor(audio=__SCREAMING_SNAKE_CASE    ,    images=__SCREAMING_SNAKE_CASE	)

           self.assertListEqual(list(inputs.keys()	)    ,    ['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']	)

           # test if it raises when no input is passed
           with pytest.raises(__SCREAMING_SNAKE_CASE	):
                 processor()






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




           """simple docstring"""







           UpperCamelCase__     : int											=					self.get_image_processor()
           UpperCamelCase__     : str											=					self.get_feature_extractor()

           UpperCamelCase__     : Dict											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           self.assertListEqual(
               processor.model_input_names    ,    image_processor.model_input_names + feature_extractor.model_input_names    ,    msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''    ,    )


