



'''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 			UpperCamelCase_ (							unittest.TestCase			):





	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		,		)			->			int:
								# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
								UpperCAmelCase     :		Optional[int]							     =		size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
								UpperCAmelCase     :		Union[str, Any]							     =		parent
								UpperCAmelCase     :		List[Any]							     =		batch_size
								UpperCAmelCase     :		List[Any]							     =		num_channels
								UpperCAmelCase     :		Dict							     =		min_resolution
								UpperCAmelCase     :		List[str]							     =		max_resolution
								UpperCAmelCase     :		Optional[int]							     =		do_resize
								UpperCAmelCase     :		Dict							     =		size
								UpperCAmelCase     :		Union[str, Any]							     =		do_rescale
								UpperCAmelCase     :		List[str]							     =		rescale_factor
								UpperCAmelCase     :		List[str]							     =		do_normalize
								UpperCAmelCase     :		Optional[int]							     =		image_mean
								UpperCAmelCase     :		List[Any]							     =		image_std
								UpperCAmelCase     :		Dict							     =		do_pad





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

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

								return expected_height, expected_width




@require_torch
@require_vision
class 			UpperCamelCase_ (							__magic_name__      ,     unittest.TestCase			):
	lowercase          = DetrImageProcessor if is_vision_available() else None





	def  _lowercase(  self    )			->			Tuple:
								UpperCAmelCase     :		Dict							     =		DetrImageProcessingTester(self    )





	@property
	def  _lowercase(  self    )			->			Optional[int]:
								return self.image_processor_tester.prepare_image_processor_dict()





	def  _lowercase(  self    )			->			Any:
								UpperCAmelCase     :		int							     =		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  _lowercase(  self    )			->			int:
								UpperCAmelCase     :		str							     =		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    )

								UpperCAmelCase     :		Tuple							     =		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  _lowercase(  self    )			->			List[Any]:
								pass





	def  _lowercase(  self    )			->			str:
								# Initialize image_processing
								UpperCAmelCase     :		List[str]							     =		self.image_processing_class(**self.image_processor_dict    )
								# create random PIL images
								UpperCAmelCase     :		List[str]							     =		prepare_image_inputs(self.image_processor_tester		,		equal_resolution=A    )
								for image in image_inputs:
															self.assertIsInstance(A		,		Image.Image    )

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

								UpperCAmelCase     ,     UpperCAmelCase     :		Dict							     =		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
								UpperCAmelCase     ,     UpperCAmelCase     :		Optional[Any]							     =		self.image_processor_tester.get_expected_values(A		,		batched=A    )

								UpperCAmelCase     :		str							     =		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  _lowercase(  self    )			->			Tuple:
								# Initialize image_processing
								UpperCAmelCase     :		Optional[int]							     =		self.image_processing_class(**self.image_processor_dict    )
								# create random numpy tensors
								UpperCAmelCase     :		Union[str, Any]							     =		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
								UpperCAmelCase     :		str							     =		image_processing(image_inputs[0]		,		return_tensors="""pt"""    ).pixel_values

								UpperCAmelCase     ,     UpperCAmelCase     :		Dict							     =		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
								UpperCAmelCase     :		Optional[Any]							     =		image_processing(A		,		return_tensors="""pt"""    ).pixel_values

								UpperCAmelCase     ,     UpperCAmelCase     :		Tuple							     =		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  _lowercase(  self    )			->			Any:
								# Initialize image_processing
								UpperCAmelCase     :		Tuple							     =		self.image_processing_class(**self.image_processor_dict    )
								# create random PyTorch tensors
								UpperCAmelCase     :		Tuple							     =		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
								UpperCAmelCase     :		Optional[int]							     =		image_processing(image_inputs[0]		,		return_tensors="""pt"""    ).pixel_values

								UpperCAmelCase     ,     UpperCAmelCase     :		Tuple							     =		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
								UpperCAmelCase     :		Union[str, Any]							     =		image_processing(A		,		return_tensors="""pt"""    ).pixel_values

								UpperCAmelCase     ,     UpperCAmelCase     :		Dict							     =		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  _lowercase(  self    )			->			Any:
								# prepare image and target
								UpperCAmelCase     :		Union[str, Any]							     =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
								with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""		,		"""r"""    ) as f:
															UpperCAmelCase     :		Union[str, Any]							     =		json.loads(f.read()    )

								UpperCAmelCase     :		int							     =		{"""image_id""": 39769, """annotations""": target}

								# encode them
								UpperCAmelCase     :		Dict							     =		DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50"""    )
								UpperCAmelCase     :		Union[str, Any]							     =		image_processing(images=A		,		annotations=A		,		return_tensors="""pt"""    )

								# verify pixel values
								UpperCAmelCase     :		List[str]							     =		torch.Size([1, 3, 800, 1066]    )
								self.assertEqual(encoding["""pixel_values"""].shape		,		A    )

								UpperCAmelCase     :		List[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]		,		A		,		atol=1e-4    )    )

								# verify area
								UpperCAmelCase     :		Tuple							     =		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"""]		,		A    )    )
								# verify boxes
								UpperCAmelCase     :		Tuple							     =		torch.Size([6, 4]    )
								self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape		,		A    )
								UpperCAmelCase     :		Union[str, Any]							     =		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]		,		A		,		atol=1e-3    )    )
								# verify image_id
								UpperCAmelCase     :		str							     =		torch.tensor([39769]    )
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]		,		A    )    )
								# verify is_crowd
								UpperCAmelCase     :		Union[str, Any]							     =		torch.tensor([0, 0, 0, 0, 0, 0]    )
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]		,		A    )    )
								# verify class_labels
								UpperCAmelCase     :		str							     =		torch.tensor([75, 75, 63, 65, 17, 17]    )
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]		,		A    )    )
								# verify orig_size
								UpperCAmelCase     :		Dict							     =		torch.tensor([480, 640]    )
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]		,		A    )    )
								# verify size
								UpperCAmelCase     :		List[str]							     =		torch.tensor([800, 1066]    )
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]		,		A    )    )






	@slow
	def  _lowercase(  self    )			->			List[str]:
								# prepare image, target and masks_path
								UpperCAmelCase     :		Tuple							     =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
								with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""		,		"""r"""    ) as f:
															UpperCAmelCase     :		Union[str, Any]							     =		json.loads(f.read()    )

								UpperCAmelCase     :		str							     =		{"""file_name""": """000000039769.png""", """image_id""": 39769, """segments_info""": target}

								UpperCAmelCase     :		List[Any]							     =		pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""    )

								# encode them
								UpperCAmelCase     :		Any							     =		DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50-panoptic"""    )
								UpperCAmelCase     :		Optional[int]							     =		image_processing(images=A		,		annotations=A		,		masks_path=A		,		return_tensors="""pt"""    )

								# verify pixel values
								UpperCAmelCase     :		int							     =		torch.Size([1, 3, 800, 1066]    )
								self.assertEqual(encoding["""pixel_values"""].shape		,		A    )

								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]		,		A		,		atol=1e-4    )    )

								# verify area
								UpperCAmelCase     :		Tuple							     =		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"""]		,		A    )    )
								# verify boxes
								UpperCAmelCase     :		Any							     =		torch.Size([6, 4]    )
								self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape		,		A    )
								UpperCAmelCase     :		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]		,		A		,		atol=1e-3    )    )
								# verify image_id
								UpperCAmelCase     :		Dict							     =		torch.tensor([39769]    )
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]		,		A    )    )
								# verify is_crowd
								UpperCAmelCase     :		List[Any]							     =		torch.tensor([0, 0, 0, 0, 0, 0]    )
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]		,		A    )    )
								# verify class_labels
								UpperCAmelCase     :		Dict							     =		torch.tensor([17, 17, 63, 75, 75, 93]    )
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]		,		A    )    )
								# verify masks
								UpperCAmelCase     :		Union[str, Any]							     =		822873
								self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()		,		A    )
								# verify orig_size
								UpperCAmelCase     :		int							     =		torch.tensor([480, 640]    )
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]		,		A    )    )
								# verify size
								UpperCAmelCase     :		int							     =		torch.tensor([800, 1066]    )
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]		,		A    )    )






'''simple docstring'''




import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


a   :			List[Any]					  =    """__DUMMY_TRANSFORMERS_USER__"""
a   :			Tuple					  =    """Dummy User"""
a   :			Optional[Any]					  =    """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"""

a   :			Optional[Any]					  =    """https://hub-ci.huggingface.co"""
a   :			List[Any]					  =    CI_HUB_ENDPOINT + """/datasets/{repo_id}/resolve/{revision}/{path}"""
a   :			Tuple					  =    CI_HUB_ENDPOINT + """/{repo_id}/resolve/{revision}/{filename}"""
a   :			str					  =    Path("""~/.huggingface/hub_ci_token""").expanduser()



@pytest.fixture
def       __lowerCamelCase  (					_lowercase		)      ->			Optional[int]:
							monkeypatch.setattr(
							    """huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE"""	,				_lowercase		)



@pytest.fixture
def       __lowerCamelCase  (					_lowercase		)      ->			List[str]:
							monkeypatch.setattr("""datasets.config.HF_ENDPOINT"""	,				_lowercase		)
							monkeypatch.setattr("""datasets.config.HUB_DATASETS_URL"""	,				_lowercase		)



@pytest.fixture
def       __lowerCamelCase  (					_lowercase		)      ->			Any:
							monkeypatch.setattr("""huggingface_hub.hf_api.HfFolder.path_token"""	,				_lowercase		)



@pytest.fixture
def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			Optional[Any]:
							HfFolder.save_token(_lowercase		)
							yield
							HfFolder.delete_token()



@pytest.fixture(scope="""session"""		)
def       __lowerCamelCase  (					)      ->			str:
							return HfApi(endpoint=_lowercase		)



@pytest.fixture(scope="""session"""		)
def       __lowerCamelCase  (					_lowercase		)      ->			Union[str, Any]:
							UpperCAmelCase     :		str							     =		HfFolder.get_token()
							HfFolder.save_token(_lowercase		)
							yield CI_HUB_USER_TOKEN
							if previous_token is not None:
														HfFolder.save_token(_lowercase		)



@pytest.fixture
def       __lowerCamelCase  (					_lowercase		)      ->			Any:
							def _cleanup_repo(_lowercase		):
														hf_api.delete_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""		)

							return _cleanup_repo



@pytest.fixture
def       __lowerCamelCase  (					_lowercase		)      ->			List[str]:
							@contextmanager
							def _temporary_repo(_lowercase		):
														try:
																					yield repo_id
														finally:
																					cleanup_repo(_lowercase		)

							return _temporary_repo



@pytest.fixture(scope="""session"""		)
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			List[str]:
							UpperCAmelCase     :		str							     =		F'''repo_txt_data-{int(time.time() * 10e3		)}'''
							UpperCAmelCase     :		List[Any]							     =		F'''{CI_HUB_USER}/{repo_name}'''
							hf_api.create_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""	,				private=_lowercase		)
							hf_api.upload_file(
							    token=_lowercase	,				path_or_fileobj=str(_lowercase		)	,				path_in_repo="""data/text_data.txt"""	,				repo_id=_lowercase	,				repo_type="""dataset"""	,				)
							yield repo_id
							try:
														hf_api.delete_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""		)
							except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
														pass



@pytest.fixture()
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			List[Any]:
							return hf_private_dataset_repo_txt_data_



@pytest.fixture(scope="""session"""		)
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     :		Optional[int]							     =		F'''repo_zipped_txt_data-{int(time.time() * 10e3		)}'''
							UpperCAmelCase     :		Optional[int]							     =		F'''{CI_HUB_USER}/{repo_name}'''
							hf_api.create_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""	,				private=_lowercase		)
							hf_api.upload_file(
							    token=_lowercase	,				path_or_fileobj=str(_lowercase		)	,				path_in_repo="""data.zip"""	,				repo_id=_lowercase	,				repo_type="""dataset"""	,				)
							yield repo_id
							try:
														hf_api.delete_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""		)
							except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
														pass



@pytest.fixture()
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			List[str]:
							return hf_private_dataset_repo_zipped_txt_data_



@pytest.fixture(scope="""session"""		)
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			Tuple:
							UpperCAmelCase     :		List[Any]							     =		F'''repo_zipped_img_data-{int(time.time() * 10e3		)}'''
							UpperCAmelCase     :		List[str]							     =		F'''{CI_HUB_USER}/{repo_name}'''
							hf_api.create_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""	,				private=_lowercase		)
							hf_api.upload_file(
							    token=_lowercase	,				path_or_fileobj=str(_lowercase		)	,				path_in_repo="""data.zip"""	,				repo_id=_lowercase	,				repo_type="""dataset"""	,				)
							yield repo_id
							try:
														hf_api.delete_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""		)
							except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
														pass



@pytest.fixture()
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			List[Any]:
							return hf_private_dataset_repo_zipped_img_data_

