





'''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		,      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		,      ):
			# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
			lowerCAmelCase     :    Optional[Any]						=   size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
			lowerCAmelCase     :    Any						=   parent
			lowerCAmelCase     :    Dict						=   batch_size
			lowerCAmelCase     :    int						=   num_channels
			lowerCAmelCase     :    str						=   min_resolution
			lowerCAmelCase     :    Union[str, Any]						=   max_resolution
			lowerCAmelCase     :    int						=   do_resize
			lowerCAmelCase     :    Dict						=   size
			lowerCAmelCase     :    Dict						=   do_normalize
			lowerCAmelCase     :    Dict						=   image_mean
			lowerCAmelCase     :    Dict						=   image_std
			lowerCAmelCase     :    Any						=   do_rescale
			lowerCAmelCase     :    Any						=   rescale_factor
			lowerCAmelCase     :    Tuple						=   do_pad



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

			else:
					lowerCAmelCase     :    List[Any]						=   []
					for image in image_inputs:
							lowerCAmelCase	,		lowerCAmelCase     :    str						=   self.get_expected_values([image]	)
							expected_values.append((expected_height, expected_width)	)
					lowerCAmelCase     :    Union[str, Any]						=   max(a_		,      key=lambda a_	: item[0]	)[0]
					lowerCAmelCase     :    Dict						=   max(a_		,      key=lambda a_	: item[1]	)[1]

			return expected_height, expected_width






@require_torch
@require_vision
class 		lowerCamelCase  (      _A      ,   unittest.TestCase       ):
	snake_case_      =   ConditionalDetrImageProcessor if is_vision_available() else None



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   ConditionalDetrImageProcessingTester(self	)



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



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[Any]						=   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_resize"	)	)
			self.assertTrue(hasattr(a_		,      "size"	)	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Union[str, Any]						=   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_	)

			lowerCAmelCase     :    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   _lowerCamelCase    (   self	):
			pass



	def   _lowerCamelCase    (   self	):
			# Initialize image_processing
			lowerCAmelCase     :    List[Any]						=   self.image_processing_class(**self.image_processor_dict	)
			# create random PIL images
			lowerCAmelCase     :    Optional[int]						=   prepare_image_inputs(self.image_processor_tester		,      equal_resolution=a_	)
			for image in image_inputs:
					self.assertIsInstance(a_		,      Image.Image	)

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

			lowerCAmelCase	,		lowerCAmelCase     :    str						=   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
			lowerCAmelCase	,		lowerCAmelCase     :    int						=   self.image_processor_tester.get_expected_values(a_		,      batched=a_	)

			lowerCAmelCase     :    Any						=   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   _lowerCamelCase    (   self	):
			# Initialize image_processing
			lowerCAmelCase     :    Any						=   self.image_processing_class(**self.image_processor_dict	)
			# create random numpy tensors
			lowerCAmelCase     :    str						=   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
			lowerCAmelCase     :    Dict						=   image_processing(image_inputs[0]		,      return_tensors="pt"	).pixel_values

			lowerCAmelCase	,		lowerCAmelCase     :    Optional[Any]						=   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
			lowerCAmelCase     :    int						=   image_processing(a_		,      return_tensors="pt"	).pixel_values

			lowerCAmelCase	,		lowerCAmelCase     :    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   _lowerCamelCase    (   self	):
			# Initialize image_processing
			lowerCAmelCase     :    List[Any]						=   self.image_processing_class(**self.image_processor_dict	)
			# create random PyTorch tensors
			lowerCAmelCase     :    Any						=   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
			lowerCAmelCase     :    Any						=   image_processing(image_inputs[0]		,      return_tensors="pt"	).pixel_values

			lowerCAmelCase	,		lowerCAmelCase     :    int						=   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
			lowerCAmelCase     :    List[str]						=   image_processing(a_		,      return_tensors="pt"	).pixel_values

			lowerCAmelCase	,		lowerCAmelCase     :    int						=   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   _lowerCamelCase    (   self	):
			# prepare image and target
			lowerCAmelCase     :    str						=   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
			with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"		,      "r"	) as f:
					lowerCAmelCase     :    Optional[Any]						=   json.loads(f.read()	)

			lowerCAmelCase     :    List[str]						=   {"image_id": 39_769, "annotations": target}

			# encode them
			lowerCAmelCase     :    int						=   ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50"	)
			lowerCAmelCase     :    Any						=   image_processing(images=a_		,      annotations=a_		,      return_tensors="pt"	)

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

			lowerCAmelCase     :    Tuple						=   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
			lowerCAmelCase     :    Optional[int]						=   torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]	)
			self.assertTrue(torch.allclose(encoding["labels"][0]["area"]		,      a_	)	)
			# verify boxes
			lowerCAmelCase     :    List[str]						=   torch.Size([6, 4]	)
			self.assertEqual(encoding["labels"][0]["boxes"].shape		,      a_	)
			lowerCAmelCase     :    Dict						=   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
			lowerCAmelCase     :    Any						=   torch.tensor([39_769]	)
			self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,      a_	)	)
			# verify is_crowd
			lowerCAmelCase     :    Union[str, Any]						=   torch.tensor([0, 0, 0, 0, 0, 0]	)
			self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,      a_	)	)
			# verify class_labels
			lowerCAmelCase     :    int						=   torch.tensor([75, 75, 63, 65, 17, 17]	)
			self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,      a_	)	)
			# verify orig_size
			lowerCAmelCase     :    Union[str, Any]						=   torch.tensor([480, 640]	)
			self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,      a_	)	)
			# verify size
			lowerCAmelCase     :    List[str]						=   torch.tensor([800, 1_066]	)
			self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,      a_	)	)





	@slow
	def   _lowerCamelCase    (   self	):
			# prepare image, target and masks_path
			lowerCAmelCase     :    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:
					lowerCAmelCase     :    Optional[Any]						=   json.loads(f.read()	)

			lowerCAmelCase     :    List[str]						=   {"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

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

			# encode them
			lowerCAmelCase     :    Union[str, Any]						=   ConditionalDetrImageProcessor(format="coco_panoptic"	)
			lowerCAmelCase     :    Optional[int]						=   image_processing(images=a_		,      annotations=a_		,      masks_path=a_		,      return_tensors="pt"	)

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

			lowerCAmelCase     :    str						=   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
			lowerCAmelCase     :    Any						=   torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]	)
			self.assertTrue(torch.allclose(encoding["labels"][0]["area"]		,      a_	)	)
			# verify boxes
			lowerCAmelCase     :    Union[str, Any]						=   torch.Size([6, 4]	)
			self.assertEqual(encoding["labels"][0]["boxes"].shape		,      a_	)
			lowerCAmelCase     :    Dict						=   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
			lowerCAmelCase     :    List[str]						=   torch.tensor([39_769]	)
			self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,      a_	)	)
			# verify is_crowd
			lowerCAmelCase     :    Union[str, Any]						=   torch.tensor([0, 0, 0, 0, 0, 0]	)
			self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,      a_	)	)
			# verify class_labels
			lowerCAmelCase     :    Union[str, Any]						=   torch.tensor([17, 17, 63, 75, 75, 93]	)
			self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,      a_	)	)
			# verify masks
			lowerCAmelCase     :    Dict						=   822_873
			self.assertEqual(encoding["labels"][0]["masks"].sum().item()		,      a_	)
			# verify orig_size
			lowerCAmelCase     :    Optional[Any]						=   torch.tensor([480, 640]	)
			self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,      a_	)	)
			# verify size
			lowerCAmelCase     :    Tuple						=   torch.tensor([800, 1_066]	)
			self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,      a_	)	)










'''simple docstring'''






import numpy as np






def      __A						(							a_						:  np.array	):
		return (2 / (1 + np.exp(-2 * vector	))) - 1


if __name__ == "__main__":
					import doctest

					doctest.testmod()



