


"""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     _UpperCamelCase    (  unittest.TestCase						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a=7       ,  __a=3       ,  __a=30       ,  __a=4_00       ,  __a=True       ,  __a=None       ,  __a=True       ,  __a=[0.5, 0.5, 0.5]       ,  __a=[0.5, 0.5, 0.5]       ,  __a=True       ,  __a=1 / 2_55       ,  __a=True       ,  ):
										# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
										__lowerCAmelCase							       =     size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
										__lowerCAmelCase							       =     parent
										__lowerCAmelCase							       =     batch_size
										__lowerCAmelCase							       =     num_channels
										__lowerCAmelCase							       =     min_resolution
										__lowerCAmelCase							       =     max_resolution
										__lowerCAmelCase							       =     do_resize
										__lowerCAmelCase							       =     size
										__lowerCAmelCase							       =     do_normalize
										__lowerCAmelCase							       =     image_mean
										__lowerCAmelCase							       =     image_std
										__lowerCAmelCase							       =     do_rescale
										__lowerCAmelCase							       =     rescale_factor
										__lowerCAmelCase							       =     do_pad

							def      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      snake_case							(	self       ,  __a       ,  __a=False      ):
										if not batched:
													__lowerCAmelCase							       =     image_inputs[0]
													if isinstance(__a       ,  Image.Image      ):
																__lowerCAmelCase       ,      __lowerCAmelCase							       =     image.size
													else:
																__lowerCAmelCase       ,      __lowerCAmelCase							       =     image.shape[1], image.shape[2]
													if w < h:
																__lowerCAmelCase							       =     int(self.size["shortest_edge"] * h / w      )
																__lowerCAmelCase							       =     self.size["shortest_edge"]
													elif w > h:
																__lowerCAmelCase							       =     self.size["shortest_edge"]
																__lowerCAmelCase							       =     int(self.size["shortest_edge"] * w / h      )
													else:
																__lowerCAmelCase							       =     self.size["shortest_edge"]
																__lowerCAmelCase							       =     self.size["shortest_edge"]

										else:
													__lowerCAmelCase							       =     []
													for image in image_inputs:
																__lowerCAmelCase       ,      __lowerCAmelCase							       =     self.get_expected_values([image]      )
																expected_values.append((expected_height, expected_width)      )
													__lowerCAmelCase							       =     max(__a       ,  key=lambda __a      : item[0]      )[0]
													__lowerCAmelCase							       =     max(__a       ,  key=lambda __a      : item[1]      )[1]

										return expected_height, expected_width
@require_torch
@require_vision
class     _UpperCamelCase    (  lowerCAmelCase__			,unittest.TestCase						):
							'''simple docstring'''







							__UpperCAmelCase		:       Optional[Any]							 =ConditionalDetrImageProcessor if is_vision_available() else None

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     ConditionalDetrImageProcessingTester(self      )

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

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     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      snake_case							(	self      ):
										__lowerCAmelCase							       =     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       ,  __a      )

										__lowerCAmelCase							       =     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      snake_case							(	self      ):
										pass

							def      snake_case							(	self      ):
										# Initialize image_processing
										__lowerCAmelCase							       =     self.image_processing_class(**self.image_processor_dict      )
										# create random PIL images
										__lowerCAmelCase							       =     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							       =     image_processing(image_inputs[0]       ,  return_tensors="pt"      ).pixel_values

										__lowerCAmelCase       ,      __lowerCAmelCase							       =     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							       =     self.image_processor_tester.get_expected_values(__a       ,  batched=__a      )

										__lowerCAmelCase							       =     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      snake_case							(	self      ):
										# Initialize image_processing
										__lowerCAmelCase							       =     self.image_processing_class(**self.image_processor_dict      )
										# create random numpy tensors
										__lowerCAmelCase							       =     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							       =     image_processing(image_inputs[0]       ,  return_tensors="pt"      ).pixel_values

										__lowerCAmelCase       ,      __lowerCAmelCase							       =     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							       =     image_processing(__a       ,  return_tensors="pt"      ).pixel_values

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

										__lowerCAmelCase       ,      __lowerCAmelCase							       =     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							       =     image_processing(__a       ,  return_tensors="pt"      ).pixel_values

										__lowerCAmelCase       ,      __lowerCAmelCase							       =     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      snake_case							(	self      ):
										# prepare image and target
										__lowerCAmelCase							       =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
										with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"       ,  "r"      ) as f:
													__lowerCAmelCase							       =     json.loads(f.read()      )

										__lowerCAmelCase							       =     {"image_id": 3_97_69, "annotations": target}

										# encode them
										__lowerCAmelCase							       =     ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50"      )
										__lowerCAmelCase							       =     image_processing(images=__a       ,  annotations=__a       ,  return_tensors="pt"      )

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

										__lowerCAmelCase							       =     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
										__lowerCAmelCase							       =     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
										__lowerCAmelCase							       =     torch.Size([6, 4]      )
										self.assertEqual(encoding["labels"][0]["boxes"].shape       ,  __a      )
										__lowerCAmelCase							       =     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
										__lowerCAmelCase							       =     torch.tensor([3_97_69]      )
										self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,  __a      )      )
										# verify is_crowd
										__lowerCAmelCase							       =     torch.tensor([0, 0, 0, 0, 0, 0]      )
										self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,  __a      )      )
										# verify class_labels
										__lowerCAmelCase							       =     torch.tensor([75, 75, 63, 65, 17, 17]      )
										self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,  __a      )      )
										# verify orig_size
										__lowerCAmelCase							       =     torch.tensor([4_80, 6_40]      )
										self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,  __a      )      )
										# verify size
										__lowerCAmelCase							       =     torch.tensor([8_00, 10_66]      )
										self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,  __a      )      )



							@slow
							def      snake_case							(	self      ):
										# prepare image, target and masks_path
										__lowerCAmelCase							       =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
										with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"       ,  "r"      ) as f:
													__lowerCAmelCase							       =     json.loads(f.read()      )

										__lowerCAmelCase							       =     {"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

										__lowerCAmelCase							       =     pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"      )

										# encode them
										__lowerCAmelCase							       =     ConditionalDetrImageProcessor(format="coco_panoptic"      )
										__lowerCAmelCase							       =     image_processing(images=__a       ,  annotations=__a       ,  masks_path=__a       ,  return_tensors="pt"      )

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

										__lowerCAmelCase							       =     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
										__lowerCAmelCase							       =     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
										__lowerCAmelCase							       =     torch.Size([6, 4]      )
										self.assertEqual(encoding["labels"][0]["boxes"].shape       ,  __a      )
										__lowerCAmelCase							       =     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
										__lowerCAmelCase							       =     torch.tensor([3_97_69]      )
										self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,  __a      )      )
										# verify is_crowd
										__lowerCAmelCase							       =     torch.tensor([0, 0, 0, 0, 0, 0]      )
										self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,  __a      )      )
										# verify class_labels
										__lowerCAmelCase							       =     torch.tensor([17, 17, 63, 75, 75, 93]      )
										self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,  __a      )      )
										# verify masks
										__lowerCAmelCase							       =     82_28_73
										self.assertEqual(encoding["labels"][0]["masks"].sum().item()       ,  __a      )
										# verify orig_size
										__lowerCAmelCase							       =     torch.tensor([4_80, 6_40]      )
										self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,  __a      )      )
										# verify size
										__lowerCAmelCase							       =     torch.tensor([8_00, 10_66]      )
										self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,  __a      )      )








"""simple docstring"""


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase__						)
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       str							 =field(default="""image-classification"""			,metadata={"""include_in_asdict_even_if_is_default""": True}						)
							__UpperCAmelCase		:       ClassVar[Features]							 =Features({"""image""": Image()}						)
							__UpperCAmelCase		:       ClassVar[Features]							 =Features({"""labels""": ClassLabel}						)
							__UpperCAmelCase		:       str							 ="image"
							__UpperCAmelCase		:       str							 ="labels"

							def      snake_case							(	self       ,  __a      ):
										if self.label_column not in features:
													raise ValueError(f"Column {self.label_column} is not present in features."      )
										if not isinstance(features[self.label_column]       ,  __a      ):
													raise ValueError(f"Column {self.label_column} is not a ClassLabel."      )
										__lowerCAmelCase							       =     copy.deepcopy(self      )
										__lowerCAmelCase							       =     self.label_schema.copy()
										__lowerCAmelCase							       =     features[self.label_column]
										__lowerCAmelCase							       =     label_schema
										return task_template



							@property
							def      snake_case							(	self      ):
										return {
										    self.image_column: "image",
										    self.label_column: "labels",
										}




