

"""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 DetaImageProcessor




class  _lowerCAmelCase  (     unittest.TestCase       ):







 """simple docstring"""







 def __init__(      self							:      Optional[int], UpperCAmelCase__							:      Union[str, Any], UpperCAmelCase__							:      Tuple=7, UpperCAmelCase__							:      Union[str, Any]=3, UpperCAmelCase__							:      List[Any]=3_0, UpperCAmelCase__							:      Optional[int]=4_0_0, UpperCAmelCase__							:      Tuple=True, UpperCAmelCase__							:      Dict=None, UpperCAmelCase__							:      Any=True, UpperCAmelCase__							:      str=[0.5, 0.5, 0.5], UpperCAmelCase__							:      Any=[0.5, 0.5, 0.5], UpperCAmelCase__							:      Optional[Any]=True, UpperCAmelCase__							:      Dict=1 / 2_5_5, UpperCAmelCase__							:      Optional[int]=True, ):
    # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
    __lowercase								=						size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
    __lowercase								=						parent
    __lowercase								=						batch_size
    __lowercase								=						num_channels
    __lowercase								=						min_resolution
    __lowercase								=						max_resolution
    __lowercase								=						do_resize
    __lowercase								=						size
    __lowercase								=						do_normalize
    __lowercase								=						image_mean
    __lowercase								=						image_std
    __lowercase								=						do_rescale
    __lowercase								=						rescale_factor
    __lowercase								=						do_pad


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

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

    return expected_height, expected_width







@require_torch
@require_vision
class  _lowerCAmelCase  (     lowercase					,unittest.TestCase       ):







 """simple docstring"""







 __UpperCAmelCase     : Union[str, Any]       		=     DetaImageProcessor if is_vision_available() else None


 def  _lowercase			(      self							:      str     ):
    __lowercase								=						DetaImageProcessingTester(self     )


 @property
 def  _lowercase			(      self							:      List[Any]     ):
    return self.image_processor_tester.prepare_image_processor_dict()


 def  _lowercase			(      self							:      Optional[Any]     ):
    __lowercase								=						self.image_processing_class(**self.image_processor_dict     )
    self.assertTrue(hasattr(UpperCAmelCase__, "image_mean"     )     )
    self.assertTrue(hasattr(UpperCAmelCase__, "image_std"     )     )
    self.assertTrue(hasattr(UpperCAmelCase__, "do_normalize"     )     )
    self.assertTrue(hasattr(UpperCAmelCase__, "do_resize"     )     )
    self.assertTrue(hasattr(UpperCAmelCase__, "do_rescale"     )     )
    self.assertTrue(hasattr(UpperCAmelCase__, "do_pad"     )     )
    self.assertTrue(hasattr(UpperCAmelCase__, "size"     )     )


 def  _lowercase			(      self							:      str     ):
    __lowercase								=						self.image_processing_class.from_dict(self.image_processor_dict     )
    self.assertEqual(image_processor.size, {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}     )
    self.assertEqual(image_processor.do_pad, UpperCAmelCase__     )


 def  _lowercase			(      self							:      Union[str, Any]     ):
    pass


 def  _lowercase			(      self							:      Any     ):
    # Initialize image_processing
    __lowercase								=						self.image_processing_class(**self.image_processor_dict     )
    # create random PIL images
    __lowercase								=						prepare_image_inputs(self.image_processor_tester, equal_resolution=UpperCAmelCase__     )
    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__, Image.Image     )

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

    __lowercase		,__lowercase								=						self.image_processor_tester.get_expected_values(UpperCAmelCase__     )

    self.assertEqual(
        encoded_images.shape, (1, self.image_processor_tester.num_channels, expected_height, expected_width), )

    # Test batched
    __lowercase		,__lowercase								=						self.image_processor_tester.get_expected_values(UpperCAmelCase__, batched=UpperCAmelCase__     )

    __lowercase								=						image_processing(UpperCAmelCase__, 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							:      Optional[int]     ):
    # Initialize image_processing
    __lowercase								=						self.image_processing_class(**self.image_processor_dict     )
    # create random numpy tensors
    __lowercase								=						prepare_image_inputs(self.image_processor_tester, equal_resolution=UpperCAmelCase__, numpify=UpperCAmelCase__     )
    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__, np.ndarray     )

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

    __lowercase		,__lowercase								=						self.image_processor_tester.get_expected_values(UpperCAmelCase__     )

    self.assertEqual(
        encoded_images.shape, (1, self.image_processor_tester.num_channels, expected_height, expected_width), )

    # Test batched
    __lowercase								=						image_processing(UpperCAmelCase__, return_tensors="pt"     ).pixel_values

    __lowercase		,__lowercase								=						self.image_processor_tester.get_expected_values(UpperCAmelCase__, batched=UpperCAmelCase__     )

    self.assertEqual(
        encoded_images.shape, (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        ), )


 def  _lowercase			(      self							:      int     ):
    # Initialize image_processing
    __lowercase								=						self.image_processing_class(**self.image_processor_dict     )
    # create random PyTorch tensors
    __lowercase								=						prepare_image_inputs(self.image_processor_tester, equal_resolution=UpperCAmelCase__, torchify=UpperCAmelCase__     )
    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__, torch.Tensor     )

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

    __lowercase		,__lowercase								=						self.image_processor_tester.get_expected_values(UpperCAmelCase__     )

    self.assertEqual(
        encoded_images.shape, (1, self.image_processor_tester.num_channels, expected_height, expected_width), )

    # Test batched
    __lowercase								=						image_processing(UpperCAmelCase__, return_tensors="pt"     ).pixel_values

    __lowercase		,__lowercase								=						self.image_processor_tester.get_expected_values(UpperCAmelCase__, batched=UpperCAmelCase__     )

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

    __lowercase								=						{"image_id": 3_9_7_6_9, "annotations": target}

    # encode them
    __lowercase								=						DetaImageProcessor()
    __lowercase								=						image_processing(images=UpperCAmelCase__, annotations=UpperCAmelCase__, return_tensors="pt"     )

    # verify pixel values
    __lowercase								=						torch.Size([1, 3, 8_0_0, 1_0_6_6]     )
    self.assertEqual(encoding["pixel_values"].shape, UpperCAmelCase__     )

    __lowercase								=						torch.tensor([0.2_796, 0.3_138, 0.3_481]     )
    self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], UpperCAmelCase__, atol=1E-4     )     )

    # verify area
    __lowercase								=						torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]     )
    self.assertTrue(torch.allclose(encoding["labels"][0]["area"], UpperCAmelCase__     )     )
    # verify boxes
    __lowercase								=						torch.Size([6, 4]     )
    self.assertEqual(encoding["labels"][0]["boxes"].shape, UpperCAmelCase__     )
    __lowercase								=						torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]     )
    self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], UpperCAmelCase__, atol=1E-3     )     )
    # verify image_id
    __lowercase								=						torch.tensor([3_9_7_6_9]     )
    self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], UpperCAmelCase__     )     )
    # verify is_crowd
    __lowercase								=						torch.tensor([0, 0, 0, 0, 0, 0]     )
    self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], UpperCAmelCase__     )     )
    # verify class_labels
    __lowercase								=						torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]     )
    self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], UpperCAmelCase__     )     )
    # verify orig_size
    __lowercase								=						torch.tensor([4_8_0, 6_4_0]     )
    self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], UpperCAmelCase__     )     )
    # verify size
    __lowercase								=						torch.tensor([8_0_0, 1_0_6_6]     )
    self.assertTrue(torch.allclose(encoding["labels"][0]["size"], UpperCAmelCase__     )     )



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

    __lowercase								=						{"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target}

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

    # encode them
    __lowercase								=						DetaImageProcessor(format="coco_panoptic"     )
    __lowercase								=						image_processing(images=UpperCAmelCase__, annotations=UpperCAmelCase__, masks_path=UpperCAmelCase__, return_tensors="pt"     )

    # verify pixel values
    __lowercase								=						torch.Size([1, 3, 8_0_0, 1_0_6_6]     )
    self.assertEqual(encoding["pixel_values"].shape, UpperCAmelCase__     )

    __lowercase								=						torch.tensor([0.2_796, 0.3_138, 0.3_481]     )
    self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], UpperCAmelCase__, atol=1E-4     )     )

    # verify area
    __lowercase								=						torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]     )
    self.assertTrue(torch.allclose(encoding["labels"][0]["area"], UpperCAmelCase__     )     )
    # verify boxes
    __lowercase								=						torch.Size([6, 4]     )
    self.assertEqual(encoding["labels"][0]["boxes"].shape, UpperCAmelCase__     )
    __lowercase								=						torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]     )
    self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], UpperCAmelCase__, atol=1E-3     )     )
    # verify image_id
    __lowercase								=						torch.tensor([3_9_7_6_9]     )
    self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], UpperCAmelCase__     )     )
    # verify is_crowd
    __lowercase								=						torch.tensor([0, 0, 0, 0, 0, 0]     )
    self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], UpperCAmelCase__     )     )
    # verify class_labels
    __lowercase								=						torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]     )
    self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], UpperCAmelCase__     )     )
    # verify masks
    __lowercase								=						8_2_2_8_7_3
    self.assertEqual(encoding["labels"][0]["masks"].sum().item(), UpperCAmelCase__     )
    # verify orig_size
    __lowercase								=						torch.tensor([4_8_0, 6_4_0]     )
    self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], UpperCAmelCase__     )     )
    # verify size
    __lowercase								=						torch.tensor([8_0_0, 1_0_6_6]     )
    self.assertTrue(torch.allclose(encoding["labels"][0]["size"], UpperCAmelCase__     )     )







"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_a							=							logging.get_logger(__name__)




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 __UpperCAmelCase     : Optional[Any]       		=     ["pixel_values"]


 def __init__(      self							:      int, UpperCAmelCase__							:      bool = True, UpperCAmelCase__							:      Optional[Dict[str, int]] = None, UpperCAmelCase__							:      PILImageResampling = PILImageResampling.BILINEAR, UpperCAmelCase__							:      bool = True, UpperCAmelCase__							:      Dict[str, int] = None, UpperCAmelCase__							:      bool = True, UpperCAmelCase__							:      Union[int, float] = 1 / 2_5_5, UpperCAmelCase__							:      bool = True, UpperCAmelCase__							:      Optional[Union[float, List[float]]] = None, UpperCAmelCase__							:      Optional[Union[float, List[float]]] = None, **UpperCAmelCase__							:      str, ):
    super().__init__(**UpperCAmelCase__     )
    __lowercase								=						size if size is not None else {"shortest_edge": 2_5_6}
    __lowercase								=						get_size_dict(UpperCAmelCase__, default_to_square=UpperCAmelCase__     )
    __lowercase								=						crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
    __lowercase								=						get_size_dict(UpperCAmelCase__     )
    __lowercase								=						do_resize
    __lowercase								=						size
    __lowercase								=						resample
    __lowercase								=						do_center_crop
    __lowercase								=						crop_size
    __lowercase								=						do_rescale
    __lowercase								=						rescale_factor
    __lowercase								=						do_normalize
    __lowercase								=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    __lowercase								=						image_std if image_std is not None else IMAGENET_STANDARD_STD


 def  _lowercase			(      self							:      int, UpperCAmelCase__							:      np.ndarray, UpperCAmelCase__							:      Dict[str, int], UpperCAmelCase__							:      PILImageResampling = PILImageResampling.BICUBIC, UpperCAmelCase__							:      Optional[Union[str, ChannelDimension]] = None, **UpperCAmelCase__							:      Dict, ):
    __lowercase								=						get_size_dict(UpperCAmelCase__, default_to_square=UpperCAmelCase__     )
    if "shortest_edge" not in size:
       raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""     )
    __lowercase								=						get_resize_output_image_size(UpperCAmelCase__, size=size["shortest_edge"], default_to_square=UpperCAmelCase__     )
    return resize(UpperCAmelCase__, size=UpperCAmelCase__, resample=UpperCAmelCase__, data_format=UpperCAmelCase__, **UpperCAmelCase__     )


 def  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      np.ndarray, UpperCAmelCase__							:      Dict[str, int], UpperCAmelCase__							:      Optional[Union[str, ChannelDimension]] = None, **UpperCAmelCase__							:      Dict, ):
    __lowercase								=						get_size_dict(UpperCAmelCase__     )
    return center_crop(UpperCAmelCase__, size=(size["height"], size["width"]), data_format=UpperCAmelCase__, **UpperCAmelCase__     )


 def  _lowercase			(      self							:      Optional[int], UpperCAmelCase__							:      np.ndarray, UpperCAmelCase__							:      float, UpperCAmelCase__							:      Optional[Union[str, ChannelDimension]] = None, **UpperCAmelCase__							:      Union[str, Any]     ):
    return rescale(UpperCAmelCase__, scale=UpperCAmelCase__, data_format=UpperCAmelCase__, **UpperCAmelCase__     )


 def  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      np.ndarray, UpperCAmelCase__							:      Union[float, List[float]], UpperCAmelCase__							:      Union[float, List[float]], UpperCAmelCase__							:      Optional[Union[str, ChannelDimension]] = None, **UpperCAmelCase__							:      int, ):
    return normalize(UpperCAmelCase__, mean=UpperCAmelCase__, std=UpperCAmelCase__, data_format=UpperCAmelCase__, **UpperCAmelCase__     )



 def  _lowercase			(      self							:      Any, UpperCAmelCase__							:      ImageInput, UpperCAmelCase__							:      Optional[bool] = None, UpperCAmelCase__							:      Dict[str, int] = None, UpperCAmelCase__							:      PILImageResampling = None, UpperCAmelCase__							:      bool = None, UpperCAmelCase__							:      Dict[str, int] = None, UpperCAmelCase__							:      Optional[bool] = None, UpperCAmelCase__							:      Optional[float] = None, UpperCAmelCase__							:      Optional[bool] = None, UpperCAmelCase__							:      Optional[Union[float, List[float]]] = None, UpperCAmelCase__							:      Optional[Union[float, List[float]]] = None, UpperCAmelCase__							:      Optional[Union[str, TensorType]] = None, UpperCAmelCase__							:      Union[str, ChannelDimension] = ChannelDimension.FIRST, **UpperCAmelCase__							:      Optional[int], ):
    __lowercase								=						do_resize if do_resize is not None else self.do_resize
    __lowercase								=						size if size is not None else self.size
    __lowercase								=						get_size_dict(UpperCAmelCase__, default_to_square=UpperCAmelCase__     )
    __lowercase								=						resample if resample is not None else self.resample
    __lowercase								=						do_center_crop if do_center_crop is not None else self.do_center_crop
    __lowercase								=						crop_size if crop_size is not None else self.crop_size
    __lowercase								=						get_size_dict(UpperCAmelCase__     )
    __lowercase								=						do_rescale if do_rescale is not None else self.do_rescale
    __lowercase								=						rescale_factor if rescale_factor is not None else self.rescale_factor
    __lowercase								=						do_normalize if do_normalize is not None else self.do_normalize
    __lowercase								=						image_mean if image_mean is not None else self.image_mean
    __lowercase								=						image_std if image_std is not None else self.image_std

    __lowercase								=						make_list_of_images(UpperCAmelCase__     )

    if not valid_images(UpperCAmelCase__     ):
       raise ValueError(
           "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
           "torch.Tensor, tf.Tensor or jax.ndarray."     )

    if do_resize and size is None:
       raise ValueError("Size must be specified if do_resize is True."     )

    if do_center_crop and crop_size is None:
       raise ValueError("Crop size must be specified if do_center_crop is True."     )

    if do_rescale and rescale_factor is None:
       raise ValueError("Rescale factor must be specified if do_rescale is True."     )

    if do_normalize and (image_mean is None or image_std is None):
       raise ValueError("Image mean and std must be specified if do_normalize is True."     )

    # All transformations expect numpy arrays.
    __lowercase								=						[to_numpy_array(UpperCAmelCase__     ) for image in images]

    if do_resize:
       __lowercase								=						[self.resize(image=UpperCAmelCase__, size=UpperCAmelCase__, resample=UpperCAmelCase__     ) for image in images]

    if do_center_crop:
       __lowercase								=						[self.center_crop(image=UpperCAmelCase__, size=UpperCAmelCase__     ) for image in images]

    if do_rescale:
       __lowercase								=						[self.rescale(image=UpperCAmelCase__, scale=UpperCAmelCase__     ) for image in images]

    if do_normalize:
       __lowercase								=						[self.normalize(image=UpperCAmelCase__, mean=UpperCAmelCase__, std=UpperCAmelCase__     ) for image in images]

    __lowercase								=						[to_channel_dimension_format(UpperCAmelCase__, UpperCAmelCase__     ) for image in images]

    __lowercase								=						{"pixel_values": images}
    return BatchFeature(data=UpperCAmelCase__, tensor_type=UpperCAmelCase__     )




