





'''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 				a_       (    unittest.TestCase							):





     def __init__(			self		: str     ,				lowercase		: Dict     ,				lowercase		: List[str]=7     ,				lowercase		: Any=3     ,				lowercase		: Tuple=30     ,				lowercase		: Union[str, Any]=400     ,				lowercase		: List[Any]=True     ,				lowercase		: str=None     ,				lowercase		: Tuple=True     ,				lowercase		: Tuple=[0.5, 0.5, 0.5]     ,				lowercase		: Tuple=[0.5, 0.5, 0.5]     ,				lowercase		: Any=True     ,				lowercase		: Union[str, Any]=1 / 255     ,				lowercase		: List[Any]=True     ,				):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




        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		: int     ,				lowercase		: str     ,				lowercase		: Optional[Any]=False						):





        """simple docstring"""




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

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

        return expected_height, expected_width







@require_torch
@require_vision
class 				a_       (    _lowerCAmelCase     ,						unittest.TestCase							):
     __A								=      ConditionalDetrImageProcessor if is_vision_available() else None





     def 							lowercase__				(			self		: Union[str, Any]						):





        """simple docstring"""




        lowercase_      :Dict         =			ConditionalDetrImageProcessingTester(self						)





     @property
     def 							lowercase__				(			self		: str						):





        """simple docstring"""




        return self.image_processor_tester.prepare_image_processor_dict()





     def 							lowercase__				(			self		: Any						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			self.image_processing_class(**self.image_processor_dict						)
        self.assertTrue(hasattr(lowercase     ,				"image_mean"						)						)
        self.assertTrue(hasattr(lowercase     ,				"image_std"						)						)
        self.assertTrue(hasattr(lowercase     ,				"do_normalize"						)						)
        self.assertTrue(hasattr(lowercase     ,				"do_resize"						)						)
        self.assertTrue(hasattr(lowercase     ,				"size"						)						)





     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        lowercase_      :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     ,				lowercase						)

        lowercase_      :str         =			self.image_processing_class.from_dict(
            self.image_processor_dict     ,				size=42     ,				max_size=84     ,				pad_and_return_pixel_mask=lowercase						)
        self.assertEqual(image_processor.size     ,				{"shortest_edge": 42, "longest_edge": 84}						)
        self.assertEqual(image_processor.do_pad     ,				lowercase						)





     def 							lowercase__				(			self		: str						):





        """simple docstring"""




        pass





     def 							lowercase__				(			self		: Dict						):





        """simple docstring"""




        lowercase_      :int         =			self.image_processing_class(**self.image_processor_dict						)
        # create random PIL images
        lowercase_      :Any         =			prepare_image_inputs(self.image_processor_tester     ,				equal_resolution=lowercase						)
        for image in image_inputs:
           self.assertIsInstance(lowercase     ,				Image.Image						)

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

        lowercase_      ,							lowercase_      :int         =			self.image_processor_tester.get_expected_values(lowercase						)

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

        # Test batched
        lowercase_      ,							lowercase_      :Any         =			self.image_processor_tester.get_expected_values(lowercase     ,				batched=lowercase						)

        lowercase_      :Optional[int]         =			image_processing(lowercase     ,				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						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			self.image_processing_class(**self.image_processor_dict						)
        # create random numpy tensors
        lowercase_      :Any         =			prepare_image_inputs(self.image_processor_tester     ,				equal_resolution=lowercase     ,				numpify=lowercase						)
        for image in image_inputs:
           self.assertIsInstance(lowercase     ,				np.ndarray						)

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

        lowercase_      ,							lowercase_      :List[str]         =			self.image_processor_tester.get_expected_values(lowercase						)

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

        # Test batched
        lowercase_      :str         =			image_processing(lowercase     ,				return_tensors="pt"						).pixel_values

        lowercase_      ,							lowercase_      :Dict         =			self.image_processor_tester.get_expected_values(lowercase     ,				batched=lowercase						)

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





     def 							lowercase__				(			self		: List[Any]						):





        """simple docstring"""




        lowercase_      :List[str]         =			self.image_processing_class(**self.image_processor_dict						)
        # create random PyTorch tensors
        lowercase_      :int         =			prepare_image_inputs(self.image_processor_tester     ,				equal_resolution=lowercase     ,				torchify=lowercase						)
        for image in image_inputs:
           self.assertIsInstance(lowercase     ,				torch.Tensor						)

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

        lowercase_      ,							lowercase_      :Tuple         =			self.image_processor_tester.get_expected_values(lowercase						)

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

        # Test batched
        lowercase_      :Any         =			image_processing(lowercase     ,				return_tensors="pt"						).pixel_values

        lowercase_      ,							lowercase_      :str         =			self.image_processor_tester.get_expected_values(lowercase     ,				batched=lowercase						)

        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]						):





        """simple docstring"""




        lowercase_      :List[str]         =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
        with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"     ,				"r"						) as f:
           lowercase_      :str         =			json.loads(f.read()						)

        lowercase_      :Optional[Any]         =			{"image_id": 39_769, "annotations": target}

        # encode them
        lowercase_      :Any         =			ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50"						)
        lowercase_      :List[str]         =			image_processing(images=lowercase     ,				annotations=lowercase     ,				return_tensors="pt"						)

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

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

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







     @slow
     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
        with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"     ,				"r"						) as f:
           lowercase_      :Optional[int]         =			json.loads(f.read()						)

        lowercase_      :Tuple         =			{"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

        lowercase_      :str         =			pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"						)

        # encode them
        lowercase_      :Tuple         =			ConditionalDetrImageProcessor(format="coco_panoptic"						)
        lowercase_      :Tuple         =			image_processing(images=lowercase     ,				annotations=lowercase     ,				masks_path=lowercase     ,				return_tensors="pt"						)

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

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

        # verify area
        lowercase_      :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"]     ,				lowercase						)						)
        # verify boxes
        lowercase_      :Optional[int]         =			torch.Size([6, 4]						)
        self.assertEqual(encoding["labels"][0]["boxes"].shape     ,				lowercase						)
        lowercase_      :Optional[int]         =			torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]						)
        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]     ,				lowercase     ,				atol=1e-3						)						)
        # verify image_id
        lowercase_      :Optional[Any]         =			torch.tensor([39_769]						)
        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]     ,				lowercase						)						)
        # verify is_crowd
        lowercase_      :Optional[Any]         =			torch.tensor([0, 0, 0, 0, 0, 0]						)
        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]     ,				lowercase						)						)
        # verify class_labels
        lowercase_      :str         =			torch.tensor([17, 17, 63, 75, 75, 93]						)
        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]     ,				lowercase						)						)
        # verify masks
        lowercase_      :Dict         =			822_873
        self.assertEqual(encoding["labels"][0]["masks"].sum().item()     ,				lowercase						)
        # verify orig_size
        lowercase_      :List[str]         =			torch.tensor([480, 640]						)
        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]     ,				lowercase						)						)
        # verify size
        lowercase_      :Union[str, Any]         =			torch.tensor([800, 1_066]						)
        self.assertTrue(torch.allclose(encoding["labels"][0]["size"]     ,				lowercase						)						)









'''simple docstring'''


import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 				a_       (    _lowerCAmelCase							):
     __A								=      ["image_processor", "tokenizer"]
     __A								=      "LayoutLMv3ImageProcessor"
     __A								=      ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")





     def __init__(			self		: int     ,				lowercase		: Optional[Any]=None     ,				lowercase		: List[str]=None     ,				**lowercase		: Optional[int]						):





        """simple docstring"""




        lowercase_      :int         =			None
        if "feature_extractor" in kwargs:
           warnings.warn(
               "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
               " instead."     ,				lowercase     ,				)
           lowercase_      :Optional[int]         =			kwargs.pop("feature_extractor"						)

        lowercase_      :Union[str, Any]         =			image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
           raise ValueError("You need to specify an `image_processor`."						)
        if tokenizer is None:
           raise ValueError("You need to specify a `tokenizer`."						)

        super().__init__(lowercase     ,				lowercase						)





     def __call__(			self		: Optional[Any]     ,				lowercase		: List[str]     ,				lowercase		: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,				lowercase		: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None     ,				lowercase		: Union[List[List[int]], List[List[List[int]]]] = None     ,				lowercase		: Optional[Union[List[int], List[List[int]]]] = None     ,				lowercase		: bool = True     ,				lowercase		: Union[bool, str, PaddingStrategy] = False     ,				lowercase		: Union[bool, str, TruncationStrategy] = None     ,				lowercase		: Optional[int] = None     ,				lowercase		: int = 0     ,				lowercase		: Optional[int] = None     ,				lowercase		: Optional[bool] = None     ,				lowercase		: Optional[bool] = None     ,				lowercase		: bool = False     ,				lowercase		: bool = False     ,				lowercase		: bool = False     ,				lowercase		: bool = False     ,				lowercase		: bool = True     ,				lowercase		: Optional[Union[str, TensorType]] = None     ,				**lowercase		: List[Any]     ,				):





        """simple docstring"""




        if self.image_processor.apply_ocr and (boxes is not None):
           raise ValueError(
               "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."						)

        if self.image_processor.apply_ocr and (word_labels is not None):
           raise ValueError(
               "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."						)

        # first, apply the image processor
        lowercase_      :Dict         =			self.image_processor(images=lowercase     ,				return_tensors=lowercase						)

        # second, apply the tokenizer
        if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(lowercase     ,				lowercase						):
              lowercase_      :str         =			[text]  # add batch dimension (as the image processor always adds a batch dimension)
           lowercase_      :Union[str, Any]         =			features["words"]

        lowercase_      :Optional[Any]         =			self.tokenizer(
            text=text if text is not None else features["words"]     ,				text_pair=text_pair if text_pair is not None else None     ,				boxes=boxes if boxes is not None else features["boxes"]     ,				word_labels=lowercase     ,				add_special_tokens=lowercase     ,				padding=lowercase     ,				truncation=lowercase     ,				max_length=lowercase     ,				stride=lowercase     ,				pad_to_multiple_of=lowercase     ,				return_token_type_ids=lowercase     ,				return_attention_mask=lowercase     ,				return_overflowing_tokens=lowercase     ,				return_special_tokens_mask=lowercase     ,				return_offsets_mapping=lowercase     ,				return_length=lowercase     ,				verbose=lowercase     ,				return_tensors=lowercase     ,				**lowercase     ,				)

        # add pixel values
        lowercase_      :Any         =			features.pop("pixel_values"						)
        if return_overflowing_tokens is True:
           lowercase_      :Any         =			self.get_overflowing_images(lowercase     ,				encoded_inputs["overflow_to_sample_mapping"]						)
        lowercase_      :Any         =			images

        return encoded_inputs





     def 							lowercase__				(			self		: List[Any]     ,				lowercase		: Any     ,				lowercase		: Optional[Any]						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			[]
        for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]						)

        if len(lowercase						) != len(lowercase						):
           raise ValueError(
               "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
               F' {len(lowercase						)} and {len(lowercase						)}'						)

        return images_with_overflow





     def 							lowercase__				(			self		: Union[str, Any]     ,				*lowercase		: List[Any]     ,				**lowercase		: Optional[Any]						):





        """simple docstring"""




        return self.tokenizer.batch_decode(*lowercase     ,				**lowercase						)





     def 							lowercase__				(			self		: List[Any]     ,				*lowercase		: Any     ,				**lowercase		: str						):





        """simple docstring"""




        return self.tokenizer.decode(*lowercase     ,				**lowercase						)





     @property
     def 							lowercase__				(			self		: Optional[Any]						):





        """simple docstring"""




        return ["input_ids", "bbox", "attention_mask", "pixel_values"]





     @property
     def 							lowercase__				(			self		: str						):





        """simple docstring"""




        warnings.warn(
            "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."     ,				lowercase     ,				)
        return self.image_processor_class







     @property
     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        warnings.warn(
            "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,				lowercase     ,				)
        return self.image_processor


