




'''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 					_a (unittest.TestCase	):





     '''simple docstring'''



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







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

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

          return expected_height, expected_width







@require_torch
@require_vision
class 					_a (a__,		unittest.TestCase	):





     '''simple docstring'''



     lowerCAmelCase_  :     List[str]          =					DetaImageProcessor if is_vision_available() else None







     def 			snake_case_      (				self							)      ->				Union[str, Any]:
          snake_case			:      List[Any]        =			DetaImageProcessingTester(self							)







     @property
     def 			snake_case_      (				self							)      ->				str:
          return self.image_processor_tester.prepare_image_processor_dict()







     def 			snake_case_      (				self							)      ->				Dict:
          snake_case			:      Union[str, 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							,"""do_rescale"""							)							)
          self.assertTrue(hasattr(__a							,"""do_pad"""							)							)
          self.assertTrue(hasattr(__a							,"""size"""							)							)







     def 			snake_case_      (				self							)      ->				Optional[Any]:
          snake_case			:      List[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							)







     def 			snake_case_      (				self							)      ->				int:
          pass







     def 			snake_case_      (				self							)      ->				Dict:
          # Initialize image_processing
          snake_case			:      Any        =			self.image_processing_class(**self.image_processor_dict							)
          # create random PIL images
          snake_case			:      Dict        =			prepare_image_inputs(self.image_processor_tester							,equal_resolution=__a							)
          for image in image_inputs:
               self.assertIsInstance(__a							,Image.Image							)

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

          snake_case ,		snake_case			:      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
          snake_case ,		snake_case			:      str        =			self.image_processor_tester.get_expected_values(__a							,batched=__a							)

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

          snake_case ,		snake_case			:      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
          snake_case			:      Dict        =			image_processing(__a							,return_tensors="""pt"""							).pixel_values

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

          snake_case ,		snake_case			:      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
          snake_case			:      str        =			image_processing(__a							,return_tensors="""pt"""							).pixel_values

          snake_case ,		snake_case			:      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,
              )							,)







     @slow
     def 			snake_case_      (				self							)      ->				Any:
          # prepare image and target
          snake_case			:      Dict        =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
          with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""							,"""r"""							) as f:
               snake_case			:      Optional[Any]        =			json.loads(f.read()							)

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

          # encode them
          snake_case			:      str        =			DetaImageProcessor()
          snake_case			:      List[Any]        =			image_processing(images=__a							,annotations=__a							,return_tensors="""pt"""							)

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

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

          # verify area
          snake_case			:      int        =			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"""]							,__a							)							)
          # verify boxes
          snake_case			:      Any        =			torch.Size([6, 4]							)
          self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape							,__a							)
          snake_case			:      Dict        =			torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]							)
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]							,__a							,atol=1E-3							)							)
          # verify image_id
          snake_case			:      Dict        =			torch.tensor([39_769]							)
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]							,__a							)							)
          # verify is_crowd
          snake_case			:      Union[str, Any]        =			torch.tensor([0, 0, 0, 0, 0, 0]							)
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]							,__a							)							)
          # verify class_labels
          snake_case			:      Optional[int]        =			torch.tensor([75, 75, 63, 65, 17, 17]							)
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]							,__a							)							)
          # verify orig_size
          snake_case			:      Union[str, Any]        =			torch.tensor([480, 640]							)
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]							,__a							)							)
          # verify size
          snake_case			:      Optional[Any]        =			torch.tensor([800, 1_066]							)
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]							,__a							)							)







     @slow
     def 			snake_case_      (				self							)      ->				int:
          # prepare image, target and masks_path
          snake_case			:      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:
               snake_case			:      Optional[int]        =			json.loads(f.read()							)

          snake_case			:      Optional[int]        =			{"""file_name""": """000000039769.png""", """image_id""": 39_769, """segments_info""": target}

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

          # encode them
          snake_case			:      Optional[int]        =			DetaImageProcessor(format="""coco_panoptic"""							)
          snake_case			:      Dict        =			image_processing(images=__a							,annotations=__a							,masks_path=__a							,return_tensors="""pt"""							)

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

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

          # verify area
          snake_case			:      List[str]        =			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"""]							,__a							)							)
          # verify boxes
          snake_case			:      Union[str, Any]        =			torch.Size([6, 4]							)
          self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape							,__a							)
          snake_case			:      List[str]        =			torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]							)
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]							,__a							,atol=1E-3							)							)
          # verify image_id
          snake_case			:      List[Any]        =			torch.tensor([39_769]							)
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]							,__a							)							)
          # verify is_crowd
          snake_case			:      int        =			torch.tensor([0, 0, 0, 0, 0, 0]							)
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]							,__a							)							)
          # verify class_labels
          snake_case			:      str        =			torch.tensor([17, 17, 63, 75, 75, 93]							)
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]							,__a							)							)
          # verify masks
          snake_case			:      int        =			822_873
          self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()							,__a							)
          # verify orig_size
          snake_case			:      Any        =			torch.tensor([480, 640]							)
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]							,__a							)							)
          # verify size
          snake_case			:      Tuple        =			torch.tensor([800, 1_066]							)
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]							,__a							)							)









'''simple docstring'''







from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
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, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
      import PIL

# soft dependency
if is_pytesseract_available():
      import pytesseract

lowercase      :   Optional[Any]      =       logging.get_logger(__name__)



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase				):
     return [
         int(1_000 * (box[0] / width)				),
         int(1_000 * (box[1] / height)				),
         int(1_000 * (box[2] / width)				),
         int(1_000 * (box[3] / height)				),
     ]



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase				):
     snake_case			:      List[Any]        =			to_pil_image(__lowercase				)
     snake_case ,		snake_case			:      Union[str, Any]        =			pil_image.size
     snake_case			:      Optional[Any]        =			pytesseract.image_to_data(__lowercase     ,   lang=__lowercase     ,   output_type="""dict"""     ,   config=__lowercase				)
     snake_case ,		snake_case ,		snake_case ,		snake_case ,		snake_case			:      Dict        =			data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

     # filter empty words and corresponding coordinates
     snake_case			:      Tuple        =			[idx for idx, word in enumerate(__lowercase				) if not word.strip()]
     snake_case			:      Tuple        =			[word for idx, word in enumerate(__lowercase				) if idx not in irrelevant_indices]
     snake_case			:      str        =			[coord for idx, coord in enumerate(__lowercase				) if idx not in irrelevant_indices]
     snake_case			:      Tuple        =			[coord for idx, coord in enumerate(__lowercase				) if idx not in irrelevant_indices]
     snake_case			:      Optional[Any]        =			[coord for idx, coord in enumerate(__lowercase				) if idx not in irrelevant_indices]
     snake_case			:      Optional[Any]        =			[coord for idx, coord in enumerate(__lowercase				) if idx not in irrelevant_indices]

     # turn coordinates into (left, top, left+width, top+height) format
     snake_case			:      Dict        =			[]
     for x, y, w, h in zip(__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase				):
          snake_case			:      Tuple        =			[x, y, x + w, y + h]
          actual_boxes.append(__lowercase				)

     # finally, normalize the bounding boxes
     snake_case			:      str        =			[]
     for box in actual_boxes:
          normalized_boxes.append(normalize_box(__lowercase     ,   __lowercase     ,   __lowercase				)				)

     assert len(__lowercase				) == len(__lowercase				), "Not as many words as there are bounding boxes"

     return words, normalized_boxes







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Dict          =					["""pixel_values"""]







     def __init__(				self							,__a = True							,__a = None							,__a = PILImageResampling.BILINEAR							,__a = True							,__a = 1 / 255							,__a = True							,__a = None							,__a = None							,__a = True							,__a = None							,__a = ""							,**__a							,)      ->				None:
          super().__init__(**__a							)
          snake_case			:      int        =			size if size is not None else {"""height""": 224, """width""": 224}
          snake_case			:      Union[str, Any]        =			get_size_dict(__a							)

          snake_case			:      List[str]        =			do_resize
          snake_case			:      Dict        =			size
          snake_case			:      Dict        =			resample
          snake_case			:      Union[str, Any]        =			do_rescale
          snake_case			:      Union[str, Any]        =			rescale_value
          snake_case			:      List[str]        =			do_normalize
          snake_case			:      int        =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          snake_case			:      int        =			image_std if image_std is not None else IMAGENET_STANDARD_STD
          snake_case			:      List[Any]        =			apply_ocr
          snake_case			:      Any        =			ocr_lang
          snake_case			:      Optional[int]        =			tesseract_config







     def 			snake_case_      (				self							,__a							,__a							,__a = PILImageResampling.BILINEAR							,__a = None							,**__a							,)      ->				np.ndarray:
          snake_case			:      List[str]        =			get_size_dict(__a							)
          if "height" not in size or "width" not in size:
               raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''							)
          snake_case			:      Tuple        =			(size["""height"""], size["""width"""])
          return resize(__a							,size=__a							,resample=__a							,data_format=__a							,**__a							)







     def 			snake_case_      (				self							,__a							,__a							,__a = None							,**__a							,)      ->				np.ndarray:
          return rescale(__a							,scale=__a							,data_format=__a							,**__a							)







     def 			snake_case_      (				self							,__a							,__a							,__a							,__a = None							,**__a							,)      ->				np.ndarray:
          return normalize(__a							,mean=__a							,std=__a							,data_format=__a							,**__a							)







     def 			snake_case_      (				self							,__a							,__a = None							,__a = None							,__a=None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = None							,__a = ChannelDimension.FIRST							,**__a							,)      ->				PIL.Image.Image:
          snake_case			:      int        =			do_resize if do_resize is not None else self.do_resize
          snake_case			:      List[Any]        =			size if size is not None else self.size
          snake_case			:      Any        =			get_size_dict(__a							)
          snake_case			:      Any        =			resample if resample is not None else self.resample
          snake_case			:      str        =			do_rescale if do_rescale is not None else self.do_rescale
          snake_case			:      List[str]        =			rescale_factor if rescale_factor is not None else self.rescale_factor
          snake_case			:      Dict        =			do_normalize if do_normalize is not None else self.do_normalize
          snake_case			:      Optional[int]        =			image_mean if image_mean is not None else self.image_mean
          snake_case			:      Tuple        =			image_std if image_std is not None else self.image_std
          snake_case			:      List[str]        =			apply_ocr if apply_ocr is not None else self.apply_ocr
          snake_case			:      str        =			ocr_lang if ocr_lang is not None else self.ocr_lang
          snake_case			:      Dict        =			tesseract_config if tesseract_config is not None else self.tesseract_config

          snake_case			:      Any        =			make_list_of_images(__a							)

          if not valid_images(__a							):
               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_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("""If do_normalize is True, image_mean and image_std must be specified."""							)

          # All transformations expect numpy arrays.
          snake_case			:      Union[str, Any]        =			[to_numpy_array(__a							) for image in images]

          # Tesseract OCR to get words + normalized bounding boxes
          if apply_ocr:
               requires_backends(self							,"""pytesseract"""							)
               snake_case			:      int        =			[]
               snake_case			:      Optional[Any]        =			[]
               for image in images:
                    snake_case ,		snake_case			:      Tuple        =			apply_tesseract(__a							,__a							,__a							)
                    words_batch.append(__a							)
                    boxes_batch.append(__a							)

          if do_resize:
               snake_case			:      Any        =			[self.resize(image=__a							,size=__a							,resample=__a							) for image in images]

          if do_rescale:
               snake_case			:      Tuple        =			[self.rescale(image=__a							,scale=__a							) for image in images]

          if do_normalize:
               snake_case			:      Optional[Any]        =			[self.normalize(image=__a							,mean=__a							,std=__a							) for image in images]

          snake_case			:      Dict        =			[to_channel_dimension_format(__a							,__a							) for image in images]

          snake_case			:      List[Any]        =			BatchFeature(data={"""pixel_values""": images}							,tensor_type=__a							)

          if apply_ocr:
               snake_case			:      Optional[Any]        =			words_batch
               snake_case			:      Optional[int]        =			boxes_batch
          return data



