



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       _SCREAMING_SNAKE_CASE	(unittest.TestCase ):





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





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

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

      return expected_height, expected_width






@require_torch
@require_vision
class       _SCREAMING_SNAKE_CASE	(UpperCamelCase			,    unittest.TestCase ):
     lowerCAmelCase					=			DetaImageProcessor if is_vision_available() else None





     def 	__snake_case    (       self :							Optional[Any]				)->Tuple:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	DetaImageProcessingTester(self				)





     @property
     def 	__snake_case    (       self :							List[str]				)->Optional[int]:
      return self.image_processor_tester.prepare_image_processor_dict()





     def 	__snake_case    (       self :							List[Any]				)->Any:
      __SCREAMING_SNAKE_CASE       :    Any       =	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 	__snake_case    (       self :							Optional[int]				)->List[Any]:
      __SCREAMING_SNAKE_CASE       :    Any       =	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 	__snake_case    (       self :							Any				)->int:
      pass





     def 	__snake_case    (       self :							List[Any]				)->Union[str, Any]:
      # Initialize image_processing
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.image_processing_class(**self.image_processor_dict				)
      # create random PIL images
      __SCREAMING_SNAKE_CASE       :    List[str]       =	prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=UpperCamelCase				)
      for image in image_inputs:
       self.assertIsInstance(UpperCamelCase     ,  Image.Image				)

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

      __SCREAMING_SNAKE_CASE       :    List[str]       =	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
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.image_processor_tester.get_expected_values(UpperCamelCase     ,  batched=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	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 	__snake_case    (       self :							List[Any]				)->List[Any]:
      # Initialize image_processing
      __SCREAMING_SNAKE_CASE       :    Dict       =	self.image_processing_class(**self.image_processor_dict				)
      # create random numpy tensors
      __SCREAMING_SNAKE_CASE       :    int       =	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
      __SCREAMING_SNAKE_CASE       :    Any       =	image_processing(image_inputs[0]     ,  return_tensors="pt"				).pixel_values

      __SCREAMING_SNAKE_CASE       :    List[str]       =	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
      __SCREAMING_SNAKE_CASE       :    Any       =	image_processing(UpperCamelCase     ,  return_tensors="pt"				).pixel_values

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

      __SCREAMING_SNAKE_CASE       :    int       =	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
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	image_processing(UpperCamelCase     ,  return_tensors="pt"				).pixel_values

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	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 	__snake_case    (       self :							Tuple				)->Any:
      # prepare image and target
      __SCREAMING_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:
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	json.loads(f.read()				)

      __SCREAMING_SNAKE_CASE       :    Any       =	{"image_id": 3_9_7_6_9, "annotations": target}

      # encode them
      __SCREAMING_SNAKE_CASE       :    Dict       =	DetaImageProcessor()
      __SCREAMING_SNAKE_CASE       :    List[str]       =	image_processing(images=UpperCamelCase     ,  annotations=UpperCamelCase     ,  return_tensors="pt"				)

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

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	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]     ,  UpperCamelCase     ,  atol=1E-4				)				)

      # verify area
      __SCREAMING_SNAKE_CASE       :    List[str]       =	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"]     ,  UpperCamelCase				)				)
      # verify boxes
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	torch.Size([6, 4]				)
      self.assertEqual(encoding["labels"][0]["boxes"].shape     ,  UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	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]     ,  UpperCamelCase     ,  atol=1E-3				)				)
      # verify image_id
      __SCREAMING_SNAKE_CASE       :    Tuple       =	torch.tensor([3_9_7_6_9]				)
      self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]     ,  UpperCamelCase				)				)
      # verify is_crowd
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	torch.tensor([0, 0, 0, 0, 0, 0]				)
      self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]     ,  UpperCamelCase				)				)
      # verify class_labels
      __SCREAMING_SNAKE_CASE       :    str       =	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
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	torch.tensor([4_8_0, 6_4_0]				)
      self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]     ,  UpperCamelCase				)				)
      # verify size
      __SCREAMING_SNAKE_CASE       :    Tuple       =	torch.tensor([8_0_0, 1_0_6_6]				)
      self.assertTrue(torch.allclose(encoding["labels"][0]["size"]     ,  UpperCamelCase				)				)






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

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

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

      # encode them
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	DetaImageProcessor(format="coco_panoptic"				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	image_processing(images=UpperCamelCase     ,  annotations=UpperCamelCase     ,  masks_path=UpperCamelCase     ,  return_tensors="pt"				)

      # verify pixel values
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	torch.Size([1, 3, 8_0_0, 1_0_6_6]				)
      self.assertEqual(encoding["pixel_values"].shape     ,  UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	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]     ,  UpperCamelCase     ,  atol=1E-4				)				)

      # verify area
      __SCREAMING_SNAKE_CASE       :    str       =	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"]     ,  UpperCamelCase				)				)
      # verify boxes
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	torch.Size([6, 4]				)
      self.assertEqual(encoding["labels"][0]["boxes"].shape     ,  UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    int       =	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]     ,  UpperCamelCase     ,  atol=1E-3				)				)
      # verify image_id
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	torch.tensor([3_9_7_6_9]				)
      self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]     ,  UpperCamelCase				)				)
      # verify is_crowd
      __SCREAMING_SNAKE_CASE       :    Any       =	torch.tensor([0, 0, 0, 0, 0, 0]				)
      self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]     ,  UpperCamelCase				)				)
      # verify class_labels
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	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
      __SCREAMING_SNAKE_CASE       :    List[str]       =	8_2_2_8_7_3
      self.assertEqual(encoding["labels"][0]["masks"].sum().item()     ,  UpperCamelCase				)
      # verify orig_size
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	torch.tensor([4_8_0, 6_4_0]				)
      self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]     ,  UpperCamelCase				)				)
      # verify size
      __SCREAMING_SNAKE_CASE       :    List[str]       =	torch.tensor([8_0_0, 1_0_6_6]				)
      self.assertTrue(torch.allclose(encoding["labels"][0]["size"]     ,  UpperCamelCase				)				)











import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


_lowerCamelCase						    =  logging.get_logger(__name__)

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     def __init__(       self :							int     ,  *UpperCamelCase :							Optional[int]     ,  **UpperCamelCase :							Optional[int]				)->None:
      warnings.warn(
          "The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
          " Please use LayoutLMv2ImageProcessor instead."     ,  UpperCamelCase     ,  )
      super().__init__(*UpperCamelCase     ,  **UpperCamelCase				)






