






'''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 DetrImageProcessor
class 				lowercase       (							unittest.TestCase      ):


    '''simple docstring'''

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

     '''simple docstring'''




     lowerCamelCase__  =     size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
     lowerCamelCase__  =     parent
     lowerCamelCase__  =     batch_size
     lowerCamelCase__  =     num_channels
     lowerCamelCase__  =     min_resolution
     lowerCamelCase__  =     max_resolution
     lowerCamelCase__  =     do_resize
     lowerCamelCase__  =     size
     lowerCamelCase__  =     do_rescale
     lowerCamelCase__  =     rescale_factor
     lowerCamelCase__  =     do_normalize
     lowerCamelCase__  =     image_mean
     lowerCamelCase__  =     image_std
     lowerCamelCase__  =     do_pad

    def    a__				(					self     :     str     )							->							str:

     '''simple docstring'''




     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_rescale": self.do_rescale,
         "rescale_factor": self.rescale_factor,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_pad": self.do_pad,
     }




    def    a__				(					self     :     Dict				,     __lowerCamelCase     :     Dict				,     __lowerCamelCase     :     Dict=False     )							->							Tuple:

     '''simple docstring'''




     if not batched:
      lowerCamelCase__  =     image_inputs[0]
      if isinstance(__lowerCamelCase				,     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(__lowerCamelCase				,     key=lambda __lowerCamelCase     : item[0]     )[0]
      lowerCamelCase__  =     max(__lowerCamelCase				,     key=lambda __lowerCamelCase     : item[1]     )[1]

     return expected_height, expected_width
@require_torch
@require_vision
class 				lowercase       (							_lowerCamelCase ,       unittest.TestCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							DetrImageProcessor if is_vision_available() else None

    def    a__				(					self     :     Optional[int]     )							->							Union[str, Any]:

     '''simple docstring'''




     lowerCamelCase__  =     DetrImageProcessingTester(self     )

    @property
    def    a__				(					self     :     Optional[Any]     )							->							List[str]:

     '''simple docstring'''




     return self.image_processor_tester.prepare_image_processor_dict()

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

     '''simple docstring'''




     lowerCamelCase__  =     self.image_processing_class(**self.image_processor_dict     )
     self.assertTrue(hasattr(__lowerCamelCase				,     "image_mean"     )     )
     self.assertTrue(hasattr(__lowerCamelCase				,     "image_std"     )     )
     self.assertTrue(hasattr(__lowerCamelCase				,     "do_normalize"     )     )
     self.assertTrue(hasattr(__lowerCamelCase				,     "do_rescale"     )     )
     self.assertTrue(hasattr(__lowerCamelCase				,     "rescale_factor"     )     )
     self.assertTrue(hasattr(__lowerCamelCase				,     "do_resize"     )     )
     self.assertTrue(hasattr(__lowerCamelCase				,     "size"     )     )
     self.assertTrue(hasattr(__lowerCamelCase				,     "do_pad"     )     )

    def    a__				(					self     :     str     )							->							int:

     '''simple docstring'''




     lowerCamelCase__  =     self.image_processing_class.from_dict(self.image_processor_dict     )
     self.assertEqual(image_processor.size				,     {"shortest_edge": 18, "longest_edge": 1333}     )
     self.assertEqual(image_processor.do_pad				,     __lowerCamelCase     )

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

    def    a__				(					self     :     Any     )							->							int:

     '''simple docstring'''




     pass

    def    a__				(					self     :     int     )							->							Any:

     '''simple docstring'''




     lowerCamelCase__  =     self.image_processing_class(**self.image_processor_dict     )
     # create random PIL images
     lowerCamelCase__  =     prepare_image_inputs(self.image_processor_tester				,     equal_resolution=__lowerCamelCase     )
     for image in image_inputs:
      self.assertIsInstance(__lowerCamelCase				,     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(__lowerCamelCase     )

     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(__lowerCamelCase				,     batched=__lowerCamelCase     )

     lowerCamelCase__  =     image_processing(__lowerCamelCase				,     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    a__				(					self     :     Tuple     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     self.image_processing_class(**self.image_processor_dict     )
     # create random numpy tensors
     lowerCamelCase__  =     prepare_image_inputs(self.image_processor_tester				,     equal_resolution=__lowerCamelCase				,     numpify=__lowerCamelCase     )
     for image in image_inputs:
      self.assertIsInstance(__lowerCamelCase				,     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(__lowerCamelCase     )

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

     # Test batched
     lowerCamelCase__  =     image_processing(__lowerCamelCase				,     return_tensors="pt"     ).pixel_values

     lowerCamelCase__  ,	lowerCamelCase__  =     self.image_processor_tester.get_expected_values(__lowerCamelCase				,     batched=__lowerCamelCase     )

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

    def    a__				(					self     :     List[Any]     )							->							Optional[int]:

     '''simple docstring'''




     lowerCamelCase__  =     self.image_processing_class(**self.image_processor_dict     )
     # create random PyTorch tensors
     lowerCamelCase__  =     prepare_image_inputs(self.image_processor_tester				,     equal_resolution=__lowerCamelCase				,     torchify=__lowerCamelCase     )
     for image in image_inputs:
      self.assertIsInstance(__lowerCamelCase				,     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(__lowerCamelCase     )

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

     # Test batched
     lowerCamelCase__  =     image_processing(__lowerCamelCase				,     return_tensors="pt"     ).pixel_values

     lowerCamelCase__  ,	lowerCamelCase__  =     self.image_processor_tester.get_expected_values(__lowerCamelCase				,     batched=__lowerCamelCase     )

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

    @slow
    def    a__				(					self     :     Union[str, Any]     )							->							Any:

     '''simple docstring'''




     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": 39769, "annotations": target}

     # encode them
     lowerCamelCase__  =     DetrImageProcessor.from_pretrained("facebook/detr-resnet-50"     )
     lowerCamelCase__  =     image_processing(images=__lowerCamelCase				,     annotations=__lowerCamelCase				,     return_tensors="pt"     )

     # verify pixel values
     lowerCamelCase__  =     torch.Size([1, 3, 800, 1066]     )
     self.assertEqual(encoding["pixel_values"].shape				,     __lowerCamelCase     )

     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]				,     __lowerCamelCase				,     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"]				,     __lowerCamelCase     )     )
     # verify boxes
     lowerCamelCase__  =     torch.Size([6, 4]     )
     self.assertEqual(encoding["labels"][0]["boxes"].shape				,     __lowerCamelCase     )
     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]				,     __lowerCamelCase				,     atol=1E-3     )     )
     # verify image_id
     lowerCamelCase__  =     torch.tensor([39769]     )
     self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,     __lowerCamelCase     )     )
     # verify is_crowd
     lowerCamelCase__  =     torch.tensor([0, 0, 0, 0, 0, 0]     )
     self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,     __lowerCamelCase     )     )
     # verify class_labels
     lowerCamelCase__  =     torch.tensor([75, 75, 63, 65, 17, 17]     )
     self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,     __lowerCamelCase     )     )
     # verify orig_size
     lowerCamelCase__  =     torch.tensor([480, 640]     )
     self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,     __lowerCamelCase     )     )
     # verify size
     lowerCamelCase__  =     torch.tensor([800, 1066]     )
     self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,     __lowerCamelCase     )     )




    @slow
    def    a__				(					self     :     str     )							->							List[str]:

     '''simple docstring'''




     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": 39769, "segments_info": target}

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

     # encode them
     lowerCamelCase__  =     DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic"     )
     lowerCamelCase__  =     image_processing(images=__lowerCamelCase				,     annotations=__lowerCamelCase				,     masks_path=__lowerCamelCase				,     return_tensors="pt"     )

     # verify pixel values
     lowerCamelCase__  =     torch.Size([1, 3, 800, 1066]     )
     self.assertEqual(encoding["pixel_values"].shape				,     __lowerCamelCase     )

     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]				,     __lowerCamelCase				,     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"]				,     __lowerCamelCase     )     )
     # verify boxes
     lowerCamelCase__  =     torch.Size([6, 4]     )
     self.assertEqual(encoding["labels"][0]["boxes"].shape				,     __lowerCamelCase     )
     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]				,     __lowerCamelCase				,     atol=1E-3     )     )
     # verify image_id
     lowerCamelCase__  =     torch.tensor([39769]     )
     self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,     __lowerCamelCase     )     )
     # verify is_crowd
     lowerCamelCase__  =     torch.tensor([0, 0, 0, 0, 0, 0]     )
     self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,     __lowerCamelCase     )     )
     # verify class_labels
     lowerCamelCase__  =     torch.tensor([17, 17, 63, 75, 75, 93]     )
     self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,     __lowerCamelCase     )     )
     # verify masks
     lowerCamelCase__  =     822873
     self.assertEqual(encoding["labels"][0]["masks"].sum().item()				,     __lowerCamelCase     )
     # verify orig_size
     lowerCamelCase__  =     torch.tensor([480, 640]     )
     self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,     __lowerCamelCase     )     )
     # verify size
     lowerCamelCase__  =     torch.tensor([800, 1066]     )
     self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,     __lowerCamelCase     )     )









'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

__A							:							int  							=		{
    """google/vit-base-patch16-224""": """https://huggingface.co/vit-base-patch16-224/resolve/main/config.json""",
    # See all ViT models at https://huggingface.co/models?filter=vit
}
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							"vit"

    def __init__(					self     :     Optional[int]				,     __lowerCamelCase     :     Tuple=768				,     __lowerCamelCase     :     Tuple=12				,     __lowerCamelCase     :     Tuple=12				,     __lowerCamelCase     :     int=3072				,     __lowerCamelCase     :     Optional[Any]="gelu"				,     __lowerCamelCase     :     Optional[int]=0.0				,     __lowerCamelCase     :     Tuple=0.0				,     __lowerCamelCase     :     Optional[Any]=0.0_2				,     __lowerCamelCase     :     int=1E-12				,     __lowerCamelCase     :     Tuple=224				,     __lowerCamelCase     :     int=16				,     __lowerCamelCase     :     Optional[int]=3				,     __lowerCamelCase     :     List[str]=True				,     __lowerCamelCase     :     str=16				,     **__lowerCamelCase     :     Optional[int]				,     )							->							str:

     '''simple docstring'''




     super().__init__(**__lowerCamelCase     )

     lowerCamelCase__  =     hidden_size
     lowerCamelCase__  =     num_hidden_layers
     lowerCamelCase__  =     num_attention_heads
     lowerCamelCase__  =     intermediate_size
     lowerCamelCase__  =     hidden_act
     lowerCamelCase__  =     hidden_dropout_prob
     lowerCamelCase__  =     attention_probs_dropout_prob
     lowerCamelCase__  =     initializer_range
     lowerCamelCase__  =     layer_norm_eps
     lowerCamelCase__  =     image_size
     lowerCamelCase__  =     patch_size
     lowerCamelCase__  =     num_channels
     lowerCamelCase__  =     qkv_bias
     lowerCamelCase__  =     encoder_stride
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							version.parse("1.11"      )

    @property
    def    a__				(					self     :     Optional[int]     )							->							Mapping[str, Mapping[int, str]]:

     '''simple docstring'''




     return OrderedDict(
         [
             ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
         ]     )




    @property
    def    a__				(					self     :     Optional[int]     )							->							float:

     '''simple docstring'''




     return 1E-4

