






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 YolosImageProcessor







class 	_UpperCAmelCase  (  unittest.TestCase		):




      """simple docstring"""



      def __init__(   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase=7    ,	_lowerCAmelCase=3    ,	_lowerCAmelCase=30    ,	_lowerCAmelCase=400    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=[0.5, 0.5, 0.5]    ,	_lowerCAmelCase=[0.5, 0.5, 0.5]    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=1 / 255    ,	_lowerCAmelCase=True    ,	):



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



          '''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 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase=False		):



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

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

          return expected_height, expected_width







@require_torch
@require_vision
class 	_UpperCAmelCase  (  _A				,							unittest.TestCase		):




      """simple docstring"""



      A          =    YolosImageProcessor if is_vision_available() else None
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[Any]      							=				YolosImageProcessingTester(self		)
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return self.image_processor_tester.prepare_image_processor_dict()
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Dict      							=				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_resize"		)		)
          self.assertTrue(hasattr(_lowerCAmelCase    ,	"size"		)		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Union[str, 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    ,	_lowerCAmelCase		)

          lowerCAmelCase__   :int      							=				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 			snake_case_      (   self		):



          '''simple docstring'''
          pass
      def 			snake_case_      (   self		):



          '''simple docstring'''
          # Initialize image_processing
          lowerCAmelCase__   :Union[str, Any]      							=				self.image_processing_class(**self.image_processor_dict		)
          # create random PIL images
          lowerCAmelCase__   :List[Any]      							=				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__   :Optional[Any]      							=				image_processing(image_inputs[0]    ,	return_tensors="pt"		).pixel_values

          lowerCAmelCase__							,lowerCAmelCase__   :Dict      							=				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__   :List[str]      							=				self.image_processor_tester.get_expected_values(_lowerCAmelCase    ,	batched=_lowerCAmelCase		)

          lowerCAmelCase__   :Optional[int]      							=				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 			snake_case_      (   self		):



          '''simple docstring'''
          # Initialize image_processing
          lowerCAmelCase__   :Optional[int]      							=				self.image_processing_class(**self.image_processor_dict		)
          # create random numpy tensors
          lowerCAmelCase__   :Tuple      							=				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__   :Union[str, Any]      							=				image_processing(image_inputs[0]    ,	return_tensors="pt"		).pixel_values

          lowerCAmelCase__							,lowerCAmelCase__   :Union[str, Any]      							=				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__   :Union[str, Any]      							=				image_processing(_lowerCAmelCase    ,	return_tensors="pt"		).pixel_values

          lowerCAmelCase__							,lowerCAmelCase__   :Dict      							=				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 			snake_case_      (   self		):



          '''simple docstring'''
          # Initialize image_processing
          lowerCAmelCase__   :Union[str, Any]      							=				self.image_processing_class(**self.image_processor_dict		)
          # create random PyTorch tensors
          lowerCAmelCase__   :int      							=				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__   :Tuple      							=				image_processing(image_inputs[0]    ,	return_tensors="pt"		).pixel_values

          lowerCAmelCase__							,lowerCAmelCase__   :Optional[int]      							=				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__   :Union[str, Any]      							=				image_processing(_lowerCAmelCase    ,	return_tensors="pt"		).pixel_values

          lowerCAmelCase__							,lowerCAmelCase__   :Dict      							=				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 			snake_case_      (   self		):



          '''simple docstring'''
          # Initialize image_processings
          lowerCAmelCase__   :Any      							=				self.image_processing_class(**self.image_processor_dict		)
          lowerCAmelCase__   :int      							=				self.image_processing_class(do_resize=_lowerCAmelCase    ,	do_normalize=_lowerCAmelCase    ,	do_rescale=_lowerCAmelCase		)
          # create random PyTorch tensors
          lowerCAmelCase__   :str      							=				prepare_image_inputs(self.image_processor_tester    ,	equal_resolution=_lowerCAmelCase    ,	torchify=_lowerCAmelCase		)
          for image in image_inputs:
              self.assertIsInstance(_lowerCAmelCase    ,	torch.Tensor		)

          # Test whether the method "pad" and calling the image processor return the same tensors
          lowerCAmelCase__   :Tuple      							=				image_processing_a.pad(_lowerCAmelCase    ,	return_tensors="pt"		)
          lowerCAmelCase__   :List[str]      							=				image_processing_a(_lowerCAmelCase    ,	return_tensors="pt"		)

          self.assertTrue(
              torch.allclose(encoded_images_with_method["pixel_values"]    ,	encoded_images["pixel_values"]    ,	atol=1e-4		)		)
      @slow
      def 			snake_case_      (   self		):



          '''simple docstring'''
          # prepare image and target
          lowerCAmelCase__   :int      							=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
          with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"    ,	"r"		) as f:
              lowerCAmelCase__   :Dict      							=				json.loads(f.read()		)

          lowerCAmelCase__   :Any      							=				{"image_id": 39_769, "annotations": target}

          # encode them
          lowerCAmelCase__   :List[Any]      							=				YolosImageProcessor.from_pretrained("hustvl/yolos-small"		)
          lowerCAmelCase__   :str      							=				image_processing(images=_lowerCAmelCase    ,	annotations=_lowerCAmelCase    ,	return_tensors="pt"		)

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

          lowerCAmelCase__   :int      							=				torch.tensor([0.2796, 0.3138, 0.3481]		)
          self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]    ,	_lowerCAmelCase    ,	atol=1e-4		)		)

          # verify area
          lowerCAmelCase__   :str      							=				torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["area"]    ,	_lowerCAmelCase		)		)
          # verify boxes
          lowerCAmelCase__   :Dict      							=				torch.Size([6, 4]		)
          self.assertEqual(encoding["labels"][0]["boxes"].shape    ,	_lowerCAmelCase		)
          lowerCAmelCase__   :Tuple      							=				torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]    ,	_lowerCAmelCase    ,	atol=1e-3		)		)
          # verify image_id
          lowerCAmelCase__   :Any      							=				torch.tensor([39_769]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]    ,	_lowerCAmelCase		)		)
          # verify is_crowd
          lowerCAmelCase__   :int      							=				torch.tensor([0, 0, 0, 0, 0, 0]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]    ,	_lowerCAmelCase		)		)
          # verify class_labels
          lowerCAmelCase__   :Dict      							=				torch.tensor([75, 75, 63, 65, 17, 17]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]    ,	_lowerCAmelCase		)		)
          # verify orig_size
          lowerCAmelCase__   :Dict      							=				torch.tensor([480, 640]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]    ,	_lowerCAmelCase		)		)
          # verify size
          lowerCAmelCase__   :Dict      							=				torch.tensor([800, 1_066]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["size"]    ,	_lowerCAmelCase		)		)



      @slow
      def 			snake_case_      (   self		):



          '''simple docstring'''
          # prepare image, target and masks_path
          lowerCAmelCase__   :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:
              lowerCAmelCase__   :int      							=				json.loads(f.read()		)

          lowerCAmelCase__   :Union[str, Any]      							=				{"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

          lowerCAmelCase__   :Any      							=				pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"		)

          # encode them
          lowerCAmelCase__   :List[Any]      							=				YolosImageProcessor(format="coco_panoptic"		)
          lowerCAmelCase__   :Optional[int]      							=				image_processing(images=_lowerCAmelCase    ,	annotations=_lowerCAmelCase    ,	masks_path=_lowerCAmelCase    ,	return_tensors="pt"		)

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

          lowerCAmelCase__   :List[str]      							=				torch.tensor([0.2796, 0.3138, 0.3481]		)
          self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]    ,	_lowerCAmelCase    ,	atol=1e-4		)		)

          # verify area
          lowerCAmelCase__   :Union[str, Any]      							=				torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["area"]    ,	_lowerCAmelCase		)		)
          # verify boxes
          lowerCAmelCase__   :List[str]      							=				torch.Size([6, 4]		)
          self.assertEqual(encoding["labels"][0]["boxes"].shape    ,	_lowerCAmelCase		)
          lowerCAmelCase__   :List[Any]      							=				torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]    ,	_lowerCAmelCase    ,	atol=1e-3		)		)
          # verify image_id
          lowerCAmelCase__   :Optional[Any]      							=				torch.tensor([39_769]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]    ,	_lowerCAmelCase		)		)
          # verify is_crowd
          lowerCAmelCase__   :Optional[int]      							=				torch.tensor([0, 0, 0, 0, 0, 0]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]    ,	_lowerCAmelCase		)		)
          # verify class_labels
          lowerCAmelCase__   :List[str]      							=				torch.tensor([17, 17, 63, 75, 75, 93]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]    ,	_lowerCAmelCase		)		)
          # verify masks
          lowerCAmelCase__   :int      							=				822_873
          self.assertEqual(encoding["labels"][0]["masks"].sum().item()    ,	_lowerCAmelCase		)
          # verify orig_size
          lowerCAmelCase__   :List[str]      							=				torch.tensor([480, 640]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]    ,	_lowerCAmelCase		)		)
          # verify size
          lowerCAmelCase__   :Optional[int]      							=				torch.tensor([800, 1_066]		)
          self.assertTrue(torch.allclose(encoding["labels"][0]["size"]    ,	_lowerCAmelCase		)		)











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    :   Tuple        =			logging.get_logger(__name__)

_a    :   Optional[int]        =			{
    """facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}







class 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    '''levit'''
      def __init__(   self    ,	_lowerCAmelCase=224    ,	_lowerCAmelCase=3    ,	_lowerCAmelCase=3    ,	_lowerCAmelCase=2    ,	_lowerCAmelCase=1    ,	_lowerCAmelCase=16    ,	_lowerCAmelCase=[128, 256, 384]    ,	_lowerCAmelCase=[4, 8, 12]    ,	_lowerCAmelCase=[4, 4, 4]    ,	_lowerCAmelCase=[16, 16, 16]    ,	_lowerCAmelCase=0    ,	_lowerCAmelCase=[2, 2, 2]    ,	_lowerCAmelCase=[2, 2, 2]    ,	_lowerCAmelCase=0.02    ,	**_lowerCAmelCase    ,	):



          '''simple docstring'''
          super().__init__(**_lowerCAmelCase		)
          lowerCAmelCase__   :List[str]      							=				image_size
          lowerCAmelCase__   :Dict      							=				num_channels
          lowerCAmelCase__   :List[Any]      							=				kernel_size
          lowerCAmelCase__   :List[Any]      							=				stride
          lowerCAmelCase__   :Tuple      							=				padding
          lowerCAmelCase__   :str      							=				hidden_sizes
          lowerCAmelCase__   :Union[str, Any]      							=				num_attention_heads
          lowerCAmelCase__   :Optional[int]      							=				depths
          lowerCAmelCase__   :Union[str, Any]      							=				key_dim
          lowerCAmelCase__   :str      							=				drop_path_rate
          lowerCAmelCase__   :int      							=				patch_size
          lowerCAmelCase__   :Dict      							=				attention_ratio
          lowerCAmelCase__   :Dict      							=				mlp_ratio
          lowerCAmelCase__   :Optional[int]      							=				initializer_range
          lowerCAmelCase__   :Tuple      							=				[
              ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
              ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
          ]







class 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    version.parse('''1.11'''		)
      @property
      def 			snake_case_      (   self		):



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



      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return 1e-4



