




"""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 	lowerCamelCase__			(     unittest.TestCase	):


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



					"""simple docstring"""







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


			def 							lowerCamelCase_ (   self							):



					"""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 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE=False							):



					"""simple docstring"""







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

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

					return expected_height, expected_width







@require_torch
@require_vision
class 	lowerCamelCase__			(     lowerCamelCase_ ,     unittest.TestCase	):
			a__       :					Dict	    =   DetrImageProcessor if is_vision_available() else None


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					snake_case						:  List[Any]    	= DetrImageProcessingTester(self							)


			@property
			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					return self.image_processor_tester.prepare_image_processor_dict()


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					snake_case						:  List[Any]    	= self.image_processing_class(**self.image_processor_dict							)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,       "image_mean"							)							)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,       "image_std"							)							)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,       "do_normalize"							)							)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,       "do_rescale"							)							)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,       "rescale_factor"							)							)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,       "do_resize"							)							)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,       "size"							)							)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,       "do_pad"							)							)


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					snake_case						:  int    	= 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 ,       _SCREAMING_SNAKE_CASE							)

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


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					pass


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					snake_case						:  Optional[Any]    	= self.image_processing_class(**self.image_processor_dict							)
					# create random PIL images
					snake_case						:  Optional[int]    	= prepare_image_inputs(self.image_processor_tester ,       equal_resolution=_SCREAMING_SNAKE_CASE							)
					for image in image_inputs:
							self.assertIsInstance(_SCREAMING_SNAKE_CASE ,       Image.Image							)

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

					snake_case						:  Any    	= self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE							)

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

					# Test batched
					snake_case						:  Any    	= self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE ,       batched=_SCREAMING_SNAKE_CASE							)

					snake_case						:  str    	= image_processing(_SCREAMING_SNAKE_CASE ,       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 							lowerCamelCase_ (   self							):



					"""simple docstring"""







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

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

					snake_case						:  List[Any]    	= self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE							)

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

					# Test batched
					snake_case						:  List[str]    	= image_processing(_SCREAMING_SNAKE_CASE ,       return_tensors="pt"							).pixel_values

					snake_case						:  Optional[Any]    	= self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE ,       batched=_SCREAMING_SNAKE_CASE							)

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


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					snake_case						:  Tuple    	= self.image_processing_class(**self.image_processor_dict							)
					# create random PyTorch tensors
					snake_case						:  List[Any]    	= prepare_image_inputs(self.image_processor_tester ,       equal_resolution=_SCREAMING_SNAKE_CASE ,       torchify=_SCREAMING_SNAKE_CASE							)
					for image in image_inputs:
							self.assertIsInstance(_SCREAMING_SNAKE_CASE ,       torch.Tensor							)

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

					snake_case						:  Optional[Any]    	= self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE							)

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

					# Test batched
					snake_case						:  Union[str, Any]    	= image_processing(_SCREAMING_SNAKE_CASE ,       return_tensors="pt"							).pixel_values

					snake_case						:  Dict    	= self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE ,       batched=_SCREAMING_SNAKE_CASE							)

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


			@slow
			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







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

					snake_case						:  Any    	= {'''image_id''': 39_769, '''annotations''': target}

					# encode them
					snake_case						:  Optional[int]    	= DetrImageProcessor.from_pretrained("facebook/detr-resnet-50"							)
					snake_case						:  Optional[int]    	= image_processing(images=_SCREAMING_SNAKE_CASE ,       annotations=_SCREAMING_SNAKE_CASE ,       return_tensors="pt"							)

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

					snake_case						:  Union[str, Any]    	= torch.tensor([0.27_96, 0.31_38, 0.34_81]							)
					self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] ,       _SCREAMING_SNAKE_CASE ,       atol=1E-4							)							)

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



			@slow
			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					snake_case						:  Dict    	= 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						:  Union[str, Any]    	= json.loads(f.read()							)

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

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

					# encode them
					snake_case						:  Union[str, Any]    	= DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic"							)
					snake_case						:  List[Any]    	= image_processing(images=_SCREAMING_SNAKE_CASE ,       annotations=_SCREAMING_SNAKE_CASE ,       masks_path=_SCREAMING_SNAKE_CASE ,       return_tensors="pt"							)

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

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

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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase								=	{
    """configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MobileViTConfig""", """MobileViTOnnxConfig"""],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	["""MobileViTFeatureExtractor"""]
     UpperCamelCase								=	["""MobileViTImageProcessor"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """MobileViTForImageClassification""",
         """MobileViTForSemanticSegmentation""",
         """MobileViTModel""",
         """MobileViTPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFMobileViTForImageClassification""",
         """TFMobileViTForSemanticSegmentation""",
         """TFMobileViTModel""",
         """TFMobileViTPreTrainedModel""",
     ]

if TYPE_CHECKING:
     from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_mobilevit import MobileViTFeatureExtractor
          from .image_processing_mobilevit import MobileViTImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilevit import (
              MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileViTForImageClassification,
              MobileViTForSemanticSegmentation,
              MobileViTModel,
              MobileViTPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mobilevit import (
              TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFMobileViTForImageClassification,
              TFMobileViTForSemanticSegmentation,
              TFMobileViTModel,
              TFMobileViTPreTrainedModel,
          )


else:
     import sys

     UpperCamelCase								=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




