


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 ConditionalDetrImageProcessor




class       lowercase_					(unittest.TestCase   ):





				def __init__(				self       ,  lowercase_       ,  lowercase_=7       ,  lowercase_=3       ,  lowercase_=30       ,  lowercase_=400       ,  lowercase_=True       ,  lowercase_=None       ,  lowercase_=True       ,  lowercase_=[0.5, 0.5, 0.5]       ,  lowercase_=[0.5, 0.5, 0.5]       ,  lowercase_=True       ,  lowercase_=1 / 255       ,  lowercase_=True       ,  )  -> List[Any]:
											# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
											a__      =size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
											a__      =parent
											a__      =batch_size
											a__      =num_channels
											a__      =min_resolution
											a__      =max_resolution
											a__      =do_resize
											a__      =size
											a__      =do_normalize
											a__      =image_mean
											a__      =image_std
											a__      =do_rescale
											a__      =rescale_factor
											a__      =do_pad





				def 						__UpperCamelCase    (				self)  -> Dict:
											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 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_=False)  -> Any:
											if not batched:
																		a__      =image_inputs[0]
																		if isinstance(lowercase_       ,  Image.Image):
																									a__    ,     a__      =image.size
																		else:
																									a__    ,     a__      =image.shape[1], image.shape[2]
																		if w < h:
																									a__      =int(self.size['shortest_edge'] * h / w)
																									a__      =self.size['shortest_edge']
																		elif w > h:
																									a__      =self.size['shortest_edge']
																									a__      =int(self.size['shortest_edge'] * w / h)
																		else:
																									a__      =self.size['shortest_edge']
																									a__      =self.size['shortest_edge']

											else:
																		a__      =[]
																		for image in image_inputs:
																									a__    ,     a__      =self.get_expected_values([image])
																									expected_values.append((expected_height, expected_width))
																		a__      =max(lowercase_       ,  key=lambda lowercase_: item[0])[0]
																		a__      =max(lowercase_       ,  key=lambda lowercase_: item[1])[1]

											return expected_height, expected_width







@require_torch
@require_vision
class       lowercase_					(lowercase__      ,					unittest.TestCase   ):
				snake_case	    =ConditionalDetrImageProcessor if is_vision_available() else None





				def 						__UpperCamelCase    (				self)  -> Tuple:
											a__      =ConditionalDetrImageProcessingTester(self)





				@property
				def 						__UpperCamelCase    (				self)  -> Dict:
											return self.image_processor_tester.prepare_image_processor_dict()





				def 						__UpperCamelCase    (				self)  -> Any:
											a__      =self.image_processing_class(**self.image_processor_dict)
											self.assertTrue(hasattr(lowercase_       ,  'image_mean'))
											self.assertTrue(hasattr(lowercase_       ,  'image_std'))
											self.assertTrue(hasattr(lowercase_       ,  'do_normalize'))
											self.assertTrue(hasattr(lowercase_       ,  'do_resize'))
											self.assertTrue(hasattr(lowercase_       ,  'size'))





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											a__      =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       ,  lowercase_)

											a__      =self.image_processing_class.from_dict(
											    self.image_processor_dict       ,  size=42       ,  max_size=84       ,  pad_and_return_pixel_mask=lowercase_)
											self.assertEqual(image_processor.size       ,  {'shortest_edge': 42, 'longest_edge': 84})
											self.assertEqual(image_processor.do_pad       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											pass





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											# Initialize image_processing
											a__      =self.image_processing_class(**self.image_processor_dict)
											# create random PIL images
											a__      =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowercase_)
											for image in image_inputs:
																		self.assertIsInstance(lowercase_       ,  Image.Image)

											# Test not batched input
											a__      =image_processing(image_inputs[0]       ,  return_tensors='pt').pixel_values

											a__    ,     a__      =self.image_processor_tester.get_expected_values(lowercase_)

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

											# Test batched
											a__    ,     a__      =self.image_processor_tester.get_expected_values(lowercase_       ,  batched=lowercase_)

											a__      =image_processing(lowercase_       ,  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 						__UpperCamelCase    (				self)  -> Optional[int]:
											# Initialize image_processing
											a__      =self.image_processing_class(**self.image_processor_dict)
											# create random numpy tensors
											a__      =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowercase_       ,  numpify=lowercase_)
											for image in image_inputs:
																		self.assertIsInstance(lowercase_       ,  np.ndarray)

											# Test not batched input
											a__      =image_processing(image_inputs[0]       ,  return_tensors='pt').pixel_values

											a__    ,     a__      =self.image_processor_tester.get_expected_values(lowercase_)

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

											# Test batched
											a__      =image_processing(lowercase_       ,  return_tensors='pt').pixel_values

											a__    ,     a__      =self.image_processor_tester.get_expected_values(lowercase_       ,  batched=lowercase_)

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





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											# Initialize image_processing
											a__      =self.image_processing_class(**self.image_processor_dict)
											# create random PyTorch tensors
											a__      =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowercase_       ,  torchify=lowercase_)
											for image in image_inputs:
																		self.assertIsInstance(lowercase_       ,  torch.Tensor)

											# Test not batched input
											a__      =image_processing(image_inputs[0]       ,  return_tensors='pt').pixel_values

											a__    ,     a__      =self.image_processor_tester.get_expected_values(lowercase_)

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

											# Test batched
											a__      =image_processing(lowercase_       ,  return_tensors='pt').pixel_values

											a__    ,     a__      =self.image_processor_tester.get_expected_values(lowercase_       ,  batched=lowercase_)

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





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

											a__      ={'image_id': 39769, 'annotations': target}

											# encode them
											a__      =ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50')
											a__      =image_processing(images=lowercase_       ,  annotations=lowercase_       ,  return_tensors='pt')

											# verify pixel values
											a__      =torch.Size([1, 3, 800, 1066])
											self.assertEqual(encoding['pixel_values'].shape       ,  lowercase_)

											a__      =torch.tensor([0.27_96, 0.31_38, 0.34_81])
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,  lowercase_       ,  atol=1e-4))

											# verify area
											a__      =torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38])
											self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,  lowercase_))
											# verify boxes
											a__      =torch.Size([6, 4])
											self.assertEqual(encoding['labels'][0]['boxes'].shape       ,  lowercase_)
											a__      =torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15])
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,  lowercase_       ,  atol=1e-3))
											# verify image_id
											a__      =torch.tensor([39769])
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,  lowercase_))
											# verify is_crowd
											a__      =torch.tensor([0, 0, 0, 0, 0, 0])
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,  lowercase_))
											# verify class_labels
											a__      =torch.tensor([75, 75, 63, 65, 17, 17])
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,  lowercase_))
											# verify orig_size
											a__      =torch.tensor([480, 640])
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,  lowercase_))
											# verify size
											a__      =torch.tensor([800, 1066])
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,  lowercase_))





				@slow
				def 						__UpperCamelCase    (				self)  -> Dict:
											# prepare image, target and masks_path
											a__      =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
											with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'       ,  'r') as f:
																		a__      =json.loads(f.read())

											a__      ={'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

											a__      =pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic')

											# encode them
											a__      =ConditionalDetrImageProcessor(format='coco_panoptic')
											a__      =image_processing(images=lowercase_       ,  annotations=lowercase_       ,  masks_path=lowercase_       ,  return_tensors='pt')

											# verify pixel values
											a__      =torch.Size([1, 3, 800, 1066])
											self.assertEqual(encoding['pixel_values'].shape       ,  lowercase_)

											a__      =torch.tensor([0.27_96, 0.31_38, 0.34_81])
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,  lowercase_       ,  atol=1e-4))

											# verify area
											a__      =torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47])
											self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,  lowercase_))
											# verify boxes
											a__      =torch.Size([6, 4])
											self.assertEqual(encoding['labels'][0]['boxes'].shape       ,  lowercase_)
											a__      =torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25])
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,  lowercase_       ,  atol=1e-3))
											# verify image_id
											a__      =torch.tensor([39769])
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,  lowercase_))
											# verify is_crowd
											a__      =torch.tensor([0, 0, 0, 0, 0, 0])
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,  lowercase_))
											# verify class_labels
											a__      =torch.tensor([17, 17, 63, 75, 75, 93])
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,  lowercase_))
											# verify masks
											a__      =822873
											self.assertEqual(encoding['labels'][0]['masks'].sum().item()       ,  lowercase_)
											# verify orig_size
											a__      =torch.tensor([480, 640])
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,  lowercase_))
											# verify size
											a__      =torch.tensor([800, 1066])
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,  lowercase_))





from typing import TYPE_CHECKING

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


_lowerCAmelCase:						Optional[Any]   				=       {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase:						List[str]   				=       [
	    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SwiftFormerForImageClassification',
	    'SwiftFormerModel',
	    'SwiftFormerPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_swiftformer import (
	    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    SwiftFormerConfig,
	    SwiftFormerOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swiftformer import (
		    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwiftFormerForImageClassification,
		    SwiftFormerModel,
		    SwiftFormerPreTrainedModel,
		)

else:
	import sys

	_lowerCAmelCase:						List[Any]   				=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

