




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 							__UpperCamelCase			( unittest.TestCase  ):





				'''simple docstring'''



				def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_=7       , UpperCAmelCase_=3       , UpperCAmelCase_=30       , UpperCAmelCase_=4_00       , UpperCAmelCase_=True       , UpperCAmelCase_=None       , UpperCAmelCase_=True       , UpperCAmelCase_=[0.5, 0.5, 0.5]       , UpperCAmelCase_=[0.5, 0.5, 0.5]       , UpperCAmelCase_=True       , UpperCAmelCase_=1 / 2_55       , UpperCAmelCase_=True       , ):
											# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
											lowerCAmelCase   =		size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
											lowerCAmelCase   =		parent
											lowerCAmelCase   =		batch_size
											lowerCAmelCase   =		num_channels
											lowerCAmelCase   =		min_resolution
											lowerCAmelCase   =		max_resolution
											lowerCAmelCase   =		do_resize
											lowerCAmelCase   =		size
											lowerCAmelCase   =		do_normalize
											lowerCAmelCase   =		image_mean
											lowerCAmelCase   =		image_std
											lowerCAmelCase   =		do_rescale
											lowerCAmelCase   =		rescale_factor
											lowerCAmelCase   =		do_pad



				def       __snake_case		( self						):
											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       , UpperCAmelCase_       , UpperCAmelCase_=False						):
											if not batched:
																		lowerCAmelCase   =		image_inputs[0]
																		if isinstance(UpperCAmelCase_       , 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(UpperCAmelCase_       , key=lambda UpperCAmelCase_						: item[0]						)[0]
																		lowerCAmelCase   =		max(UpperCAmelCase_       , key=lambda UpperCAmelCase_						: item[1]						)[1]

											return expected_height, expected_width




@require_torch
@require_vision
class 							__UpperCamelCase			( __UpperCAmelCase     ,					unittest.TestCase  ):





				'''simple docstring'''



				__a							: Union[str, Any]										=ConditionalDetrImageProcessor if is_vision_available() else None



				def       __snake_case		( self						):
											lowerCAmelCase   =		ConditionalDetrImageProcessingTester(self						)



				@property
				def       __snake_case		( self						):
											return self.image_processor_tester.prepare_image_processor_dict()



				def       __snake_case		( self						):
											lowerCAmelCase   =		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_       , '''size'''						)						)



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.image_processing_class.from_dict(self.image_processor_dict						)
											self.assertEqual(image_processor.size       , {'''shortest_edge''': 18, '''longest_edge''': 13_33}						)
											self.assertEqual(image_processor.do_pad       , UpperCAmelCase_						)

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



				def       __snake_case		( self						):
											pass



				def       __snake_case		( self						):
											# Initialize image_processing
											lowerCAmelCase   =		self.image_processing_class(**self.image_processor_dict						)
											# create random PIL images
											lowerCAmelCase   =		prepare_image_inputs(self.image_processor_tester       , equal_resolution=UpperCAmelCase_						)
											for image in image_inputs:
																		self.assertIsInstance(UpperCAmelCase_       , 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(UpperCAmelCase_						)

											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(UpperCAmelCase_       , batched=UpperCAmelCase_						)

											lowerCAmelCase   =		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						):
											# Initialize image_processing
											lowerCAmelCase   =		self.image_processing_class(**self.image_processor_dict						)
											# create random numpy tensors
											lowerCAmelCase   =		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
											lowerCAmelCase   =		image_processing(image_inputs[0]       , return_tensors='''pt'''						).pixel_values

											lowerCAmelCase   ,     lowerCAmelCase   =		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
											lowerCAmelCase   =		image_processing(UpperCAmelCase_       , return_tensors='''pt'''						).pixel_values

											lowerCAmelCase   ,     lowerCAmelCase   =		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						):
											# Initialize image_processing
											lowerCAmelCase   =		self.image_processing_class(**self.image_processor_dict						)
											# create random PyTorch tensors
											lowerCAmelCase   =		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
											lowerCAmelCase   =		image_processing(image_inputs[0]       , return_tensors='''pt'''						).pixel_values

											lowerCAmelCase   ,     lowerCAmelCase   =		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
											lowerCAmelCase   =		image_processing(UpperCAmelCase_       , return_tensors='''pt'''						).pixel_values

											lowerCAmelCase   ,     lowerCAmelCase   =		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						):
											# prepare image and target
											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''': 3_97_69, '''annotations''': target}

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

											# verify pixel values
											lowerCAmelCase   =		torch.Size([1, 3, 8_00, 10_66]						)
											self.assertEqual(encoding['''pixel_values'''].shape       , UpperCAmelCase_						)

											lowerCAmelCase   =		torch.tensor([0.2796, 0.3138, 0.3481]						)
											self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       , UpperCAmelCase_       , atol=1E-4						)						)

											# verify area
											lowerCAmelCase   =		torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]						)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       , UpperCAmelCase_						)						)
											# verify boxes
											lowerCAmelCase   =		torch.Size([6, 4]						)
											self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       , UpperCAmelCase_						)
											lowerCAmelCase   =		torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]						)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       , UpperCAmelCase_       , atol=1E-3						)						)
											# verify image_id
											lowerCAmelCase   =		torch.tensor([3_97_69]						)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       , UpperCAmelCase_						)						)
											# verify is_crowd
											lowerCAmelCase   =		torch.tensor([0, 0, 0, 0, 0, 0]						)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       , UpperCAmelCase_						)						)
											# verify class_labels
											lowerCAmelCase   =		torch.tensor([75, 75, 63, 65, 17, 17]						)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       , UpperCAmelCase_						)						)
											# verify orig_size
											lowerCAmelCase   =		torch.tensor([4_80, 6_40]						)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       , UpperCAmelCase_						)						)
											# verify size
											lowerCAmelCase   =		torch.tensor([8_00, 10_66]						)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       , UpperCAmelCase_						)						)






				@slow
				def       __snake_case		( self						):
											# prepare image, target and masks_path
											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''': 3_97_69, '''segments_info''': target}

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

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

											# verify pixel values
											lowerCAmelCase   =		torch.Size([1, 3, 8_00, 10_66]						)
											self.assertEqual(encoding['''pixel_values'''].shape       , UpperCAmelCase_						)

											lowerCAmelCase   =		torch.tensor([0.2796, 0.3138, 0.3481]						)
											self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       , UpperCAmelCase_       , atol=1E-4						)						)

											# verify area
											lowerCAmelCase   =		torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]						)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       , UpperCAmelCase_						)						)
											# verify boxes
											lowerCAmelCase   =		torch.Size([6, 4]						)
											self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       , UpperCAmelCase_						)
											lowerCAmelCase   =		torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]						)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       , UpperCAmelCase_       , atol=1E-3						)						)
											# verify image_id
											lowerCAmelCase   =		torch.tensor([3_97_69]						)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       , UpperCAmelCase_						)						)
											# verify is_crowd
											lowerCAmelCase   =		torch.tensor([0, 0, 0, 0, 0, 0]						)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       , UpperCAmelCase_						)						)
											# verify class_labels
											lowerCAmelCase   =		torch.tensor([17, 17, 63, 75, 75, 93]						)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       , UpperCAmelCase_						)						)
											# verify masks
											lowerCAmelCase   =		82_28_73
											self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()       , UpperCAmelCase_						)
											# verify orig_size
											lowerCAmelCase   =		torch.tensor([4_80, 6_40]						)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       , UpperCAmelCase_						)						)
											# verify size
											lowerCAmelCase   =		torch.tensor([8_00, 10_66]						)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       , UpperCAmelCase_						)						)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_						={
    """configuration_jukebox""": [
        """JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """JukeboxConfig""",
        """JukeboxPriorConfig""",
        """JukeboxVQVAEConfig""",
    ],
    """tokenization_jukebox""": ["""JukeboxTokenizer"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_						=[
		    """JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """JukeboxModel""",
		    """JukeboxPreTrainedModel""",
		    """JukeboxVQVAE""",
		    """JukeboxPrior""",
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

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



