

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 SCREAMING_SNAKE_CASE  (		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 ,				):

				"""simple docstring"""


				snake_case_      		= size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
				snake_case_      		= parent
				snake_case_      		= batch_size
				snake_case_      		= num_channels
				snake_case_      		= min_resolution
				snake_case_      		= max_resolution
				snake_case_      		= do_resize
				snake_case_      		= size
				snake_case_      		= do_normalize
				snake_case_      		= image_mean
				snake_case_      		= image_std
				snake_case_      		= do_rescale
				snake_case_      		= rescale_factor
				snake_case_      		= do_pad






			def 			__lowerCAmelCase (    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 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase=False							):

				"""simple docstring"""


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

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

				return expected_height, expected_width







@require_torch
@require_vision
class SCREAMING_SNAKE_CASE  (		__snake_case							,						unittest.TestCase							):






			"""simple docstring"""




			__A						  =  YolosImageProcessor if is_vision_available() else None






			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= YolosImageProcessingTester(self							)






			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return self.image_processor_tester.prepare_image_processor_dict()






			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= 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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= 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							)

				snake_case_      		= 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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass






			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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

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

				snake_case_		,       snake_case_      		= 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
				snake_case_		,       snake_case_      		= self.image_processor_tester.get_expected_values(__UpperCamelCase ,				batched=__UpperCamelCase							)

				snake_case_      		= 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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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

				snake_case_		,       snake_case_      		= 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
				snake_case_      		= image_processing(__UpperCamelCase ,				return_tensors='pt'							).pixel_values

				snake_case_		,       snake_case_      		= 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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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

				snake_case_		,       snake_case_      		= 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
				snake_case_      		= image_processing(__UpperCamelCase ,				return_tensors='pt'							).pixel_values

				snake_case_		,       snake_case_      		= 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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.image_processing_class(**self.image_processor_dict							)
				snake_case_      		= self.image_processing_class(do_resize=__UpperCamelCase ,				do_normalize=__UpperCamelCase ,				do_rescale=__UpperCamelCase							)
				# create random PyTorch tensors
				snake_case_      		= prepare_image_inputs(self.image_processor_tester ,				equal_resolution=__UpperCamelCase ,				torchify=__UpperCamelCase							)
				for image in image_inputs:
					self.assertIsInstance(__UpperCamelCase ,				torch.Tensor							)

				# Test whether the method "pad" and calling the image processor return the same tensors
				snake_case_      		= image_processing_a.pad(__UpperCamelCase ,				return_tensors='pt'							)
				snake_case_      		= image_processing_a(__UpperCamelCase ,				return_tensors='pt'							)

				self.assertTrue(
				    torch.allclose(encoded_images_with_method['pixel_values'] ,				encoded_images['pixel_values'] ,				atol=1E-4							)							)






			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
				with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt' ,				'r'							) as f:
					snake_case_      		= json.loads(f.read()							)

				snake_case_      		= {'image_id': 3_97_69, 'annotations': target}

				# encode them
				snake_case_      		= YolosImageProcessor.from_pretrained('hustvl/yolos-small'							)
				snake_case_      		= image_processing(images=__UpperCamelCase ,				annotations=__UpperCamelCase ,				return_tensors='pt'							)

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

				snake_case_      		= 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
				snake_case_      		= torch.tensor([5887.9600, 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'] ,				__UpperCamelCase							)							)
				# verify boxes
				snake_case_      		= torch.Size([6, 4]							)
				self.assertEqual(encoding['labels'][0]['boxes'].shape ,				__UpperCamelCase							)
				snake_case_      		= 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
				snake_case_      		= torch.tensor([3_97_69]							)
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,				__UpperCamelCase							)							)
				# verify is_crowd
				snake_case_      		= torch.tensor([0, 0, 0, 0, 0, 0]							)
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,				__UpperCamelCase							)							)
				# verify class_labels
				snake_case_      		= torch.tensor([75, 75, 63, 65, 17, 17]							)
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,				__UpperCamelCase							)							)
				# verify orig_size
				snake_case_      		= torch.tensor([4_80, 6_40]							)
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,				__UpperCamelCase							)							)
				# verify size
				snake_case_      		= torch.tensor([8_00, 10_66]							)
				self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,				__UpperCamelCase							)							)







			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= 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_      		= json.loads(f.read()							)

				snake_case_      		= {'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

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

				# encode them
				snake_case_      		= YolosImageProcessor(format='coco_panoptic'							)
				snake_case_      		= image_processing(images=__UpperCamelCase ,				annotations=__UpperCamelCase ,				masks_path=__UpperCamelCase ,				return_tensors='pt'							)

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

				snake_case_      		= 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
				snake_case_      		= torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 5879.6562, 7634.1147]							)
				self.assertTrue(torch.allclose(encoding['labels'][0]['area'] ,				__UpperCamelCase							)							)
				# verify boxes
				snake_case_      		= torch.Size([6, 4]							)
				self.assertEqual(encoding['labels'][0]['boxes'].shape ,				__UpperCamelCase							)
				snake_case_      		= 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
				snake_case_      		= torch.tensor([3_97_69]							)
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,				__UpperCamelCase							)							)
				# verify is_crowd
				snake_case_      		= torch.tensor([0, 0, 0, 0, 0, 0]							)
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,				__UpperCamelCase							)							)
				# verify class_labels
				snake_case_      		= torch.tensor([17, 17, 63, 75, 75, 93]							)
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,				__UpperCamelCase							)							)
				# verify masks
				snake_case_      		= 82_28_73
				self.assertEqual(encoding['labels'][0]['masks'].sum().item() ,				__UpperCamelCase							)
				# verify orig_size
				snake_case_      		= torch.tensor([4_80, 6_40]							)
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,				__UpperCamelCase							)							)
				# verify size
				snake_case_      		= 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_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


A											=     {
    'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
    'tokenization_perceiver': ['PerceiverTokenizer'],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     ['PerceiverFeatureExtractor']
		A											=     ['PerceiverImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     [
		    'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'PerceiverForImageClassificationConvProcessing',
		    'PerceiverForImageClassificationFourier',
		    'PerceiverForImageClassificationLearned',
		    'PerceiverForMaskedLM',
		    'PerceiverForMultimodalAutoencoding',
		    'PerceiverForOpticalFlow',
		    'PerceiverForSequenceClassification',
		    'PerceiverLayer',
		    'PerceiverModel',
		    'PerceiverPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
		from .tokenization_perceiver import PerceiverTokenizer

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_perceiver import PerceiverFeatureExtractor
				from .image_processing_perceiver import PerceiverImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_perceiver import (
				    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PerceiverForImageClassificationConvProcessing,
				    PerceiverForImageClassificationFourier,
				    PerceiverForImageClassificationLearned,
				    PerceiverForMaskedLM,
				    PerceiverForMultimodalAutoencoding,
				    PerceiverForOpticalFlow,
				    PerceiverForSequenceClassification,
				    PerceiverLayer,
				    PerceiverModel,
				    PerceiverPreTrainedModel,
				)

else:
		import sys

		A											=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
