
"""simple docstring"""







import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
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 transformers import OneFormerImageProcessor
														from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
														from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
							from PIL import Image
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase="shi-labs/oneformer_demo"	):
	with open(hf_hub_download(_UpperCamelCase ,						_UpperCamelCase ,						repo_type='dataset'	) ,						'r'	) as f:
		__lowerCAmelCase    :		Tuple		    =   json.load(_UpperCamelCase	)
	__lowerCAmelCase    :		int		    =   {}
	__lowerCAmelCase    :		Optional[Any]		    =   []
	__lowerCAmelCase    :		List[str]		    =   []
	for key, info in class_info.items():
		__lowerCAmelCase    :		Optional[Any]		    =   info['name']
		class_names.append(info['name']	)
		if info["isthing"]:
			thing_ids.append(int(_UpperCamelCase	)	)
	__lowerCAmelCase    :		List[Any]		    =   thing_ids
	__lowerCAmelCase    :		str		    =   class_names
	return metadata




class 				A__ (			unittest.TestCase):

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=7 ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=30 ,	_SCREAMING_SNAKE_CASE=4_00 ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=True ,	_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=10 ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=2_55 ,	_SCREAMING_SNAKE_CASE="shi-labs/oneformer_demo" ,	_SCREAMING_SNAKE_CASE="ade20k_panoptic.json" ,	_SCREAMING_SNAKE_CASE=10 ,	):
			__lowerCAmelCase    :		str		    =   parent
			__lowerCAmelCase    :		Optional[int]		    =   batch_size
			__lowerCAmelCase    :		Optional[Any]		    =   num_channels
			__lowerCAmelCase    :		int		    =   min_resolution
			__lowerCAmelCase    :		int		    =   max_resolution
			__lowerCAmelCase    :		List[str]		    =   do_resize
			__lowerCAmelCase    :		Tuple		    =   {'shortest_edge': 32, 'longest_edge': 13_33} if size is None else size
			__lowerCAmelCase    :		Optional[Any]		    =   do_normalize
			__lowerCAmelCase    :		List[str]		    =   image_mean
			__lowerCAmelCase    :		int		    =   image_std
			__lowerCAmelCase    :		Tuple		    =   class_info_file
			__lowerCAmelCase    :		Union[str, Any]		    =   prepare_metadata(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		int		    =   num_text
			__lowerCAmelCase    :		str		    =   repo_path

			# for the post_process_functions
			__lowerCAmelCase    :		Any		    =   2
			__lowerCAmelCase    :		Optional[int]		    =   10
			__lowerCAmelCase    :		str		    =   10
			__lowerCAmelCase    :		Dict		    =   3
			__lowerCAmelCase    :		Union[str, Any]		    =   4
			__lowerCAmelCase    :		int		    =   num_labels
			__lowerCAmelCase    :		str		    =   do_reduce_labels
			__lowerCAmelCase    :		Optional[Any]		    =   ignore_index

		def 							__lowerCamelCase							(   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,
			    "num_labels": self.num_labels,
			    "do_reduce_labels": self.do_reduce_labels,
			    "ignore_index": self.ignore_index,
			    "class_info_file": self.class_info_file,
			    "metadata": self.metadata,
			    "num_text": self.num_text,
			}

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

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

			return expected_height, expected_width







		def 							__lowerCamelCase							(   self				):
			return OneFormerForUniversalSegmentationOutput(
			    # +1 for null class
			    class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)				) ,	masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)				) ,	)




@require_torch
@require_vision
class 				A__ (			_lowerCamelCase ,  unittest.TestCase):
		A_		: List[Any]            =			OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
		# only for test_image_processing_common.test_image_proc_to_json_string
		A_		: Union[str, Any]            =			image_processing_class

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   OneFormerImageProcessorTester(self				)

		@property
		def 							__lowerCamelCase							(   self				):
			return self.image_processing_tester.prepare_image_processor_dict()

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   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_resize'				)				)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,	'size'				)				)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,	'ignore_index'				)				)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,	'class_info_file'				)				)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,	'num_text'				)				)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,	'repo_path'				)				)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,	'metadata'				)				)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,	'do_reduce_labels'				)				)

		def 							__lowerCamelCase							(   self				):
			pass

		def 							__lowerCamelCase							(   self				):
			# Initialize image_processor
			__lowerCAmelCase    :		Optional[int]		    =   self.image_processing_class(**self.image_processor_dict				)
			# create random PIL images
			__lowerCAmelCase    :		Any		    =   prepare_image_inputs(self.image_processing_tester ,	equal_resolution=_SCREAMING_SNAKE_CASE				)
			for image in image_inputs:
				self.assertIsInstance(_SCREAMING_SNAKE_CASE ,	Image.Image				)

			# Test not batched input
			__lowerCAmelCase    :		int		    =   image_processor(image_inputs[0] ,	['semantic'] ,	return_tensors='pt'				).pixel_values

			__lowerCAmelCase    ,     __lowerCAmelCase    :		Optional[int]		    =   self.image_processing_tester.get_expected_values(_SCREAMING_SNAKE_CASE				)

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

			# Test batched
			__lowerCAmelCase    ,     __lowerCAmelCase    :		List[str]		    =   self.image_processing_tester.get_expected_values(_SCREAMING_SNAKE_CASE ,	batched=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		int		    =   image_processor(
			    _SCREAMING_SNAKE_CASE ,	['semantic'] * len(_SCREAMING_SNAKE_CASE				) ,	return_tensors='pt'				).pixel_values
			self.assertEqual(
			    encoded_images.shape ,	(
			        self.image_processing_tester.batch_size,
			        self.image_processing_tester.num_channels,
			        expected_height,
			        expected_width,
			    ) ,	)

		def 							__lowerCamelCase							(   self				):
			# Initialize image_processor
			__lowerCAmelCase    :		Dict		    =   self.image_processing_class(**self.image_processor_dict				)
			# create random numpy tensors
			__lowerCAmelCase    :		Optional[int]		    =   prepare_image_inputs(self.image_processing_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
			__lowerCAmelCase    :		List[Any]		    =   image_processor(image_inputs[0] ,	['semantic'] ,	return_tensors='pt'				).pixel_values

			__lowerCAmelCase    ,     __lowerCAmelCase    :		str		    =   self.image_processing_tester.get_expected_values(_SCREAMING_SNAKE_CASE				)

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

			# Test batched
			__lowerCAmelCase    ,     __lowerCAmelCase    :		List[str]		    =   self.image_processing_tester.get_expected_values(_SCREAMING_SNAKE_CASE ,	batched=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Any		    =   image_processor(
			    _SCREAMING_SNAKE_CASE ,	['semantic'] * len(_SCREAMING_SNAKE_CASE				) ,	return_tensors='pt'				).pixel_values
			self.assertEqual(
			    encoded_images.shape ,	(
			        self.image_processing_tester.batch_size,
			        self.image_processing_tester.num_channels,
			        expected_height,
			        expected_width,
			    ) ,	)

		def 							__lowerCamelCase							(   self				):
			# Initialize image_processor
			__lowerCAmelCase    :		Optional[int]		    =   self.image_processing_class(**self.image_processor_dict				)
			# create random PyTorch tensors
			__lowerCAmelCase    :		Optional[Any]		    =   prepare_image_inputs(self.image_processing_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
			__lowerCAmelCase    :		Union[str, Any]		    =   image_processor(image_inputs[0] ,	['semantic'] ,	return_tensors='pt'				).pixel_values

			__lowerCAmelCase    ,     __lowerCAmelCase    :		List[Any]		    =   self.image_processing_tester.get_expected_values(_SCREAMING_SNAKE_CASE				)

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

			# Test batched
			__lowerCAmelCase    ,     __lowerCAmelCase    :		List[str]		    =   self.image_processing_tester.get_expected_values(_SCREAMING_SNAKE_CASE ,	batched=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		int		    =   image_processor(
			    _SCREAMING_SNAKE_CASE ,	['semantic'] * len(_SCREAMING_SNAKE_CASE				) ,	return_tensors='pt'				).pixel_values
			self.assertEqual(
			    encoded_images.shape ,	(
			        self.image_processing_tester.batch_size,
			        self.image_processing_tester.num_channels,
			        expected_height,
			        expected_width,
			    ) ,	)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE="np"				):
			__lowerCAmelCase    :		List[str]		    =   self.image_processing_class(**self.image_processor_dict				)
			# prepare image and target
			__lowerCAmelCase    :		Dict		    =   self.image_processing_tester.num_labels
			__lowerCAmelCase    :		Tuple		    =   None
			__lowerCAmelCase    :		Optional[int]		    =   None
			__lowerCAmelCase    :		List[Any]		    =   prepare_image_inputs(self.image_processing_tester ,	equal_resolution=_SCREAMING_SNAKE_CASE				)
			if with_segmentation_maps:
				__lowerCAmelCase    :		int		    =   num_labels
				if is_instance_map:
					__lowerCAmelCase    :		Tuple		    =   list(range(_SCREAMING_SNAKE_CASE				)				) * 2
					__lowerCAmelCase    :		List[Any]		    =   dict(enumerate(_SCREAMING_SNAKE_CASE				)				)
				__lowerCAmelCase    :		int		    =   [
				    np.random.randint(0 ,	high * 2 ,	(img.size[1], img.size[0])				).astype(np.uinta				) for img in image_inputs
				]
				if segmentation_type == "pil":
					__lowerCAmelCase    :		List[Any]		    =   [Image.fromarray(_SCREAMING_SNAKE_CASE				) for annotation in annotations]

			__lowerCAmelCase    :		Optional[Any]		    =   image_processor(
			    _SCREAMING_SNAKE_CASE ,	['semantic'] * len(_SCREAMING_SNAKE_CASE				) ,	_SCREAMING_SNAKE_CASE ,	return_tensors='pt' ,	instance_id_to_semantic_id=_SCREAMING_SNAKE_CASE ,	pad_and_return_pixel_mask=_SCREAMING_SNAKE_CASE ,	)

			return inputs

		def 							__lowerCamelCase							(   self				):
			pass

		def 							__lowerCamelCase							(   self				):
			def common(_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=None				):
				__lowerCAmelCase    :		Dict		    =   self.comm_get_image_processor_inputs(
				    with_segmentation_maps=_SCREAMING_SNAKE_CASE ,	is_instance_map=_SCREAMING_SNAKE_CASE ,	segmentation_type=_SCREAMING_SNAKE_CASE				)

				__lowerCAmelCase    :		int		    =   inputs['mask_labels']
				__lowerCAmelCase    :		Optional[int]		    =   inputs['class_labels']
				__lowerCAmelCase    :		Union[str, Any]		    =   inputs['pixel_values']
				__lowerCAmelCase    :		Dict		    =   inputs['text_inputs']

				# check the batch_size
				for mask_label, class_label, text_input in zip(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
					self.assertEqual(mask_label.shape[0] ,	class_label.shape[0]				)
					# this ensure padding has happened
					self.assertEqual(mask_label.shape[1:] ,	pixel_values.shape[2:]				)
					self.assertEqual(len(_SCREAMING_SNAKE_CASE				) ,	self.image_processing_tester.num_text				)

			common()
			common(is_instance_map=_SCREAMING_SNAKE_CASE				)
			common(is_instance_map=_SCREAMING_SNAKE_CASE ,	segmentation_type='pil'				)
			common(is_instance_map=_SCREAMING_SNAKE_CASE ,	segmentation_type='pil'				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   np.zeros((20, 50)				)
			__lowerCAmelCase    :		Tuple		    =   1
			__lowerCAmelCase    :		int		    =   1
			__lowerCAmelCase    :		Optional[Any]		    =   1

			__lowerCAmelCase    :		Union[str, Any]		    =   binary_mask_to_rle(_SCREAMING_SNAKE_CASE				)
			self.assertEqual(len(_SCREAMING_SNAKE_CASE				) ,	4				)
			self.assertEqual(rle[0] ,	21				)
			self.assertEqual(rle[1] ,	45				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Tuple		    =   self.image_processing_class(
			    num_labels=self.image_processing_tester.num_classes ,	max_seq_length=77 ,	task_seq_length=77 ,	class_info_file='ade20k_panoptic.json' ,	num_text=self.image_processing_tester.num_text ,	repo_path='shi-labs/oneformer_demo' ,	)
			__lowerCAmelCase    :		Dict		    =   self.image_processing_tester.get_fake_oneformer_outputs()

			__lowerCAmelCase    :		List[str]		    =   fature_extractor.post_process_semantic_segmentation(_SCREAMING_SNAKE_CASE				)

			self.assertEqual(len(_SCREAMING_SNAKE_CASE				) ,	self.image_processing_tester.batch_size				)
			self.assertEqual(
			    segmentation[0].shape ,	(
			        self.image_processing_tester.height,
			        self.image_processing_tester.width,
			    ) ,	)

			__lowerCAmelCase    :		int		    =   [(1, 4) for i in range(self.image_processing_tester.batch_size				)]
			__lowerCAmelCase    :		List[str]		    =   fature_extractor.post_process_semantic_segmentation(_SCREAMING_SNAKE_CASE ,	target_sizes=_SCREAMING_SNAKE_CASE				)

			self.assertEqual(segmentation[0].shape ,	target_sizes[0]				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[Any]		    =   self.image_processing_class(
			    num_labels=self.image_processing_tester.num_classes ,	max_seq_length=77 ,	task_seq_length=77 ,	class_info_file='ade20k_panoptic.json' ,	num_text=self.image_processing_tester.num_text ,	repo_path='shi-labs/oneformer_demo' ,	)
			__lowerCAmelCase    :		Any		    =   self.image_processing_tester.get_fake_oneformer_outputs()
			__lowerCAmelCase    :		Tuple		    =   image_processor.post_process_instance_segmentation(_SCREAMING_SNAKE_CASE ,	threshold=0				)

			self.assertTrue(len(_SCREAMING_SNAKE_CASE				) == self.image_processing_tester.batch_size				)
			for el in segmentation:
				self.assertTrue('segmentation' in el				)
				self.assertTrue('segments_info' in el				)
				self.assertEqual(type(el['segments_info']				) ,	_SCREAMING_SNAKE_CASE				)
				self.assertEqual(
				    el['segmentation'].shape ,	(self.image_processing_tester.height, self.image_processing_tester.width)				)







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		str		    =   self.image_processing_class(
			    num_labels=self.image_processing_tester.num_classes ,	max_seq_length=77 ,	task_seq_length=77 ,	class_info_file='ade20k_panoptic.json' ,	num_text=self.image_processing_tester.num_text ,	repo_path='shi-labs/oneformer_demo' ,	)
			__lowerCAmelCase    :		Optional[int]		    =   self.image_processing_tester.get_fake_oneformer_outputs()
			__lowerCAmelCase    :		Optional[Any]		    =   image_processor.post_process_panoptic_segmentation(_SCREAMING_SNAKE_CASE ,	threshold=0				)

			self.assertTrue(len(_SCREAMING_SNAKE_CASE				) == self.image_processing_tester.batch_size				)
			for el in segmentation:
				self.assertTrue('segmentation' in el				)
				self.assertTrue('segments_info' in el				)
				self.assertEqual(type(el['segments_info']				) ,	_SCREAMING_SNAKE_CASE				)
				self.assertEqual(
				    el['segmentation'].shape ,	(self.image_processing_tester.height, self.image_processing_tester.width)				)

"""simple docstring"""







import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import RegNetForImageClassification, RegNetModel
							from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor




class 				A__ :

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=32 ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=10 ,	_SCREAMING_SNAKE_CASE=[10, 20, 30, 40] ,	_SCREAMING_SNAKE_CASE=[1, 1, 2, 1] ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE="relu" ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=None ,	):
			__lowerCAmelCase    :		Optional[Any]		    =   parent
			__lowerCAmelCase    :		int		    =   batch_size
			__lowerCAmelCase    :		Union[str, Any]		    =   image_size
			__lowerCAmelCase    :		Optional[Any]		    =   num_channels
			__lowerCAmelCase    :		List[Any]		    =   embeddings_size
			__lowerCAmelCase    :		Dict		    =   hidden_sizes
			__lowerCAmelCase    :		Optional[int]		    =   depths
			__lowerCAmelCase    :		Optional[Any]		    =   is_training
			__lowerCAmelCase    :		int		    =   use_labels
			__lowerCAmelCase    :		List[Any]		    =   hidden_act
			__lowerCAmelCase    :		Optional[Any]		    =   num_labels
			__lowerCAmelCase    :		List[str]		    =   scope
			__lowerCAmelCase    :		Optional[Any]		    =   len(_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

			__lowerCAmelCase    :		List[str]		    =   None
			if self.use_labels:
				__lowerCAmelCase    :		Tuple		    =   ids_tensor([self.batch_size] ,	self.num_labels				)

			__lowerCAmelCase    :		int		    =   self.get_config()

			return config, pixel_values, labels

		def 							__lowerCamelCase							(   self				):
			return RegNetConfig(
			    num_channels=self.num_channels ,	embeddings_size=self.embeddings_size ,	hidden_sizes=self.hidden_sizes ,	depths=self.depths ,	hidden_act=self.hidden_act ,	num_labels=self.num_labels ,	)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Any		    =   RegNetModel(config=_SCREAMING_SNAKE_CASE				)
			model.to(_SCREAMING_SNAKE_CASE				)
			model.eval()
			__lowerCAmelCase    :		Any		    =   model(_SCREAMING_SNAKE_CASE				)
			# expected last hidden states: B, C, H // 32, W // 32
			self.parent.assertEqual(
			    result.last_hidden_state.shape ,	(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,	)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Optional[Any]		    =   self.num_labels
			__lowerCAmelCase    :		str		    =   RegNetForImageClassification(_SCREAMING_SNAKE_CASE				)
			model.to(_SCREAMING_SNAKE_CASE				)
			model.eval()
			__lowerCAmelCase    :		Any		    =   model(_SCREAMING_SNAKE_CASE ,	labels=_SCREAMING_SNAKE_CASE				)
			self.parent.assertEqual(result.logits.shape ,	(self.batch_size, self.num_labels)				)







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   self.prepare_config_and_inputs()
			__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Any		    =   config_and_inputs
			__lowerCAmelCase    :		List[str]		    =   {'pixel_values': pixel_values}
			return config, inputs_dict




@require_torch
class 				A__ (			_lowerCamelCase ,  _lowerCamelCase ,  unittest.TestCase):
		A_		: List[str]            =			(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
		A_		: List[Any]            =			(
		    {'feature-extraction': RegNetModel, 'image-classification': RegNetForImageClassification}
		    if is_torch_available()
		    else {}
		)

		A_		: Dict            =			False
		A_		: str            =			False
		A_		: int            =			False
		A_		: Union[str, Any]            =			False

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   RegNetModelTester(self				)
			__lowerCAmelCase    :		int		    =   ConfigTester(self ,	config_class=_SCREAMING_SNAKE_CASE ,	has_text_modality=_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			self.create_and_test_config_common_properties()
			self.config_tester.create_and_test_config_to_json_string()
			self.config_tester.create_and_test_config_to_json_file()
			self.config_tester.create_and_test_config_from_and_save_pretrained()
			self.config_tester.create_and_test_config_with_num_labels()
			self.config_tester.check_config_can_be_init_without_params()
			self.config_tester.check_config_arguments_init()

		def 							__lowerCamelCase							(   self				):
			return

		@unittest.skip(reason='RegNet does not use inputs_embeds'				)
		def 							__lowerCamelCase							(   self				):
			pass

		@unittest.skip(reason='RegNet does not support input and output embeddings'				)
		def 							__lowerCamelCase							(   self				):
			pass

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		List[Any]		    =   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				__lowerCAmelCase    :		Optional[int]		    =   model_class(_SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase    :		Dict		    =   inspect.signature(model.forward				)
				# signature.parameters is an OrderedDict => so arg_names order is deterministic
				__lowerCAmelCase    :		Tuple		    =   [*signature.parameters.keys()]

				__lowerCAmelCase    :		Optional[int]		    =   ['pixel_values']
				self.assertListEqual(arg_names[:1] ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		List[str]		    =   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				__lowerCAmelCase    :		Union[str, Any]		    =   model_class(config=_SCREAMING_SNAKE_CASE				)
				for name, module in model.named_modules():
					if isinstance(_SCREAMING_SNAKE_CASE ,	(nn.BatchNormad, nn.GroupNorm)				):
						self.assertTrue(
						    torch.all(module.weight == 1				) ,	msg=f"Parameter {name} of model {model_class} seems not properly initialized" ,	)
						self.assertTrue(
						    torch.all(module.bias == 0				) ,	msg=f"Parameter {name} of model {model_class} seems not properly initialized" ,	)

		def 							__lowerCamelCase							(   self				):
			def check_hidden_states_output(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase    :		Any		    =   model_class(_SCREAMING_SNAKE_CASE				)
				model.to(_SCREAMING_SNAKE_CASE				)
				model.eval()

				with torch.no_grad():
					__lowerCAmelCase    :		List[str]		    =   model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)				)

				__lowerCAmelCase    :		Any		    =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

				__lowerCAmelCase    :		List[str]		    =   self.model_tester.num_stages
				self.assertEqual(len(_SCREAMING_SNAKE_CASE				) ,	expected_num_stages + 1				)

				# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
				self.assertListEqual(
				    list(hidden_states[0].shape[-2:]				) ,	[self.model_tester.image_size // 2, self.model_tester.image_size // 2] ,	)

			__lowerCAmelCase    ,     __lowerCAmelCase    :		Dict		    =   self.model_tester.prepare_config_and_inputs_for_common()
			__lowerCAmelCase    :		List[Any]		    =   ['basic', 'bottleneck']
			for model_class in self.all_model_classes:
				for layer_type in layers_type:
					__lowerCAmelCase    :		List[Any]		    =   layer_type
					__lowerCAmelCase    :		Tuple		    =   True
					check_hidden_states_output(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

					# check that output_hidden_states also work using config
					del inputs_dict["output_hidden_states"]
					__lowerCAmelCase    :		Dict		    =   True

					check_hidden_states_output(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[Any]		    =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE				)







		@slow
		def 							__lowerCamelCase							(   self				):
			for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				__lowerCAmelCase    :		Dict		    =   RegNetModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
				self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)



def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		str		    =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
	return image




@require_torch
@require_vision
class 				A__ (			unittest.TestCase):

		@cached_property
		def 							__lowerCamelCase							(   self				):
			return (
			    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
			    if is_vision_available()
			    else None
			)







		@slow
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		str		    =   self.default_image_processor
			__lowerCAmelCase    :		Any		    =   prepare_img()
			__lowerCAmelCase    :		Any		    =   image_processor(images=_SCREAMING_SNAKE_CASE ,	return_tensors='pt'				).to(_SCREAMING_SNAKE_CASE				)

			# forward pass
			with torch.no_grad():
				__lowerCAmelCase    :		List[str]		    =   model(**_SCREAMING_SNAKE_CASE				)

			# verify the logits
			__lowerCAmelCase    :		Union[str, Any]		    =   torch.Size((1, 10_00)				)
			self.assertEqual(outputs.logits.shape ,	_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Optional[int]		    =   torch.tensor([-0.4180, -1.5051, -3.4836]				).to(_SCREAMING_SNAKE_CASE				)

			self.assertTrue(torch.allclose(outputs.logits[0, :3] ,	_SCREAMING_SNAKE_CASE ,	atol=1E-4				)				)