



'''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   :      Tuple				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Dict=7				,    lowerCAmelCase__   :      int=3				,    lowerCAmelCase__   :      Any=30				,    lowerCAmelCase__   :      int=4_00				,    lowerCAmelCase__   :      Union[str, Any]=True				,    lowerCAmelCase__   :      Union[str, Any]=None				,    lowerCAmelCase__   :      Union[str, Any]=True				,    lowerCAmelCase__   :      Dict=1 / 2_55				,    lowerCAmelCase__   :      List[str]=True				,    lowerCAmelCase__   :      Optional[Any]=[0.5, 0.5, 0.5]				,    lowerCAmelCase__   :      Optional[int]=[0.5, 0.5, 0.5]				,    lowerCAmelCase__   :      Optional[int]=True				,    ):

													"""simple docstring"""
													# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
													SCREAMING_SNAKE_CASE					:      int     					=			parent
													SCREAMING_SNAKE_CASE					:      str     					=			batch_size
													SCREAMING_SNAKE_CASE					:      int     					=			num_channels
													SCREAMING_SNAKE_CASE					:      str     					=			min_resolution
													SCREAMING_SNAKE_CASE					:      int     					=			max_resolution
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			do_resize
													SCREAMING_SNAKE_CASE					:      Dict     					=			size
													SCREAMING_SNAKE_CASE					:      List[str]     					=			do_rescale
													SCREAMING_SNAKE_CASE					:      Dict     					=			rescale_factor
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			do_normalize
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			image_mean
													SCREAMING_SNAKE_CASE					:      Tuple     					=			image_std
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			do_pad







						def 					__lowercase    (  self   :      int				):

													"""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 					__lowercase    (  self   :      List[str]				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Optional[int]=False				):

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

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

													return expected_height, expected_width


@require_torch
@require_vision
class 	lowerCamelCase_							(					snake_case_    ,				unittest.TestCase			):
						_lowerCAmelCase     :   Tuple	     =				DetrImageProcessor if is_vision_available() else None







						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			DetrImageProcessingTester(self				)







						@property
						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													return self.image_processor_tester.prepare_image_processor_dict()







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self.image_processing_class(**self.image_processor_dict				)
													self.assertTrue(hasattr(lowerCAmelCase__				,    '''image_mean'''				)				)
													self.assertTrue(hasattr(lowerCAmelCase__				,    '''image_std'''				)				)
													self.assertTrue(hasattr(lowerCAmelCase__				,    '''do_normalize'''				)				)
													self.assertTrue(hasattr(lowerCAmelCase__				,    '''do_rescale'''				)				)
													self.assertTrue(hasattr(lowerCAmelCase__				,    '''rescale_factor'''				)				)
													self.assertTrue(hasattr(lowerCAmelCase__				,    '''do_resize'''				)				)
													self.assertTrue(hasattr(lowerCAmelCase__				,    '''size'''				)				)
													self.assertTrue(hasattr(lowerCAmelCase__				,    '''do_pad'''				)				)







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			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				,    lowerCAmelCase__				)

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







						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													pass







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													# Initialize image_processing
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.image_processing_class(**self.image_processor_dict				)
													# create random PIL images
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			prepare_image_inputs(self.image_processor_tester				,    equal_resolution=lowerCAmelCase__				)
													for image in image_inputs:
																				self.assertIsInstance(lowerCAmelCase__				,    Image.Image				)

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

													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      str     					=			self.image_processor_tester.get_expected_values(lowerCAmelCase__				)

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

													# Test batched
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Dict     					=			self.image_processor_tester.get_expected_values(lowerCAmelCase__				,    batched=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      int     					=			image_processing(lowerCAmelCase__				,    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 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													# Initialize image_processing
													SCREAMING_SNAKE_CASE					:      int     					=			self.image_processing_class(**self.image_processor_dict				)
													# create random numpy tensors
													SCREAMING_SNAKE_CASE					:      str     					=			prepare_image_inputs(self.image_processor_tester				,    equal_resolution=lowerCAmelCase__				,    numpify=lowerCAmelCase__				)
													for image in image_inputs:
																				self.assertIsInstance(lowerCAmelCase__				,    np.ndarray				)

													# Test not batched input
													SCREAMING_SNAKE_CASE					:      List[str]     					=			image_processing(image_inputs[0]				,    return_tensors='''pt'''				).pixel_values

													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      str     					=			self.image_processor_tester.get_expected_values(lowerCAmelCase__				)

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

													# Test batched
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			image_processing(lowerCAmelCase__				,    return_tensors='''pt'''				).pixel_values

													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[str]     					=			self.image_processor_tester.get_expected_values(lowerCAmelCase__				,    batched=lowerCAmelCase__				)

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







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													# Initialize image_processing
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.image_processing_class(**self.image_processor_dict				)
													# create random PyTorch tensors
													SCREAMING_SNAKE_CASE					:      Any     					=			prepare_image_inputs(self.image_processor_tester				,    equal_resolution=lowerCAmelCase__				,    torchify=lowerCAmelCase__				)
													for image in image_inputs:
																				self.assertIsInstance(lowerCAmelCase__				,    torch.Tensor				)

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

													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Dict     					=			self.image_processor_tester.get_expected_values(lowerCAmelCase__				)

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

													# Test batched
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			image_processing(lowerCAmelCase__				,    return_tensors='''pt'''				).pixel_values

													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Tuple     					=			self.image_processor_tester.get_expected_values(lowerCAmelCase__				,    batched=lowerCAmelCase__				)

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







						@slow
						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													# prepare image and target
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
													with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''				,    '''r'''				) as f:
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			json.loads(f.read()				)

													SCREAMING_SNAKE_CASE					:      Any     					=			{'''image_id''': 3_97_69, '''annotations''': target}

													# encode them
													SCREAMING_SNAKE_CASE					:      Dict     					=			DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''				)
													SCREAMING_SNAKE_CASE					:      Any     					=			image_processing(images=lowerCAmelCase__				,    annotations=lowerCAmelCase__				,    return_tensors='''pt'''				)

													# verify pixel values
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			torch.Size([1, 3, 8_00, 10_66]				)
													self.assertEqual(encoding['''pixel_values'''].shape				,    lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			torch.tensor([0.2796, 0.3138, 0.3481]				)
													self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]				,    lowerCAmelCase__				,    atol=1e-4				)				)

													# verify area
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']				,    lowerCAmelCase__				)				)
													# verify boxes
													SCREAMING_SNAKE_CASE					:      Tuple     					=			torch.Size([6, 4]				)
													self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape				,    lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      str     					=			torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]				,    lowerCAmelCase__				,    atol=1e-3				)				)
													# verify image_id
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			torch.tensor([3_97_69]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']				,    lowerCAmelCase__				)				)
													# verify is_crowd
													SCREAMING_SNAKE_CASE					:      Tuple     					=			torch.tensor([0, 0, 0, 0, 0, 0]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']				,    lowerCAmelCase__				)				)
													# verify class_labels
													SCREAMING_SNAKE_CASE					:      Dict     					=			torch.tensor([75, 75, 63, 65, 17, 17]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']				,    lowerCAmelCase__				)				)
													# verify orig_size
													SCREAMING_SNAKE_CASE					:      List[str]     					=			torch.tensor([4_80, 6_40]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']				,    lowerCAmelCase__				)				)
													# verify size
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			torch.tensor([8_00, 10_66]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']				,    lowerCAmelCase__				)				)







						@slow
						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													# prepare image, target and masks_path
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
													with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''				,    '''r'''				) as f:
																				SCREAMING_SNAKE_CASE					:      Dict     					=			json.loads(f.read()				)

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			{'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}

													SCREAMING_SNAKE_CASE					:      str     					=			pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''				)

													# encode them
													SCREAMING_SNAKE_CASE					:      str     					=			DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			image_processing(images=lowerCAmelCase__				,    annotations=lowerCAmelCase__				,    masks_path=lowerCAmelCase__				,    return_tensors='''pt'''				)

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

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			torch.tensor([0.2796, 0.3138, 0.3481]				)
													self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]				,    lowerCAmelCase__				,    atol=1e-4				)				)

													# verify area
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']				,    lowerCAmelCase__				)				)
													# verify boxes
													SCREAMING_SNAKE_CASE					:      Any     					=			torch.Size([6, 4]				)
													self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape				,    lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      int     					=			torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]				,    lowerCAmelCase__				,    atol=1e-3				)				)
													# verify image_id
													SCREAMING_SNAKE_CASE					:      Tuple     					=			torch.tensor([3_97_69]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']				,    lowerCAmelCase__				)				)
													# verify is_crowd
													SCREAMING_SNAKE_CASE					:      Any     					=			torch.tensor([0, 0, 0, 0, 0, 0]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']				,    lowerCAmelCase__				)				)
													# verify class_labels
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			torch.tensor([17, 17, 63, 75, 75, 93]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']				,    lowerCAmelCase__				)				)
													# verify masks
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			82_28_73
													self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()				,    lowerCAmelCase__				)
													# verify orig_size
													SCREAMING_SNAKE_CASE					:      Dict     					=			torch.tensor([4_80, 6_40]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']				,    lowerCAmelCase__				)				)
													# verify size
													SCREAMING_SNAKE_CASE					:      Dict     					=			torch.tensor([8_00, 10_66]				)
													self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']				,    lowerCAmelCase__				)				)









'''simple docstring'''



from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor
class 	lowerCamelCase_							:







						def __init__(  self   :      Optional[Any]				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      int=3				,    lowerCAmelCase__   :      Tuple=32				,    lowerCAmelCase__   :      Union[str, Any]=3				,    lowerCAmelCase__   :      Dict=10				,    lowerCAmelCase__   :      List[Any]=[10, 20, 30, 40]				,    lowerCAmelCase__   :      List[Any]=[1, 1, 2, 1]				,    lowerCAmelCase__   :      Optional[int]=True				,    lowerCAmelCase__   :      Any=True				,    lowerCAmelCase__   :      List[str]="relu"				,    lowerCAmelCase__   :      int=3				,    lowerCAmelCase__   :      Tuple=None				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			parent
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			batch_size
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			image_size
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			num_channels
													SCREAMING_SNAKE_CASE					:      str     					=			embeddings_size
													SCREAMING_SNAKE_CASE					:      str     					=			hidden_sizes
													SCREAMING_SNAKE_CASE					:      Tuple     					=			depths
													SCREAMING_SNAKE_CASE					:      Dict     					=			is_training
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			use_labels
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			hidden_act
													SCREAMING_SNAKE_CASE					:      str     					=			num_labels
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			scope
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			len(lowerCAmelCase__				)







						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			None
													if self.use_labels:
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			ids_tensor([self.batch_size]				,    self.num_labels				)

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self.get_config()
													return config, pixel_values, labels







						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													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 					__lowercase    (  self   :      Dict				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			TFRegNetModel(config=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      int     					=			model(lowerCAmelCase__				,    training=lowerCAmelCase__				)
													# 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 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.num_labels
													SCREAMING_SNAKE_CASE					:      Any     					=			TFRegNetForImageClassification(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			model(lowerCAmelCase__				,    labels=lowerCAmelCase__				,    training=lowerCAmelCase__				)
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_labels)				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			self.prepare_config_and_inputs()
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[str]     					=			config_and_inputs
													SCREAMING_SNAKE_CASE					:      Tuple     					=			{'''pixel_values''': pixel_values}
													return config, inputs_dict


@require_tf
class 	lowerCamelCase_							(					snake_case_    ,				snake_case_    ,				unittest.TestCase			):
						_lowerCAmelCase     :   str	     =				(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
						_lowerCAmelCase     :   str	     =				(
						    {'feature-extraction': TFRegNetModel, 'image-classification': TFRegNetForImageClassification}
						    if is_tf_available()
						    else {}
						)

						_lowerCAmelCase     :   Tuple	     =				False
						_lowerCAmelCase     :   List[Any]	     =				False
						_lowerCAmelCase     :   List[str]	     =				False
						_lowerCAmelCase     :   str	     =				False
						_lowerCAmelCase     :   str	     =				False







						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			TFRegNetModelTester(self				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			ConfigTester(self				,    config_class=lowerCAmelCase__				,    has_text_modality=lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													return







						@unittest.skip(reason='''RegNet does not use inputs_embeds'''				)
						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													pass







						@unittest.skipIf(
						    not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''				)				) == 0				,    reason='''TF does not support backprop for grouped convolutions on CPU.'''				,    )
						@slow
						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													super().test_keras_fit()







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

													"""simple docstring"""
													pass







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				SCREAMING_SNAKE_CASE					:      int     					=			model_class(lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Any     					=			inspect.signature(model.call				)
																				# signature.parameters is an OrderedDict => so arg_names order is deterministic
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			[*signature.parameters.keys()]

																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			['''pixel_values''']
																				self.assertListEqual(arg_names[:1]				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													def check_hidden_states_output(lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      Tuple				):
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			model_class(lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Dict     					=			model(**self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				)				,    training=lowerCAmelCase__				)

																				SCREAMING_SNAKE_CASE					:      int     					=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																				SCREAMING_SNAKE_CASE					:      int     					=			self.model_tester.num_stages
																				self.assertEqual(len(lowerCAmelCase__				)				,    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]				,    )

													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.model_tester.prepare_config_and_inputs_for_common()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			['''basic''', '''bottleneck''']
													for model_class in self.all_model_classes:
																				for layer_type in layers_type:
																											SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			layer_type
																											SCREAMING_SNAKE_CASE					:      Tuple     					=			True
																											check_hidden_states_output(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				)

																											# check that output_hidden_states also work using config
																											del inputs_dict["output_hidden_states"]
																											SCREAMING_SNAKE_CASE					:      Optional[int]     					=			True

																											check_hidden_states_output(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.model_tester.prepare_config_and_inputs_for_common()

													def check_equivalence(lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      Dict				,    lowerCAmelCase__   :      str={}				):
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model(lowerCAmelCase__				,    return_dict=lowerCAmelCase__				,    **lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Any     					=			model(lowerCAmelCase__				,    return_dict=lowerCAmelCase__				,    **lowerCAmelCase__				).to_tuple()

																				def recursive_check(lowerCAmelCase__   :      Dict				,    lowerCAmelCase__   :      str				):
																											if isinstance(lowerCAmelCase__				,    (List, Tuple)				):
																																		for tuple_iterable_value, dict_iterable_value in zip(lowerCAmelCase__				,    lowerCAmelCase__				):
																																									recursive_check(lowerCAmelCase__				,    lowerCAmelCase__				)
																											elif tuple_object is None:
																																		return
																											else:
																																		self.assertTrue(
																																		    all(tf.equal(lowerCAmelCase__				,    lowerCAmelCase__				)				)				,    msg=(
																																		        '''Tuple and dict output are not equal. Difference:'''
																																		        F""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object				)				)}"""
																																		    )				,    )

																											recursive_check(lowerCAmelCase__				,    lowerCAmelCase__				)

													for model_class in self.all_model_classes:
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			model_class(lowerCAmelCase__				)

																				SCREAMING_SNAKE_CASE					:      int     					=			self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				)
																				check_equivalence(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				)

																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				,    return_labels=lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      str     					=			self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				,    return_labels=lowerCAmelCase__				)
																				check_equivalence(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				)

																				SCREAMING_SNAKE_CASE					:      Tuple     					=			self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Dict     					=			self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				)
																				check_equivalence(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    {'''output_hidden_states''': True}				)

																				SCREAMING_SNAKE_CASE					:      Any     					=			self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				,    return_labels=lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				,    return_labels=lowerCAmelCase__				)
																				check_equivalence(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    {'''output_hidden_states''': True}				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__				)







						@slow
						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			TFRegNetModel.from_pretrained(lowerCAmelCase__				)
																				self.assertIsNotNone(lowerCAmelCase__				)


def 					UpperCAmelCase   (						):
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
							return image






@require_tf
@require_vision
class 	lowerCamelCase_							(					unittest.TestCase			):







						@cached_property
						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													return (
													    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
													    if is_vision_available()
													    else None
													)







						@slow
						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)

													SCREAMING_SNAKE_CASE					:      Dict     					=			self.default_image_processor
													SCREAMING_SNAKE_CASE					:      int     					=			prepare_img()
													SCREAMING_SNAKE_CASE					:      List[str]     					=			image_processor(images=lowerCAmelCase__				,    return_tensors='''tf'''				)

													# forward pass
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model(**lowerCAmelCase__				,    training=lowerCAmelCase__				)

													# verify the logits
													SCREAMING_SNAKE_CASE					:      List[str]     					=			tf.TensorShape((1, 10_00)				)
													self.assertEqual(outputs.logits.shape				,    lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      List[str]     					=			tf.constant([-0.4180, -1.5051, -3.4836]				)

													tf.debugging.assert_near(outputs.logits[0, :3]				,    lowerCAmelCase__				,    atol=1e-4				)




