






'''simple docstring'''





import warnings
from functools import wraps
from typing import Callable

def      __lowercase					( snake_case ):







				"""simple docstring"""






				@wraps(snake_case )
				def _inner_fn(*snake_case,  **snake_case ):
								warnings.warn(
								    (f'''\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.'''),  snake_case,  )
								return fn(*snake_case,  **snake_case )

				return _inner_fn














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 						lowerCamelCase_				:







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase=3       ,					__lowerCAmelCase=3_2       ,					__lowerCAmelCase=3       ,					__lowerCAmelCase=1_0       ,					__lowerCAmelCase=[1_0, 2_0, 3_0, 4_0]       ,					__lowerCAmelCase=[1, 1, 2, 1]       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase="relu"       ,					__lowerCAmelCase=3       ,					__lowerCAmelCase=None       ,					):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      parent
								__magic_name__		:str        =      batch_size
								__magic_name__		:Union[str, Any]        =      image_size
								__magic_name__		:Optional[Any]        =      num_channels
								__magic_name__		:str        =      embeddings_size
								__magic_name__		:List[Any]        =      hidden_sizes
								__magic_name__		:List[str]        =      depths
								__magic_name__		:str        =      is_training
								__magic_name__		:List[Any]        =      use_labels
								__magic_name__		:int        =      hidden_act
								__magic_name__		:Dict        =      num_labels
								__magic_name__		:Any        =      scope
								__magic_name__		:Optional[int]        =      len(__lowerCAmelCase     )







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

								__magic_name__		:int        =      None
								if self.use_labels:
												__magic_name__		:int        =      ids_tensor([self.batch_size]       ,					self.num_labels     )

								__magic_name__		:List[str]        =      self.get_config()

								return config, pixel_values, labels







				def 			A	(						self     ):
								"""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 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      RegNetModel(config=__lowerCAmelCase     )
								model.to(__lowerCAmelCase     )
								model.eval()
								__magic_name__		:Dict        =      model(__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 // 3_2, self.image_size // 3_2)       ,					)







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      self.num_labels
								__magic_name__		:str        =      RegNetForImageClassification(__lowerCAmelCase     )
								model.to(__lowerCAmelCase     )
								model.eval()
								__magic_name__		:str        =      model(__lowerCAmelCase       ,					labels=__lowerCAmelCase     )
								self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_labels)     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      self.prepare_config_and_inputs()
								__magic_name__     ,				__magic_name__     ,				__magic_name__		:str        =      config_and_inputs
								__magic_name__		:Optional[int]        =      {'''pixel_values''': pixel_values}
								return config, inputs_dict







@require_torch
class 						lowerCamelCase_				(      lowerCamelCase			,				lowerCamelCase			,				unittest.TestCase ):
				a__					       =  (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
				a__					       =  (
				    {'''feature-extraction''': RegNetModel, '''image-classification''': RegNetForImageClassification}
				    if is_torch_available()
				    else {}
				)

				a__					       =  False
				a__					       =  False
				a__					       =  False
				a__					       =  False







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      RegNetModelTester(self     )
								__magic_name__		:Union[str, Any]        =      ConfigTester(self       ,					config_class=__lowerCAmelCase       ,					has_text_modality=__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								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 			A	(						self     ):
								"""simple docstring"""
								return







				@unittest.skip(reason='''RegNet does not use inputs_embeds'''     )
				def 			A	(						self     ):
								"""simple docstring"""
								pass







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







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

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

												__magic_name__		:Any        =      ['''pixel_values''']
												self.assertListEqual(arg_names[:1]       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__     ,				__magic_name__		:Any        =      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												__magic_name__		:Tuple        =      model_class(config=__lowerCAmelCase     )
												for name, module in model.named_modules():
																if isinstance(__lowerCAmelCase       ,					(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 			A	(						self     ):
								"""simple docstring"""
								def check_hidden_states_output(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
												__magic_name__		:Any        =      model_class(__lowerCAmelCase     )
												model.to(__lowerCAmelCase     )
												model.eval()

												with torch.no_grad():
																__magic_name__		:int        =      model(**self._prepare_for_class(__lowerCAmelCase       ,					__lowerCAmelCase     )     )

												__magic_name__		:Optional[int]        =      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

												__magic_name__		:List[str]        =      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]       ,					)

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

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																__magic_name__		:List[str]        =      True

																check_hidden_states_output(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												__magic_name__		:Optional[int]        =      RegNetModel.from_pretrained(__lowerCAmelCase     )
												self.assertIsNotNone(__lowerCAmelCase     )







def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:List[str]        =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
				return image

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







				@cached_property
				def 			A	(						self     ):
								"""simple docstring"""
								return (
								    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
								    if is_vision_available()
								    else None
								)







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(__lowerCAmelCase     )

								__magic_name__		:List[str]        =      self.default_image_processor
								__magic_name__		:Any        =      prepare_img()
								__magic_name__		:Optional[Any]        =      image_processor(images=__lowerCAmelCase       ,					return_tensors='''pt'''     ).to(__lowerCAmelCase     )

								# forward pass
								with torch.no_grad():
												__magic_name__		:int        =      model(**__lowerCAmelCase     )

								# verify the logits
								__magic_name__		:List[Any]        =      torch.Size((1, 1_0_0_0)     )
								self.assertEqual(outputs.logits.shape       ,					__lowerCAmelCase     )

								__magic_name__		:Any        =      torch.tensor([-0.4180, -1.5051, -3.4836]     ).to(__lowerCAmelCase     )

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






