


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__	:






			"""simple docstring"""






			def __init__(							self      ,						snake_case      ,						snake_case=3      ,						snake_case=32      ,						snake_case=3      ,						snake_case=10      ,						snake_case=[10, 20, 30, 40]      ,						snake_case=[1, 1, 2, 1]      ,						snake_case=True      ,						snake_case=True      ,						snake_case="relu"      ,						snake_case=3      ,						snake_case=None      ,						):

						'''simple docstring'''


						UpperCamelCase__					=       parent
						UpperCamelCase__					=       batch_size
						UpperCamelCase__					=       image_size
						UpperCamelCase__					=       num_channels
						UpperCamelCase__					=       embeddings_size
						UpperCamelCase__					=       hidden_sizes
						UpperCamelCase__					=       depths
						UpperCamelCase__					=       is_training
						UpperCamelCase__					=       use_labels
						UpperCamelCase__					=       hidden_act
						UpperCamelCase__					=       num_labels
						UpperCamelCase__					=       scope
						UpperCamelCase__					=       len(snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

						UpperCamelCase__					=       self.get_config()

						return config, pixel_values, labels




			def 				snake_case__				(							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 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       RegNetModel(config=snake_case     )
						model.to(snake_case     )
						model.eval()
						UpperCamelCase__					=       model(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 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.num_labels
						UpperCamelCase__					=       RegNetForImageClassification(snake_case     )
						model.to(snake_case     )
						model.eval()
						UpperCamelCase__					=       model(snake_case      ,						labels=snake_case     )
						self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.num_labels)     )







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.prepare_config_and_inputs()
						UpperCamelCase__,      UpperCamelCase__,      UpperCamelCase__					=       config_and_inputs
						UpperCamelCase__					=       {"pixel_values": pixel_values}
						return config, inputs_dict







@require_torch
class        lowerCamelCase__	(							UpperCAmelCase					,	UpperCAmelCase					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Optional[Any]    =		(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
			_UpperCamelCase		:					Optional[Any]    =		(
			    {'feature-extraction': RegNetModel, 'image-classification': RegNetForImageClassification}
			    if is_torch_available()
			    else {}
			)

			_UpperCamelCase		:					str    =		False
			_UpperCamelCase		:					Optional[Any]    =		False
			_UpperCamelCase		:					Optional[int]    =		False
			_UpperCamelCase		:					Any    =		False




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       RegNetModelTester(self     )
						UpperCamelCase__					=       ConfigTester(self      ,						config_class=snake_case      ,						has_text_modality=snake_case     )




			def 				snake_case__				(							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 				snake_case__				(							self     ):

						'''simple docstring'''


						return




			@unittest.skip(reason="RegNet does not use inputs_embeds"     )
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						pass




			@unittest.skip(reason="RegNet does not support input and output embeddings"     )
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						pass




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()

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

									UpperCamelCase__					=       ["pixel_values"]
									self.assertListEqual(arg_names[:1]      ,						snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCamelCase__					=       model_class(config=snake_case     )
									for name, module in model.named_modules():
												if isinstance(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 				snake_case__				(							self     ):

						'''simple docstring'''


						def check_hidden_states_output(snake_case      ,						snake_case      ,						snake_case     ):
									UpperCamelCase__					=       model_class(snake_case     )
									model.to(snake_case     )
									model.eval()

									with torch.no_grad():
												UpperCamelCase__					=       model(**self._prepare_for_class(snake_case      ,						snake_case     )     )

									UpperCamelCase__					=       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									UpperCamelCase__					=       self.model_tester.num_stages
									self.assertEqual(len(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]      ,						)

						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()
						UpperCamelCase__					=       ["basic", "bottleneck"]
						for model_class in self.all_model_classes:
									for layer_type in layers_type:
												UpperCamelCase__					=       layer_type
												UpperCamelCase__					=       True
												check_hidden_states_output(snake_case      ,						snake_case      ,						snake_case     )

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												UpperCamelCase__					=       True

												check_hidden_states_output(snake_case      ,						snake_case      ,						snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*snake_case     )







			@slow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCamelCase__					=       RegNetModel.from_pretrained(snake_case     )
									self.assertIsNotNone(snake_case     )



def        UpperCamelCase_(				)->		List[Any]:
			UpperCamelCase__					=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
			return image







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






			"""simple docstring"""






			@cached_property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return (
						    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
						    if is_vision_available()
						    else None
						)







			@slow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(snake_case     )

						UpperCamelCase__					=       self.default_image_processor
						UpperCamelCase__					=       prepare_img()
						UpperCamelCase__					=       image_processor(images=snake_case      ,						return_tensors="pt"     ).to(snake_case     )

						# forward pass
						with torch.no_grad():
									UpperCamelCase__					=       model(**snake_case     )

						# verify the logits
						UpperCamelCase__					=       torch.Size((1, 1000)     )
						self.assertEqual(outputs.logits.shape      ,						snake_case     )

						UpperCamelCase__					=       torch.tensor([-0.4180, -1.5051, -3.4836]     ).to(snake_case     )

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






from abc import ABC, abstractmethod
from argparse import ArgumentParser







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			@staticmethod
			@abstractmethod
			def 				snake_case__				(							snake_case     ):

						'''simple docstring'''


						raise NotImplementedError()







			@abstractmethod
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						raise NotImplementedError()


