"""simple docstring"""
import inspect
import unittest

from transformers import ResNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
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 ResNetBackbone, ResNetForImageClassification, ResNetModel
					from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor



class 	lowerCAmelCase_     :



				"""simple docstring"""

				def __init__(					self		,     lowerCAmelCase		,     lowerCAmelCase=3		,     lowerCAmelCase=32		,     lowerCAmelCase=3		,     lowerCAmelCase=10		,     lowerCAmelCase=[10, 20, 30, 40]		,     lowerCAmelCase=[1, 1, 2, 1]		,     lowerCAmelCase=True		,     lowerCAmelCase=True		,     lowerCAmelCase="relu"		,     lowerCAmelCase=3		,     lowerCAmelCase=None		,     lowerCAmelCase=["stage2", "stage3", "stage4"]		,     lowerCAmelCase=[2, 3, 4]		,     ):






										"""simple docstring"""

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

				def 		snake_case			(					self     ):






										"""simple docstring"""

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

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

										snake_case             =      self.get_config()

										return config, pixel_values, labels

				def 		snake_case			(					self     ):






										"""simple docstring"""

										return ResNetConfig(
										    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		,     out_features=self.out_features		,     out_indices=self.out_indices		,     )

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case             =      ResNetModel(config=lowerCAmelCase     )
										model.to(lowerCAmelCase     )
										model.eval()
										snake_case             =      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 // 32, self.image_size // 32)		,     )

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     ):






										"""simple docstring"""

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

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case             =      ResNetBackbone(config=lowerCAmelCase     )
										model.to(lowerCAmelCase     )
										model.eval()
										snake_case             =      model(lowerCAmelCase     )

										# verify feature maps
										self.parent.assertEqual(len(result.feature_maps     )		,     len(config.out_features     )     )
										self.parent.assertListEqual(list(result.feature_maps[0].shape     )		,     [self.batch_size, self.hidden_sizes[1], 4, 4]     )

										# verify channels
										self.parent.assertEqual(len(model.channels     )		,     len(config.out_features     )     )
										self.parent.assertListEqual(model.channels		,     config.hidden_sizes[1:]     )

										# verify backbone works with out_features=None
										snake_case             =      None
										snake_case             =      ResNetBackbone(config=lowerCAmelCase     )
										model.to(lowerCAmelCase     )
										model.eval()
										snake_case             =      model(lowerCAmelCase     )

										# verify feature maps
										self.parent.assertEqual(len(result.feature_maps     )		,     1     )
										self.parent.assertListEqual(list(result.feature_maps[0].shape     )		,     [self.batch_size, self.hidden_sizes[-1], 1, 1]     )

										# verify channels
										self.parent.assertEqual(len(model.channels     )		,     1     )
										self.parent.assertListEqual(model.channels		,     [config.hidden_sizes[-1]]     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.prepare_config_and_inputs()
										snake_case       ,snake_case       ,snake_case             =      config_and_inputs
										snake_case             =      {'pixel_values': pixel_values}
										return config, inputs_dict





@require_torch
class 	lowerCAmelCase_     (      lowerCAmelCase  ,     lowerCAmelCase  ,     unittest.TestCase       ):



				"""simple docstring"""
				_lowerCAmelCase  :     str      	=    (
				    (
				        ResNetModel,
				        ResNetForImageClassification,
				        ResNetBackbone,
				    )
				    if is_torch_available()
				    else ()
				)
				_lowerCAmelCase  :     Tuple      	=    (
				    {"""feature-extraction""": ResNetModel, """image-classification""": ResNetForImageClassification}
				    if is_torch_available()
				    else {}
				)

				_lowerCAmelCase  :     Dict      	=    True
				_lowerCAmelCase  :     Union[str, Any]      	=    False
				_lowerCAmelCase  :     int      	=    False
				_lowerCAmelCase  :     Optional[int]      	=    False
				_lowerCAmelCase  :     List[str]      	=    False

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      ResNetModelTester(self     )
										snake_case             =      ConfigTester(self		,     config_class=lowerCAmelCase		,     has_text_modality=lowerCAmelCase     )

				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='ResNet does not use inputs_embeds'     )
				def 		snake_case			(					self     ):






										"""simple docstring"""

										pass

				@unittest.skip(reason='ResNet does not support input and output embeddings'     )
				def 		snake_case			(					self     ):






										"""simple docstring"""

										pass

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case       ,snake_case             =      self.model_tester.prepare_config_and_inputs_for_common()

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

																snake_case             =      ['pixel_values']
																self.assertListEqual(arg_names[:1]		,     lowerCAmelCase     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

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

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_backbone(*lowerCAmelCase     )

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case       ,snake_case             =      self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																snake_case             =      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 		snake_case			(					self     ):






										"""simple docstring"""

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

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

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

																snake_case             =      self.model_tester.num_stages
																self.assertEqual(len(lowerCAmelCase     )		,     expected_num_stages + 1     )

																# ResNet'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 // 4, self.model_tester.image_size // 4]		,     )

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

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

																						check_hidden_states_output(lowerCAmelCase		,     lowerCAmelCase		,     lowerCAmelCase     )

				@unittest.skip(reason='ResNet does not use feedforward chunking'     )
				def 		snake_case			(					self     ):






										"""simple docstring"""

										pass

				def 		snake_case			(					self     ):






										"""simple docstring"""

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

				@slow
				def 		snake_case			(					self     ):






										"""simple docstring"""

										for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																snake_case             =      ResNetModel.from_pretrained(lowerCAmelCase     )
																self.assertIsNotNone(lowerCAmelCase     )





def  lowerCAmelCase__     (      )       ->					str:





						"""simple docstring"""






						snake_case             =      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(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
										    if is_vision_available()
										    else None
										)

				@slow
				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(lowerCAmelCase     )

										snake_case             =      self.default_image_processor
										snake_case             =      prepare_img()
										snake_case             =      image_processor(images=lowerCAmelCase		,     return_tensors='pt'     ).to(lowerCAmelCase     )

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

										# verify the logits
										snake_case             =      torch.Size((1, 10_00)     )
										self.assertEqual(outputs.logits.shape		,     lowerCAmelCase     )

										snake_case             =      torch.tensor([-11.10_69, -9.78_77, -8.37_77]     ).to(lowerCAmelCase     )

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





@require_torch
class 	lowerCAmelCase_     (      lowerCAmelCase  ,     unittest.TestCase       ):



				"""simple docstring"""
				_lowerCAmelCase  :     Optional[Any]      	=    (ResNetBackbone,) if is_torch_available() else ()
				_lowerCAmelCase  :     Tuple      	=    False
				_lowerCAmelCase  :     List[Any]      	=    ResNetConfig

				def 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      ResNetModelTester(self     )



"""simple docstring"""
def  lowerCAmelCase__     (      _UpperCamelCase							:					list       )       ->					list:





						"""simple docstring"""






						if len(_UpperCamelCase       ) <= 1:
												return data
						else:
												return [
												    *quick_sort([e for e in data[1:] if e <= data[0]]       ),
												    data[0],
												    *quick_sort([e for e in data[1:] if e > data[0]]       ),
												]


if __name__ == "__main__":
					import doctest

					doctest.testmod()


