




'''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 						__UpperCamelCase :






       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"""







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






       def   lowercase__  (							self						):



              """simple docstring"""







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

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

              lowerCamelCase_							=self.get_config()

              return config, pixel_values, labels






       def   lowercase__  (							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   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=ResNetModel(config=lowerCAmelCase						)
              model.to(lowerCAmelCase						)
              model.eval()
              lowerCamelCase_							=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   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







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






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=ResNetBackbone(config=lowerCAmelCase						)
              model.to(lowerCAmelCase						)
              model.eval()
              lowerCamelCase_							=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
              lowerCamelCase_							=None
              lowerCamelCase_							=ResNetBackbone(config=lowerCAmelCase						)
              model.to(lowerCAmelCase						)
              model.eval()
              lowerCamelCase_							=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   lowercase__  (							self						):



              """simple docstring"""







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





@require_torch
class 						__UpperCamelCase (				lowerCamelCase__    ,      lowerCamelCase__    ,      unittest.TestCase       ):
       lowercase			:					List[str]   		=(
           (
               ResNetModel,
               ResNetForImageClassification,
               ResNetBackbone,
           )
           if is_torch_available()
           else ()
       )
       lowercase			:					Tuple   		=(
           {'feature-extraction': ResNetModel, 'image-classification': ResNetForImageClassification}
           if is_torch_available()
           else {}
       )

       lowercase			:					List[Any]   		=True
       lowercase			:					str   		=False
       lowercase			:					Any   		=False
       lowercase			:					Dict   		=False
       lowercase			:					Optional[int]   		=False






       def   lowercase__  (							self						):



              """simple docstring"""







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






       def   lowercase__  (							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   lowercase__  (							self						):



              """simple docstring"""







              return






       @unittest.skip(reason='''ResNet does not use inputs_embeds'''						)
       def   lowercase__  (							self						):



              """simple docstring"""







              pass






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



              """simple docstring"""







              pass






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_,   lowerCamelCase_							=self.model_tester.prepare_config_and_inputs_for_common()

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

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






       def   lowercase__  (							self						):



              """simple docstring"""







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






       def   lowercase__  (							self						):



              """simple docstring"""







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






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_,   lowerCamelCase_							=self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     lowerCamelCase_							=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   lowercase__  (							self						):



              """simple docstring"""







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

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

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

                     lowerCamelCase_							=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],	)

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

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

                            check_hidden_states_output(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)






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



              """simple docstring"""







              pass






       def   lowercase__  (							self						):



              """simple docstring"""







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






       @slow
       def   lowercase__  (							self						):



              """simple docstring"""







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





def   a_							(   )							->		Optional[int]:



       """simple docstring"""
       lowerCamelCase_							=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
       return image


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






       @cached_property
       def   lowercase__  (							self						):



              """simple docstring"""







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






       @slow
       def   lowercase__  (							self						):



              """simple docstring"""







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

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

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

              # verify the logits
              lowerCamelCase_							=torch.Size((1, 1_000)						)
              self.assertEqual(outputs.logits.shape,	lowerCAmelCase						)

              lowerCamelCase_							=torch.tensor([-11.1_069, -9.7_8_7_7, -8.3_7_7_7]						).to(lowerCAmelCase						)

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





@require_torch
class 						__UpperCamelCase (				lowerCamelCase__    ,      unittest.TestCase       ):
       lowercase			:					Any   		=(ResNetBackbone,) if is_torch_available() else ()
       lowercase			:					str   		=False
       lowercase			:					Optional[int]   		=ResNetConfig






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=ResNetModelTester(self						)










'''simple docstring'''




from .hash_table import HashTable


class 						__UpperCamelCase (				lowerCamelCase__       ):






       def __init__(							self,	*lowerCAmelCase,	**lowerCAmelCase						):



              """simple docstring"""







              super().__init__(*lowerCAmelCase,	**lowerCAmelCase						)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase=None						):



              """simple docstring"""







              lowerCamelCase_							=1
              lowerCamelCase_							=self.hash_function(key + i * i						)

              while self.values[new_key] is not None and self.values[new_key] != key:
                     i += 1
                     lowerCamelCase_							=(
                         self.hash_function(key + i * i						)
                         if not self.balanced_factor() >= self.lim_charge
                         else None
                     )

                     if new_key is None:
                            break

              return new_key




