
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 						__lowercase:


       """simple docstring"""







       def __init__(     self					:			List[Any]  ,      _lowerCAmelCase					:			Union[str, Any]  ,      _lowerCAmelCase					:			Any=3  ,      _lowerCAmelCase					:			Any=32  ,      _lowerCAmelCase					:			str=3  ,      _lowerCAmelCase					:			int=10  ,      _lowerCAmelCase					:			Optional[int]=[10, 20, 30, 40]  ,      _lowerCAmelCase					:			Optional[Any]=[1, 1, 2, 1]  ,      _lowerCAmelCase					:			Union[str, Any]=True  ,      _lowerCAmelCase					:			Optional[Any]=True  ,      _lowerCAmelCase					:			List[Any]="relu"  ,      _lowerCAmelCase					:			List[str]=3  ,      _lowerCAmelCase					:			int=None  ,      _lowerCAmelCase					:			List[str]=["stage2", "stage3", "stage4"]  ,      _lowerCAmelCase					:			Optional[Any]=[2, 3, 4]  ,      )					->   Dict:
         _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 SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]       )					->   Union[str, Any]:
         _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 SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   Any:
         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 SCREAMING_SNAKE_CASE_				(     self					:			List[Any]  ,      _lowerCAmelCase					:			List[Any]  ,      _lowerCAmelCase					:			List[str]  ,      _lowerCAmelCase					:			Dict       )					->   Optional[int]:
         _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 SCREAMING_SNAKE_CASE_				(     self					:			str  ,      _lowerCAmelCase					:			List[str]  ,      _lowerCAmelCase					:			Union[str, Any]  ,      _lowerCAmelCase					:			int       )					->   Tuple:
         _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 SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]  ,      _lowerCAmelCase					:			Optional[Any]  ,      _lowerCAmelCase					:			int  ,      _lowerCAmelCase					:			Union[str, Any]       )					->   int:
         _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 SCREAMING_SNAKE_CASE_				(     self					:			int       )					->   Optional[int]:
         _lowerCAmelCase						=		self.prepare_config_and_inputs()
         _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase						=		config_and_inputs
         _lowerCAmelCase						=		{'pixel_values': pixel_values}
         return config, inputs_dict





@require_torch
class 						__lowercase(     SCREAMING_SNAKE_CASE		,  SCREAMING_SNAKE_CASE		,  unittest.TestCase   ):


       """simple docstring"""







       UpperCamelCase_				 =							(
           (
               ResNetModel,
               ResNetForImageClassification,
               ResNetBackbone,
           )
           if is_torch_available()
           else ()
       )
       UpperCamelCase_				 =							(
           {'''feature-extraction''': ResNetModel, '''image-classification''': ResNetForImageClassification}
           if is_torch_available()
           else {}
       )

       UpperCamelCase_				 =							True
       UpperCamelCase_				 =							False
       UpperCamelCase_				 =							False
       UpperCamelCase_				 =							False
       UpperCamelCase_				 =							False



       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   Dict:
         _lowerCAmelCase						=		ResNetModelTester(self       )
         _lowerCAmelCase						=		ConfigTester(self  ,      config_class=_lowerCAmelCase  ,      has_text_modality=_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]       )					->   List[str]:
         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 SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]       )					->   Optional[int]:
         return



       @unittest.skip(reason='ResNet does not use inputs_embeds'       )
       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Tuple:
         pass



       @unittest.skip(reason='ResNet does not support input and output embeddings'       )
       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   Any:
         pass



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   Tuple:
         _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 SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   Any:
         _lowerCAmelCase						=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   int:
         _lowerCAmelCase						=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   Dict:
         _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 SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   List[str]:
         def check_hidden_states_output(_lowerCAmelCase					:			Optional[Any]  ,      _lowerCAmelCase					:			Any  ,      _lowerCAmelCase					:			List[Any]       ):
           _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 SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   Optional[Any]:
         pass



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   Optional[Any]:
         _lowerCAmelCase						=		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase       )






       @slow
       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   Any:
         for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowerCAmelCase						=		ResNetModel.from_pretrained(_lowerCAmelCase       )
           self.assertIsNotNone(_lowerCAmelCase       )



def 	_a  (					):






  """simple docstring"""




  _lowerCAmelCase						=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
  return image





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


       """simple docstring"""







       @cached_property
       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Any:
         return (
             AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
             if is_vision_available()
             else None
         )






       @slow
       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Dict:
         _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, 1000)       )
         self.assertEqual(outputs.logits.shape  ,      _lowerCAmelCase       )

         _lowerCAmelCase						=		torch.tensor([-11.1069, -9.7877, -8.3777]       ).to(_lowerCAmelCase       )

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





@require_torch
class 						__lowercase(     SCREAMING_SNAKE_CASE		,  unittest.TestCase   ):


       """simple docstring"""







       UpperCamelCase_				 =							(ResNetBackbone,) if is_torch_available() else ()
       UpperCamelCase_				 =							False
       UpperCamelCase_				 =							ResNetConfig



       def SCREAMING_SNAKE_CASE_				(     self					:			List[Any]       )					->   int:
         _lowerCAmelCase						=		ResNetModelTester(self       )


import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


_UpperCamelCase:					List[Any]     =TypeVar('T', bound='TaskTemplate')





@dataclass(frozen=SCREAMING_SNAKE_CASE   )
class 						__lowercase(     abc.ABC   ):


       """simple docstring"""







       UpperCamelCase_				 =							42
       UpperCamelCase_				 =							42
       UpperCamelCase_				 =							42



       def SCREAMING_SNAKE_CASE_				(     self					:			T  ,      _lowerCAmelCase					:			Features       )					->   T:
         return copy.deepcopy(self       )



       @property
       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   Features:
         return Features(**self.input_schema  ,      **self.label_schema       )



       @property
       @abc.abstractmethod
       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Dict[str, str]:
         raise NotImplementedError






       @classmethod
       def SCREAMING_SNAKE_CASE_				(     cls					:			Type[T]  ,      _lowerCAmelCase					:			dict       )					->   T:
         _lowerCAmelCase						=		{f.name for f in dataclasses.fields(cls       )}
         return cls(**{k: v for k, v in template_dict.items() if k in field_names}       )
