
'''simple docstring'''




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  A				:





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






         """simple docstring"""






         A       :   List[str]					=						parent
         A       :   int					=						batch_size
         A       :   Optional[int]					=						image_size
         A       :   int					=						num_channels
         A       :   List[Any]					=						embeddings_size
         A       :   str					=						hidden_sizes
         A       :   Any					=						depths
         A       :   List[str]					=						is_training
         A       :   Any					=						use_labels
         A       :   Any					=						hidden_act
         A       :   Tuple					=						num_labels
         A       :   List[str]					=						scope
         A       :   Union[str, Any]					=						len(SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         A       :   Any					=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

         A       :   Optional[Any]					=						None
         if self.use_labels:
             A       :   str					=						ids_tensor([self.batch_size]						,			self.num_labels	)

         A       :   List[Any]					=						self.get_config()

         return config, pixel_values, labels





     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """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 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				Tuple:






         """simple docstring"""






         A       :   int					=						RegNetModel(config=SCREAMING_SNAKE_CASE	)
         model.to(SCREAMING_SNAKE_CASE	)
         model.eval()
         A       :   List[str]					=						model(SCREAMING_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 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				int:






         """simple docstring"""






         A       :   str					=						self.num_labels
         A       :   List[Any]					=						RegNetForImageClassification(SCREAMING_SNAKE_CASE	)
         model.to(SCREAMING_SNAKE_CASE	)
         model.eval()
         A       :   Optional[Any]					=						model(SCREAMING_SNAKE_CASE						,			labels=SCREAMING_SNAKE_CASE	)
         self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.num_labels)	)





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         A       :   Tuple					=						self.prepare_config_and_inputs()
         A,  A,  A       :   Optional[int]					=						config_and_inputs
         A       :   Union[str, Any]					=						{'''pixel_values''': pixel_values}
         return config, inputs_dict





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

     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		False





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         A       :   Any					=						RegNetModelTester(self	)
         A       :   List[str]					=						ConfigTester(self						,			config_class=SCREAMING_SNAKE_CASE						,			has_text_modality=SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				List[str]:






         """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 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         return





     @unittest.skip(reason='''RegNet does not use inputs_embeds'''	)
     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         pass





     @unittest.skip(reason='''RegNet does not support input and output embeddings'''	)
     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         pass





     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         A,  A       :   Dict					=						self.model_tester.prepare_config_and_inputs_for_common()

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

             A       :   Optional[Any]					=						['''pixel_values''']
             self.assertListEqual(arg_names[:1]						,			SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         A       :   Optional[int]					=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         A,  A       :   Optional[Any]					=						self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             A       :   Tuple					=						model_class(config=SCREAMING_SNAKE_CASE	)
             for name, module in model.named_modules():
                 if isinstance(SCREAMING_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 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         def check_hidden_states_output(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	):
             A       :   Dict					=						model_class(SCREAMING_SNAKE_CASE	)
             model.to(SCREAMING_SNAKE_CASE	)
             model.eval()

             with torch.no_grad():
                 A       :   Any					=						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)	)

             A       :   Any					=						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

             A       :   Any					=						self.model_tester.num_stages
             self.assertEqual(len(SCREAMING_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]						,			)

         A,  A       :   str					=						self.model_tester.prepare_config_and_inputs_for_common()
         A       :   Dict					=						['''basic''', '''bottleneck''']
         for model_class in self.all_model_classes:
             for layer_type in layers_type:
                 A       :   Dict					=						layer_type
                 A       :   Union[str, Any]					=						True
                 check_hidden_states_output(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 A       :   Dict					=						True

                 check_hidden_states_output(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         A       :   Union[str, Any]					=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE	)





     @slow
     def 				__lowerCAmelCase							(							self	) ->				Dict:






         """simple docstring"""






         for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             A       :   Dict					=						RegNetModel.from_pretrained(SCREAMING_SNAKE_CASE	)
             self.assertIsNotNone(SCREAMING_SNAKE_CASE	)







def 		lowerCAmelCase_				(   ):




    '''simple docstring'''



    A       :   Union[str, Any]					=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
    return image







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





     @cached_property
     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






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





     @slow
     def 				__lowerCAmelCase							(							self	) ->				List[str]:






         """simple docstring"""






         A       :   Optional[Any]					=						RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(SCREAMING_SNAKE_CASE	)

         A       :   Dict					=						self.default_image_processor
         A       :   int					=						prepare_img()
         A       :   Optional[Any]					=						image_processor(images=SCREAMING_SNAKE_CASE						,			return_tensors='''pt'''	).to(SCREAMING_SNAKE_CASE	)

         # forward pass
         with torch.no_grad():
             A       :   List[str]					=						model(**SCREAMING_SNAKE_CASE	)

         # verify the logits
         A       :   Dict					=						torch.Size((1, 1000)	)
         self.assertEqual(outputs.logits.shape						,			SCREAMING_SNAKE_CASE	)

         A       :   Tuple					=						torch.tensor([-0.4_180, -1.5_051, -3.4_836]	).to(SCREAMING_SNAKE_CASE	)

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





def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Any							=							len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							[[False for _ in range(m + 1						)] for _ in range(n + 1						)]
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							True
						for i in range(_lowercase						):
												for j in range(m + 1						):
																		if dp[i][j]:
																								if j < m and a[i].upper() == b[j]:
																														SCREAMING_SNAKE_CASE							:    List[str]							=							True
																								if a[i].islower():
																														SCREAMING_SNAKE_CASE							:    Dict							=							True
						return dp[n][m]


if __name__ == "__main__":
					import doctest

					doctest.testmod()





