





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 						UpperCamelCase_  :



					def __init__(		self     :     str       ,      lowerCamelCase     :     str       ,      lowerCamelCase     :     Any=3       ,      lowerCamelCase     :     int=32       ,      lowerCamelCase     :     Optional[int]=3       ,      lowerCamelCase     :     Dict=10       ,      lowerCamelCase     :     List[str]=[10, 20, 30, 40]       ,      lowerCamelCase     :     int=[1, 1, 2, 1]       ,      lowerCamelCase     :     Optional[Any]=True       ,      lowerCamelCase     :     Any=True       ,      lowerCamelCase     :     int="relu"       ,      lowerCamelCase     :     List[str]=3       ,      lowerCamelCase     :     Union[str, Any]=None       ,      ):
							lowerCamelCase_	:     Any 						=      parent
							lowerCamelCase_	:     int 						=      batch_size
							lowerCamelCase_	:     List[Any] 						=      image_size
							lowerCamelCase_	:     Optional[Any] 						=      num_channels
							lowerCamelCase_	:     Optional[Any] 						=      embeddings_size
							lowerCamelCase_	:     List[Any] 						=      hidden_sizes
							lowerCamelCase_	:     Dict 						=      depths
							lowerCamelCase_	:     List[str] 						=      is_training
							lowerCamelCase_	:     Any 						=      use_labels
							lowerCamelCase_	:     List[Any] 						=      hidden_act
							lowerCamelCase_	:     str 						=      num_labels
							lowerCamelCase_	:     Tuple 						=      scope
							lowerCamelCase_	:     Any 						=      len(lowerCamelCase						)



					def   __a     (		self     :     Any						):
							lowerCamelCase_	:     Dict 						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

							lowerCamelCase_	:     int 						=      self.get_config()

							return config, pixel_values, labels



					def   __a     (		self     :     Any						):
							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   __a     (		self     :     int       ,      lowerCamelCase     :     Tuple       ,      lowerCamelCase     :     str       ,      lowerCamelCase     :     List[str]						):
							lowerCamelCase_	:     Any 						=      RegNetModel(config=lowerCamelCase						)
							model.to(lowerCamelCase						)
							model.eval()
							lowerCamelCase_	:     int 						=      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   __a     (		self     :     Union[str, Any]       ,      lowerCamelCase     :     Dict       ,      lowerCamelCase     :     List[str]       ,      lowerCamelCase     :     Optional[int]						):
							lowerCamelCase_	:     Dict 						=      self.num_labels
							lowerCamelCase_	:     str 						=      RegNetForImageClassification(lowerCamelCase						)
							model.to(lowerCamelCase						)
							model.eval()
							lowerCamelCase_	:     Tuple 						=      model(lowerCamelCase       ,      labels=lowerCamelCase						)
							self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.num_labels)						)





					def   __a     (		self     :     str						):
							lowerCamelCase_	:     int 						=      self.prepare_config_and_inputs()
							lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_	:     Optional[Any] 						=      config_and_inputs
							lowerCamelCase_	:     str 						=      {'pixel_values': pixel_values}
							return config, inputs_dict






@require_torch
class 						UpperCamelCase_  (  snake_case__  ,     snake_case__  ,     unittest.TestCase							):
					_a      :						int						    =					(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
					_a      :						Optional[int]						    =					(
					    {'feature-extraction': RegNetModel, 'image-classification': RegNetForImageClassification}
					    if is_torch_available()
					    else {}
					)

					_a      :						List[str]						    =					False
					_a      :						Optional[Any]						    =					False
					_a      :						Optional[Any]						    =					False
					_a      :						Dict						    =					False



					def   __a     (		self     :     str						):
							lowerCamelCase_	:     int 						=      RegNetModelTester(self						)
							lowerCamelCase_	:     int 						=      ConfigTester(self       ,      config_class=lowerCamelCase       ,      has_text_modality=lowerCamelCase						)



					def   __a     (		self     :     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   __a     (		self     :     Union[str, Any]						):
							return



					@unittest.skip(reason='RegNet does not use inputs_embeds'						)
					def   __a     (		self     :     int						):
							pass



					@unittest.skip(reason='RegNet does not support input and output embeddings'						)
					def   __a     (		self     :     str						):
							pass



					def   __a     (		self     :     Union[str, Any]						):
							lowerCamelCase_						,      lowerCamelCase_	:     Optional[Any] 						=      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									lowerCamelCase_	:     Optional[int] 						=      model_class(lowerCamelCase						)
									lowerCamelCase_	:     Union[str, Any] 						=      inspect.signature(model.forward						)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									lowerCamelCase_	:     Dict 						=      [*signature.parameters.keys()]

									lowerCamelCase_	:     Union[str, Any] 						=      ['pixel_values']
									self.assertListEqual(arg_names[:1]       ,      lowerCamelCase						)



					def   __a     (		self     :     Dict						):
							lowerCamelCase_	:     Dict 						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase						)



					def   __a     (		self     :     Dict						):
							lowerCamelCase_						,      lowerCamelCase_	:     Optional[int] 						=      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									lowerCamelCase_	:     int 						=      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   __a     (		self     :     Optional[int]						):
							def check_hidden_states_output(lowerCamelCase     :     List[Any]       ,      lowerCamelCase     :     Union[str, Any]       ,      lowerCamelCase     :     List[Any]						):
									lowerCamelCase_	:     Tuple 						=      model_class(lowerCamelCase						)
									model.to(lowerCamelCase						)
									model.eval()

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

									lowerCamelCase_	:     Optional[int] 						=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									lowerCamelCase_	:     Union[str, Any] 						=      self.model_tester.num_stages
									self.assertEqual(len(lowerCamelCase						)       ,      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]       ,      )

							lowerCamelCase_						,      lowerCamelCase_	:     List[str] 						=      self.model_tester.prepare_config_and_inputs_for_common()
							lowerCamelCase_	:     List[Any] 						=      ['basic', 'bottleneck']
							for model_class in self.all_model_classes:
									for layer_type in layers_type:
											lowerCamelCase_	:     Any 						=      layer_type
											lowerCamelCase_	:     Optional[Any] 						=      True
											check_hidden_states_output(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)

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

											check_hidden_states_output(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)



					def   __a     (		self     :     Optional[Any]						):
							lowerCamelCase_	:     Tuple 						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCamelCase						)





					@slow
					def   __a     (		self     :     List[Any]						):
							for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCamelCase_	:     int 						=      RegNetModel.from_pretrained(lowerCamelCase						)
									self.assertIsNotNone(lowerCamelCase						)


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






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



					@cached_property
					def   __a     (		self     :     Optional[int]						):
							return (
							    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
							    if is_vision_available()
							    else None
							)





					@slow
					def   __a     (		self     :     List[str]						):
							lowerCamelCase_	:     Any 						=      RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(lowerCamelCase						)

							lowerCamelCase_	:     Any 						=      self.default_image_processor
							lowerCamelCase_	:     List[Any] 						=      prepare_img()
							lowerCamelCase_	:     Any 						=      image_processor(images=lowerCamelCase       ,      return_tensors='pt'						).to(lowerCamelCase						)

							# forward pass
							with torch.no_grad():
									lowerCamelCase_	:     List[Any] 						=      model(**lowerCamelCase						)

							# verify the logits
							lowerCamelCase_	:     Union[str, Any] 						=      torch.Size((1, 10_00)						)
							self.assertEqual(outputs.logits.shape       ,      lowerCamelCase						)

							lowerCamelCase_	:     List[str] 						=      torch.tensor([-0.4_180, -1.5_051, -3.4_836]						).to(lowerCamelCase						)

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







_lowercase		:  Any			       ={"""a""": ["""c""", """b"""], """b""": ["""d""", """e"""], """c""": [], """d""": [], """e""": []}
_lowercase		:  Union[str, Any]			       =["""a""", """b""", """c""", """d""", """e"""]


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		lowerCamelCase_	:     int 						=      start
		# add current to visited
		visited.append(lowerCAmelCase__						)
		lowerCamelCase_	:     Optional[int] 						=      edges[current]
		for neighbor in neighbors:
				# if neighbor not in visited, visit
				if neighbor not in visited:
						lowerCamelCase_	:     Dict 						=      topological_sort(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)
    # if all neighbors visited add current to sort
		sort.append(lowerCAmelCase__						)
		# if all vertices haven't been visited select a new one to visit
		if len(lowerCAmelCase__						) != len(lowerCAmelCase__						):
				for vertice in vertices:
						if vertice not in visited:
								lowerCamelCase_	:     Any 						=      topological_sort(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)
    # return sort
		return sort


if __name__ == "__main__":
		_lowercase		:  str			       =topological_sort("""a""", [], [])
		print(sort)
