
"""simple docstring"""


import inspect
import unittest

from transformers import BitConfig
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 BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
		from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image







class 					lowerCAmelCase_					:



						'''simple docstring'''


						def __init__(			self  :   List[Any]	,A_  :   Dict	,A_  :   Optional[Any]=3	,A_  :   Optional[int]=32	,A_  :   Dict=3	,A_  :   Tuple=10	,A_  :   Tuple=[8, 16, 32, 64]	,A_  :   Optional[int]=[1, 1, 2, 1]	,A_  :   int=True	,A_  :   Dict=True	,A_  :   Union[str, Any]="relu"	,A_  :   Dict=3	,A_  :   Optional[int]=None	,A_  :   int=["stage2", "stage3", "stage4"]	,A_  :   Tuple=[2, 3, 4]	,A_  :   List[str]=1	,)  -> str:
							A     = parent
							A     = batch_size
							A     = image_size
							A     = num_channels
							A     = embeddings_size
							A     = hidden_sizes
							A     = depths
							A     = is_training
							A     = use_labels
							A     = hidden_act
							A     = num_labels
							A     = scope
							A     = len(A_   )
							A     = out_features
							A     = out_indices
							A     = num_groups


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> int:
							A     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

							A     = self.get_config()

							return config, pixel_values, labels


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Any:
							return BitConfig(
							    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	,num_groups=self.num_groups	,)


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,A_  :   Union[str, Any]	,A_  :   Any	,A_  :   List[Any]   )  -> Optional[Any]:
							A     = BitModel(config=A_   )
							model.to(A_   )
							model.eval()
							A     = model(A_   )
							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  :   Optional[Any]	,A_  :   Union[str, Any]	,A_  :   int	,A_  :   Tuple   )  -> Tuple:
							A     = self.num_labels
							A     = BitForImageClassification(A_   )
							model.to(A_   )
							model.eval()
							A     = model(A_	,labels=A_   )
							self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.num_labels)   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,A_  :   Union[str, Any]	,A_  :   str	,A_  :   Optional[Any]   )  -> str:
							A     = BitBackbone(config=A_   )
							model.to(A_   )
							model.eval()
							A     = model(A_   )

							# 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
							A     = None
							A     = BitBackbone(config=A_   )
							model.to(A_   )
							model.eval()
							A     = model(A_   )

							# 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  :   List[Any]   )  -> Union[str, Any]:
							A     = self.prepare_config_and_inputs()
							A ,   A ,   A     = config_and_inputs
							A     = {'pixel_values': pixel_values}
							return config, inputs_dict







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



						'''simple docstring'''


						_lowerCamelCase:     Optional[int]   =     (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
						_lowerCamelCase:     List[Any]   =     (
						    {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
						    if is_torch_available()
						    else {}
						)

						_lowerCamelCase:     Union[str, Any]   =     False
						_lowerCamelCase:     Optional[int]   =     False
						_lowerCamelCase:     List[str]   =     False
						_lowerCamelCase:     int   =     False
						_lowerCamelCase:     Any   =     False


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Optional[int]:
							A     = BitModelTester(self   )
							A     = ConfigTester(self	,config_class=A_	,has_text_modality=A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Optional[int]:
							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  :   Optional[Any]   )  -> Optional[Any]:
							return


						@unittest.skip(reason='Bit does not output attentions'   )
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> List[str]:
							pass


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


						@unittest.skip(reason='Bit does not support input and output embeddings'   )
						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> Dict:
							pass


						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> List[Any]:
							A ,   A     = self.model_tester.prepare_config_and_inputs_for_common()

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

								A     = ['pixel_values']
								self.assertListEqual(arg_names[:1]	,A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> List[Any]:
							A     = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Tuple:
							A     = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> int:
							A ,   A     = self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								A     = model_class(config=A_   )
								for name, module in model.named_modules():
									if isinstance(A_	,(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   )  -> int:
							def check_hidden_states_output(A_  :   Any	,A_  :   List[str]	,A_  :   int   ):
								A     = model_class(A_   )
								model.to(A_   )
								model.eval()

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

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

								A     = self.model_tester.num_stages
								self.assertEqual(len(A_   )	,expected_num_stages + 1   )

								# Bit'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]	,)

							A ,   A     = self.model_tester.prepare_config_and_inputs_for_common()
							A     = ['preactivation', 'bottleneck']
							for model_class in self.all_model_classes:
								for layer_type in layers_type:
									A     = layer_type
									A     = True
									check_hidden_states_output(A_	,A_	,A_   )

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

									check_hidden_states_output(A_	,A_	,A_   )


						@unittest.skip(reason='Bit does not use feedforward chunking'   )
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Optional[Any]:
							pass


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> List[Any]:
							A     = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*A_   )




						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Optional[Any]:
							for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A     = BitModel.from_pretrained(A_   )
								self.assertIsNotNone(A_   )






def    _snake_case					(		):
	A     = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
	return image







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



						'''simple docstring'''


						@cached_property
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> int:
							return (
							    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ) if is_vision_available() else None
							)




						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Any:
							A     = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(A_   )

							A     = self.default_image_processor
							A     = prepare_img()
							A     = image_processor(images=A_	,return_tensors='pt'   ).to(A_   )

							# forward pass
							with torch.no_grad():
								A     = model(**A_   )

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

							A     = torch.tensor([[-0.65_26, -0.52_63, -1.43_98]]   ).to(A_   )

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







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



						'''simple docstring'''


						_lowerCamelCase:     Optional[Any]   =     (BitBackbone,) if is_torch_available() else ()
						_lowerCamelCase:     List[str]   =     BitConfig

						_lowerCamelCase:     Optional[Any]   =     False


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Union[str, Any]:
							A     = BitModelTester(self   )

"""simple docstring"""


from maths.prime_check import is_prime






def    _snake_case					(		snake_case__ :  int  ):
	if not isinstance(snake_case__						,   snake_case__  ):
		A     = F'Input value of [number={number}] must be an integer'
		raise TypeError(snake_case__  )
	if is_prime(snake_case__  ) and is_prime(number + 2  ):
		return number + 2
	else:
		return -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()