
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 							UpperCAmelCase      :




	'''simple docstring'''





	def __init__(       self					: List[Any]							,A					: List[str]							,A					: List[Any]=3							,A					: Any=32							,A					: Optional[int]=3							,A					: Optional[int]=10							,A					: Optional[Any]=[8, 16, 32, 64]							,A					: Optional[Any]=[1, 1, 2, 1]							,A					: Any=True							,A					: str=True							,A					: Any="relu"							,A					: Dict=3							,A					: Optional[Any]=None							,A					: Dict=["stage2", "stage3", "stage4"]							,A					: List[str]=[2, 3, 4]							,A					: Union[str, Any]=1							,):
					__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 				UpperCamelCase_  (       self					: Any ):
					__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 				UpperCamelCase_  (       self					: Tuple ):
					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 				UpperCamelCase_  (       self					: Dict							,A					: int							,A					: Dict							,A					: List[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 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: List[Any]							,A					: List[str]							,A					: 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 				UpperCamelCase_  (       self					: Optional[Any]							,A					: List[Any]							,A					: List[str]							,A					: List[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 				UpperCamelCase_  (       self					: Optional[int] ):
					__A   			= self.prepare_config_and_inputs()
					__A			,							__A			,							__A   			= config_and_inputs
					__A   			= {"pixel_values": pixel_values}
					return config, inputs_dict





@require_torch
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			unittest.TestCase  ):




	'''simple docstring'''





	snake_case_					    =     (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
	snake_case_					    =     (
	    {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
	    if is_torch_available()
	    else {}
	)

	snake_case_					    =     False
	snake_case_					    =     False
	snake_case_					    =     False
	snake_case_					    =     False
	snake_case_					    =     False






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					__A   			= BitModelTester(self )
					__A   			= ConfigTester(self							,config_class=A							,has_text_modality=A )






	def 				UpperCamelCase_  (       self					: 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 				UpperCamelCase_  (       self					: Optional[int] ):
					return






	@unittest.skip(reason="Bit does not output attentions" )
	def 				UpperCamelCase_  (       self					: Any ):
					pass






	@unittest.skip(reason="Bit does not use inputs_embeds" )
	def 				UpperCamelCase_  (       self					: List[Any] ):
					pass






	@unittest.skip(reason="Bit does not support input and output embeddings" )
	def 				UpperCamelCase_  (       self					: List[str] ):
					pass






	def 				UpperCamelCase_  (       self					: Union[str, 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 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*A )






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_backbone(*A )






	def 				UpperCamelCase_  (       self					: List[str] ):
					__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 				UpperCamelCase_  (       self					: Tuple ):
					def check_hidden_states_output(A					: Any							,A					: List[str]							,A					: Tuple ):
									__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 				UpperCamelCase_  (       self					: Optional[int] ):
					pass






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*A )







	@slow
	def 				UpperCamelCase_  (       self					: List[str] ):
					for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A   			= BitModel.from_pretrained(A )
									self.assertIsNotNone(A )





def 						UpperCAmelCase			(	)		->	int:







				"""simple docstring"""



				__A   			= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
				return image





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




	'''simple docstring'''





	@cached_property
	def 				UpperCamelCase_  (       self					: List[str] ):
					return (
					    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
					)







	@slow
	def 				UpperCamelCase_  (       self					: 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, 10_00) )
					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 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			unittest.TestCase  ):




	'''simple docstring'''





	snake_case_					    =     (BitBackbone,) if is_torch_available() else ()
	snake_case_					    =     BitConfig

	snake_case_					    =     False






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					__A   			= BitModelTester(self )


SCREAMING_SNAKE_CASE							:Any							 =				256
# Modulus to hash a string
SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				100_0003


def 						UpperCAmelCase			(	a_		,   a_						)		->	bool:







				"""simple docstring"""



				__A   			= len(a_						)
				__A   			= len(a_						)
				if p_len > t_len:
								return False

				__A   			= 0
				__A   			= 0
				__A   			= 1

				# Calculating the hash of pattern and substring of text
				for i in range(a_						):
								__A   			= (ord(pattern[i]						) + p_hash * alphabet_size) % modulus
								__A   			= (ord(text[i]						) + text_hash * alphabet_size) % modulus
								if i == p_len - 1:
												continue
								__A   			= (modulus_power * alphabet_size) % modulus

				for i in range(0		,   t_len - p_len + 1						):
								if text_hash == p_hash and text[i : i + p_len] == pattern:
												return True
								if i == t_len - p_len:
												continue
								# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
								__A   			= (
								    (text_hash - ord(text[i]						) * modulus_power) * alphabet_size
								    + ord(text[i + p_len]						)
								) % modulus
				return False


def 						UpperCAmelCase			(	)		->	None:







				"""simple docstring"""



				__A   			= "abc1abc12"
				__A   			= "alskfjaldsabc1abc1abc12k23adsfabcabc"
				__A   			= "alskfjaldsk23adsfabcabc"
				assert rabin_karp(a_		,   a_						) and not rabin_karp(a_		,   a_						)

				# Test 2)
				__A   			= "ABABX"
				__A   			= "ABABZABABYABABX"
				assert rabin_karp(a_		,   a_						)

				# Test 3)
				__A   			= "AAAB"
				__A   			= "ABAAAAAB"
				assert rabin_karp(a_		,   a_						)

				# Test 4)
				__A   			= "abcdabcy"
				__A   			= "abcxabcdabxabcdabcdabcy"
				assert rabin_karp(a_		,   a_						)

				# Test 5)
				__A   			= "Lü"
				__A   			= "Lüsai"
				assert rabin_karp(a_		,   a_						)
				__A   			= "Lue"
				assert not rabin_karp(a_		,   a_						)
				print("Success."						)


if __name__ == "__main__":
					test_rabin_karp()
