


'''simple docstring'''
import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
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 transformers import (
					    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
					    MODEL_MAPPING,
					    LevitForImageClassification,
					    LevitForImageClassificationWithTeacher,
					    LevitModel,
					)
					from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import LevitImageProcessor




class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				def 	a__		(     self							)    ->  Dict:
					lowercase						:		Tuple   					=		self.config_class(**self.inputs_dict							)
					self.parent.assertTrue(hasattr(a_							,      "hidden_sizes"							)							)
					self.parent.assertTrue(hasattr(a_							,      "num_attention_heads"							)							)




class        _UpperCamelCase	:


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_=1_3							,      a_=6_4							,      a_=3							,      a_=3							,      a_=2							,      a_=1							,      a_=1_6							,      a_=[1_2_8, 2_5_6, 3_8_4]							,      a_=[4, 6, 8]							,      a_=[2, 3, 4]							,      a_=[1_6, 1_6, 1_6]							,      a_=0							,      a_=[2, 2, 2]							,      a_=[2, 2, 2]							,      a_=0.02							,      a_=True							,      a_=True							,      a_=2							,      )    ->  Any:
					lowercase						:		str   					=		parent
					lowercase						:		int   					=		batch_size
					lowercase						:		Any   					=		image_size
					lowercase						:		Dict   					=		num_channels
					lowercase						:		Optional[Any]   					=		kernel_size
					lowercase						:		int   					=		stride
					lowercase						:		int   					=		padding
					lowercase						:		Tuple   					=		hidden_sizes
					lowercase						:		Tuple   					=		num_attention_heads
					lowercase						:		Union[str, Any]   					=		depths
					lowercase						:		List[Any]   					=		key_dim
					lowercase						:		Optional[int]   					=		drop_path_rate
					lowercase						:		int   					=		patch_size
					lowercase						:		Dict   					=		attention_ratio
					lowercase						:		Dict   					=		mlp_ratio
					lowercase						:		List[str]   					=		initializer_range
					lowercase						:		Dict   					=		[
					    ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
					    ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
					]
					lowercase						:		List[str]   					=		is_training
					lowercase						:		Any   					=		use_labels
					lowercase						:		List[Any]   					=		num_labels
					lowercase						:		Tuple   					=		initializer_range

				def 	a__		(     self							)    ->  Union[str, Any]:
					lowercase						:		List[Any]   					=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

					lowercase						:		Dict   					=		self.get_config()
					return config, pixel_values, labels

				def 	a__		(     self							)    ->  List[Any]:
					return LevitConfig(
					    image_size=self.image_size							,      num_channels=self.num_channels							,      kernel_size=self.kernel_size							,      stride=self.stride							,      padding=self.padding							,      patch_size=self.patch_size							,      hidden_sizes=self.hidden_sizes							,      num_attention_heads=self.num_attention_heads							,      depths=self.depths							,      key_dim=self.key_dim							,      drop_path_rate=self.drop_path_rate							,      mlp_ratio=self.mlp_ratio							,      attention_ratio=self.attention_ratio							,      initializer_range=self.initializer_range							,      down_ops=self.down_ops							,      )

				def 	a__		(     self							,      a_							,      a_							,      a_							)    ->  Union[str, Any]:
					lowercase						:		Optional[Any]   					=		LevitModel(config=a_							)
					model.to(a_							)
					model.eval()
					lowercase						:		Tuple   					=		model(a_							)
					lowercase						:		Any   					=		(self.image_size, self.image_size)
					lowercase					,		lowercase						:		Optional[Any]   					=		image_size[0], image_size[1]
					for _ in range(4							):
						lowercase						:		str   					=		floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1							)
						lowercase						:		Dict   					=		floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1							)
					self.parent.assertEqual(
					    result.last_hidden_state.shape							,      (self.batch_size, ceil(height / 4							) * ceil(width / 4							), self.hidden_sizes[-1])							,      )

				def 	a__		(     self							,      a_							,      a_							,      a_							)    ->  Any:
					lowercase						:		Any   					=		self.num_labels
					lowercase						:		Optional[Any]   					=		LevitForImageClassification(a_							)
					model.to(a_							)
					model.eval()
					lowercase						:		Dict   					=		model(a_							,      labels=a_							)
					self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.num_labels)							)


				def 	a__		(     self							)    ->  Optional[Any]:
					lowercase						:		Optional[Any]   					=		self.prepare_config_and_inputs()
					lowercase					,		lowercase					,		lowercase						:		List[str]   					=		config_and_inputs
					lowercase						:		Optional[Any]   					=		{"pixel_values": pixel_values}
					return config, inputs_dict




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


				'''simple docstring'''


				_snake_case						       =				(
				    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
				    if is_torch_available()
				    else ()
				)
				_snake_case						       =				(
				    {
				        '''feature-extraction''': LevitModel,
				        '''image-classification''': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
				    }
				    if is_torch_available()
				    else {}
				)

				_snake_case						       =				False
				_snake_case						       =				False
				_snake_case						       =				False
				_snake_case						       =				False
				_snake_case						       =				False

				def 	a__		(     self							)    ->  int:
					lowercase						:		str   					=		LevitModelTester(self							)
					lowercase						:		List[Any]   					=		ConfigTester(self							,      config_class=a_							,      has_text_modality=a_							,      hidden_size=3_7							)

				def 	a__		(     self							)    ->  List[Any]:
					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							)    ->  Tuple:
					return

				@unittest.skip(reason="Levit does not use inputs_embeds"							)
				def 	a__		(     self							)    ->  Tuple:
					pass

				@unittest.skip(reason="Levit does not support input and output embeddings"							)
				def 	a__		(     self							)    ->  Union[str, Any]:
					pass

				@unittest.skip(reason="Levit does not output attentions"							)
				def 	a__		(     self							)    ->  Dict:
					pass

				def 	a__		(     self							)    ->  Tuple:
					lowercase					,		lowercase						:		List[Any]   					=		self.model_tester.prepare_config_and_inputs_for_common()

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

						lowercase						:		List[Any]   					=		["pixel_values"]
						self.assertListEqual(arg_names[:1]							,      a_							)

				def 	a__		(     self							)    ->  str:
					def check_hidden_states_output(a_							,      a_							,      a_							):
						lowercase						:		Optional[Any]   					=		model_class(a_							)
						model.to(a_							)
						model.eval()

						with torch.no_grad():
							lowercase						:		Optional[int]   					=		model(**self._prepare_for_class(a_							,      a_							)							)

						lowercase						:		Optional[int]   					=		outputs.hidden_states

						lowercase						:		Union[str, Any]   					=		len(self.model_tester.depths							) + 1
						self.assertEqual(len(a_							)							,      a_							)

						lowercase						:		Optional[int]   					=		(self.model_tester.image_size, self.model_tester.image_size)
						lowercase					,		lowercase						:		Optional[int]   					=		image_size[0], image_size[1]
						for _ in range(4							):
							lowercase						:		Any   					=		floor(
							    (
							        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
							        / self.model_tester.stride
							    )
							    + 1							)
							lowercase						:		Tuple   					=		floor(
							    (
							        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
							        / self.model_tester.stride
							    )
							    + 1							)
						# verify the first hidden states (first block)
						self.assertListEqual(
						    list(hidden_states[0].shape[-2:]							)							,      [
						        height * width,
						        self.model_tester.hidden_sizes[0],
						    ]							,      )

					lowercase					,		lowercase						:		Any   					=		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						lowercase						:		Optional[int]   					=		True
						check_hidden_states_output(a_							,      a_							,      a_							)

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

						check_hidden_states_output(a_							,      a_							,      a_							)

				@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."							)
				def 	a__		(     self							)    ->  Dict:
					pass

				def 	a__		(     self							,      a_							,      a_							,      a_=False							)    ->  List[str]:
					lowercase						:		Any   					=		super()._prepare_for_class(a_							,      a_							,      return_labels=a_							)

					if return_labels:
						if model_class.__name__ == "LevitForImageClassificationWithTeacher":
							del inputs_dict["labels"]

					return inputs_dict

				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		int   					=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*a_							)

				def 	a__		(     self							)    ->  Dict:
					lowercase						:		List[Any]   					=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*a_							)

				def 	a__		(     self							)    ->  Dict:
					if not self.model_tester.is_training:
						return

					lowercase					,		lowercase						:		Dict   					=		self.model_tester.prepare_config_and_inputs_for_common()
					lowercase						:		Dict   					=		True

					for model_class in self.all_model_classes:
						# LevitForImageClassificationWithTeacher supports inference-only
						if (
						    model_class in get_values(a_							)
						    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
						):
							continue
						lowercase						:		Tuple   					=		model_class(a_							)
						model.to(a_							)
						model.train()
						lowercase						:		Dict   					=		self._prepare_for_class(a_							,      a_							,      return_labels=a_							)
						lowercase						:		int   					=		model(**a_							).loss
						loss.backward()

				def 	a__		(     self							)    ->  Any:
					lowercase					,		lowercase						:		Optional[int]   					=		self.model_tester.prepare_config_and_inputs_for_common()
					if not self.model_tester.is_training:
						return

					lowercase						:		Dict   					=		False
					lowercase						:		List[str]   					=		True

					for model_class in self.all_model_classes:
						if model_class in get_values(a_							) or not model_class.supports_gradient_checkpointing:
							continue
						# LevitForImageClassificationWithTeacher supports inference-only
						if model_class.__name__ == "LevitForImageClassificationWithTeacher":
							continue
						lowercase						:		Optional[int]   					=		model_class(a_							)
						model.gradient_checkpointing_enable()
						model.to(a_							)
						model.train()
						lowercase						:		Union[str, Any]   					=		self._prepare_for_class(a_							,      a_							,      return_labels=a_							)
						lowercase						:		Optional[int]   					=		model(**a_							).loss
						loss.backward()

				def 	a__		(     self							)    ->  Dict:
					lowercase					,		lowercase						:		List[Any]   					=		self.model_tester.prepare_config_and_inputs_for_common()

					lowercase						:		str   					=		[
					    {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
					    {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
					    {"title": "regression", "num_labels": 1, "dtype": torch.float},
					]

					for model_class in self.all_model_classes:
						if (
						    model_class
						    not in [
						        *get_values(a_							),
						    ]
						    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
						):
							continue

						for problem_type in problem_types:
							with self.subTest(msg=F'''Testing {model_class} with {problem_type['title']}'''							):
								lowercase						:		Optional[int]   					=		problem_type["title"]
								lowercase						:		List[Any]   					=		problem_type["num_labels"]

								lowercase						:		str   					=		model_class(a_							)
								model.to(a_							)
								model.train()

								lowercase						:		List[str]   					=		self._prepare_for_class(a_							,      a_							,      return_labels=a_							)

								if problem_type["num_labels"] > 1:
									lowercase						:		Dict   					=		inputs["labels"].unsqueeze(1							).repeat(1							,      problem_type["num_labels"]							)

								lowercase						:		List[str]   					=		inputs["labels"].to(problem_type["dtype"]							)

								# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
								# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
								# they have the same size." which is a symptom something in wrong for the regression problem.
								# See https://github.com/huggingface/transformers/issues/11780
								with warnings.catch_warnings(record=a_							) as warning_list:
									lowercase						:		Union[str, Any]   					=		model(**a_							).loss
								for w in warning_list:
									if "Using a target size that is different to the input size" in str(w.message							):
										raise ValueError(
										    F'''Something is going wrong in the regression problem: intercepted {w.message}'''							)

								loss.backward()


				@slow
				def 	a__		(     self							)    ->  Any:
					for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase						:		int   					=		LevitModel.from_pretrained(a_							)
						self.assertIsNotNone(a_							)




def 	_A  (       )  ->				List[Any]:
	lowercase						:		List[str]   					=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
	return image






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


				'''simple docstring'''


				@cached_property
				def 	a__		(     self							)    ->  str:
					return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)


				@slow
				def 	a__		(     self							)    ->  Union[str, Any]:
					lowercase						:		List[str]   					=		LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(
					    a_							)

					lowercase						:		Union[str, Any]   					=		self.default_image_processor
					lowercase						:		Optional[Any]   					=		prepare_img()
					lowercase						:		int   					=		image_processor(images=a_							,      return_tensors="pt"							).to(a_							)

					# forward pass
					with torch.no_grad():
						lowercase						:		Optional[Any]   					=		model(**a_							)

					# verify the logits
					lowercase						:		Optional[int]   					=		torch.Size((1, 1_0_0_0)							)
					self.assertEqual(outputs.logits.shape							,      a_							)

					lowercase						:		Any   					=		torch.tensor([1.04_48, -0.37_45, -1.83_17]							).to(a_							)

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






'''simple docstring'''
lowerCAmelCase   : List[str]             =				2_5_6
# Modulus to hash a string
lowerCAmelCase   : Tuple             =				1_0_0_0_0_0_3




def 	_A  (       A							,A							)  ->				bool:
	lowercase						:		List[Any]   					=		len(A							)
	lowercase						:		List[Any]   					=		len(A							)
	if p_len > t_len:
		return False

	lowercase						:		List[str]   					=		0
	lowercase						:		Dict   					=		0
	lowercase						:		Any   					=		1

	# Calculating the hash of pattern and substring of text
	for i in range(A							):
		lowercase						:		Optional[int]   					=		(ord(pattern[i]							) + p_hash * alphabet_size) % modulus
		lowercase						:		Any   					=		(ord(text[i]							) + text_hash * alphabet_size) % modulus
		if i == p_len - 1:
			continue
		lowercase						:		List[str]   					=		(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
		lowercase						:		Any   					=		(
		    (text_hash - ord(text[i]							) * modulus_power) * alphabet_size
		    + ord(text[i + p_len]							)
		) % modulus
	return False




def 	_A  (       )  ->				None:
	lowercase						:		Dict   					=		"abc1abc12"
	lowercase						:		Union[str, Any]   					=		"alskfjaldsabc1abc1abc12k23adsfabcabc"
	lowercase						:		Any   					=		"alskfjaldsk23adsfabcabc"
	assert rabin_karp(A							,A							) and not rabin_karp(A							,A							)

	# Test 2)
	lowercase						:		str   					=		"ABABX"
	lowercase						:		Union[str, Any]   					=		"ABABZABABYABABX"
	assert rabin_karp(A							,A							)

	# Test 3)
	lowercase						:		str   					=		"AAAB"
	lowercase						:		List[str]   					=		"ABAAAAAB"
	assert rabin_karp(A							,A							)

	# Test 4)
	lowercase						:		List[str]   					=		"abcdabcy"
	lowercase						:		str   					=		"abcxabcdabxabcdabcdabcy"
	assert rabin_karp(A							,A							)

	# Test 5)
	lowercase						:		int   					=		"Lü"
	lowercase						:		Optional[Any]   					=		"Lüsai"
	assert rabin_karp(A							,A							)
	lowercase						:		Tuple   					=		"Lue"
	assert not rabin_karp(A							,A							)
	print("Success."							)


if __name__ == "__main__":
					test_rabin_karp()


