



'''simple docstring'''
import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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_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 SwiftFormerForImageClassification, SwiftFormerModel
					from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import ViTImageProcessor






class 				lowercase__      :





	def __init__(					self   :							List[str]					,lowerCamelCase__   :							Any					,lowerCamelCase__   :							str=13					,lowerCamelCase__   :							int=3					,lowerCamelCase__   :							Tuple=True					,lowerCamelCase__   :							List[str]=True					,lowerCamelCase__   :							Union[str, Any]=0.1					,lowerCamelCase__   :							int=0.1					,lowerCamelCase__   :							int=224					,lowerCamelCase__   :							Dict=1000					,lowerCamelCase__   :							Any=[3, 3, 6, 4]					,lowerCamelCase__   :							List[str]=[48, 56, 112, 220]					,):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[int]												=  parent
							_UpperCamelCase   :						str												=  batch_size
							_UpperCamelCase   :						Tuple												=  num_channels
							_UpperCamelCase   :						Optional[Any]												=  is_training
							_UpperCamelCase   :						Any												=  use_labels
							_UpperCamelCase   :						List[Any]												=  hidden_dropout_prob
							_UpperCamelCase   :						Optional[Any]												=  attention_probs_dropout_prob
							_UpperCamelCase   :						List[str]												=  num_labels
							_UpperCamelCase   :						Dict												=  image_size
							_UpperCamelCase   :						Any												=  layer_depths
							_UpperCamelCase   :						Tuple												=  embed_dims





	def       UpperCamelCase_    (					self   :							Union[str, Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

							_UpperCamelCase   :						Dict												=  self.get_config()

							return config, pixel_values, labels





	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							return SwiftFormerConfig(
							    depths=self.layer_depths					,embed_dims=self.embed_dims					,mlp_ratio=4					,downsamples=[True, True, True, True]					,hidden_act='gelu'					,num_labels=self.num_labels					,down_patch_size=3					,down_stride=2					,down_pad=1					,drop_rate=0.0					,drop_path_rate=0.0					,use_layer_scale=UpperCAmelCase_					,layer_scale_init_value=1E-5					,)





	def       UpperCamelCase_    (					self   :							Optional[int]					,lowerCamelCase__   :							int					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							Tuple					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  SwiftFormerModel(config=UpperCAmelCase_					)
							model.to(UpperCAmelCase_					)
							model.eval()
							_UpperCamelCase   :						Optional[int]												=  model(UpperCAmelCase_					)
							self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, self.embed_dims[-1], 7, 7)					)





	def       UpperCamelCase_    (					self   :							int					,lowerCamelCase__   :							Optional[int]					,lowerCamelCase__   :							str					,lowerCamelCase__   :							Optional[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  self.num_labels
							_UpperCamelCase   :						int												=  SwiftFormerForImageClassification(UpperCAmelCase_					)
							model.to(UpperCAmelCase_					)
							model.eval()
							_UpperCamelCase   :						Optional[int]												=  model(UpperCAmelCase_					,labels=UpperCAmelCase_					)
							self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.num_labels)					)

							_UpperCamelCase   :						str												=  SwiftFormerForImageClassification(UpperCAmelCase_					)
							model.to(UpperCAmelCase_					)
							model.eval()

							_UpperCamelCase   :						Union[str, Any]												=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)
							_UpperCamelCase   :						Tuple												=  model(UpperCAmelCase_					)
							self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.num_labels)					)





	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							((_UpperCamelCase)							,     (_UpperCamelCase)							,     (_UpperCamelCase))   :						Dict												=  self.prepare_config_and_inputs()
							_UpperCamelCase   :						Dict												=  {'pixel_values': pixel_values}
							return config, inputs_dict






@require_torch
class 				lowercase__      (				lowercase		,   lowercase		,   unittest.TestCase  ):
	lowercase__    							=     (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
	lowercase__    							=     (
	    {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
	    if is_torch_available()
	    else {}
	)

	lowercase__    							=     False
	lowercase__    							=     False
	lowercase__    							=     False
	lowercase__    							=     False
	lowercase__    							=     False





	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  SwiftFormerModelTester(self					)
							_UpperCamelCase   :						Dict												=  ConfigTester(
							    self					,config_class=UpperCAmelCase_					,has_text_modality=UpperCAmelCase_					,hidden_size=37					,num_attention_heads=12					,num_hidden_layers=12					,)





	def       UpperCamelCase_    (					self   :							Optional[Any]					):







							'''simple docstring'''
							self.config_tester.run_common_tests()





	@unittest.skip(reason='SwiftFormer does not use inputs_embeds'					)
	def       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							pass





	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							_UpperCamelCase							,     _UpperCamelCase   :						Optional[int]												=  self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													_UpperCamelCase   :						Optional[int]												=  model_class(UpperCAmelCase_					)
													_UpperCamelCase   :						List[Any]												=  model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(UpperCAmelCase_					,nn.Linear					)					)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							_UpperCamelCase							,     _UpperCamelCase   :						Union[str, Any]												=  self.model_tester.prepare_config_and_inputs_for_common()

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

													_UpperCamelCase   :						Dict												=  ['pixel_values']
													self.assertListEqual(arg_names[:1]					,UpperCAmelCase_					)





	def       UpperCamelCase_    (					self   :							Optional[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						str												=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCAmelCase_					)





	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							_UpperCamelCase   :						int												=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_					)





	@slow
	def       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_UpperCamelCase   :						int												=  SwiftFormerModel.from_pretrained(UpperCAmelCase_					)
													self.assertIsNotNone(UpperCAmelCase_					)





	@unittest.skip(reason='SwiftFormer does not output attentions'					)
	def       UpperCamelCase_    (					self   :							Union[str, Any]					):







							'''simple docstring'''
							pass





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							def check_hidden_states_output(lowerCamelCase__   :							Any					,lowerCamelCase__   :							int					,lowerCamelCase__   :							List[str]					):
													_UpperCamelCase   :						str												=  model_class(UpperCAmelCase_					)
													model.to(UpperCAmelCase_					)
													model.eval()

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

													_UpperCamelCase   :						Union[str, Any]												=  outputs.hidden_states

													_UpperCamelCase   :						Optional[Any]												=  8
													self.assertEqual(len(UpperCAmelCase_					)					,UpperCAmelCase_					)  # TODO

													# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
													# with the width and height being successively divided by 2, after every 2 blocks
													for i in range(len(UpperCAmelCase_					)					):
																			self.assertEqual(
																			    hidden_states[i].shape					,torch.Size(
																			        [
																			            self.model_tester.batch_size,
																			            self.model_tester.embed_dims[i // 2],
																			            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																			            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																			        ]					)					,)

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

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

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

													check_hidden_states_output(UpperCAmelCase_					,UpperCAmelCase_					,UpperCAmelCase_					)





	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							def _config_zero_init(lowerCamelCase__   :							List[Any]					):
													_UpperCamelCase   :						Optional[int]												=  copy.deepcopy(UpperCAmelCase_					)
													for key in configs_no_init.__dict__.keys():
																			if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
																									setattr(UpperCAmelCase_					,UpperCAmelCase_					,1E-10					)
																			if isinstance(getattr(UpperCAmelCase_					,UpperCAmelCase_					,UpperCAmelCase_					)					,UpperCAmelCase_					):
																									_UpperCamelCase   :						Union[str, Any]												=  _config_zero_init(getattr(UpperCAmelCase_					,UpperCAmelCase_					)					)
																									setattr(UpperCAmelCase_					,UpperCAmelCase_					,UpperCAmelCase_					)
													return configs_no_init

							_UpperCamelCase							,     _UpperCamelCase   :						Tuple												=  self.model_tester.prepare_config_and_inputs_for_common()

							_UpperCamelCase   :						Optional[int]												=  _config_zero_init(UpperCAmelCase_					)
							for model_class in self.all_model_classes:
													_UpperCamelCase   :						Dict												=  model_class(config=UpperCAmelCase_					)
													for name, param in model.named_parameters():
																			if param.requires_grad:
																									self.assertIn(
																									    ((param.data.mean() * 1E9) / 1E9).round().item()					,[0.0, 1.0]					,msg=F'Parameter {name} of model {model_class} seems not properly initialized'					,)





	@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'					)
	def       UpperCamelCase_    (					self   :							Optional[Any]					):







							'''simple docstring'''
							pass






def A__       (	):
						_UpperCamelCase   :						Dict												=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
						return image






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





	@cached_property
	def       UpperCamelCase_    (					self   :							Optional[Any]					):







							'''simple docstring'''
							return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs'					) if is_vision_available() else None





	@slow
	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'					).to(UpperCAmelCase_					)

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

							# forward pass
							with torch.no_grad():
													_UpperCamelCase   :						Optional[int]												=  model(**UpperCAmelCase_					)

							# verify the logits
							_UpperCamelCase   :						Any												=  torch.Size((1, 1000)					)
							self.assertEqual(outputs.logits.shape					,UpperCAmelCase_					)

							_UpperCamelCase   :						Optional[Any]												=  torch.tensor([[-2.17_03E00, 2.11_07E00, -2.08_11E00]]					).to(UpperCAmelCase_					)
							self.assertTrue(torch.allclose(outputs.logits[0, :3]					,UpperCAmelCase_					,atol=1E-4					)					)







'''simple docstring'''





SCREAMING_SNAKE_CASE__          =  256
# Modulus to hash a string
SCREAMING_SNAKE_CASE__          =  100_0003




def 							lowerCamelCase	(					_snake_case	:    str  ,_snake_case	:    str							):





    '''simple docstring'''


    lowercase__									=    len(_snake_case							)
    lowercase__									=    len(_snake_case							)
    if p_len > t_len:
        return False

    lowercase__									=    0
    lowercase__									=    0
    lowercase__									=    1

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




def 							lowerCamelCase	(					):





    '''simple docstring'''


    lowercase__									=    "abc1abc12"
    lowercase__									=    "alskfjaldsabc1abc1abc12k23adsfabcabc"
    lowercase__									=    "alskfjaldsk23adsfabcabc"
    assert rabin_karp(_snake_case  ,_snake_case							) and not rabin_karp(_snake_case  ,_snake_case							)

    # Test 2)
    lowercase__									=    "ABABX"
    lowercase__									=    "ABABZABABYABABX"
    assert rabin_karp(_snake_case  ,_snake_case							)

    # Test 3)
    lowercase__									=    "AAAB"
    lowercase__									=    "ABAAAAAB"
    assert rabin_karp(_snake_case  ,_snake_case							)

    # Test 4)
    lowercase__									=    "abcdabcy"
    lowercase__									=    "abcxabcdabxabcdabcdabcy"
    assert rabin_karp(_snake_case  ,_snake_case							)

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


if __name__ == "__main__":
 test_rabin_karp()




