





'''simple docstring'''

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
  from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor






class   UpperCAmelCase      :
 def __init__(							self :Any		,			lowercase_ :Optional[Any]		,			lowercase_ :Any=13		,			lowercase_ :Optional[int]=30		,			lowercase_ :Union[str, Any]=2		,			lowercase_ :Union[str, Any]=3		,			lowercase_ :Any=True		,			lowercase_ :str=True		,			lowercase_ :Tuple=32		,			lowercase_ :int=5		,			lowercase_ :Optional[int]=4		,			lowercase_ :Optional[Any]=37		,			lowercase_ :Union[str, Any]="gelu"		,			lowercase_ :Any=0.1		,			lowercase_ :Tuple=0.1		,			lowercase_ :Any=10		,			lowercase_ :Tuple=0.0_2		,			lowercase_ :Dict=None		,			lowercase_ :Tuple=2		,			)->				Optional[int]:
     A__							=     parent
     A__							=     batch_size
     A__							=     image_size
     A__							=     patch_size
     A__							=     num_channels
     A__							=     is_training
     A__							=     use_labels
     A__							=     hidden_size
     A__							=     num_hidden_layers
     A__							=     num_attention_heads
     A__							=     intermediate_size
     A__							=     hidden_act
     A__							=     hidden_dropout_prob
     A__							=     attention_probs_dropout_prob
     A__							=     type_sequence_label_size
     A__							=     initializer_range
     A__							=     scope
     A__							=     encoder_stride

     # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
     A__							=     (image_size // patch_size) ** 2
     A__							=     num_patches + 1
 def 	UpperCAmelCase_				(							self :int				)->				List[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.type_sequence_label_size				)

     A__							=     self.get_config()

     return config, pixel_values, labels
 def 	UpperCAmelCase_				(							self :Dict				)->				List[Any]:
     return ViTConfig(
         image_size=self.image_size		,			patch_size=self.patch_size		,			num_channels=self.num_channels		,			hidden_size=self.hidden_size		,			num_hidden_layers=self.num_hidden_layers		,			num_attention_heads=self.num_attention_heads		,			intermediate_size=self.intermediate_size		,			hidden_act=self.hidden_act		,			hidden_dropout_prob=self.hidden_dropout_prob		,			attention_probs_dropout_prob=self.attention_probs_dropout_prob		,			is_decoder=__lowerCamelCase		,			initializer_range=self.initializer_range		,			encoder_stride=self.encoder_stride		,			)
 def 	UpperCAmelCase_				(							self :Union[str, Any]		,			lowercase_ :int		,			lowercase_ :List[str]		,			lowercase_ :Optional[int]				)->				Tuple:
     A__							=     ViTModel(config=__lowerCamelCase				)
     model.to(__lowerCamelCase				)
     model.eval()
     A__							=     model(__lowerCamelCase				)
     self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)				)
 def 	UpperCAmelCase_				(							self :Tuple		,			lowercase_ :Tuple		,			lowercase_ :List[str]		,			lowercase_ :List[str]				)->				str:
     A__							=     ViTForMaskedImageModeling(config=__lowerCamelCase				)
     model.to(__lowerCamelCase				)
     model.eval()
     A__							=     model(__lowerCamelCase				)
     self.parent.assertEqual(
         result.reconstruction.shape		,			(self.batch_size, self.num_channels, self.image_size, self.image_size)				)

     # test greyscale images
     A__							=     1
     A__							=     ViTForMaskedImageModeling(__lowerCamelCase				)
     model.to(__lowerCamelCase				)
     model.eval()

     A__							=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
     A__							=     model(__lowerCamelCase				)
     self.parent.assertEqual(result.reconstruction.shape		,			(self.batch_size, 1, self.image_size, self.image_size)				)
 def 	UpperCAmelCase_				(							self :Any		,			lowercase_ :int		,			lowercase_ :Dict		,			lowercase_ :Dict				)->				Optional[int]:
     A__							=     self.type_sequence_label_size
     A__							=     ViTForImageClassification(__lowerCamelCase				)
     model.to(__lowerCamelCase				)
     model.eval()
     A__							=     model(__lowerCamelCase		,			labels=__lowerCamelCase				)
     self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.type_sequence_label_size)				)

     # test greyscale images
     A__							=     1
     A__							=     ViTForImageClassification(__lowerCamelCase				)
     model.to(__lowerCamelCase				)
     model.eval()

     A__							=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
     A__							=     model(__lowerCamelCase				)
     self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.type_sequence_label_size)				)



 def 	UpperCAmelCase_				(							self :Dict				)->				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      (			__snake_case     ,			__snake_case     ,			unittest.TestCase							):
 __lowercase  						=							(
     (
         ViTModel,
         ViTForImageClassification,
         ViTForMaskedImageModeling,
     )
     if is_torch_available()
     else ()
 )
 __lowercase  						=							(
     {"""feature-extraction""": ViTModel, """image-classification""": ViTForImageClassification}
     if is_torch_available()
     else {}
 )
 __lowercase  						=							True

 __lowercase  						=							False
 __lowercase  						=							False
 __lowercase  						=							False
 def 	UpperCAmelCase_				(							self :Dict				)->				Optional[Any]:
     A__							=     ViTModelTester(self				)
     A__							=     ConfigTester(self		,			config_class=__lowerCamelCase		,			has_text_modality=__lowerCamelCase		,			hidden_size=37				)
 def 	UpperCAmelCase_				(							self :List[Any]				)->				List[str]:
     self.config_tester.run_common_tests()
 @unittest.skip(reason="ViT does not use inputs_embeds"				)
 def 	UpperCAmelCase_				(							self :Tuple				)->				Union[str, Any]:
     pass
 def 	UpperCAmelCase_				(							self :List[str]				)->				str:
     A__,      A__							=     self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         A__							=     model_class(__lowerCamelCase				)
         self.assertIsInstance(model.get_input_embeddings()		,			(nn.Module)				)
         A__							=     model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(__lowerCamelCase		,			nn.Linear				)				)
 def 	UpperCAmelCase_				(							self :List[Any]				)->				List[Any]:
     A__,      A__							=     self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         A__							=     model_class(__lowerCamelCase				)
         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]		,			__lowerCamelCase				)
 def 	UpperCAmelCase_				(							self :Optional[Any]				)->				str:
     A__							=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__lowerCamelCase				)
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Any:
     A__							=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*__lowerCamelCase				)
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Dict:
     A__							=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase				)



 @slow
 def 	UpperCAmelCase_				(							self :int				)->				Any:







     for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         A__							=     ViTModel.from_pretrained(__lowerCamelCase				)
         self.assertIsNotNone(__lowerCamelCase				)





def 		UpperCamelCase							(				):
    A__							=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
    return image






@require_torch
@require_vision
class   UpperCAmelCase      (			unittest.TestCase							):
 @cached_property
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Optional[int]:
     return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224"				) if is_vision_available() else None
 @slow
 def 	UpperCAmelCase_				(							self :List[str]				)->				str:
     A__							=     ViTForImageClassification.from_pretrained("google/vit-base-patch16-224"				).to(__lowerCamelCase				)

     A__							=     self.default_image_processor
     A__							=     prepare_img()
     A__							=     image_processor(images=__lowerCamelCase		,			return_tensors="pt"				).to(__lowerCamelCase				)

     # forward pass
     with torch.no_grad():
         A__							=     model(**__lowerCamelCase				)

     # verify the logits
     A__							=     torch.Size((1, 10_00)				)
     self.assertEqual(outputs.logits.shape		,			__lowerCamelCase				)

     A__							=     torch.tensor([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6]				).to(__lowerCamelCase				)

     self.assertTrue(torch.allclose(outputs.logits[0, :3]		,			__lowerCamelCase		,			atol=1E-4				)				)
 @slow
 def 	UpperCAmelCase_				(							self :List[Any]				)->				Any:
     A__							=     ViTModel.from_pretrained("facebook/dino-vits8"				).to(__lowerCamelCase				)

     A__							=     ViTImageProcessor.from_pretrained("facebook/dino-vits8"		,			size=4_80				)
     A__							=     prepare_img()
     A__							=     image_processor(images=__lowerCamelCase		,			return_tensors="pt"				)
     A__							=     inputs.pixel_values.to(__lowerCamelCase				)

     # forward pass
     with torch.no_grad():
         A__							=     model(__lowerCamelCase		,			interpolate_pos_encoding=__lowerCamelCase				)

     # verify the logits
     A__							=     torch.Size((1, 36_01, 3_84)				)
     self.assertEqual(outputs.last_hidden_state.shape		,			__lowerCamelCase				)

     A__							=     torch.tensor(
         [[4.2_3_4_0, 4.3_9_0_6, -6.6_6_9_2], [4.5_4_6_3, 1.8_9_2_8, -6.7_2_5_7], [4.4_4_2_9, 0.8_4_9_6, -5.8_5_8_5]]				).to(__lowerCamelCase				)

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



 @slow
 @require_accelerate
 @require_torch_gpu
 def 	UpperCAmelCase_				(							self :List[Any]				)->				Optional[int]:
     A__							=     ViTModel.from_pretrained("facebook/dino-vits8"		,			torch_dtype=torch.floataa		,			device_map="auto"				)
     A__							=     self.default_image_processor

     A__							=     prepare_img()
     A__							=     image_processor(images=__lowerCamelCase		,			return_tensors="pt"				)
     A__							=     inputs.pixel_values.to(__lowerCamelCase				)

     # forward pass to make sure inference works in fp16
     with torch.no_grad():
         A__							=     model(__lowerCamelCase				)







__a	 =      256
# Modulus to hash a string
__a	 =      100_0003
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->bool:
   UpperCAmelCase      					=  len(lowerCAmelCase_ )
   UpperCAmelCase      					=  len(lowerCAmelCase_ )
   if p_len > t_len:
      return False

   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  1

   # Calculating the hash of pattern and substring of text
   for i in range(lowerCAmelCase_ ):
      UpperCAmelCase      					=  (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
      UpperCAmelCase      					=  (ord(text[i] ) + text_hash * alphabet_size) % modulus
      if i == p_len - 1:
         continue
      UpperCAmelCase      					=  (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
      UpperCAmelCase      					=  (
          (text_hash - ord(text[i] ) * modulus_power) * alphabet_size
          + ord(text[i + p_len] )
      ) % modulus
   return False
def 	_UpperCamelCase					(    )    ->None:
   UpperCAmelCase      					=  """abc1abc12"""
   UpperCAmelCase      					=  """alskfjaldsabc1abc1abc12k23adsfabcabc"""
   UpperCAmelCase      					=  """alskfjaldsk23adsfabcabc"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ ) and not rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )

   # Test 2)
   UpperCAmelCase      					=  """ABABX"""
   UpperCAmelCase      					=  """ABABZABABYABABX"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )

   # Test 3)
   UpperCAmelCase      					=  """AAAB"""
   UpperCAmelCase      					=  """ABAAAAAB"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )

   # Test 4)
   UpperCAmelCase      					=  """abcdabcy"""
   UpperCAmelCase      					=  """abcxabcdabxabcdabcdabcy"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )

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


if __name__ == "__main__":
 test_rabin_karp()



