

"""simple docstring"""



import inspect
import unittest

from transformers import ViTMSNConfig
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 ViTMSNForImageClassification, ViTMSNModel
 from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor



class 					a						:



    """simple docstring"""



    def __init__(     self: str    ,						UpperCamelCase: List[Any]    ,						UpperCamelCase: Dict=13    ,						UpperCamelCase: List[str]=30    ,						UpperCamelCase: Union[str, Any]=2    ,						UpperCamelCase: str=3    ,						UpperCamelCase: Union[str, Any]=True    ,						UpperCamelCase: List[Any]=True    ,						UpperCamelCase: List[str]=32    ,						UpperCamelCase: Optional[Any]=5    ,						UpperCamelCase: List[Any]=4    ,						UpperCamelCase: Tuple=37    ,						UpperCamelCase: Optional[Any]="gelu"    ,						UpperCamelCase: Union[str, Any]=0.1    ,						UpperCamelCase: Tuple=0.1    ,						UpperCamelCase: Union[str, Any]=10    ,						UpperCamelCase: int=0.02    ,						UpperCamelCase: Optional[int]=None    ,						):






         """simple docstring"""


         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

         # in ViT MSN, 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: Optional[int]       ):






         """simple docstring"""


         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: Tuple       ):






         """simple docstring"""


         return ViTMSNConfig(
             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    ,						initializer_range=self.initializer_range    ,						)

    def 				UpperCamelCase	(     self: Tuple    ,						UpperCamelCase: Dict    ,						UpperCamelCase: List[Any]    ,						UpperCamelCase: Dict       ):






         """simple docstring"""


         A__										=  ViTMSNModel(config=UpperCamelCase       )
         model.to(UpperCamelCase       )
         model.eval()
         A__										=  model(UpperCamelCase       )
         self.parent.assertEqual(result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length, self.hidden_size)       )

    def 				UpperCamelCase	(     self: Tuple    ,						UpperCamelCase: Optional[int]    ,						UpperCamelCase: str    ,						UpperCamelCase: int       ):






         """simple docstring"""


         A__										=  self.type_sequence_label_size
         A__										=  ViTMSNForImageClassification(UpperCamelCase       )
         model.to(UpperCamelCase       )
         model.eval()
         A__										=  model(UpperCamelCase    ,						labels=UpperCamelCase       )
         print("""Pixel and labels shape: {pixel_values.shape}, {labels.shape}"""       )
         print("""Labels: {labels}"""       )
         self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.type_sequence_label_size)       )

         # test greyscale images
         A__										=  1
         A__										=  ViTMSNForImageClassification(UpperCamelCase       )
         model.to(UpperCamelCase       )
         model.eval()

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





    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         A__										=  self.prepare_config_and_inputs()
         A__			,				A__			,				A__										=  config_and_inputs
         A__										=  {"""pixel_values""": pixel_values}
         return config, inputs_dict



@require_torch
class 					a						(       _lowerCamelCase,	_lowerCamelCase,	unittest.TestCase				):



    """simple docstring"""



    UpperCAmelCase						 =					(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
    UpperCAmelCase						 =					(
        {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
        if is_torch_available()
        else {}
    )

    UpperCAmelCase						 =					False
    UpperCAmelCase						 =					False
    UpperCAmelCase						 =					False
    UpperCAmelCase						 =					False

    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         A__										=  ViTMSNModelTester(self       )
         A__										=  ConfigTester(self    ,						config_class=UpperCamelCase    ,						has_text_modality=UpperCamelCase    ,						hidden_size=37       )

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






         """simple docstring"""


         self.config_tester.run_common_tests()

    @unittest.skip(reason="""ViTMSN does not use inputs_embeds"""       )
    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         pass

    def 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


         A__			,				A__										=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              A__										=  model_class(UpperCamelCase       )
              self.assertIsInstance(model.get_input_embeddings()    ,						(nn.Module)       )
              A__										=  model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(UpperCamelCase    ,						nn.Linear       )       )

    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         A__			,				A__										=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              A__										=  model_class(UpperCamelCase       )
              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]    ,						UpperCamelCase       )

    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         A__										=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*UpperCamelCase       )

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






         """simple docstring"""


         A__										=  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 VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              A__										=  ViTMSNModel.from_pretrained(UpperCamelCase       )
              self.assertIsNotNone(UpperCamelCase       )





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



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



    """simple docstring"""



    @cached_property
    def 				UpperCamelCase	(     self: List[Any]       ):






         """simple docstring"""


         return ViTImageProcessor.from_pretrained("""facebook/vit-msn-small"""       ) if is_vision_available() else None





    @slow
    def 				UpperCamelCase	(     self: Union[str, Any]       ):






         """simple docstring"""


         torch.manual_seed(2       )
         A__										=  ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small"""       ).to(UpperCamelCase       )

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

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

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

         A__										=  torch.tensor([-0.0_803, -0.4_454, -0.2_375]       ).to(UpperCamelCase       )

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









"""simple docstring"""



from __future__ import annotations



class 					a						:



    """simple docstring"""



    def __init__(     self: Any    ,						UpperCamelCase: str    ,						UpperCamelCase: str       ):






         """simple docstring"""


         A__			,				A__										=  text, pattern
         A__			,				A__										=  len(UpperCamelCase       ), len(UpperCamelCase       )

    def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: str       ):






         """simple docstring"""


         for i in range(self.patLen - 1    ,						-1    ,						-1       ):
              if char == self.pattern[i]:
                   return i
         return -1

    def 				UpperCamelCase	(     self: str    ,						UpperCamelCase: int       ):






         """simple docstring"""


         for i in range(self.patLen - 1    ,						-1    ,						-1       ):
              if self.pattern[i] != self.text[current_pos + i]:
                   return current_pos + i
         return -1





    def 				UpperCamelCase	(     self: List[Any]       ):






         """simple docstring"""


         A__										=  []
         for i in range(self.textLen - self.patLen + 1       ):
              A__										=  self.mismatch_in_text(UpperCamelCase       )
              if mismatch_index == -1:
                   positions.append(UpperCamelCase       )
              else:
                   A__										=  self.match_in_pattern(self.text[mismatch_index]       )
                   A__										=  (
                       mismatch_index - match_index
                   )  # shifting index lgtm [py/multiple-definition]
         return positions

SCREAMING_SNAKE_CASE_					:	List[Any]							  =						'ABAABA'
SCREAMING_SNAKE_CASE_					:	List[Any]							  =						'AB'
SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						BoyerMooreSearch(text, pattern)
SCREAMING_SNAKE_CASE_					:	int							  =						bms.bad_character_heuristic()

if len(positions) == 0:
 print('No match found')
else:
 print('Pattern found in following positions: ')
 print(positions)






