


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       lowerCamelCase__   :





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase=1_3		,     UpperCAmelCase=3_0		,     UpperCAmelCase=2		,     UpperCAmelCase=3		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=3_2		,     UpperCAmelCase=5		,     UpperCAmelCase=4		,     UpperCAmelCase=3_7		,     UpperCAmelCase="gelu"		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=1_0		,     UpperCAmelCase=0.02		,     UpperCAmelCase=None		,     )     ->   List[str]:
      _lowercase							      =parent
      _lowercase							      =batch_size
      _lowercase							      =image_size
      _lowercase							      =patch_size
      _lowercase							      =num_channels
      _lowercase							      =is_training
      _lowercase							      =use_labels
      _lowercase							      =hidden_size
      _lowercase							      =num_hidden_layers
      _lowercase							      =num_attention_heads
      _lowercase							      =intermediate_size
      _lowercase							      =hidden_act
      _lowercase							      =hidden_dropout_prob
      _lowercase							      =attention_probs_dropout_prob
      _lowercase							      =type_sequence_label_size
      _lowercase							      =initializer_range
      _lowercase							      =scope

      # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
      _lowercase							      =(image_size // patch_size) ** 2
      _lowercase							      =num_patches + 1





     def      __A					(self       )     ->   Dict:
      _lowercase							      =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

      _lowercase							      =None
      if self.use_labels:
       _lowercase							      =ids_tensor([self.batch_size]		,     self.type_sequence_label_size       )

      _lowercase							      =self.get_config()

      return config, pixel_values, labels





     def      __A					(self       )     ->   Dict:
      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      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   List[str]:
      _lowercase							      =ViTMSNModel(config=UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(UpperCAmelCase       )
      self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Union[str, Any]:
      _lowercase							      =self.type_sequence_label_size
      _lowercase							      =ViTMSNForImageClassification(UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =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
      _lowercase							      =1
      _lowercase							      =ViTMSNForImageClassification(UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()

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





     def      __A					(self       )     ->   str:
      _lowercase							      =self.prepare_config_and_inputs()
      _lowercase      ,    _lowercase      ,    _lowercase							      =config_and_inputs
      _lowercase							      ={'''pixel_values''': pixel_values}
      return config, inputs_dict







@require_torch
class       lowerCamelCase__   (       lowerCAmelCase   ,							lowerCAmelCase   ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__      			=			(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE__      			=			(
         {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
         if is_torch_available()
         else {}
     )

     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False





     def      __A					(self       )     ->   int:
      _lowercase							      =ViTMSNModelTester(self       )
      _lowercase							      =ConfigTester(self		,     config_class=UpperCAmelCase		,     has_text_modality=UpperCAmelCase		,     hidden_size=3_7       )





     def      __A					(self       )     ->   Optional[int]:
      self.config_tester.run_common_tests()





     @unittest.skip(reason='''ViTMSN does not use inputs_embeds'''       )
     def      __A					(self       )     ->   List[str]:
      pass





     def      __A					(self       )     ->   Dict:
      _lowercase      ,    _lowercase							      =self.model_tester.prepare_config_and_inputs_for_common()

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





     def      __A					(self       )     ->   List[Any]:
      _lowercase      ,    _lowercase							      =self.model_tester.prepare_config_and_inputs_for_common()

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

       _lowercase							      =['''pixel_values''']
       self.assertListEqual(arg_names[:1]		,     UpperCAmelCase       )





     def      __A					(self       )     ->   Optional[int]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCAmelCase       )





     def      __A					(self       )     ->   str:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase       )





     @slow
     def      __A					(self       )     ->   Optional[Any]:
      for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _lowercase							      =ViTMSNModel.from_pretrained(UpperCAmelCase       )
       self.assertIsNotNone(UpperCAmelCase       )



def 				UpperCAmelCase_					(							)     ->			Optional[int]:







 """simple docstring"""


 _lowercase							      =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
 return image







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





     @cached_property
     def      __A					(self       )     ->   Tuple:
      return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small'''       ) if is_vision_available() else None





     @slow
     def      __A					(self       )     ->   Optional[Any]:
      torch.manual_seed(2       )
      _lowercase							      =ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''       ).to(UpperCAmelCase       )

      _lowercase							      =self.default_image_processor
      _lowercase							      =prepare_img()
      _lowercase							      =image_processor(images=UpperCAmelCase		,     return_tensors='''pt'''       ).to(UpperCAmelCase       )

      # forward pass
      with torch.no_grad():
       _lowercase							      =model(**UpperCAmelCase       )

      # verify the logits
      _lowercase							      =torch.Size((1, 1_0_0_0)       )
      self.assertEqual(outputs.logits.shape		,     UpperCAmelCase       )

      _lowercase							      =torch.tensor([-0.0803, -0.4454, -0.2375]       ).to(UpperCAmelCase       )

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


import itertools
import string
from collections.abc import Generator, Iterable
def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							iter(_lowercase						)
						while True:
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tuple(itertools.islice(_lowercase		,  _lowercase						)						)
												if not chunk:
																		return
												yield chunk
def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    int							=							''''''.join([c.upper() for c in dirty if c in string.ascii_letters]						)
						SCREAMING_SNAKE_CASE							:    List[str]							=							''''''

						if len(_lowercase						) < 2:
												return dirty

						for i in range(len(_lowercase						) - 1						):
												clean += dirty[i]

												if dirty[i] == dirty[i + 1]:
																		clean += "X"

						clean += dirty[-1]

						if len(_lowercase						) & 1:
												clean += "X"

						return clean
def       A	(    _lowercase						):
						# I and J are used interchangeably to allow
						# us to use a 5x5 table (25 letters)
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							'''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
						# we're using a list instead of a '2d' array because it makes the math
						# for setting up the table and doing the actual encoding/decoding simpler
						SCREAMING_SNAKE_CASE							:    List[Any]							=							[]

						# copy key chars into the table if they are in `alphabet` ignoring duplicates
						for char in key.upper():
												if char not in table and char in alphabet:
																		table.append(_lowercase						)

    # fill the rest of the table in with the remaining alphabet chars
						for char in alphabet:
												if char not in table:
																		table.append(_lowercase						)

						return table
def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Any							=							generate_table(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Any							=							prepare_input(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							''''''

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(_lowercase		,  2						):
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    int							=							divmod(table.index(_lowercase						)		,  5						)
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							divmod(table.index(_lowercase						)		,  5						)

												if rowa == rowa:
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
												elif cola == cola:
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
												else:  # rectangle
																		ciphertext += table[rowa * 5 + cola]
																		ciphertext += table[rowa * 5 + cola]

						return ciphertext



def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Any							=							generate_table(_lowercase						)
						SCREAMING_SNAKE_CASE							:    List[Any]							=							''''''

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(_lowercase		,  2						):
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    List[str]							=							divmod(table.index(_lowercase						)		,  5						)
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Optional[Any]							=							divmod(table.index(_lowercase						)		,  5						)

												if rowa == rowa:
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
												elif cola == cola:
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
												else:  # rectangle
																		plaintext += table[rowa * 5 + cola]
																		plaintext += table[rowa * 5 + cola]

						return plaintext





