


"""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 						_UpperCamelCase						:
							'''simple docstring'''







							def __init__(  self					,     __a					,     __a=13					,     __a=30					,     __a=2					,     __a=3					,     __a=True					,     __a=True					,     __a=32					,     __a=5					,     __a=4					,     __a=37					,     __a="gelu"					,     __a=0.1					,     __a=0.1					,     __a=10					,     __a=0.0_2					,     __a=None					,     ):
										__lowerCAmelCase       					=    parent
										__lowerCAmelCase       					=    batch_size
										__lowerCAmelCase       					=    image_size
										__lowerCAmelCase       					=    patch_size
										__lowerCAmelCase       					=    num_channels
										__lowerCAmelCase       					=    is_training
										__lowerCAmelCase       					=    use_labels
										__lowerCAmelCase       					=    hidden_size
										__lowerCAmelCase       					=    num_hidden_layers
										__lowerCAmelCase       					=    num_attention_heads
										__lowerCAmelCase       					=    intermediate_size
										__lowerCAmelCase       					=    hidden_act
										__lowerCAmelCase       					=    hidden_dropout_prob
										__lowerCAmelCase       					=    attention_probs_dropout_prob
										__lowerCAmelCase       					=    type_sequence_label_size
										__lowerCAmelCase       					=    initializer_range
										__lowerCAmelCase       					=    scope

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

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

										__lowerCAmelCase       					=    self.get_config()

										return config, pixel_values, labels

							def     snake_case       (  self			):
										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     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    ViTMSNModel(config=__SCREAMING_SNAKE_CASE			)
										model.to(__SCREAMING_SNAKE_CASE			)
										model.eval()
										__lowerCAmelCase       					=    model(__SCREAMING_SNAKE_CASE			)
										self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, self.seq_length, self.hidden_size)			)

							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    self.type_sequence_label_size
										__lowerCAmelCase       					=    ViTMSNForImageClassification(__SCREAMING_SNAKE_CASE			)
										model.to(__SCREAMING_SNAKE_CASE			)
										model.eval()
										__lowerCAmelCase       					=    model(__SCREAMING_SNAKE_CASE					,     labels=__SCREAMING_SNAKE_CASE			)
										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
										__lowerCAmelCase       					=    1
										__lowerCAmelCase       					=    ViTMSNForImageClassification(__SCREAMING_SNAKE_CASE			)
										model.to(__SCREAMING_SNAKE_CASE			)
										model.eval()

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



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.prepare_config_and_inputs()
										__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    config_and_inputs
										__lowerCAmelCase       					=    {"pixel_values": pixel_values}
										return config, inputs_dict
@require_torch
class 						_UpperCamelCase						(     __UpperCAmelCase ,__UpperCAmelCase ,unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					List[Any]          =(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
							__UpperCAmelCase		:					Optional[int]          =(
							    {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
							    if is_torch_available()
							    else {}
							)

							__UpperCAmelCase		:					int          =False
							__UpperCAmelCase		:					Optional[Any]          =False
							__UpperCAmelCase		:					Any          =False
							__UpperCAmelCase		:					Any          =False

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    ViTMSNModelTester(self			)
										__lowerCAmelCase       					=    ConfigTester(self					,     config_class=__SCREAMING_SNAKE_CASE					,     has_text_modality=__SCREAMING_SNAKE_CASE					,     hidden_size=37			)

							def     snake_case       (  self			):
										self.config_tester.run_common_tests()

							@unittest.skip(reason="ViTMSN does not use inputs_embeds"			)
							def     snake_case       (  self			):
										pass

							def     snake_case       (  self			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs_for_common()

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

							def     snake_case       (  self			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs_for_common()

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

													__lowerCAmelCase       					=    ["pixel_values"]
													self.assertListEqual(arg_names[:1]					,     __SCREAMING_SNAKE_CASE			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE			)



							@slow
							def     snake_case       (  self			):

										for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__lowerCAmelCase       					=    ViTMSNModel.from_pretrained(__SCREAMING_SNAKE_CASE			)
													self.assertIsNotNone(__SCREAMING_SNAKE_CASE			)


def _lowerCamelCase			(	):

			'''simple docstring'''




			__lowerCAmelCase       					=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
			return image


@require_torch
@require_vision
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							@cached_property
							def     snake_case       (  self			):
										return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"			) if is_vision_available() else None



							@slow
							def     snake_case       (  self			):
										torch.manual_seed(2			)
										__lowerCAmelCase       					=    ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"			).to(__SCREAMING_SNAKE_CASE			)

										__lowerCAmelCase       					=    self.default_image_processor
										__lowerCAmelCase       					=    prepare_img()
										__lowerCAmelCase       					=    image_processor(images=__SCREAMING_SNAKE_CASE					,     return_tensors="pt"			).to(__SCREAMING_SNAKE_CASE			)

										# forward pass
										with torch.no_grad():
													__lowerCAmelCase       					=    model(**__SCREAMING_SNAKE_CASE			)

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

										__lowerCAmelCase       					=    torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5]			).to(__SCREAMING_SNAKE_CASE			)

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








"""simple docstring"""


import string


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    ""
			for i in sequence:
						__lowerCAmelCase       					=    ord(_UpperCamelCase   )
						if 65 <= extract <= 90:
									output += chr(155 - extract   )
						elif 97 <= extract <= 122:
									output += chr(219 - extract   )
						else:
									output += i
			return output


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    string.ascii_letters
			__lowerCAmelCase       					=    string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
			return "".join(
			    letters_reversed[letters.index(_UpperCamelCase   )] if c in letters else c for c in sequence   )


def _lowerCamelCase			(	):

			'''simple docstring'''




			from timeit import timeit

			print("Running performance benchmarks..."   )
			__lowerCAmelCase       					=    "from string import printable ; from __main__ import atbash, atbash_slow"
			print(f"> atbash_slow(): {timeit('atbash_slow(printable)'					,				setup=_UpperCamelCase   )} seconds"   )
			print(f">      atbash(): {timeit('atbash(printable)'					,				setup=_UpperCamelCase   )} seconds"   )


if __name__ == "__main__":
	for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
		print(f'''{example} encrypted in atbash: {atbash(example)}''')
	benchmark()




