





"""simple docstring"""





import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
						from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image





class        _lowerCamelCase      :


						def __init__(self   ,							__a   ,							__a=13   ,							__a=64   ,							__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=0.02   ,							__a=[1, 16, 4, 4]   ,							__a=None   ,							) ->   Tuple:
								UpperCamelCase   =	parent
								UpperCamelCase   =	batch_size
								UpperCamelCase   =	image_size
								UpperCamelCase   =	patch_size
								UpperCamelCase   =	num_channels
								UpperCamelCase   =	is_training
								UpperCamelCase   =	use_labels
								UpperCamelCase   =	hidden_size
								UpperCamelCase   =	num_hidden_layers
								UpperCamelCase   =	num_attention_heads
								UpperCamelCase   =	intermediate_size
								UpperCamelCase   =	hidden_act
								UpperCamelCase   =	hidden_dropout_prob
								UpperCamelCase   =	attention_probs_dropout_prob
								UpperCamelCase   =	type_sequence_label_size
								UpperCamelCase   =	initializer_range
								UpperCamelCase   =	scope
								UpperCamelCase   =	backbone_featmap_shape

								# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								# the number of patches is based on the feature map of the backbone, which by default uses an output stride
								# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
								UpperCamelCase   =	(self.image_size // 32) ** 2
								UpperCamelCase   =	num_patches + 1


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

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

								UpperCamelCase   =	self.get_config()

								return config, pixel_values, labels


						def     snake_case_   (self		) ->   Dict:
								UpperCamelCase   =	{
								    "global_padding": "same",
								    "layer_type": "bottleneck",
								    "depths": [3, 4, 9],
								    "out_features": ["stage1", "stage2", "stage3"],
								    "embedding_dynamic_padding": True,
								    "hidden_sizes": [4, 8, 16, 32],
								    "num_groups": 2,
								}

								return ViTHybridConfig(
								    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=__a   ,							initializer_range=self.initializer_range   ,							backbone_featmap_shape=self.backbone_featmap_shape   ,							backbone_config=__a   ,							)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a		) ->   List[Any]:
								UpperCamelCase   =	ViTHybridModel(config=__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	model(__a		)
								self.parent.assertEqual(result.last_hidden_state.shape   ,							(self.batch_size, self.seq_length, self.hidden_size)		)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a		) ->   int:
								UpperCamelCase   =	self.type_sequence_label_size
								UpperCamelCase   =	ViTHybridForImageClassification(__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	model(__a   ,							labels=__a		)
								self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.type_sequence_label_size)		)




						def     snake_case_   (self		) ->   Union[str, Any]:
								UpperCamelCase   =	self.prepare_config_and_inputs()
								UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	config_and_inputs
								UpperCamelCase   =	{"pixel_values": pixel_values}
								return config, inputs_dict





@require_torch
class        _lowerCamelCase      (       _lowercase  ,			_lowercase  ,			unittest.TestCase ):
						UpperCAmelCase_ =			(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
						UpperCAmelCase_ =			(
						    {"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
						    if is_torch_available()
						    else {}
						)
						UpperCAmelCase_ =			False
						UpperCAmelCase_ =			False
						UpperCAmelCase_ =			False


						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	ViTHybridModelTester(self		)
								UpperCamelCase   =	ConfigTester(self   ,							config_class=__a   ,							has_text_modality=__a   ,							hidden_size=37		)


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


						@unittest.skip(reason="ViT does not use inputs_embeds"		)
						def     snake_case_   (self		) ->   Optional[int]:
								pass


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

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


						def     snake_case_   (self		) ->   Dict:
								UpperCamelCase   ,				UpperCamelCase   =	self.model_tester.prepare_config_and_inputs_for_common()

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

										UpperCamelCase   =	["pixel_values"]
										self.assertListEqual(arg_names[:1]   ,							__a		)


						def     snake_case_   (self		) ->   Tuple:
								UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__a		)


						def     snake_case_   (self		) ->   int:
								UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*__a		)


						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   ,				UpperCamelCase   =	self.model_tester.prepare_config_and_inputs_for_common()

								UpperCamelCase   =	_config_zero_init(__a		)
								for model_class in self.all_model_classes:
										UpperCamelCase   =	model_class(config=__a		)
										# Skip the check for the backbone
										for name, module in model.named_modules():
												if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
														UpperCamelCase   =	[F"{name}.{key}" for key in module.state_dict().keys()]
														break

										for name, param in model.named_parameters():
												if param.requires_grad:
														if name in backbone_params:
																continue
														self.assertIn(
														    ((param.data.mean() * 1e9).round() / 1e9).item()   ,							[0.0, 1.0]   ,							msg=F"Parameter {name} of model {model_class} seems not properly initialized"   ,							)




						@slow
						def     snake_case_   (self		) ->   Tuple:
								for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										UpperCamelCase   =	ViTHybridModel.from_pretrained(__a		)
										self.assertIsNotNone(__a		)



def       a__					(					):




		"""simple docstring"""


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






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


						@cached_property
						def     snake_case_   (self		) ->   Union[str, Any]:
								return (
								    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
								    if is_vision_available()
								    else None
								)


						@slow
						def     snake_case_   (self		) ->   Dict:
								UpperCamelCase   =	ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(
								    __a		)

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

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

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

								UpperCamelCase   =	torch.tensor([-1.9090, -0.4993, -0.2389]		).to(__a		)

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




						@slow
						@require_accelerate
						def     snake_case_   (self		) ->   List[Any]:
								UpperCamelCase   =	ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384"		)
								UpperCamelCase   =	ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384"   ,							device_map="auto"		)

								UpperCamelCase   =	prepare_img()

								UpperCamelCase   =	image_processor(images=__a   ,							return_tensors="pt"		)
								UpperCamelCase   =	model(**__a		)
								UpperCamelCase   =	outputs.logits
								# model predicts one of the 1000 ImageNet classes
								UpperCamelCase   =	logits.argmax(-1		).item()

								self.assertTrue(model.config.idalabel[predicted_class_idx]   ,							"tabby, tabby cat"		)













"""simple docstring"""





import string


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		for key in range(len(string.ascii_uppercase    )    ):
				UpperCamelCase   =	""
				for symbol in message:
						if symbol in string.ascii_uppercase:
								UpperCamelCase   =	string.ascii_uppercase.find(_SCREAMING_SNAKE_CASE    )
								UpperCamelCase   =	num - key
								if num < 0:
										UpperCamelCase   =	num + len(string.ascii_uppercase    )
								UpperCamelCase   =	translated + string.ascii_uppercase[num]
						else:
								UpperCamelCase   =	translated + symbol
				print(F"Decryption using Key #{key}: {translated}"    )


def       a__					(					):




		"""simple docstring"""


		UpperCamelCase   =	input("Encrypted message: "    )
		UpperCamelCase   =	message.upper()
		decrypt(_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()






