

import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
			import torch

			from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin

class 							UpperCAmelCase_						:
							"""simple docstring"""







							def __init__(     self			,				_a			,				_a=None			,				_a=None			,				_a=None			,				_a="resnet50"			,				_a=3			,				_a=3_2			,				_a=3			,				_a=True			,				_a=True			,				)       ->       List[str]:
												_a      :       Optional[int]   =						parent
												_a      :       Dict   =						out_indices if out_indices is not None else [4]
												_a      :       Dict   =						stage_names
												_a      :       Optional[Any]   =						out_features
												_a      :       Dict   =						backbone
												_a      :       Tuple   =						batch_size
												_a      :       List[Any]   =						image_size
												_a      :       Union[str, Any]   =						num_channels
												_a      :       Optional[int]   =						use_pretrained_backbone
												_a      :       Union[str, Any]   =						is_training


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       int   =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)
												_a      :       Tuple   =						self.get_config()

												return config, pixel_values


							def 							__lowercase							(     self			)       ->       int:
												return TimmBackboneConfig(
												    image_size=self.image_size			,				num_channels=self.num_channels			,				out_features=self.out_features			,				out_indices=self.out_indices			,				stage_names=self.stage_names			,				use_pretrained_backbone=self.use_pretrained_backbone			,				backbone=self.backbone			,				)


							def 							__lowercase							(     self			,				_a			,				_a			)       ->       Dict:
												_a      :       int   =						TimmBackbone(config=__lowercase			)
												model.to(__lowercase			)
												model.eval()
												with torch.no_grad():
																	_a      :       List[str]   =						model(__lowercase			)
												self.parent.assertEqual(
												    result.feature_map[-1].shape			,				(self.batch_size, model.channels[-1], 1_4, 1_4)			,				)




							def 							__lowercase							(     self			)       ->       List[str]:
												_a      :       Any   =						self.prepare_config_and_inputs()
												_a ,   _a      :       List[Any]   =						config_and_inputs
												_a      :       Union[str, Any]   =						{'''pixel_values''': pixel_values}
												return config, inputs_dict



@require_torch
@require_timm
class 							UpperCAmelCase_						(     __lowercase							,			__lowercase							,			__lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Tuple             =       (TimmBackbone,) if is_torch_available() else ()
							UpperCAmelCase__			:		str             =       {"feature-extraction": TimmBackbone} if is_torch_available() else {}
							UpperCAmelCase__			:		Any             =       False
							UpperCAmelCase__			:		Tuple             =       False
							UpperCAmelCase__			:		int             =       False
							UpperCAmelCase__			:		Union[str, Any]             =       False


							def 							__lowercase							(     self			)       ->       int:
												_a      :       Dict   =						TimmBackboneModelTester(self			)
												_a      :       Tuple   =						ConfigTester(self			,				config_class=__lowercase			,				has_text_modality=__lowercase			)


							def 							__lowercase							(     self			)       ->       Dict:
												self.config_tester.create_and_test_config_to_json_string()
												self.config_tester.create_and_test_config_to_json_file()
												self.config_tester.create_and_test_config_from_and_save_pretrained()
												self.config_tester.create_and_test_config_with_num_labels()
												self.config_tester.check_config_can_be_init_without_params()
												self.config_tester.check_config_arguments_init()


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       Tuple   =						'''resnet18'''
												_a      :       Any   =						'''microsoft/resnet-18'''

												_a      :       Union[str, Any]   =						AutoBackbone.from_pretrained(__lowercase			,				use_timm_backbone=__lowercase			)
												_a      :       Union[str, Any]   =						AutoBackbone.from_pretrained(__lowercase			)

												self.assertEqual(len(timm_model.out_features			)			,				len(transformers_model.out_features			)			)
												self.assertEqual(len(timm_model.stage_names			)			,				len(transformers_model.stage_names			)			)
												self.assertEqual(timm_model.channels			,				transformers_model.channels			)
												# Out indices are set to the last layer by default. For timm models, we don't know
												# the number of layers in advance, so we set it to (-1,), whereas for transformers
												# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
												self.assertEqual(timm_model.out_indices			,				(-1,)			)
												self.assertEqual(transformers_model.out_indices			,				[len(timm_model.stage_names			) - 1]			)

												_a      :       List[str]   =						AutoBackbone.from_pretrained(__lowercase			,				use_timm_backbone=__lowercase			,				out_indices=[1, 2, 3]			)
												_a      :       List[str]   =						AutoBackbone.from_pretrained(__lowercase			,				out_indices=[1, 2, 3]			)

												self.assertEqual(timm_model.out_indices			,				transformers_model.out_indices			)
												self.assertEqual(len(timm_model.out_features			)			,				len(transformers_model.out_features			)			)
												self.assertEqual(timm_model.channels			,				transformers_model.channels			)


							@unittest.skip('''TimmBackbone doesn\'t support feed forward chunking'''			)
							def 							__lowercase							(     self			)       ->       List[str]:
												pass


							@unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute'''			)
							def 							__lowercase							(     self			)       ->       Tuple:
												pass


							@unittest.skip('''TimmBackbone initialization is managed on the timm side'''			)
							def 							__lowercase							(     self			)       ->       Dict:
												pass


							@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''			)
							def 							__lowercase							(     self			)       ->       List[Any]:
												pass


							@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''			)
							def 							__lowercase							(     self			)       ->       Optional[int]:
												pass


							@unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint'''			)
							def 							__lowercase							(     self			)       ->       List[str]:
												pass


							@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''			)
							def 							__lowercase							(     self			)       ->       str:
												pass


							@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''			)
							def 							__lowercase							(     self			)       ->       Any:
												pass


							@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''			)
							def 							__lowercase							(     self			)       ->       Any:
												pass


							@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''			)
							def 							__lowercase							(     self			)       ->       Tuple:
												pass


							@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''			)
							def 							__lowercase							(     self			)       ->       Any:
												pass


							@unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.'''			)
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												pass


							@unittest.skip('''TimmBackbone doesn\'t support output_attentions.'''			)
							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												pass


							@unittest.skip('''Safetensors is not supported by timm.'''			)
							def 							__lowercase							(     self			)       ->       int:
												pass


							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''			)
							def 							__lowercase							(     self			)       ->       int:
												pass


							def 							__lowercase							(     self			)       ->       Dict:
												_a ,   _a      :       Tuple   =						self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	_a      :       List[str]   =						model_class(__lowercase			)
																	_a      :       Any   =						inspect.signature(model.forward			)
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	_a      :       Dict   =						[*signature.parameters.keys()]

																	_a      :       int   =						['''pixel_values''']
																	self.assertListEqual(arg_names[:1]			,				__lowercase			)


							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a ,   _a      :       int   =						self.model_tester.prepare_config_and_inputs_for_common()
												_a      :       str   =						True
												_a      :       Dict   =						self.has_attentions

												# no need to test all models as different heads yield the same functionality
												_a      :       Dict   =						self.all_model_classes[0]
												_a      :       Optional[Any]   =						model_class(__lowercase			)
												model.to(__lowercase			)

												_a      :       List[str]   =						self._prepare_for_class(__lowercase			,				__lowercase			)
												_a      :       Dict   =						model(**__lowercase			)
												_a      :       Any   =						outputs[0][-1]

												# Encoder-/Decoder-only models
												_a      :       List[str]   =						outputs.hidden_states[0]
												hidden_states.retain_grad()

												if self.has_attentions:
																	_a      :       List[Any]   =						outputs.attentions[0]
																	attentions.retain_grad()

												output.flatten()[0].backward(retain_graph=__lowercase			)

												self.assertIsNotNone(hidden_states.grad			)

												if self.has_attentions:
																	self.assertIsNotNone(attentions.grad			)




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

												for model_class in self.all_model_classes:
																	_a      :       Optional[Any]   =						model_class(__lowercase			)
																	model.to(__lowercase			)
																	model.eval()
																	_a      :       List[str]   =						model(**__lowercase			)

																	self.assertEqual(len(result.feature_maps			)			,				len(config.out_indices			)			)
																	self.assertEqual(len(model.channels			)			,				len(config.out_indices			)			)

																	# Check output of last stage is taken if out_features=None, out_indices=None
																	_a      :       Dict   =						copy.deepcopy(__lowercase			)
																	_a      :       Optional[int]   =						None
																	_a      :       List[str]   =						model_class(__lowercase			)
																	model.to(__lowercase			)
																	model.eval()
																	_a      :       Tuple   =						model(**__lowercase			)

																	self.assertEqual(len(result.feature_maps			)			,				1			)
																	self.assertEqual(len(model.channels			)			,				1			)

																	# Check backbone can be initialized with fresh weights
																	_a      :       List[Any]   =						copy.deepcopy(__lowercase			)
																	_a      :       str   =						False
																	_a      :       Optional[Any]   =						model_class(__lowercase			)
																	model.to(__lowercase			)
																	model.eval()
																	_a      :       Any   =						model(**__lowercase			)






'''simple docstring'''

import string

def 					__UpperCamelCase				(						lowercase__    :	str			):






     '''simple docstring'''





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

def 					__UpperCamelCase				(						):






     '''simple docstring'''





     __lowercase      		=input('Encrypted message: '			)
     __lowercase      		=message.upper()
     decrypt(lowercase__			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()


