



import inspect
import unittest

from transformers import ResNetConfig
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_backbone_common import BackboneTesterMixin
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 ResNetBackbone, ResNetForImageClassification, ResNetModel
		from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor


class 	a__       :


					"""simple docstring"""



					def __init__(		self     :int      ,  lowercase__     :int      ,  lowercase__     :List[Any]=3      ,  lowercase__     :List[Any]=32      ,  lowercase__     :List[str]=3      ,  lowercase__     :Union[str, Any]=10      ,  lowercase__     :str=[10, 20, 30, 40]      ,  lowercase__     :int=[1, 1, 2, 1]      ,  lowercase__     :Optional[int]=True      ,  lowercase__     :Union[str, Any]=True      ,  lowercase__     :str="relu"      ,  lowercase__     :Optional[int]=3      ,  lowercase__     :Union[str, Any]=None      ,  lowercase__     :str=["stage2", "stage3", "stage4"]      ,  lowercase__     :Optional[int]=[2, 3, 4]      ,  ):
										lowercase						   =       parent
										lowercase						   =       batch_size
										lowercase						   =       image_size
										lowercase						   =       num_channels
										lowercase						   =       embeddings_size
										lowercase						   =       hidden_sizes
										lowercase						   =       depths
										lowercase						   =       is_training
										lowercase						   =       use_labels
										lowercase						   =       hidden_act
										lowercase						   =       num_labels
										lowercase						   =       scope
										lowercase						   =       len(__UpperCAmelCase    )
										lowercase						   =       out_features
										lowercase						   =       out_indices

					def 				__UpperCAmelCase		(		self     :Tuple    ):
										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.num_labels    )

										lowercase						   =       self.get_config()

										return config, pixel_values, labels

					def 				__UpperCAmelCase		(		self     :List[str]    ):
										return ResNetConfig(
										    num_channels=self.num_channels      ,  embeddings_size=self.embeddings_size      ,  hidden_sizes=self.hidden_sizes      ,  depths=self.depths      ,  hidden_act=self.hidden_act      ,  num_labels=self.num_labels      ,  out_features=self.out_features      ,  out_indices=self.out_indices      ,  )

					def 				__UpperCAmelCase		(		self     :Dict      ,  lowercase__     :Any      ,  lowercase__     :List[Any]      ,  lowercase__     :Tuple    ):
										lowercase						   =       ResNetModel(config=__UpperCAmelCase    )
										model.to(__UpperCAmelCase    )
										model.eval()
										lowercase						   =       model(__UpperCAmelCase    )
										# expected last hidden states: B, C, H // 32, W // 32
										self.parent.assertEqual(
										    result.last_hidden_state.shape      ,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)      ,  )

					def 				__UpperCAmelCase		(		self     :Optional[int]      ,  lowercase__     :Optional[Any]      ,  lowercase__     :int      ,  lowercase__     :List[str]    ):
										lowercase						   =       self.num_labels
										lowercase						   =       ResNetForImageClassification(__UpperCAmelCase    )
										model.to(__UpperCAmelCase    )
										model.eval()
										lowercase						   =       model(__UpperCAmelCase      ,  labels=__UpperCAmelCase    )
										self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.num_labels)    )

					def 				__UpperCAmelCase		(		self     :Tuple      ,  lowercase__     :Union[str, Any]      ,  lowercase__     :Any      ,  lowercase__     :Optional[int]    ):
										lowercase						   =       ResNetBackbone(config=__UpperCAmelCase    )
										model.to(__UpperCAmelCase    )
										model.eval()
										lowercase						   =       model(__UpperCAmelCase    )

										# verify feature maps
										self.parent.assertEqual(len(result.feature_maps    )      ,  len(config.out_features    )    )
										self.parent.assertListEqual(list(result.feature_maps[0].shape    )      ,  [self.batch_size, self.hidden_sizes[1], 4, 4]    )

										# verify channels
										self.parent.assertEqual(len(model.channels    )      ,  len(config.out_features    )    )
										self.parent.assertListEqual(model.channels      ,  config.hidden_sizes[1:]    )

										# verify backbone works with out_features=None
										lowercase						   =       None
										lowercase						   =       ResNetBackbone(config=__UpperCAmelCase    )
										model.to(__UpperCAmelCase    )
										model.eval()
										lowercase						   =       model(__UpperCAmelCase    )

										# verify feature maps
										self.parent.assertEqual(len(result.feature_maps    )      ,  1    )
										self.parent.assertListEqual(list(result.feature_maps[0].shape    )      ,  [self.batch_size, self.hidden_sizes[-1], 1, 1]    )

										# verify channels
										self.parent.assertEqual(len(model.channels    )      ,  1    )
										self.parent.assertListEqual(model.channels      ,  [config.hidden_sizes[-1]]    )



					def 				__UpperCAmelCase		(		self     :Dict    ):
										lowercase						   =       self.prepare_config_and_inputs()
										lowercase   ,      lowercase   ,      lowercase						   =       config_and_inputs
										lowercase						   =       {'pixel_values': pixel_values}
										return config, inputs_dict




@require_torch
class 	a__       (       lowerCamelCase_  ,			lowerCamelCase_  ,			unittest.TestCase       ):


					"""simple docstring"""



					A__  :				int					 =				(
					    (
					        ResNetModel,
					        ResNetForImageClassification,
					        ResNetBackbone,
					    )
					    if is_torch_available()
					    else ()
					)
					A__  :				int					 =				(
					    {'''feature-extraction''': ResNetModel, '''image-classification''': ResNetForImageClassification}
					    if is_torch_available()
					    else {}
					)

					A__  :				Any					 =				True
					A__  :				Optional[Any]					 =				False
					A__  :				Dict					 =				False
					A__  :				Optional[int]					 =				False
					A__  :				Tuple					 =				False

					def 				__UpperCAmelCase		(		self     :Union[str, Any]    ):
										lowercase						   =       ResNetModelTester(self    )
										lowercase						   =       ConfigTester(self      ,  config_class=__UpperCAmelCase      ,  has_text_modality=__UpperCAmelCase    )

					def 				__UpperCAmelCase		(		self     :Optional[Any]    ):
										self.create_and_test_config_common_properties()
										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 				__UpperCAmelCase		(		self     :str    ):
										return

					@unittest.skip(reason='ResNet does not use inputs_embeds'    )
					def 				__UpperCAmelCase		(		self     :List[Any]    ):
										pass

					@unittest.skip(reason='ResNet does not support input and output embeddings'    )
					def 				__UpperCAmelCase		(		self     :Any    ):
										pass

					def 				__UpperCAmelCase		(		self     :int    ):
										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 				__UpperCAmelCase		(		self     :Any    ):
										lowercase						   =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__UpperCAmelCase    )

					def 				__UpperCAmelCase		(		self     :Tuple    ):
										lowercase						   =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_backbone(*__UpperCAmelCase    )

					def 				__UpperCAmelCase		(		self     :Any    ):
										lowercase   ,      lowercase						   =       self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
															lowercase						   =       model_class(config=__UpperCAmelCase    )
															for name, module in model.named_modules():
																				if isinstance(__UpperCAmelCase      ,  (nn.BatchNormad, nn.GroupNorm)    ):
																									self.assertTrue(
																									    torch.all(module.weight == 1    )      ,  msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""      ,  )
																									self.assertTrue(
																									    torch.all(module.bias == 0    )      ,  msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""      ,  )

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



										def check_hidden_states_output(lowercase__     :str      ,  lowercase__     :int      ,  lowercase__     :Union[str, Any]    ):
															lowercase						   =       model_class(__UpperCAmelCase    )
															model.to(__UpperCAmelCase    )
															model.eval()

															with torch.no_grad():
																				lowercase						   =       model(**self._prepare_for_class(__UpperCAmelCase      ,  __UpperCAmelCase    )    )

															lowercase						   =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															lowercase						   =       self.model_tester.num_stages
															self.assertEqual(len(__UpperCAmelCase    )      ,  expected_num_stages + 1    )

															# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
															self.assertListEqual(
															    list(hidden_states[0].shape[-2:]    )      ,  [self.model_tester.image_size // 4, self.model_tester.image_size // 4]      ,  )

										lowercase   ,      lowercase						   =       self.model_tester.prepare_config_and_inputs_for_common()
										lowercase						   =       ['basic', 'bottleneck']
										for model_class in self.all_model_classes:
															for layer_type in layers_type:
																				lowercase						   =       layer_type
																				lowercase						   =       True
																				check_hidden_states_output(__UpperCAmelCase      ,  __UpperCAmelCase      ,  __UpperCAmelCase    )

																				# check that output_hidden_states also work using config
																				del inputs_dict["output_hidden_states"]
																				lowercase						   =       True

																				check_hidden_states_output(__UpperCAmelCase      ,  __UpperCAmelCase      ,  __UpperCAmelCase    )

					@unittest.skip(reason='ResNet does not use feedforward chunking'    )
					def 				__UpperCAmelCase		(		self     :List[str]    ):
										pass

					def 				__UpperCAmelCase		(		self     :str    ):
										lowercase						   =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase    )



					@slow
					def 				__UpperCAmelCase		(		self     :Any    ):



										for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															lowercase						   =       ResNetModel.from_pretrained(__UpperCAmelCase    )
															self.assertIsNotNone(__UpperCAmelCase    )





def 		__snake_case						(    ):
					"""simple docstring"""






					lowercase						   =       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     :Optional[int]    ):
										return (
										    AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )
										    if is_vision_available()
										    else None
										)



					@slow
					def 				__UpperCAmelCase		(		self     :Union[str, Any]    ):
										lowercase						   =       ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).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, 1000)    )
										self.assertEqual(outputs.logits.shape      ,  __UpperCAmelCase    )

										lowercase						   =       torch.tensor([-11.1069, -9.7877, -8.3777]    ).to(__UpperCAmelCase    )

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




@require_torch
class 	a__       (       lowerCamelCase_  ,			unittest.TestCase       ):


					"""simple docstring"""



					A__  :				Dict					 =				(ResNetBackbone,) if is_torch_available() else ()
					A__  :				str					 =				False
					A__  :				Any					 =				ResNetConfig

					def 				__UpperCAmelCase		(		self     :Tuple    ):
										lowercase						   =       ResNetModelTester(self    )












__a			:	Optional[Any]							     =		[chr(i) for i in range(32, 1_26)]
__a			:	Dict							     =		list(range(len(alphabets)))
__a			:	List[Any]							     =		list(range(len(alphabets)))
__a			:	Tuple							     =		list(range(len(alphabets)))
__a			:	List[Any]							     =		list(reversed(range(len(alphabets))))
__a			:	List[Any]							     =		[]
__a			:	Optional[int]							     =		0




def  a_		(							)	->			Dict:

			'''simple docstring'''


			global gear_one_pos
			global gear_two_pos
			global gear_three_pos
			UpperCamelCase_     				= gear_one[0]
			gear_one.append(__snake_case					)
			del gear_one[0]
			gear_one_pos += 1
			if gear_one_pos % int(len(__snake_case					)					) == 0:
						UpperCamelCase_     				= gear_two[0]
						gear_two.append(__snake_case					)
						del gear_two[0]
						gear_two_pos += 1
						if gear_two_pos % int(len(__snake_case					)					) == 0:
									UpperCamelCase_     				= gear_three[0]
									gear_three.append(__snake_case					)
									del gear_three[0]
									gear_three_pos += 1




def  a_		(							__snake_case					)	->			Any:

			'''simple docstring'''


			UpperCamelCase_     				= alphabets.index(__snake_case					)
			UpperCamelCase_     				= gear_one[target]
			UpperCamelCase_     				= gear_two[target]
			UpperCamelCase_     				= gear_three[target]
			UpperCamelCase_     				= reflector[target]
			UpperCamelCase_     				= gear_three.index(__snake_case					)
			UpperCamelCase_     				= gear_two.index(__snake_case					)
			UpperCamelCase_     				= gear_one.index(__snake_case					)
			code.append(alphabets[target]					)
			rotator()


if __name__ == "__main__":
				__a			:	Union[str, Any]							     =		list(input("""Type your message:\n"""))
				while True:
								try:
												__a			:	Optional[Any]							     =		int(input("""Please set token:(must be only digits)\n"""))
												break
								except Exception as error:
												print(error)
				for _ in range(token):
								rotator()
				for j in decode:
								engine(j)
				print("""\n""" + """""".join(code))
				print(
				    f"""\nYour Token is {token} please write it down.\nIf you want to decode """
				    """this message again you should input same digits as token!"""
				)
