
'''simple docstring'''


import collections
import inspect
import unittest

from transformers import DinatConfig
from transformers.testing_utils import require_natten, 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, _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 DinatBackbone, DinatForImageClassification, DinatModel
	from transformers.models.dinat.modeling_dinat import DINAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor




class     __snake_case			:




		def __init__(  self					:str			,    _SCREAMING_SNAKE_CASE					:Dict			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]=1_3			,    _SCREAMING_SNAKE_CASE					:Tuple=6_4			,    _SCREAMING_SNAKE_CASE					:Optional[int]=4			,    _SCREAMING_SNAKE_CASE					:Optional[int]=3			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]=1_6			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]=[1, 2, 1]			,    _SCREAMING_SNAKE_CASE					:List[str]=[2, 4, 8]			,    _SCREAMING_SNAKE_CASE					:List[str]=3			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]=[[3], [1, 2], [1]]			,    _SCREAMING_SNAKE_CASE					:int=2.0			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]=True			,    _SCREAMING_SNAKE_CASE					:List[str]=0.0			,    _SCREAMING_SNAKE_CASE					:List[str]=0.0			,    _SCREAMING_SNAKE_CASE					:Optional[int]=0.1			,    _SCREAMING_SNAKE_CASE					:int="gelu"			,    _SCREAMING_SNAKE_CASE					:Any=True			,    _SCREAMING_SNAKE_CASE					:str=0.02			,    _SCREAMING_SNAKE_CASE					:int=1E-5			,    _SCREAMING_SNAKE_CASE					:Optional[int]=True			,    _SCREAMING_SNAKE_CASE					:Dict=None			,    _SCREAMING_SNAKE_CASE					:Optional[int]=True			,    _SCREAMING_SNAKE_CASE					:Any=1_0			,    _SCREAMING_SNAKE_CASE					:Any=["stage1", "stage2"]			,    _SCREAMING_SNAKE_CASE					:Optional[int]=[1, 2]			,    )					->			List[str]:






								'''simple docstring'''






								UpperCamelCase_     :	Optional[int]					     =     parent
								UpperCamelCase_     :	Union[str, Any]					     =     batch_size
								UpperCamelCase_     :	Any					     =     image_size
								UpperCamelCase_     :	int					     =     patch_size
								UpperCamelCase_     :	Union[str, Any]					     =     num_channels
								UpperCamelCase_     :	List[str]					     =     embed_dim
								UpperCamelCase_     :	List[Any]					     =     depths
								UpperCamelCase_     :	int					     =     num_heads
								UpperCamelCase_     :	Dict					     =     kernel_size
								UpperCamelCase_     :	int					     =     dilations
								UpperCamelCase_     :	List[str]					     =     mlp_ratio
								UpperCamelCase_     :	Any					     =     qkv_bias
								UpperCamelCase_     :	List[str]					     =     hidden_dropout_prob
								UpperCamelCase_     :	str					     =     attention_probs_dropout_prob
								UpperCamelCase_     :	int					     =     drop_path_rate
								UpperCamelCase_     :	List[Any]					     =     hidden_act
								UpperCamelCase_     :	List[Any]					     =     patch_norm
								UpperCamelCase_     :	Union[str, Any]					     =     layer_norm_eps
								UpperCamelCase_     :	Tuple					     =     initializer_range
								UpperCamelCase_     :	List[Any]					     =     is_training
								UpperCamelCase_     :	List[Any]					     =     scope
								UpperCamelCase_     :	List[str]					     =     use_labels
								UpperCamelCase_     :	Dict					     =     num_labels
								UpperCamelCase_     :	Tuple					     =     out_features
								UpperCamelCase_     :	List[Any]					     =     out_indices




		def 	_snake_case	(  self					:Tuple)					->			Union[str, Any]:






								'''simple docstring'''






								UpperCamelCase_     :	int					     =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

								UpperCamelCase_     :	int					     =     None
								if self.use_labels:
														UpperCamelCase_     :	int					     =     ids_tensor([self.batch_size]			,    self.num_labels)

								UpperCamelCase_     :	Tuple					     =     self.get_config()

								return config, pixel_values, labels




		def 	_snake_case	(  self					:List[Any])					->			Optional[int]:






								'''simple docstring'''






								return DinatConfig(
								    num_labels=self.num_labels			,    image_size=self.image_size			,    patch_size=self.patch_size			,    num_channels=self.num_channels			,    embed_dim=self.embed_dim			,    depths=self.depths			,    num_heads=self.num_heads			,    kernel_size=self.kernel_size			,    dilations=self.dilations			,    mlp_ratio=self.mlp_ratio			,    qkv_bias=self.qkv_bias			,    hidden_dropout_prob=self.hidden_dropout_prob			,    attention_probs_dropout_prob=self.attention_probs_dropout_prob			,    drop_path_rate=self.drop_path_rate			,    hidden_act=self.hidden_act			,    patch_norm=self.patch_norm			,    layer_norm_eps=self.layer_norm_eps			,    initializer_range=self.initializer_range			,    out_features=self.out_features			,    out_indices=self.out_indices			,    )




		def 	_snake_case	(  self					:Union[str, Any]			,    _SCREAMING_SNAKE_CASE					:str			,    _SCREAMING_SNAKE_CASE					:List[Any]			,    _SCREAMING_SNAKE_CASE					:Union[str, Any])					->			Any:






								'''simple docstring'''






								UpperCamelCase_     :	Tuple					     =     DinatModel(config=_SCREAMING_SNAKE_CASE)
								model.to(_SCREAMING_SNAKE_CASE)
								model.eval()
								UpperCamelCase_     :	Dict					     =     model(_SCREAMING_SNAKE_CASE)

								UpperCamelCase_     :	Any					     =     (config.image_size // config.patch_size) // (2 ** (len(config.depths) - 1))
								UpperCamelCase_     :	List[str]					     =     int(config.embed_dim * 2 ** (len(config.depths) - 1))

								self.parent.assertEqual(
								    result.last_hidden_state.shape			,    (self.batch_size, expected_height, expected_width, expected_dim))




		def 	_snake_case	(  self					:int			,    _SCREAMING_SNAKE_CASE					:List[str]			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]			,    _SCREAMING_SNAKE_CASE					:Optional[Any])					->			List[Any]:






								'''simple docstring'''






								UpperCamelCase_     :	Optional[Any]					     =     DinatForImageClassification(_SCREAMING_SNAKE_CASE)
								model.to(_SCREAMING_SNAKE_CASE)
								model.eval()
								UpperCamelCase_     :	Tuple					     =     model(_SCREAMING_SNAKE_CASE			,    labels=_SCREAMING_SNAKE_CASE)
								self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.num_labels))

								# test greyscale images
								UpperCamelCase_     :	List[str]					     =     1
								UpperCamelCase_     :	Tuple					     =     DinatForImageClassification(_SCREAMING_SNAKE_CASE)
								model.to(_SCREAMING_SNAKE_CASE)
								model.eval()

								UpperCamelCase_     :	List[str]					     =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
								UpperCamelCase_     :	Dict					     =     model(_SCREAMING_SNAKE_CASE)
								self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.num_labels))




		def 	_snake_case	(  self					:str			,    _SCREAMING_SNAKE_CASE					:Any			,    _SCREAMING_SNAKE_CASE					:Optional[int]			,    _SCREAMING_SNAKE_CASE					:Union[str, Any])					->			int:






								'''simple docstring'''






								UpperCamelCase_     :	Union[str, Any]					     =     DinatBackbone(config=_SCREAMING_SNAKE_CASE)
								model.to(_SCREAMING_SNAKE_CASE)
								model.eval()
								UpperCamelCase_     :	Optional[Any]					     =     model(_SCREAMING_SNAKE_CASE)

								# verify hidden states
								self.parent.assertEqual(len(result.feature_maps)			,    len(config.out_features))
								self.parent.assertListEqual(list(result.feature_maps[0].shape)			,    [self.batch_size, model.channels[0], 1_6, 1_6])

								# verify channels
								self.parent.assertEqual(len(model.channels)			,    len(config.out_features))

								# verify backbone works with out_features=None
								UpperCamelCase_     :	Dict					     =     None
								UpperCamelCase_     :	int					     =     DinatBackbone(config=_SCREAMING_SNAKE_CASE)
								model.to(_SCREAMING_SNAKE_CASE)
								model.eval()
								UpperCamelCase_     :	Dict					     =     model(_SCREAMING_SNAKE_CASE)

								# verify feature maps
								self.parent.assertEqual(len(result.feature_maps)			,    1)
								self.parent.assertListEqual(list(result.feature_maps[0].shape)			,    [self.batch_size, model.channels[-1], 4, 4])

								# verify channels
								self.parent.assertEqual(len(model.channels)			,    1)





		def 	_snake_case	(  self					:str)					->			str:






								'''simple docstring'''






								UpperCamelCase_     :	Tuple					     =     self.prepare_config_and_inputs()
								UpperCamelCase_     ,UpperCamelCase_     ,UpperCamelCase_     :	List[Any]					     =     config_and_inputs
								UpperCamelCase_     :	List[Any]					     =     {"""pixel_values""": pixel_values}
								return config, inputs_dict







@require_natten
@require_torch
class     __snake_case			(    _a					,			_a					,			unittest.TestCase	):
		a_          =					(
		    (
		        DinatModel,
		        DinatForImageClassification,
		        DinatBackbone,
		    )
		    if is_torch_available()
		    else ()
		)
		a_          =					(
		    {"feature-extraction": DinatModel, "image-classification": DinatForImageClassification}
		    if is_torch_available()
		    else {}
		)
		a_          =					False

		a_          =					False
		a_          =					False
		a_          =					False
		a_          =					False




		def 	_snake_case	(  self					:List[Any])					->			List[str]:






								'''simple docstring'''






								UpperCamelCase_     :	List[Any]					     =     DinatModelTester(self)
								UpperCamelCase_     :	Tuple					     =     ConfigTester(self			,    config_class=_SCREAMING_SNAKE_CASE			,    embed_dim=3_7)




		def 	_snake_case	(  self					:Optional[Any])					->			int:






								'''simple docstring'''






								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 	_snake_case	(  self					:List[str])					->			List[str]:






								'''simple docstring'''






								return




		def 	_snake_case	(  self					:Optional[Any])					->			Optional[Any]:






								'''simple docstring'''






								UpperCamelCase_     :	Any					     =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE)




		def 	_snake_case	(  self					:Tuple)					->			Union[str, Any]:






								'''simple docstring'''






								UpperCamelCase_     :	Dict					     =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE)




		def 	_snake_case	(  self					:Optional[Any])					->			Optional[int]:






								'''simple docstring'''






								UpperCamelCase_     :	Union[str, Any]					     =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_backbone(*_SCREAMING_SNAKE_CASE)




		@unittest.skip(reason="""Dinat does not use inputs_embeds""")
		def 	_snake_case	(  self					:str)					->			List[str]:






								'''simple docstring'''






								pass




		@unittest.skip(reason="""Dinat does not use feedforward chunking""")
		def 	_snake_case	(  self					:Dict)					->			Any:






								'''simple docstring'''






								pass




		def 	_snake_case	(  self					:int)					->			Union[str, Any]:






								'''simple docstring'''






								UpperCamelCase_     ,UpperCamelCase_     :	Dict					     =     self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														UpperCamelCase_     :	Optional[int]					     =     model_class(_SCREAMING_SNAKE_CASE)
														self.assertIsInstance(model.get_input_embeddings()			,    (nn.Module))
														UpperCamelCase_     :	Optional[int]					     =     model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE			,    nn.Linear))




		def 	_snake_case	(  self					:str)					->			str:






								'''simple docstring'''






								UpperCamelCase_     ,UpperCamelCase_     :	Optional[int]					     =     self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														UpperCamelCase_     :	List[Any]					     =     model_class(_SCREAMING_SNAKE_CASE)
														UpperCamelCase_     :	Optional[int]					     =     inspect.signature(model.forward)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														UpperCamelCase_     :	List[Any]					     =     [*signature.parameters.keys()]

														UpperCamelCase_     :	List[Any]					     =     ["""pixel_values"""]
														self.assertListEqual(arg_names[:1]			,    _SCREAMING_SNAKE_CASE)




		def 	_snake_case	(  self					:int)					->			List[Any]:






								'''simple docstring'''






								self.skipTest("""Dinat's attention operation is handled entirely by NATTEN.""")




		def 	_snake_case	(  self					:List[str]			,    _SCREAMING_SNAKE_CASE					:Tuple			,    _SCREAMING_SNAKE_CASE					:Dict			,    _SCREAMING_SNAKE_CASE					:Dict			,    _SCREAMING_SNAKE_CASE					:Optional[Any])					->			int:






								'''simple docstring'''






								UpperCamelCase_     :	List[str]					     =     model_class(_SCREAMING_SNAKE_CASE)
								model.to(_SCREAMING_SNAKE_CASE)
								model.eval()

								with torch.no_grad():
														UpperCamelCase_     :	str					     =     model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE))

								UpperCamelCase_     :	int					     =     outputs.hidden_states

								UpperCamelCase_     :	int					     =     getattr(
								    self.model_tester			,    """expected_num_hidden_layers"""			,    len(self.model_tester.depths) + 1)
								self.assertEqual(len(_SCREAMING_SNAKE_CASE)			,    _SCREAMING_SNAKE_CASE)

								# Dinat has a different seq_length
								UpperCamelCase_     :	str					     =     (
								    config.patch_size
								    if isinstance(config.patch_size			,    collections.abc.Iterable)
								    else (config.patch_size, config.patch_size)
								)

								UpperCamelCase_     :	int					     =     image_size[0] // patch_size[0]
								UpperCamelCase_     :	Union[str, Any]					     =     image_size[1] // patch_size[1]

								self.assertListEqual(
								    list(hidden_states[0].shape[-3:])			,    [height, width, self.model_tester.embed_dim]			,    )

								if model_class.__name__ != "DinatBackbone":
														UpperCamelCase_     :	str					     =     outputs.reshaped_hidden_states
														self.assertEqual(len(_SCREAMING_SNAKE_CASE)			,    _SCREAMING_SNAKE_CASE)

														UpperCamelCase_     ,UpperCamelCase_     ,UpperCamelCase_     ,UpperCamelCase_     :	List[Any]					     =     reshaped_hidden_states[0].shape
														UpperCamelCase_     :	List[str]					     =     (
														    reshaped_hidden_states[0].view(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE).permute(0			,    2			,    3			,    1)
														)
														self.assertListEqual(
														    list(reshaped_hidden_states.shape[-3:])			,    [height, width, self.model_tester.embed_dim]			,    )




		def 	_snake_case	(  self					:Tuple)					->			Optional[int]:






								'''simple docstring'''






								UpperCamelCase_     ,UpperCamelCase_     :	Optional[Any]					     =     self.model_tester.prepare_config_and_inputs_for_common()

								UpperCamelCase_     :	Union[str, Any]					     =     (
								    self.model_tester.image_size
								    if isinstance(self.model_tester.image_size			,    collections.abc.Iterable)
								    else (self.model_tester.image_size, self.model_tester.image_size)
								)

								for model_class in self.all_model_classes:
														UpperCamelCase_     :	List[str]					     =     True
														self.check_hidden_states_output(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE)

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														UpperCamelCase_     :	List[str]					     =     True

														self.check_hidden_states_output(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE)




		@slow
		def 	_snake_case	(  self					:Dict)					->			List[Any]:






								'''simple docstring'''






								for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														UpperCamelCase_     :	Union[str, Any]					     =     DinatModel.from_pretrained(_SCREAMING_SNAKE_CASE)
														self.assertIsNotNone(_SCREAMING_SNAKE_CASE)





		def 	_snake_case	(  self					:List[str])					->			str:






								'''simple docstring'''






								UpperCamelCase_     ,UpperCamelCase_     :	Union[str, Any]					     =     self.model_tester.prepare_config_and_inputs_for_common()

								UpperCamelCase_     :	Union[str, Any]					     =     _config_zero_init(_SCREAMING_SNAKE_CASE)
								for model_class in self.all_model_classes:
														UpperCamelCase_     :	Any					     =     model_class(config=_SCREAMING_SNAKE_CASE)
														for name, param in model.named_parameters():
																				if "embeddings" not in name and param.requires_grad:
																										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"""			,    )







@require_natten
@require_vision
@require_torch
class     __snake_case			(    unittest.TestCase	):




		@cached_property
		def 	_snake_case	(  self					:List[str])					->			List[Any]:






								'''simple docstring'''






								return AutoImageProcessor.from_pretrained("""shi-labs/dinat-mini-in1k-224""") if is_vision_available() else None





		@slow
		def 	_snake_case	(  self					:Union[str, Any])					->			List[str]:






								'''simple docstring'''






								UpperCamelCase_     :	List[Any]					     =     DinatForImageClassification.from_pretrained("""shi-labs/dinat-mini-in1k-224""").to(_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	int					     =     self.default_image_processor

								UpperCamelCase_     :	Union[str, Any]					     =     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
								UpperCamelCase_     :	List[str]					     =     image_processor(images=_SCREAMING_SNAKE_CASE			,    return_tensors="""pt""").to(_SCREAMING_SNAKE_CASE)

								# forward pass
								with torch.no_grad():
														UpperCamelCase_     :	int					     =     model(**_SCREAMING_SNAKE_CASE)

								# verify the logits
								UpperCamelCase_     :	Optional[int]					     =     torch.Size((1, 1_0_0_0))
								self.assertEqual(outputs.logits.shape			,    _SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	Any					     =     torch.tensor([-0.1545, -0.7667, 0.4642]).to(_SCREAMING_SNAKE_CASE)
								self.assertTrue(torch.allclose(outputs.logits[0, :3]			,    _SCREAMING_SNAKE_CASE			,    atol=1E-4))







@require_torch
@require_natten
class     __snake_case			(    unittest.TestCase					,			_a	):
		a_          =					(DinatBackbone,) if is_torch_available() else ()
		a_          =					DinatConfig




		def 	_snake_case	(  self					:Any)					->			List[str]:






								'''simple docstring'''






								UpperCamelCase_     :	Dict					     =     DinatModelTester(self)






'''simple docstring'''


__SCREAMING_SNAKE_CASE						=      [chr(i) for i in range(32, 126)]
__SCREAMING_SNAKE_CASE						=      list(range(len(alphabets)))
__SCREAMING_SNAKE_CASE						=      list(range(len(alphabets)))
__SCREAMING_SNAKE_CASE						=      list(range(len(alphabets)))
__SCREAMING_SNAKE_CASE						=      list(reversed(range(len(alphabets))))
__SCREAMING_SNAKE_CASE						=      []
__SCREAMING_SNAKE_CASE						=      __SCREAMING_SNAKE_CASE						=      __SCREAMING_SNAKE_CASE						=      0
def 					__magic_name__(       ):
						global gear_one_pos
						global gear_two_pos
						global gear_three_pos
						UpperCamelCase_     :	List[Any]					     =     gear_one[0]
						gear_one.append(UpperCAmelCase__	)
						del gear_one[0]
						gear_one_pos += 1
						if gear_one_pos % int(len(UpperCAmelCase__	)	) == 0:
												UpperCamelCase_     :	List[str]					     =     gear_two[0]
												gear_two.append(UpperCAmelCase__	)
												del gear_two[0]
												gear_two_pos += 1
												if gear_two_pos % int(len(UpperCAmelCase__	)	) == 0:
																		UpperCamelCase_     :	List[Any]					     =     gear_three[0]
																		gear_three.append(UpperCAmelCase__	)
																		del gear_three[0]
																		gear_three_pos += 1
def 					__magic_name__(       UpperCAmelCase__ :       int	):
						UpperCamelCase_     :	Any					     =     alphabets.index(UpperCAmelCase__	)
						UpperCamelCase_     :	List[str]					     =     gear_one[target]
						UpperCamelCase_     :	str					     =     gear_two[target]
						UpperCamelCase_     :	int					     =     gear_three[target]
						UpperCamelCase_     :	Union[str, Any]					     =     reflector[target]
						UpperCamelCase_     :	Dict					     =     gear_three.index(UpperCAmelCase__	)
						UpperCamelCase_     :	Optional[int]					     =     gear_two.index(UpperCAmelCase__	)
						UpperCamelCase_     :	Optional[int]					     =     gear_one.index(UpperCAmelCase__	)
						code.append(alphabets[target]	)
						rotator()


if __name__ == "__main__":
	__SCREAMING_SNAKE_CASE						=      list(input("Type your message:\n"))
	while True:
		try:
			__SCREAMING_SNAKE_CASE						=      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!"
	)




