import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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 SwiftFormerForImageClassification, SwiftFormerModel
	from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor





class UpperCAmelCase						:


				'''simple docstring'''






				def __init__(						self		,     lowercase		,     lowercase=1_3		,     lowercase=3		,     lowercase=True		,     lowercase=True		,     lowercase=0.1		,     lowercase=0.1		,     lowercase=2_2_4		,     lowercase=1_0_0_0		,     lowercase=[3, 3, 6, 4]		,     lowercase=[4_8, 5_6, 1_1_2, 2_2_0]		,     ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				parent
						A_		:    Optional[int]         =				batch_size
						A_		:    Union[str, Any]         =				num_channels
						A_		:    Union[str, Any]         =				is_training
						A_		:    Optional[Any]         =				use_labels
						A_		:    Union[str, Any]         =				hidden_dropout_prob
						A_		:    Any         =				attention_probs_dropout_prob
						A_		:    Tuple         =				num_labels
						A_		:    str         =				image_size
						A_		:    str         =				layer_depths
						A_		:    Union[str, Any]         =				embed_dims
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Dict         =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

						A_		:    Any         =				None
						if self.use_labels:
								A_		:    Dict         =				ids_tensor([self.batch_size]		,     self.num_labels )

						A_		:    str         =				self.get_config()

						return config, pixel_values, labels
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return SwiftFormerConfig(
						    depths=self.layer_depths		,     embed_dims=self.embed_dims		,     mlp_ratio=4		,     downsamples=[True, True, True, True]		,     hidden_act='gelu'		,     num_labels=self.num_labels		,     down_patch_size=3		,     down_stride=2		,     down_pad=1		,     drop_rate=0.0		,     drop_path_rate=0.0		,     use_layer_scale=lowercase		,     layer_scale_init_value=1E-5		,     )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				SwiftFormerModel(config=lowercase )
						model.to(lowercase )
						model.eval()
						A_		:    Optional[int]         =				model(lowercase )
						self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.embed_dims[-1], 7, 7) )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase ):



						"""simple docstring"""





						A_		:    Tuple         =				self.num_labels
						A_		:    Union[str, Any]         =				SwiftFormerForImageClassification(lowercase )
						model.to(lowercase )
						model.eval()
						A_		:    List[str]         =				model(lowercase		,     labels=lowercase )
						self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels) )

						A_		:    Dict         =				SwiftFormerForImageClassification(lowercase )
						model.to(lowercase )
						model.eval()

						A_		:    Union[str, Any]         =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
						A_		:    Optional[Any]         =				model(lowercase )
						self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels) )







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						((A_)       ,      (A_)       ,      (A_))		:    Tuple         =				self.prepare_config_and_inputs()
						A_		:    Union[str, Any]         =				{'pixel_values': pixel_values}
						return config, inputs_dict





@require_torch
class UpperCAmelCase						( __A ,	__A ,	unittest.TestCase  ):


				'''simple docstring'''






				lowerCamelCase_    = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
				lowerCamelCase_    = (
				    {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
				    if is_torch_available()
				    else {}
				)

				lowerCamelCase_    = False
				lowerCamelCase_    = False
				lowerCamelCase_    = False
				lowerCamelCase_    = False
				lowerCamelCase_    = False
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Any         =				SwiftFormerModelTester(self )
						A_		:    List[str]         =				ConfigTester(
						    self		,     config_class=lowercase		,     has_text_modality=lowercase		,     hidden_size=3_7		,     num_attention_heads=1_2		,     num_hidden_layers=1_2		,     )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						self.config_tester.run_common_tests()
				@unittest.skip(reason='SwiftFormer does not use inputs_embeds' )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						pass
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_       ,      A_		:    Any         =				self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
								A_		:    int         =				model_class(lowercase )
								A_		:    Dict         =				model.get_output_embeddings()
								self.assertTrue(x is None or isinstance(lowercase		,     nn.Linear ) )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_       ,      A_		:    List[Any]         =				self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
								A_		:    str         =				model_class(lowercase )
								A_		:    Union[str, Any]         =				inspect.signature(model.forward )
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								A_		:    int         =				[*signature.parameters.keys()]

								A_		:    int         =				['pixel_values']
								self.assertListEqual(arg_names[:1]		,     lowercase )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[int]         =				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowercase )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*lowercase )
				@slow
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A_		:    Optional[Any]         =				SwiftFormerModel.from_pretrained(lowercase )
								self.assertIsNotNone(lowercase )
				@unittest.skip(reason='SwiftFormer does not output attentions' )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						pass
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						def check_hidden_states_output(lowercase		,     lowercase		,     lowercase ):
								A_		:    int         =				model_class(lowercase )
								model.to(lowercase )
								model.eval()

								with torch.no_grad():
										A_		:    List[str]         =				model(**self._prepare_for_class(lowercase		,     lowercase ) )

								A_		:    List[Any]         =				outputs.hidden_states

								A_		:    Union[str, Any]         =				8
								self.assertEqual(len(lowercase )		,     lowercase )  # TODO

								# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
								# with the width and height being successively divided by 2, after every 2 blocks
								for i in range(len(lowercase ) ):
										self.assertEqual(
										    hidden_states[i].shape		,     torch.Size(
										        [
										            self.model_tester.batch_size,
										            self.model_tester.embed_dims[i // 2],
										            (self.model_tester.image_size // 4) // 2 ** (i // 2),
										            (self.model_tester.image_size // 4) // 2 ** (i // 2),
										        ] )		,     )

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

						for model_class in self.all_model_classes:
								A_		:    str         =				True
								check_hidden_states_output(lowercase		,     lowercase		,     lowercase )

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								A_		:    int         =				True

								check_hidden_states_output(lowercase		,     lowercase		,     lowercase )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						def _config_zero_init(lowercase ):
								A_		:    List[Any]         =				copy.deepcopy(lowercase )
								for key in configs_no_init.__dict__.keys():
										if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
												setattr(lowercase		,     lowercase		,     1E-10 )
										if isinstance(getattr(lowercase		,     lowercase		,     lowercase )		,     lowercase ):
												A_		:    Optional[Any]         =				_config_zero_init(getattr(lowercase		,     lowercase ) )
												setattr(lowercase		,     lowercase		,     lowercase )
								return configs_no_init

						A_       ,      A_		:    Any         =				self.model_tester.prepare_config_and_inputs_for_common()

						A_		:    str         =				_config_zero_init(lowercase )
						for model_class in self.all_model_classes:
								A_		:    List[str]         =				model_class(config=lowercase )
								for name, param in model.named_parameters():
										if param.requires_grad:
												self.assertIn(
												    ((param.data.mean() * 1E9) / 1E9).round().item()		,     [0.0, 1.0]		,     msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''		,     )







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



						"""simple docstring"""





						pass




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    str         =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
		return image





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


				'''simple docstring'''






				@cached_property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs' ) if is_vision_available() else None







				@slow
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Any         =				SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs' ).to(lowercase )

						A_		:    Any         =				self.default_image_processor
						A_		:    List[str]         =				prepare_img()
						A_		:    Any         =				image_processor(images=lowercase		,     return_tensors='pt' ).to(lowercase )

						# forward pass
						with torch.no_grad():
								A_		:    Dict         =				model(**lowercase )

						# verify the logits
						A_		:    int         =				torch.Size((1, 1_0_0_0) )
						self.assertEqual(outputs.logits.shape		,     lowercase )

						A_		:    List[str]         =				torch.tensor([[-2.17_03E00, 2.11_07E00, -2.08_11E00]] ).to(lowercase )
						self.assertTrue(torch.allclose(outputs.logits[0, :3]		,     lowercase		,     atol=1E-4 ) )







_UpperCAmelCase            =		"""ABCDEFGHIJKLMNOPQRSTUVWXYZ"""




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Tuple         =				input('Enter message: '     )
		A_		:    int         =				input('Enter key [alphanumeric]: '     )
		A_		:    Optional[Any]         =				input('Encrypt/Decrypt [e/d]: '     )

		if mode.lower().startswith('e'     ):
				A_		:    List[Any]         =				'encrypt'
				A_		:    int         =				encrypt_message(__lowercase						,__lowercase     )
		elif mode.lower().startswith('d'     ):
				A_		:    Optional[Any]         =				'decrypt'
				A_		:    Dict         =				decrypt_message(__lowercase						,__lowercase     )

		print(f'''\n{mode.title()}ed message:'''     )
		print(__lowercase     )




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			str     ):
		'''simple docstring'''




		return translate_message(__lowercase						,__lowercase						,'encrypt'     )




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			str     ):
		'''simple docstring'''




		return translate_message(__lowercase						,__lowercase						,'decrypt'     )




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			str						,__lowercase						:			str     ):
		'''simple docstring'''




		A_		:    Tuple         =				[]
		A_		:    str         =				0
		A_		:    Optional[int]         =				key.upper()

		for symbol in message:
				A_		:    Optional[Any]         =				LETTERS.find(symbol.upper()     )
				if num != -1:
						if mode == "encrypt":
								num += LETTERS.find(key[key_index]     )
						elif mode == "decrypt":
								num -= LETTERS.find(key[key_index]     )

						num %= len(__lowercase     )

						if symbol.isupper():
								translated.append(LETTERS[num]     )
						elif symbol.islower():
								translated.append(LETTERS[num].lower()     )

						key_index += 1
						if key_index == len(__lowercase     ):
								A_		:    str         =				0
				else:
						translated.append(__lowercase     )
		return "".join(__lowercase     )


if __name__ == "__main__":
	main()






