





import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import 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 SwinaSRForImageSuperResolution, SwinaSRModel
     from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
     from PIL import Image

     from transformers import SwinaSRImageProcessor




class 			_lowerCamelCase	:



      """simple docstring"""






      def __init__(	self			,  UpperCAmelCase			,  UpperCAmelCase=13			,  UpperCAmelCase=32			,  UpperCAmelCase=1			,  UpperCAmelCase=3			,  UpperCAmelCase=16			,  UpperCAmelCase=[1, 2, 1]			,  UpperCAmelCase=[2, 2, 4]			,  UpperCAmelCase=2			,  UpperCAmelCase=2.0			,  UpperCAmelCase=True			,  UpperCAmelCase=0.0			,  UpperCAmelCase=0.0			,  UpperCAmelCase=0.1			,  UpperCAmelCase="gelu"			,  UpperCAmelCase=False			,  UpperCAmelCase=True			,  UpperCAmelCase=0.02			,  UpperCAmelCase=1E-5			,  UpperCAmelCase=True			,  UpperCAmelCase=None			,  UpperCAmelCase=False			,  UpperCAmelCase=2			,  )    ->				List[Any]:






        '''simple docstring'''






        __snake_case				:			List[str]            =    parent
        __snake_case				:			List[Any]            =    batch_size
        __snake_case				:			List[str]            =    image_size
        __snake_case				:			int            =    patch_size
        __snake_case				:			int            =    num_channels
        __snake_case				:			Any            =    embed_dim
        __snake_case				:			Union[str, Any]            =    depths
        __snake_case				:			List[str]            =    num_heads
        __snake_case				:			Optional[Any]            =    window_size
        __snake_case				:			Union[str, Any]            =    mlp_ratio
        __snake_case				:			Dict            =    qkv_bias
        __snake_case				:			str            =    hidden_dropout_prob
        __snake_case				:			List[Any]            =    attention_probs_dropout_prob
        __snake_case				:			Any            =    drop_path_rate
        __snake_case				:			Optional[int]            =    hidden_act
        __snake_case				:			List[Any]            =    use_absolute_embeddings
        __snake_case				:			Dict            =    patch_norm
        __snake_case				:			str            =    layer_norm_eps
        __snake_case				:			Any            =    initializer_range
        __snake_case				:			str            =    is_training
        __snake_case				:			Tuple            =    scope
        __snake_case				:			List[str]            =    use_labels
        __snake_case				:			Union[str, Any]            =    upscale

        # here we set some attributes to make tests pass
        __snake_case				:			List[Any]            =    len(UpperCAmelCase				)
        __snake_case				:			List[str]            =    embed_dim
        __snake_case				:			List[Any]            =    (image_size // patch_size) ** 2







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






        '''simple docstring'''






        __snake_case				:			str            =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

        __snake_case				:			Optional[int]            =    None
        if self.use_labels:
          __snake_case				:			Optional[int]            =    ids_tensor([self.batch_size]			,  self.type_sequence_label_size				)

        __snake_case				:			str            =    self.get_config()

        return config, pixel_values, labels







      def   UpperCAmelCase						(	self				)    ->				str:






        '''simple docstring'''






        return SwinaSRConfig(
            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			,  window_size=self.window_size			,  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			,  use_absolute_embeddings=self.use_absolute_embeddings			,  path_norm=self.patch_norm			,  layer_norm_eps=self.layer_norm_eps			,  initializer_range=self.initializer_range			,  upscale=self.upscale			,  )







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				Tuple:






        '''simple docstring'''






        __snake_case				:			List[Any]            =    SwinaSRModel(config=UpperCAmelCase				)
        model.to(UpperCAmelCase				)
        model.eval()
        __snake_case				:			Tuple            =    model(UpperCAmelCase				)

        self.parent.assertEqual(
            result.last_hidden_state.shape			,  (self.batch_size, self.embed_dim, self.image_size, self.image_size)				)







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				str:






        '''simple docstring'''






        __snake_case				:			str            =    SwinaSRForImageSuperResolution(UpperCAmelCase				)
        model.to(UpperCAmelCase				)
        model.eval()
        __snake_case				:			Optional[int]            =    model(UpperCAmelCase				)

        __snake_case				:			Optional[int]            =    self.image_size * self.upscale
        self.parent.assertEqual(
            result.reconstruction.shape			,  (self.batch_size, self.num_channels, expected_image_size, expected_image_size)				)







      def   UpperCAmelCase						(	self				)    ->				Tuple:






        '''simple docstring'''






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




@require_torch
class 			_lowerCamelCase	(      a	,				a	,				unittest.TestCase      ):



      """simple docstring"""






      UpperCAmelCase_  : List[str]				      =(SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
      UpperCAmelCase_  : str				      ={"feature-extraction": SwinaSRModel} if is_torch_available() else {}

      UpperCAmelCase_  : Union[str, Any]				      =False
      UpperCAmelCase_  : Dict				      =False
      UpperCAmelCase_  : List[Any]				      =False
      UpperCAmelCase_  : Dict				      =False
      UpperCAmelCase_  : Optional[int]				      =False







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






        '''simple docstring'''






        __snake_case				:			Tuple            =    SwinaSRModelTester(self				)
        __snake_case				:			Union[str, Any]            =    ConfigTester(self			,  config_class=UpperCAmelCase			,  embed_dim=37				)







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






        '''simple docstring'''






        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				)    ->				Optional[Any]:






        '''simple docstring'''






        __snake_case				:			Union[str, Any]            =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase				)







      def   UpperCAmelCase						(	self				)    ->				Any:






        '''simple docstring'''






        __snake_case				:			Optional[int]            =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_super_resolution(*UpperCAmelCase				)







      @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."				)
      def   UpperCAmelCase						(	self				)    ->				Optional[int]:






        '''simple docstring'''






        pass







      @unittest.skip(reason="Swin2SR does not use inputs_embeds"				)
      def   UpperCAmelCase						(	self				)    ->				Optional[Any]:






        '''simple docstring'''






        pass







      @unittest.skip(reason="Swin2SR does not support training yet"				)
      def   UpperCAmelCase						(	self				)    ->				Optional[int]:






        '''simple docstring'''






        pass







      @unittest.skip(reason="Swin2SR does not support training yet"				)
      def   UpperCAmelCase						(	self				)    ->				Optional[int]:






        '''simple docstring'''






        pass







      def   UpperCAmelCase						(	self				)    ->				List[Any]:






        '''simple docstring'''






        __snake_case      ,				__snake_case				:			List[str]            =    self.model_tester.prepare_config_and_inputs_for_common()

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







      def   UpperCAmelCase						(	self				)    ->				Tuple:






        '''simple docstring'''






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

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

          __snake_case				:			str            =    ["pixel_values"]
          self.assertListEqual(arg_names[:1]			,  UpperCAmelCase				)







      @slow
      def   UpperCAmelCase						(	self				)    ->				Tuple:






        '''simple docstring'''






        for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          __snake_case				:			Tuple            =    SwinaSRModel.from_pretrained(UpperCAmelCase				)
          self.assertIsNotNone(UpperCAmelCase				)







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






        '''simple docstring'''






        __snake_case      ,				__snake_case				:			int            =    self.model_tester.prepare_config_and_inputs_for_common()

        __snake_case				:			int            =    _config_zero_init(UpperCAmelCase				)
        for model_class in self.all_model_classes:
          __snake_case				:			Optional[int]            =    model_class(config=UpperCAmelCase				)
          for name, param in model.named_parameters():
            if "logit_scale" in name:
              continue
            if 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"""			,  )







      def   UpperCAmelCase						(	self				)    ->				Any:






        '''simple docstring'''






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

        for model_class in self.all_model_classes:
          __snake_case				:			List[str]            =    True
          __snake_case				:			int            =    False
          __snake_case				:			List[Any]            =    True
          __snake_case				:			int            =    model_class(UpperCAmelCase				)
          model.to(UpperCAmelCase				)
          model.eval()
          with torch.no_grad():
            __snake_case				:			int            =    model(**self._prepare_for_class(UpperCAmelCase			,  UpperCAmelCase				)				)
          __snake_case				:			Tuple            =    outputs.attentions
          __snake_case				:			Optional[int]            =    len(self.model_tester.depths				)
          self.assertEqual(len(UpperCAmelCase				)			,  UpperCAmelCase				)

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          __snake_case				:			Union[str, Any]            =    True
          __snake_case				:			List[str]            =    config.window_size**2
          __snake_case				:			Optional[Any]            =    model_class(UpperCAmelCase				)
          model.to(UpperCAmelCase				)
          model.eval()
          with torch.no_grad():
            __snake_case				:			List[str]            =    model(**self._prepare_for_class(UpperCAmelCase			,  UpperCAmelCase				)				)
          __snake_case				:			Dict            =    outputs.attentions
          self.assertEqual(len(UpperCAmelCase				)			,  UpperCAmelCase				)

          self.assertListEqual(
              list(attentions[0].shape[-3:]				)			,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]			,  )
          __snake_case				:			Optional[Any]            =    len(UpperCAmelCase				)

          # Check attention is always last and order is fine
          __snake_case				:			int            =    True
          __snake_case				:			Dict            =    True
          __snake_case				:			Tuple            =    model_class(UpperCAmelCase				)
          model.to(UpperCAmelCase				)
          model.eval()
          with torch.no_grad():
            __snake_case				:			List[Any]            =    model(**self._prepare_for_class(UpperCAmelCase			,  UpperCAmelCase				)				)

          self.assertEqual(out_len + 1			,  len(UpperCAmelCase				)				)

          __snake_case				:			str            =    outputs.attentions

          self.assertEqual(len(UpperCAmelCase				)			,  UpperCAmelCase				)

          self.assertListEqual(
              list(self_attentions[0].shape[-3:]				)			,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]			,  )




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



      """simple docstring"""






      def   UpperCAmelCase						(	self				)    ->				List[Any]:






        '''simple docstring'''






        __snake_case				:			List[str]            =    SwinaSRImageProcessor()
        __snake_case				:			Optional[int]            =    SwinaSRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64"				).to(UpperCAmelCase				)

        __snake_case				:			Optional[int]            =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
        __snake_case				:			Any            =    processor(images=UpperCAmelCase			,  return_tensors="pt"				).to(UpperCAmelCase				)

        # forward pass
        with torch.no_grad():
          __snake_case				:			Dict            =    model(**UpperCAmelCase				)

        # verify the logits
        __snake_case				:			Dict            =    torch.Size([1, 3, 976, 1296]				)
        self.assertEqual(outputs.reconstruction.shape			,  UpperCAmelCase				)
        __snake_case				:			Optional[Any]            =    torch.tensor(
            [[0.5_458, 0.5_546, 0.5_638], [0.5_526, 0.5_565, 0.5_651], [0.5_396, 0.5_426, 0.5_621]]				).to(UpperCAmelCase				)
        self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]			,  UpperCAmelCase			,  atol=1E-4				)				)








_UpperCamelCase    =	[chr(i) for i in range(32, 126)]
_UpperCamelCase    =	list(range(len(alphabets)))
_UpperCamelCase    =	list(range(len(alphabets)))
_UpperCamelCase    =	list(range(len(alphabets)))
_UpperCamelCase    =	list(reversed(range(len(alphabets))))
_UpperCamelCase    =	[]
_UpperCamelCase    =	_UpperCamelCase    =	_UpperCamelCase    =	0







def  lowerCAmelCase__(		)	->      int:
  global gear_one_pos
  global gear_two_pos
  global gear_three_pos
  __snake_case				:			int            =    gear_one[0]
  gear_one.append(lowercase			)
  del gear_one[0]
  gear_one_pos += 1
  if gear_one_pos % int(len(lowercase			)			) == 0:
    __snake_case				:			List[str]            =    gear_two[0]
    gear_two.append(lowercase			)
    del gear_two[0]
    gear_two_pos += 1
    if gear_two_pos % int(len(lowercase			)			) == 0:
      __snake_case				:			Any            =    gear_three[0]
      gear_three.append(lowercase			)
      del gear_three[0]
      gear_three_pos += 1







def  lowerCAmelCase__(		lowercase    :      Tuple			)	->      Union[str, Any]:
  __snake_case				:			Union[str, Any]            =    alphabets.index(lowercase			)
  __snake_case				:			Tuple            =    gear_one[target]
  __snake_case				:			Union[str, Any]            =    gear_two[target]
  __snake_case				:			int            =    gear_three[target]
  __snake_case				:			Optional[int]            =    reflector[target]
  __snake_case				:			Union[str, Any]            =    gear_three.index(lowercase			)
  __snake_case				:			Optional[Any]            =    gear_two.index(lowercase			)
  __snake_case				:			int            =    gear_one.index(lowercase			)
  code.append(alphabets[target]			)
  rotator()


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

