

'''simple docstring'''



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 			SCREAMING_SNAKE_CASE					:






      '''simple docstring'''






      def __init__(					self :		Dict     ,					snake_case :		Any     ,					snake_case :		Optional[Any]=13     ,					snake_case :		Optional[int]=32     ,					snake_case :		Any=1     ,					snake_case :		Union[str, Any]=3     ,					snake_case :		Any=16     ,					snake_case :		Tuple=[1, 2, 1]     ,					snake_case :		Optional[Any]=[2, 2, 4]     ,					snake_case :		int=2     ,					snake_case :		Optional[int]=2.0     ,					snake_case :		List[str]=True     ,					snake_case :		Any=0.0     ,					snake_case :		List[str]=0.0     ,					snake_case :		Optional[Any]=0.1     ,					snake_case :		Optional[int]="gelu"     ,					snake_case :		Any=False     ,					snake_case :		Tuple=True     ,					snake_case :		str=0.02     ,					snake_case :		Any=1e-5     ,					snake_case :		Dict=True     ,					snake_case :		Tuple=None     ,					snake_case :		Union[str, Any]=False     ,					snake_case :		int=2     ,					):



          """simple docstring"""





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

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




      def 	__UpperCAmelCase			(					self :		List[str]   ):



          """simple docstring"""





          _snake_case : Optional[Any]												=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

          _snake_case : List[Any]												=		None
          if self.use_labels:
              _snake_case : Tuple												=		ids_tensor([self.batch_size]     ,					self.type_sequence_label_size   )

          _snake_case : Union[str, Any]												=		self.get_config()

          return config, pixel_values, labels




      def 	__UpperCAmelCase			(					self :		Optional[Any]   ):



          """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 :		Optional[Any]     ,					snake_case :		Optional[int]     ,					snake_case :		Any     ,					snake_case :		List[str]   ):



          """simple docstring"""





          _snake_case : List[Any]												=		SwinaSRModel(config=snake_case   )
          model.to(snake_case   )
          model.eval()
          _snake_case : List[str]												=		model(snake_case   )

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




      def 	__UpperCAmelCase			(					self :		Dict     ,					snake_case :		Union[str, Any]     ,					snake_case :		Union[str, Any]     ,					snake_case :		Optional[int]   ):



          """simple docstring"""





          _snake_case : Optional[Any]												=		SwinaSRForImageSuperResolution(snake_case   )
          model.to(snake_case   )
          model.eval()
          _snake_case : Union[str, Any]												=		model(snake_case   )

          _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 :		List[str]   ):



          """simple docstring"""





          _snake_case : str												=		self.prepare_config_and_inputs()
          _snake_case					,							_snake_case					,							_snake_case : str												=		config_and_inputs
          _snake_case : List[Any]												=		{'pixel_values': pixel_values}
          return config, inputs_dict



@require_torch
class 			SCREAMING_SNAKE_CASE					(							lowercase_      ,lowercase_      ,unittest.TestCase					):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__ :				List[Any]    =				(SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
      SCREAMING_SNAKE_CASE__ :				str    =				{'''feature-extraction''': SwinaSRModel} if is_torch_available() else {}

      SCREAMING_SNAKE_CASE__ :				int    =				False
      SCREAMING_SNAKE_CASE__ :				str    =				False
      SCREAMING_SNAKE_CASE__ :				List[Any]    =				False
      SCREAMING_SNAKE_CASE__ :				Tuple    =				False
      SCREAMING_SNAKE_CASE__ :				str    =				False




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



          """simple docstring"""





          _snake_case : Optional[int]												=		SwinaSRModelTester(self   )
          _snake_case : Optional[Any]												=		ConfigTester(self     ,					config_class=snake_case     ,					embed_dim=37   )




      def 	__UpperCAmelCase			(					self :		Union[str, 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 : List[Any]												=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case   )




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



          """simple docstring"""





          _snake_case : Optional[Any]												=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_super_resolution(*snake_case   )




      @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'   )
      def 	__UpperCAmelCase			(					self :		List[str]   ):



          """simple docstring"""





          pass




      @unittest.skip(reason='Swin2SR does not use inputs_embeds'   )
      def 	__UpperCAmelCase			(					self :		Optional[int]   ):



          """simple docstring"""





          pass




      @unittest.skip(reason='Swin2SR does not support training yet'   )
      def 	__UpperCAmelCase			(					self :		str   ):



          """simple docstring"""





          pass




      @unittest.skip(reason='Swin2SR does not support training yet'   )
      def 	__UpperCAmelCase			(					self :		Union[str, Any]   ):



          """simple docstring"""





          pass




      def 	__UpperCAmelCase			(					self :		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 : Any												=		model_class(snake_case   )
              self.assertIsInstance(model.get_input_embeddings()     ,					(nn.Module)   )
              _snake_case : Optional[int]												=		model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(snake_case     ,					nn.Linear   )   )




      def 	__UpperCAmelCase			(					self :		Optional[Any]   ):



          """simple docstring"""





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

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

              _snake_case : List[str]												=		['pixel_values']
              self.assertListEqual(arg_names[:1]     ,					snake_case   )




      @slow
      def 	__UpperCAmelCase			(					self :		Tuple   ):



          """simple docstring"""





          for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _snake_case : List[str]												=		SwinaSRModel.from_pretrained(snake_case   )
              self.assertIsNotNone(snake_case   )




      def 	__UpperCAmelCase			(					self :		Dict   ):



          """simple docstring"""





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

          _snake_case : Tuple												=		_config_zero_init(snake_case   )
          for model_class in self.all_model_classes:
              _snake_case : Optional[Any]												=		model_class(config=snake_case   )
              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 :		Dict   ):



          """simple docstring"""





          _snake_case					,							_snake_case : Tuple												=		self.model_tester.prepare_config_and_inputs_for_common()
          _snake_case : List[Any]												=		True

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

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

              self.assertListEqual(
                  list(attentions[0].shape[-3:]   )     ,					[self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,					)
              _snake_case : str												=		len(snake_case   )

              # Check attention is always last and order is fine
              _snake_case : Tuple												=		True
              _snake_case : Optional[int]												=		True
              _snake_case : Optional[int]												=		model_class(snake_case   )
              model.to(snake_case   )
              model.eval()
              with torch.no_grad():
                  _snake_case : int												=		model(**self._prepare_for_class(snake_case     ,					snake_case   )   )

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

              _snake_case : str												=		outputs.attentions

              self.assertEqual(len(snake_case   )     ,					snake_case   )

              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 			SCREAMING_SNAKE_CASE					(							unittest.TestCase					):






      '''simple docstring'''






      def 	__UpperCAmelCase			(					self :		Optional[Any]   ):



          """simple docstring"""





          _snake_case : List[str]												=		SwinaSRImageProcessor()
          _snake_case : Any												=		SwinaSRForImageSuperResolution.from_pretrained('caidas/swin2SR-classical-sr-x2-64'   ).to(snake_case   )

          _snake_case : Optional[int]												=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
          _snake_case : Dict												=		processor(images=snake_case     ,					return_tensors='pt'   ).to(snake_case   )

          # forward pass
          with torch.no_grad():
              _snake_case : Union[str, Any]												=		model(**snake_case   )

          # verify the logits
          _snake_case : Optional[int]												=		torch.Size([1, 3, 976, 1296]   )
          self.assertEqual(outputs.reconstruction.shape     ,					snake_case   )
          _snake_case : Optional[Any]												=		torch.tensor(
              [[0.5458, 0.5546, 0.5638], [0.5526, 0.5565, 0.5651], [0.5396, 0.5426, 0.5621]]   ).to(snake_case   )
          self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]     ,					snake_case     ,					atol=1e-4   )   )






'''simple docstring'''



from __future__ import annotations

from collections import Counter

def   lowerCamelCase__       (      a__)		->    list[int]:



    """simple docstring"""







    _snake_case : Optional[int]												=		[True] * n
    _snake_case : List[str]												=		False
    _snake_case : int												=		False
    _snake_case : Tuple												=		True

    for i in range(3			,      int(n**0.5 + 1)			,      2):
        _snake_case : int												=		i * 2
        while index < n:
            _snake_case : List[str]												=		False
            _snake_case : Union[str, Any]												=		index + i

    _snake_case : List[str]												=		[2]

    for i in range(3			,      a__			,      2):
        if is_prime[i]:
            primes.append(a__)

    return primes

def   lowerCamelCase__       (      a__)		->    list[list[int]]:



    """simple docstring"""







    _snake_case : Tuple												=		str(a__)
    _snake_case : str												=		[]
    _snake_case : Union[str, Any]												=		['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']

    for duplicate in Counter(a__) - Counter(set(a__)):
        _snake_case : List[Any]												=		[int(number_str.replace(a__			,      a__)) for digit in digits]
        replacements.append(a__)

    return replacements

def   lowerCamelCase__       (      a__ = 8)		->    int:



    """simple docstring"""







    _snake_case : Any												=		set()

    # Filter primes with less than 3 replaceable digits
    _snake_case : List[Any]												=		{
        x for x in set(prime_sieve(1_0_0_0_0_0_0)) if len(str(a__)) - len(set(str(a__))) >= 3
    }

    for prime in primes:
        if prime in numbers_checked:
            continue

        _snake_case : int												=		digit_replacements(a__)

        for family in replacements:
            numbers_checked.update(a__)
            _snake_case : Union[str, Any]												=		primes.intersection(a__)

            if len(a__) != family_length:
                continue

            return min(a__)

    return -1


if __name__ == "__main__":
   print(solution())



