'''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   _lowercase:



   """simple docstring"""





   def __init__(							self:    str			,a:    Tuple			,a:    List[Any]=13			,a:    Optional[Any]=32			,a:    List[Any]=1			,a:    List[Any]=3			,a:    Dict=16			,a:    Tuple=[1, 2, 1]			,a:    Optional[Any]=[2, 2, 4]			,a:    Dict=2			,a:    List[str]=2.0			,a:    Optional[Any]=True			,a:    Tuple=0.0			,a:    str=0.0			,a:    Any=0.1			,a:    Any="gelu"			,a:    List[str]=False			,a:    Tuple=True			,a:    Any=0.02			,a:    int=1e-5			,a:    Tuple=True			,a:    str=None			,a:    Tuple=False			,a:    Dict=2			,):
    __UpperCAmelCase         =					parent
    __UpperCAmelCase         =					batch_size
    __UpperCAmelCase         =					image_size
    __UpperCAmelCase         =					patch_size
    __UpperCAmelCase         =					num_channels
    __UpperCAmelCase         =					embed_dim
    __UpperCAmelCase         =					depths
    __UpperCAmelCase         =					num_heads
    __UpperCAmelCase         =					window_size
    __UpperCAmelCase         =					mlp_ratio
    __UpperCAmelCase         =					qkv_bias
    __UpperCAmelCase         =					hidden_dropout_prob
    __UpperCAmelCase         =					attention_probs_dropout_prob
    __UpperCAmelCase         =					drop_path_rate
    __UpperCAmelCase         =					hidden_act
    __UpperCAmelCase         =					use_absolute_embeddings
    __UpperCAmelCase         =					patch_norm
    __UpperCAmelCase         =					layer_norm_eps
    __UpperCAmelCase         =					initializer_range
    __UpperCAmelCase         =					is_training
    __UpperCAmelCase         =					scope
    __UpperCAmelCase         =					use_labels
    __UpperCAmelCase         =					upscale

    # here we set some attributes to make tests pass
    __UpperCAmelCase         =					len(a	)
    __UpperCAmelCase         =					embed_dim
    __UpperCAmelCase         =					(image_size // patch_size) ** 2





   def 			snake_case	(							self:    Tuple	):
    __UpperCAmelCase         =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

    __UpperCAmelCase         =					None
    if self.use_labels:
     __UpperCAmelCase         =					ids_tensor([self.batch_size]			,self.type_sequence_label_size	)

    __UpperCAmelCase         =					self.get_config()

    return config, pixel_values, labels





   def 			snake_case	(							self:    Tuple	):
    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 			snake_case	(							self:    Optional[int]			,a:    Any			,a:    str			,a:    str	):
    __UpperCAmelCase         =					SwinaSRModel(config=a	)
    model.to(a	)
    model.eval()
    __UpperCAmelCase         =					model(a	)

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





   def 			snake_case	(							self:    List[Any]			,a:    int			,a:    Optional[Any]			,a:    Tuple	):
    __UpperCAmelCase         =					SwinaSRForImageSuperResolution(a	)
    model.to(a	)
    model.eval()
    __UpperCAmelCase         =					model(a	)

    __UpperCAmelCase         =					self.image_size * self.upscale
    self.parent.assertEqual(
        result.reconstruction.shape			,(self.batch_size, self.num_channels, expected_image_size, expected_image_size)	)





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




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



   """simple docstring"""





   __lowerCamelCase    =  (SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
   __lowerCamelCase    =  {'''feature-extraction''': SwinaSRModel} if is_torch_available() else {}

   __lowerCamelCase    =  False
   __lowerCamelCase    =  False
   __lowerCamelCase    =  False
   __lowerCamelCase    =  False
   __lowerCamelCase    =  False





   def 			snake_case	(							self:    Optional[Any]	):
    __UpperCAmelCase         =					SwinaSRModelTester(self	)
    __UpperCAmelCase         =					ConfigTester(self			,config_class=a			,embed_dim=37	)





   def 			snake_case	(							self:    str	):
    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:    Optional[int]	):
    __UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*a	)





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





   @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'	)
   def 			snake_case	(							self:    str	):
    pass





   @unittest.skip(reason='Swin2SR does not use inputs_embeds'	)
   def 			snake_case	(							self:    Union[str, Any]	):
    pass





   @unittest.skip(reason='Swin2SR does not support training yet'	)
   def 			snake_case	(							self:    int	):
    pass





   @unittest.skip(reason='Swin2SR does not support training yet'	)
   def 			snake_case	(							self:    Dict	):
    pass





   def 			snake_case	(							self:    Dict	):
    __UpperCAmelCase  ,			__UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     __UpperCAmelCase         =					model_class(a	)
     self.assertIsInstance(model.get_input_embeddings()			,(nn.Module)	)
     __UpperCAmelCase         =					model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(a			,nn.Linear	)	)





   def 			snake_case	(							self:    Optional[Any]	):
    __UpperCAmelCase  ,			__UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()

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

     __UpperCAmelCase         =					['pixel_values']
     self.assertListEqual(arg_names[:1]			,a	)





   @slow
   def 			snake_case	(							self:    int	):
    for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     __UpperCAmelCase         =					SwinaSRModel.from_pretrained(a	)
     self.assertIsNotNone(a	)





   def 			snake_case	(							self:    Dict	):
    __UpperCAmelCase  ,			__UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()

    __UpperCAmelCase         =					_config_zero_init(a	)
    for model_class in self.all_model_classes:
     __UpperCAmelCase         =					model_class(config=a	)
     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 			snake_case	(							self:    List[Any]	):
    __UpperCAmelCase  ,			__UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()
    __UpperCAmelCase         =					True

    for model_class in self.all_model_classes:
     __UpperCAmelCase         =					True
     __UpperCAmelCase         =					False
     __UpperCAmelCase         =					True
     __UpperCAmelCase         =					model_class(a	)
     model.to(a	)
     model.eval()
     with torch.no_grad():
      __UpperCAmelCase         =					model(**self._prepare_for_class(a			,a	)	)
     __UpperCAmelCase         =					outputs.attentions
     __UpperCAmelCase         =					len(self.model_tester.depths	)
     self.assertEqual(len(a	)			,a	)

     # check that output_attentions also work using config
     del inputs_dict["output_attentions"]
     __UpperCAmelCase         =					True
     __UpperCAmelCase         =					config.window_size**2
     __UpperCAmelCase         =					model_class(a	)
     model.to(a	)
     model.eval()
     with torch.no_grad():
      __UpperCAmelCase         =					model(**self._prepare_for_class(a			,a	)	)
     __UpperCAmelCase         =					outputs.attentions
     self.assertEqual(len(a	)			,a	)

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

     # Check attention is always last and order is fine
     __UpperCAmelCase         =					True
     __UpperCAmelCase         =					True
     __UpperCAmelCase         =					model_class(a	)
     model.to(a	)
     model.eval()
     with torch.no_grad():
      __UpperCAmelCase         =					model(**self._prepare_for_class(a			,a	)	)

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

     __UpperCAmelCase         =					outputs.attentions

     self.assertEqual(len(a	)			,a	)

     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   _lowercase(     unittest.TestCase				):



   """simple docstring"""





   def 			snake_case	(							self:    Any	):
    __UpperCAmelCase         =					SwinaSRImageProcessor()
    __UpperCAmelCase         =					SwinaSRForImageSuperResolution.from_pretrained('caidas/swin2SR-classical-sr-x2-64'	).to(a	)

    __UpperCAmelCase         =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
    __UpperCAmelCase         =					processor(images=a			,return_tensors='pt'	).to(a	)

    # forward pass
    with torch.no_grad():
     __UpperCAmelCase         =					model(**a	)

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




'''simple docstring'''




def 						__snake_case (       lowerCAmelCase       :     str			):
 __UpperCAmelCase         =					5381
 for x in s:
  __UpperCAmelCase         =					((hash_value << 5) + hash_value) + ord(lowerCAmelCase			)
 return hash_value & 0Xffff_ffff



