
"""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 A_      :




  '''simple docstring'''







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

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

     # here we set some attributes to make tests pass
     UpperCAmelCase_   :   Tuple     	=       len(lowercase_    )
     UpperCAmelCase_   :   Tuple     	=       embed_dim
     UpperCAmelCase_   :   int     	=       (image_size // patch_size) ** 2







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Tuple     	=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

     UpperCAmelCase_   :   int     	=       None
     if self.use_labels:
        UpperCAmelCase_   :   Union[str, Any]     	=       ids_tensor([self.batch_size]							,  self.type_sequence_label_size    )

     UpperCAmelCase_   :   Optional[int]     	=       self.get_config()

     return config, pixel_values, labels







  def 			UpperCamelCase__   (						self    ):

     """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							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   str     	=       SwinaSRModel(config=lowercase_    )
     model.to(lowercase_    )
     model.eval()
     UpperCAmelCase_   :   Optional[Any]     	=       model(lowercase_    )

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







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[Any]     	=       SwinaSRForImageSuperResolution(lowercase_    )
     model.to(lowercase_    )
     model.eval()
     UpperCAmelCase_   :   List[str]     	=       model(lowercase_    )

     UpperCAmelCase_   :   List[Any]     	=       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    ):

     """simple docstring"""
     UpperCAmelCase_   :   Any     	=       self.prepare_config_and_inputs()
     UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :   Any     	=       config_and_inputs
     UpperCAmelCase_   :   Optional[Any]     	=       {"pixel_values": pixel_values}
     return config, inputs_dict




@require_torch
class A_      (lowercase__							,lowercase__							,unittest.TestCase ):




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

  SCREAMING_SNAKE_CASE__     :		Tuple    	=						False
  SCREAMING_SNAKE_CASE__     :		Dict    	=						False
  SCREAMING_SNAKE_CASE__     :		Optional[int]    	=						False
  SCREAMING_SNAKE_CASE__     :		str    	=						False
  SCREAMING_SNAKE_CASE__     :		List[str]    	=						False







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[int]     	=       SwinaSRModelTester(self    )
     UpperCAmelCase_   :   int     	=       ConfigTester(self							,  config_class=lowercase_							,  embed_dim=37    )







  def 			UpperCamelCase__   (						self    ):

     """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    ):

     """simple docstring"""
     UpperCAmelCase_   :   Union[str, Any]     	=       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[Any]     	=       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_super_resolution(*lowercase_    )







  @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."    )
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     pass







  @unittest.skip(reason="Swin2SR does not use inputs_embeds"    )
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     pass







  @unittest.skip(reason="Swin2SR does not support training yet"    )
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     pass







  @unittest.skip(reason="Swin2SR does not support training yet"    )
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     pass







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   List[str]     	=       self.model_tester.prepare_config_and_inputs_for_common()

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







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[Any]     	=       self.model_tester.prepare_config_and_inputs_for_common()

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

        UpperCAmelCase_   :   Union[str, Any]     	=       ["pixel_values"]
        self.assertListEqual(arg_names[:1]							,  lowercase_    )







  @slow
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCAmelCase_   :   Dict     	=       SwinaSRModel.from_pretrained(lowercase_    )
        self.assertIsNotNone(lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[int]     	=       self.model_tester.prepare_config_and_inputs_for_common()

     UpperCAmelCase_   :   Union[str, Any]     	=       _config_zero_init(lowercase_    )
     for model_class in self.all_model_classes:
        UpperCAmelCase_   :   Union[str, Any]     	=       model_class(config=lowercase_    )
        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    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[Any]     	=       self.model_tester.prepare_config_and_inputs_for_common()
     UpperCAmelCase_   :   str     	=       True

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

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

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

        # Check attention is always last and order is fine
        UpperCAmelCase_   :   Optional[int]     	=       True
        UpperCAmelCase_   :   str     	=       True
        UpperCAmelCase_   :   Optional[Any]     	=       model_class(lowercase_    )
        model.to(lowercase_    )
        model.eval()
        with torch.no_grad():
           UpperCAmelCase_   :   List[str]     	=       model(**self._prepare_for_class(lowercase_							,  lowercase_    )    )

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

        UpperCAmelCase_   :   Optional[int]     	=       outputs.attentions

        self.assertEqual(len(lowercase_    )							,  lowercase_    )

        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 A_      (unittest.TestCase ):




  '''simple docstring'''







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Union[str, Any]     	=       SwinaSRImageProcessor()
     UpperCAmelCase_   :   Tuple     	=       SwinaSRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64"    ).to(lowercase_    )

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

     # forward pass
     with torch.no_grad():
        UpperCAmelCase_   :   List[Any]     	=       model(**lowercase_    )

     # verify the logits
     UpperCAmelCase_   :   Optional[Any]     	=       torch.Size([1, 3, 976, 1296]    )
     self.assertEqual(outputs.reconstruction.shape							,  lowercase_    )
     UpperCAmelCase_   :   List[Any]     	=       torch.tensor(
         [[0.54_58, 0.55_46, 0.56_38], [0.55_26, 0.55_65, 0.56_51], [0.53_96, 0.54_26, 0.56_21]]    ).to(lowercase_    )
     self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]							,  lowercase_							,  atol=1E-4    )    )





"""simple docstring"""
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uinta, zeros







def       UpperCamelCase_					(     lowerCamelCase							:					Optional[int]     , lowerCamelCase							:					Tuple   )						->     str:



       """simple docstring"""







       __magic_name__       ,							__magic_name__			:		Optional[Any]        =     image.shape
       __magic_name__			:		str        =     cols - block_size[1] + 1
       __magic_name__			:		int        =     rows - block_size[0] + 1
       __magic_name__			:		Optional[Any]        =     zeros((dst_height * dst_width, block_size[1] * block_size[0])   )
       __magic_name__			:		List[Any]        =     0
       for i in range(0     , lowerCamelCase   ):
              for j in range(0     , lowerCamelCase   ):
                     __magic_name__			:		List[Any]        =     ravel(image[i : i + block_size[0], j : j + block_size[1]]   )
                     __magic_name__			:		str        =     window
                     row += 1

       return image_array







def       UpperCamelCase_					(     lowerCamelCase							:					str     , lowerCamelCase							:					List[str]   )						->     Tuple:



       """simple docstring"""







       __magic_name__       ,							__magic_name__			:		List[str]        =     image.shape[0], image.shape[1]
       __magic_name__			:		Any        =     filter_kernel.shape[0]
       __magic_name__			:		Optional[Any]        =     k_size // 2
       # Pads image with the edge values of array.
       __magic_name__			:		Union[str, Any]        =     pad(lowerCamelCase     , lowerCamelCase     , mode='''edge'''   )

       # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
       __magic_name__			:		List[Any]        =     imacol(lowerCamelCase     , (k_size, k_size)   )

       #  turn the kernel into shape(k*k, 1)
       __magic_name__			:		Tuple        =     ravel(lowerCamelCase   )
       # reshape and get the dst image
       __magic_name__			:		int        =     dot(lowerCamelCase     , lowerCamelCase   ).reshape(lowerCamelCase     , lowerCamelCase   )
       return dst


if __name__ == "__main__":
 # read original image
 A									=						imread(R"""../image_data/lena.jpg""")
 # turn image in gray scale value
 A									=						cvtColor(img, COLOR_BGR2GRAY)
 # Laplace operator
 A									=						array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
 A									=						img_convolve(gray, Laplace_kernel).astype(uinta)
 imshow("""Laplacian""", out)
 waitKey(0)
