


'''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 			UpperCAmelCase			:

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





    """simple docstring"""



    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 		lowercase_    (			self			:						List[Any]       ):





    """simple docstring"""



    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 		lowercase_    (			self			:						List[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 		lowercase_    (			self			:						Tuple,       a_			:						Dict,       a_			:						Optional[Any],       a_			:						int       ):





    """simple docstring"""



    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 		lowercase_    (			self			:						Union[str, Any],       a_			:						Union[str, Any],       a_			:						Union[str, Any],       a_			:						str       ):





    """simple docstring"""



    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 		lowercase_    (			self			:						Optional[Any]       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.prepare_config_and_inputs()
    UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__			    =    config_and_inputs
    UpperCamelCase__			    =    {"pixel_values": pixel_values}
    return config, inputs_dict







@require_torch
class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      unittest.TestCase):
 _lowerCamelCase			:       int            =       (SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
 _lowerCamelCase			:       str            =       {'feature-extraction': SwinaSRModel} if is_torch_available() else {}

 _lowerCamelCase			:       Tuple            =       False
 _lowerCamelCase			:       str            =       False
 _lowerCamelCase			:       int            =       False
 _lowerCamelCase			:       List[str]            =       False
 _lowerCamelCase			:       Tuple            =       False

 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    UpperCamelCase__			    =    SwinaSRModelTester(self       )
    UpperCamelCase__			    =    ConfigTester(self,       config_class=a_,       embed_dim=37       )

 def 		lowercase_    (			self			:						List[str]       ):





    """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 		lowercase_    (			self			:						List[Any]       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*a_       )

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    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 		lowercase_    (			self			:						List[Any]       ):





    """simple docstring"""



    pass

 @unittest.skip(reason="Swin2SR does not use inputs_embeds"       )
 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    pass

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





    """simple docstring"""



    pass

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





    """simple docstring"""



    pass

 def 		lowercase_    (			self			:						Optional[Any]       ):





    """simple docstring"""



    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 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    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 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCamelCase__			    =    SwinaSRModel.from_pretrained(a_       )
       self.assertIsNotNone(a_       )

 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    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 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    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 			UpperCAmelCase			(							unittest.TestCase):

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    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(
        [[0.5_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(a_       )
    self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3],       a_,       atol=1e-4       )       )



'''simple docstring'''

from __future__ import annotations



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					list[list[int]]     ,		_UpperCamelCase   :					list[list[int]] )		->       list[int]:





   '''simple docstring'''







   assert len(_UpperCamelCase ) == len(_UpperCamelCase )

   UpperCamelCase__			    =    len(_UpperCamelCase )
   UpperCamelCase__			    =    list(range(_UpperCamelCase ) )
   UpperCamelCase__			    =    [-1] * n  # who the donor has donated to
   UpperCamelCase__			    =    [-1] * n  # who the recipient has received from
   UpperCamelCase__			    =    [0] * n

   while unmatched_donors:
      UpperCamelCase__			    =    unmatched_donors[0]
      UpperCamelCase__			    =    donor_pref[donor]
      UpperCamelCase__			    =    donor_preference[num_donations[donor]]
      num_donations[donor] += 1
      UpperCamelCase__			    =    recipient_pref[recipient]
      UpperCamelCase__			    =    rec_record[recipient]

      if prev_donor != -1:
         if rec_preference.index(_UpperCamelCase ) > rec_preference.index(_UpperCamelCase ):
            UpperCamelCase__			    =    donor
            UpperCamelCase__			    =    recipient
            unmatched_donors.append(_UpperCamelCase )
            unmatched_donors.remove(_UpperCamelCase )
      else:
         UpperCamelCase__			    =    donor
         UpperCamelCase__			    =    recipient
         unmatched_donors.remove(_UpperCamelCase )
   return donor_record