




"""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  _snake_case				:

    '''simple docstring'''




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

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





    def 				snake_case_ (							self      :  Dict     ):
        UpperCAmelCase_					:Tuple      			=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

        UpperCAmelCase_					:Optional[Any]      			=      None
        if self.use_labels:
            UpperCAmelCase_					:Dict      			=      ids_tensor([self.batch_size]					,							self.type_sequence_label_size     )

        UpperCAmelCase_					:Tuple      			=      self.get_config()

        return config, pixel_values, labels





    def 				snake_case_ (							self      :  Dict     ):
        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      :  Any					,							snake_case      :  Union[str, Any]					,							snake_case      :  Dict					,							snake_case      :  Any     ):
        UpperCAmelCase_					:Dict      			=      SwinaSRModel(config=snake_case     )
        model.to(snake_case     )
        model.eval()
        UpperCAmelCase_					: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 				snake_case_ (							self      :  str					,							snake_case      :  List[str]					,							snake_case      :  Union[str, Any]					,							snake_case      :  List[str]     ):
        UpperCAmelCase_					:Any      			=      SwinaSRForImageSuperResolution(snake_case     )
        model.to(snake_case     )
        model.eval()
        UpperCAmelCase_					:Tuple      			=      model(snake_case     )

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



@require_torch
class  _snake_case				(							A__							,						A__							,						unittest.TestCase       ):

    '''simple docstring'''




    UpperCamelCase__		     =(SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
    UpperCamelCase__		     ={"""feature-extraction""": SwinaSRModel} if is_torch_available() else {}

    UpperCamelCase__		     =False
    UpperCamelCase__		     =False
    UpperCamelCase__		     =False
    UpperCamelCase__		     =False
    UpperCamelCase__		     =False





    def 				snake_case_ (							self      :  Any     ):
        UpperCAmelCase_					:Dict      			=      SwinaSRModelTester(self     )
        UpperCAmelCase_					:Dict      			=      ConfigTester(self					,							config_class=snake_case					,							embed_dim=37     )





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





    def 				snake_case_ (							self      :  Optional[int]     ):
        UpperCAmelCase_					:List[str]      			=      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 				snake_case_ (							self      :  Optional[Any]     ):
        pass





    @unittest.skip(reason='''Swin2SR does not use inputs_embeds'''     )
    def 				snake_case_ (							self      :  List[Any]     ):
        pass





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





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





    def 				snake_case_ (							self      :  int     ):
        UpperCAmelCase_					:Union[str, Any]      			=      self.model_tester.prepare_config_and_inputs_for_common()

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





    def 				snake_case_ (							self      :  Optional[Any]     ):
        UpperCAmelCase_					:Union[str, Any]      			=      self.model_tester.prepare_config_and_inputs_for_common()

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

            UpperCAmelCase_					:Optional[int]      			=      ['''pixel_values''']
            self.assertListEqual(arg_names[:1]					,							snake_case     )





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





    def 				snake_case_ (							self      :  Union[str, Any]     ):
        UpperCAmelCase_					:Optional[Any]      			=      self.model_tester.prepare_config_and_inputs_for_common()

        UpperCAmelCase_					:int      			=      _config_zero_init(snake_case     )
        for model_class in self.all_model_classes:
            UpperCAmelCase_					:Dict      			=      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 				snake_case_ (							self      :  Any     ):
        UpperCAmelCase_					:List[Any]      			=      self.model_tester.prepare_config_and_inputs_for_common()
        UpperCAmelCase_					:Optional[int]      			=      True

        for model_class in self.all_model_classes:
            UpperCAmelCase_					:List[str]      			=      True
            UpperCAmelCase_					:str      			=      False
            UpperCAmelCase_					:Optional[int]      			=      True
            UpperCAmelCase_					:Dict      			=      model_class(snake_case     )
            model.to(snake_case     )
            model.eval()
            with torch.no_grad():
                UpperCAmelCase_					:int      			=      model(**self._prepare_for_class(snake_case					,							snake_case     )     )
            UpperCAmelCase_					:int      			=      outputs.attentions
            UpperCAmelCase_					:Union[str, 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"]
            UpperCAmelCase_					:List[str]      			=      True
            UpperCAmelCase_					:int      			=      config.window_size**2
            UpperCAmelCase_					:Union[str, Any]      			=      model_class(snake_case     )
            model.to(snake_case     )
            model.eval()
            with torch.no_grad():
                UpperCAmelCase_					:str      			=      model(**self._prepare_for_class(snake_case					,							snake_case     )     )
            UpperCAmelCase_					:Union[str, Any]      			=      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]					,							)
            UpperCAmelCase_					:List[Any]      			=      len(snake_case     )

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

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

            UpperCAmelCase_					: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  _snake_case				(							unittest.TestCase       ):

    '''simple docstring'''




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

        UpperCAmelCase_					:Any      			=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
        UpperCAmelCase_					:int      			=      processor(images=snake_case					,							return_tensors='''pt'''     ).to(snake_case     )

        # forward pass
        with torch.no_grad():
            UpperCAmelCase_					:Dict      			=      model(**snake_case     )

        # verify the logits
        UpperCAmelCase_					:Optional[int]      			=      torch.Size([1, 3, 976, 1_296]     )
        self.assertEqual(outputs.reconstruction.shape					,							snake_case     )
        UpperCAmelCase_					:List[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(snake_case     )
        self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]					,							snake_case					,							atol=1e-4     )     )








"""simple docstring"""

def 				a (						__snake_case     :				list[list[int]]  ):



				'''simple docstring'''





				if len(__snake_case  ) == 0 or not all(len(__snake_case  ) == 2 for pair in coordinates  ):
								raise ValueError('''The program cannot work out a fitting polynomial.'''  )

				if len({tuple(__snake_case  ) for pair in coordinates}  ) != len(__snake_case  ):
								raise ValueError('''The program cannot work out a fitting polynomial.'''  )

				UpperCAmelCase_					:Optional[Any]      			=      {x for x, _ in coordinates}
				if len(__snake_case  ) == 1:
								return f'x={coordinates[0][0]}'

				if len(__snake_case  ) != len(__snake_case  ):
								raise ValueError('''The program cannot work out a fitting polynomial.'''  )

				UpperCAmelCase_					:List[Any]      			=      len(__snake_case  )

				UpperCAmelCase_					:str      			=      0
				UpperCAmelCase_					:list[list[float]]      			=      []
				# put the x and x to the power values in a matrix
				while count_of_line < x:
								UpperCAmelCase_					:str      			=      0
								UpperCAmelCase_					:Optional[int]      			=      coordinates[count_of_line][0]
								UpperCAmelCase_					:list[float]      			=      []
								while count_in_line < x:
												count_line.append(a ** (x - (count_in_line + 1))  )
												count_in_line += 1
								matrix.append(__snake_case  )
								count_of_line += 1

				UpperCAmelCase_					:Dict      			=      0
				# put the y values into a vector
				UpperCAmelCase_					:list[float]      			=      []
				while count_of_line < x:
								vector.append(coordinates[count_of_line][1]  )
								count_of_line += 1

				UpperCAmelCase_					:Dict      			=      0

				while count < x:
								UpperCAmelCase_					:Any      			=      0
								while zahlen < x:
												if count == zahlen:
																zahlen += 1
												if zahlen == x:
																break
												UpperCAmelCase_					:str      			=      matrix[zahlen][count] / matrix[count][count]
												for counting_columns, item in enumerate(matrix[count]  ):
																# manipulating all the values in the matrix
																matrix[zahlen][counting_columns] -= item * bruch
												# manipulating the values in the vector
												vector[zahlen] -= vector[count] * bruch
												zahlen += 1
								count += 1

				UpperCAmelCase_					:Tuple      			=      0
				# make solutions
				UpperCAmelCase_					:list[str]      			=      []
				while count < x:
								solution.append(str(vector[count] / matrix[count][count]  )  )
								count += 1

				UpperCAmelCase_					:Optional[int]      			=      0
				UpperCAmelCase_					:Optional[Any]      			=      '''f(x)='''

				while count < x:
								UpperCAmelCase_					:list[str]      			=      solution[count].split('''E'''  )
								if len(__snake_case  ) > 1:
												UpperCAmelCase_					:List[Any]      			=      f'{remove_e[0]}*10^{remove_e[1]}'
								solved += f'x^{x - (count + 1)}*{solution[count]}'
								if count + 1 != x:
												solved += "+"
								count += 1

				return solved


if __name__ == "__main__":
						print(points_to_polynomial([]))
						print(points_to_polynomial([[]]))
						print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
						print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
						print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
						print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
						print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
						print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
						print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
						print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))


