






"""simple docstring"""


import collections
import inspect
import unittest

from transformers import DonutSwinConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_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 DonutSwinModel
   from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST






class 		snake_case    :





    def __init__(				self      :   List[str]							,						A      :   List[str]							,						A      :   List[str]=1_3							,						A      :   Dict=3_2							,						A      :   Tuple=2							,						A      :   int=3							,						A      :   str=1_6							,						A      :   Dict=[1, 2, 1]							,						A      :   List[str]=[2, 2, 4]							,						A      :   List[Any]=2							,						A      :   Optional[Any]=2.0							,						A      :   List[str]=True							,						A      :   Dict=0.0							,						A      :   Any=0.0							,						A      :   Any=0.1							,						A      :   List[str]="gelu"							,						A      :   Union[str, Any]=False							,						A      :   List[str]=True							,						A      :   List[str]=0.02							,						A      :   Dict=1E-5							,						A      :   str=True							,						A      :   int=None							,						A      :   Any=True							,						A      :   List[str]=1_0							,						A      :   Tuple=8							,						):


      '''simple docstring'''




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





    def 							lowerCamelCase__ (				self      :   List[Any]     ):


      '''simple docstring'''




      a	:     str							=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

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

      return config, pixel_values, labels





    def 							lowerCamelCase__ (				self      :   Optional[int]     ):


      '''simple docstring'''




      return DonutSwinConfig(
          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							,						encoder_stride=self.encoder_stride							,						)





    def 							lowerCamelCase__ (				self      :   Tuple							,						A      :   Tuple							,						A      :   List[Any]							,						A      :   List[Any]     ):


      '''simple docstring'''




      a	:     Union[str, Any]							=  DonutSwinModel(config=A     )
      model.to(A     )
      model.eval()
      a	:     Optional[Any]							=  model(A     )

      a	:     Optional[Any]							=  ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths     ) - 1))
      a	:     Tuple							=  int(config.embed_dim * 2 ** (len(config.depths     ) - 1)     )

      self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, expected_seq_len, expected_dim)     )





    def 							lowerCamelCase__ (				self      :   str     ):


      '''simple docstring'''




      a	:     Any							=  self.prepare_config_and_inputs()
      (
          (
          a
      ),	(
          a
      ),	(
          a
      ),	
      )	:     Optional[Any]							=  config_and_inputs
      a	:     Any							=  {'pixel_values': pixel_values}
      return config, inputs_dict






@require_torch
class 		snake_case    (    UpperCAmelCase  ,					UpperCAmelCase  ,					unittest.TestCase			):
    __magic_name__       	=	(DonutSwinModel,) if is_torch_available() else ()
    __magic_name__       	=	{'''feature-extraction''': DonutSwinModel} if is_torch_available() else {}
    __magic_name__       	=	True

    __magic_name__       	=	False
    __magic_name__       	=	False
    __magic_name__       	=	False





    def 							lowerCamelCase__ (				self      :   str     ):


      '''simple docstring'''




      a	:     Optional[int]							=  DonutSwinModelTester(self     )
      a	:     Union[str, Any]							=  ConfigTester(self							,						config_class=A							,						embed_dim=3_7     )





    def 							lowerCamelCase__ (				self      :   Optional[int]     ):


      '''simple docstring'''




      self.create_and_test_config_common_properties()
      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 							lowerCamelCase__ (				self      :   Optional[Any]     ):


      '''simple docstring'''




      return





    def 							lowerCamelCase__ (				self      :   int     ):


      '''simple docstring'''




      a	:     str							=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A     )





    def 							lowerCamelCase__ (				self      :   Optional[int]     ):


      '''simple docstring'''




      pass





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


      '''simple docstring'''




      a,	a	:     List[str]							=  self.model_tester.prepare_config_and_inputs_for_common()

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





    def 							lowerCamelCase__ (				self      :   str     ):


      '''simple docstring'''




      a,	a	:     List[str]							=  self.model_tester.prepare_config_and_inputs_for_common()

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

        a	:     Optional[Any]							=  ['pixel_values']
        self.assertListEqual(arg_names[:1]							,						A     )





    def 							lowerCamelCase__ (				self      :   Optional[int]     ):


      '''simple docstring'''




      a,	a	:     Optional[int]							=  self.model_tester.prepare_config_and_inputs_for_common()
      a	:     Dict							=  True

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

        # check that output_attentions also work using config
        del inputs_dict["output_attentions"]
        a	:     Union[str, Any]							=  True
        a	:     Any							=  config.window_size**2
        a	:     Union[str, Any]							=  model_class(A     )
        model.to(A     )
        model.eval()
        with torch.no_grad():
          a	:     Tuple							=  model(**self._prepare_for_class(A							,						A     )     )
        a	:     List[str]							=  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]							,						)
        a	:     str							=  len(A     )

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

        if hasattr(self.model_tester							,						'num_hidden_states_types'     ):
          a	:     Any							=  self.model_tester.num_hidden_states_types
        else:
          # also another +1 for reshaped_hidden_states
          a	:     str							=  2
        self.assertEqual(out_len + added_hidden_states							,						len(A     )     )

        a	:     Optional[int]							=  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]							,						)





    def 							lowerCamelCase__ (				self      :   List[str]							,						A      :   Optional[int]							,						A      :   Optional[Any]							,						A      :   int							,						A      :   Dict     ):


      '''simple docstring'''




      a	:     str							=  model_class(A     )
      model.to(A     )
      model.eval()

      with torch.no_grad():
        a	:     Tuple							=  model(**self._prepare_for_class(A							,						A     )     )

      a	:     Optional[Any]							=  outputs.hidden_states

      a	:     Optional[Any]							=  getattr(
          self.model_tester							,						'expected_num_hidden_layers'							,						len(self.model_tester.depths     ) + 1     )
      self.assertEqual(len(A     )							,						A     )

      # DonutSwin has a different seq_length
      a	:     Union[str, Any]							=  (
          config.patch_size
          if isinstance(config.patch_size							,						collections.abc.Iterable     )
          else (config.patch_size, config.patch_size)
      )

      a	:     str							=  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

      self.assertListEqual(
          list(hidden_states[0].shape[-2:]     )							,						[num_patches, self.model_tester.embed_dim]							,						)

      a	:     Dict							=  outputs.reshaped_hidden_states
      self.assertEqual(len(A     )							,						A     )

      a,	a,	a,	a	:     str							=  reshaped_hidden_states[0].shape
      a	:     Union[str, Any]							=  (
          reshaped_hidden_states[0].view(A							,						A							,						height * width     ).permute(0							,						2							,						1     )
      )
      self.assertListEqual(
          list(reshaped_hidden_states.shape[-2:]     )							,						[num_patches, self.model_tester.embed_dim]							,						)





    def 							lowerCamelCase__ (				self      :   Dict     ):


      '''simple docstring'''




      a,	a	:     str							=  self.model_tester.prepare_config_and_inputs_for_common()

      a	:     Tuple							=  (
          self.model_tester.image_size
          if isinstance(self.model_tester.image_size							,						collections.abc.Iterable     )
          else (self.model_tester.image_size, self.model_tester.image_size)
      )

      for model_class in self.all_model_classes:
        a	:     int							=  True
        self.check_hidden_states_output(A							,						A							,						A							,						A     )

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        a	:     List[str]							=  True

        self.check_hidden_states_output(A							,						A							,						A							,						A     )





    def 							lowerCamelCase__ (				self      :   int     ):


      '''simple docstring'''




      a,	a	:     Any							=  self.model_tester.prepare_config_and_inputs_for_common()
      a	:     Any							=  3

      a	:     int							=  (
          self.model_tester.image_size
          if isinstance(self.model_tester.image_size							,						collections.abc.Iterable     )
          else (self.model_tester.image_size, self.model_tester.image_size)
      )
      a	:     List[str]							=  (
          config.patch_size
          if isinstance(config.patch_size							,						collections.abc.Iterable     )
          else (config.patch_size, config.patch_size)
      )

      a	:     List[str]							=  image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
      a	:     Optional[Any]							=  image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

      for model_class in self.all_model_classes:
        a	:     Optional[Any]							=  True
        self.check_hidden_states_output(A							,						A							,						A							,						(padded_height, padded_width)     )

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        a	:     Tuple							=  True
        self.check_hidden_states_output(A							,						A							,						A							,						(padded_height, padded_width)     )





    @slow
    def 							lowerCamelCase__ (				self      :   Union[str, Any]     ):


      '''simple docstring'''




      for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a	:     List[str]							=  DonutSwinModel.from_pretrained(A     )
        self.assertIsNotNone(A     )





    def 							lowerCamelCase__ (				self      :   Optional[int]     ):


      '''simple docstring'''




      a,	a	:     Optional[Any]							=  self.model_tester.prepare_config_and_inputs_for_common()

      a	:     Any							=  _config_zero_init(A     )
      for model_class in self.all_model_classes:
        a	:     Dict							=  model_class(config=A     )
        for name, param in model.named_parameters():
          if "embeddings" not in name and 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'''							,						)












"""simple docstring"""


from cva import destroyAllWindows, imread, imshow, waitKey




def      snake_case    (A_ :Optional[int]      ,     A_ :int ):







  '''simple docstring'''

  a,	a	:     Tuple							=  img.shape[0], img.shape[1]

  def to_grayscale(A_ :Tuple      ,     A_ :List[str]      ,     A_ :Union[str, Any] ):
    return 0.2126 * red + 0.587 * green + 0.114 * blue

  def normalize(A_ :Union[str, Any] ):
    return min(A_      ,     2_5_5 )

  for i in range(A_ ):
    for j in range(A_ ):
      a	:     Union[str, Any]							=  int(to_grayscale(*img[i][j] ) )
      a	:     Any							=  [
          normalize(A_ ),
          normalize(greyscale + factor ),
          normalize(greyscale + 2 * factor ),
      ]

  return img


if __name__ == "__main__":
   # read original image
   _UpperCamelCase							:       List[str]       					=		{
       percentage: imread('image_data/lena.jpg', 1) for percentage in (10, 20, 30, 40)
   }

   for percentage, img in images.items():
      make_sepia(img, percentage)

   for percentage, img in images.items():
      imshow(f'''Original image with sepia (factor: {percentage})''', img)

   waitKey(0)
   destroyAllWindows()




