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 				lowerCamelCase			:






      def __init__(      self  ,		__lowerCamelCase  ,		__lowerCamelCase=13  ,		__lowerCamelCase=32  ,		__lowerCamelCase=2  ,		__lowerCamelCase=3  ,		__lowerCamelCase=16  ,		__lowerCamelCase=[1, 2, 1]  ,		__lowerCamelCase=[2, 2, 4]  ,		__lowerCamelCase=2  ,		__lowerCamelCase=2.0  ,		__lowerCamelCase=True  ,		__lowerCamelCase=0.0  ,		__lowerCamelCase=0.0  ,		__lowerCamelCase=0.1  ,		__lowerCamelCase="gelu"  ,		__lowerCamelCase=False  ,		__lowerCamelCase=True  ,		__lowerCamelCase=0.02  ,		__lowerCamelCase=1e-5  ,		__lowerCamelCase=True  ,		__lowerCamelCase=None  ,		__lowerCamelCase=True  ,		__lowerCamelCase=10  ,		__lowerCamelCase=8  ,		)     ->				str:


          '''simple docstring'''







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






      def 			lowerCAmelCase_   (      self						)     ->				str:


          '''simple docstring'''







          snake_case:						Union[str, Any]         =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

          snake_case:						str         =   self.get_config()

          return config, pixel_values, labels






      def 			lowerCAmelCase_   (      self						)     ->				Any:


          '''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  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase						)     ->				Union[str, Any]:


          '''simple docstring'''







          snake_case:						Optional[Any]         =   DonutSwinModel(config=__lowerCamelCase						)
          model.to(__lowerCamelCase						)
          model.eval()
          snake_case:						str         =   model(__lowerCamelCase						)

          snake_case:						Any         =   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
          snake_case:						str         =   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						)     ->				Optional[Any]:


          '''simple docstring'''







          snake_case:						str         =   self.prepare_config_and_inputs()
          (
              (
              snake_case
          )    , (
              snake_case
          )    , (
              snake_case
          )    , 
          ):						List[Any]         =   config_and_inputs
          snake_case:						str         =   {"""pixel_values""": pixel_values}
          return config, inputs_dict





@require_torch
class 				lowerCamelCase			(							__snake_case				,     __snake_case				,     unittest.TestCase					):
      __lowerCamelCase							=					(DonutSwinModel,) if is_torch_available() else ()
      __lowerCamelCase							=					{'feature-extraction': DonutSwinModel} if is_torch_available() else {}
      __lowerCamelCase							=					True

      __lowerCamelCase							=					False
      __lowerCamelCase							=					False
      __lowerCamelCase							=					False






      def 			lowerCAmelCase_   (      self						)     ->				str:


          '''simple docstring'''







          snake_case:						Optional[int]         =   DonutSwinModelTester(self						)
          snake_case:						List[Any]         =   ConfigTester(self  ,		config_class=__lowerCamelCase  ,		embed_dim=37						)






      def 			lowerCAmelCase_   (      self						)     ->				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						)     ->				str:


          '''simple docstring'''







          return






      def 			lowerCAmelCase_   (      self						)     ->				Tuple:


          '''simple docstring'''







          snake_case:						Optional[int]         =   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__lowerCamelCase						)






      def 			lowerCAmelCase_   (      self						)     ->				Tuple:


          '''simple docstring'''







          pass






      def 			lowerCAmelCase_   (      self						)     ->				str:


          '''simple docstring'''







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

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






      def 			lowerCAmelCase_   (      self						)     ->				List[str]:


          '''simple docstring'''







          snake_case    , snake_case:						int         =   self.model_tester.prepare_config_and_inputs_for_common()

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

              snake_case:						int         =   ["""pixel_values"""]
              self.assertListEqual(arg_names[:1]  ,		__lowerCamelCase						)






      def 			lowerCAmelCase_   (      self						)     ->				Any:


          '''simple docstring'''







          snake_case    , snake_case:						Dict         =   self.model_tester.prepare_config_and_inputs_for_common()
          snake_case:						List[str]         =   True

          for model_class in self.all_model_classes:
              snake_case:						str         =   True
              snake_case:						Optional[Any]         =   False
              snake_case:						int         =   True
              snake_case:						Optional[Any]         =   model_class(__lowerCamelCase						)
              model.to(__lowerCamelCase						)
              model.eval()
              with torch.no_grad():
                  snake_case:						Union[str, Any]         =   model(**self._prepare_for_class(__lowerCamelCase  ,		__lowerCamelCase						)						)
              snake_case:						Optional[int]         =   outputs.attentions
              snake_case:						Optional[Any]         =   len(self.model_tester.depths						)
              self.assertEqual(len(__lowerCamelCase						)  ,		__lowerCamelCase						)

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

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

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

              if hasattr(self.model_tester  ,		"""num_hidden_states_types"""						):
                  snake_case:						int         =   self.model_tester.num_hidden_states_types
              else:
                  # also another +1 for reshaped_hidden_states
                  snake_case:						str         =   2
              self.assertEqual(out_len + added_hidden_states  ,		len(__lowerCamelCase						)						)

              snake_case:						List[Any]         =   outputs.attentions

              self.assertEqual(len(__lowerCamelCase						)  ,		__lowerCamelCase						)

              self.assertListEqual(
                  list(self_attentions[0].shape[-3:]						)  ,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared]  ,		)






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase						)     ->				Optional[Any]:


          '''simple docstring'''







          snake_case:						Optional[Any]         =   model_class(__lowerCamelCase						)
          model.to(__lowerCamelCase						)
          model.eval()

          with torch.no_grad():
              snake_case:						Any         =   model(**self._prepare_for_class(__lowerCamelCase  ,		__lowerCamelCase						)						)

          snake_case:						int         =   outputs.hidden_states

          snake_case:						List[str]         =   getattr(
              self.model_tester  ,		"""expected_num_hidden_layers"""  ,		len(self.model_tester.depths						) + 1						)
          self.assertEqual(len(__lowerCamelCase						)  ,		__lowerCamelCase						)

          # DonutSwin has a different seq_length
          snake_case:						Tuple         =   (
              config.patch_size
              if isinstance(config.patch_size  ,		collections.abc.Iterable						)
              else (config.patch_size, config.patch_size)
          )

          snake_case:						Optional[int]         =   (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]  ,		)

          snake_case:						Optional[Any]         =   outputs.reshaped_hidden_states
          self.assertEqual(len(__lowerCamelCase						)  ,		__lowerCamelCase						)

          snake_case    , snake_case    , snake_case    , snake_case:						Any         =   reshaped_hidden_states[0].shape
          snake_case:						int         =   (
              reshaped_hidden_states[0].view(__lowerCamelCase  ,		__lowerCamelCase  ,		height * width						).permute(0  ,		2  ,		1						)
          )
          self.assertListEqual(
              list(reshaped_hidden_states.shape[-2:]						)  ,		[num_patches, self.model_tester.embed_dim]  ,		)






      def 			lowerCAmelCase_   (      self						)     ->				Optional[Any]:


          '''simple docstring'''







          snake_case    , snake_case:						Any         =   self.model_tester.prepare_config_and_inputs_for_common()

          snake_case:						str         =   (
              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:
              snake_case:						Optional[int]         =   True
              self.check_hidden_states_output(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase						)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              snake_case:						Optional[Any]         =   True

              self.check_hidden_states_output(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase						)






      def 			lowerCAmelCase_   (      self						)     ->				Optional[int]:


          '''simple docstring'''







          snake_case    , snake_case:						Tuple         =   self.model_tester.prepare_config_and_inputs_for_common()
          snake_case:						Tuple         =   3

          snake_case:						Dict         =   (
              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)
          )
          snake_case:						Optional[Any]         =   (
              config.patch_size
              if isinstance(config.patch_size  ,		collections.abc.Iterable						)
              else (config.patch_size, config.patch_size)
          )

          snake_case:						str         =   image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
          snake_case:						Any         =   image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

          for model_class in self.all_model_classes:
              snake_case:						Dict         =   True
              self.check_hidden_states_output(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		(padded_height, padded_width)						)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              snake_case:						List[Any]         =   True
              self.check_hidden_states_output(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		(padded_height, padded_width)						)






      @slow
      def 			lowerCAmelCase_   (      self						)     ->				List[str]:


          '''simple docstring'''







          for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case:						List[Any]         =   DonutSwinModel.from_pretrained(__lowerCamelCase						)
              self.assertIsNotNone(__lowerCamelCase						)






      def 			lowerCAmelCase_   (      self						)     ->				Optional[int]:


          '''simple docstring'''







          snake_case    , snake_case:						Optional[int]         =   self.model_tester.prepare_config_and_inputs_for_common()

          snake_case:						str         =   _config_zero_init(__lowerCamelCase						)
          for model_class in self.all_model_classes:
              snake_case:						List[Any]         =   model_class(config=__lowerCamelCase						)
              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"  ,		)



def   a_					(_lowerCAmelCase  :   int       )->				str:
    if not isinstance(_lowerCAmelCase			,	_lowerCAmelCase       ):
        snake_case:						Optional[Any]         =   (
            """operation can not be conducted on a object of type """
            F"{type(_lowerCAmelCase       ).__name__}"
        )
        raise TypeError(_lowerCAmelCase       )
    snake_case:						List[Any]         =   """"""
    for _ in range(0			,	32       ):
        bit_string += str(number % 2       )
        snake_case:						Any         =   number >> 1
    return bit_string

def   a_					(_lowerCAmelCase  :   int       )->				str:
    if number < 0:
        raise ValueError("""the value of input must be positive"""       )
    elif isinstance(_lowerCAmelCase			,	_lowerCAmelCase       ):
        raise TypeError("""Input value must be a 'int' type"""       )
    elif isinstance(_lowerCAmelCase			,	_lowerCAmelCase       ):
        raise TypeError("""'<' not supported between instances of 'str' and 'int'"""       )
    snake_case:						Optional[Any]         =   0
    # iterator over [1 to 32],since we are dealing with 32 bit integer
    for _ in range(1			,	33       ):
        # left shift the bits by unity
        snake_case:						Dict         =   result << 1
        # get the end bit
        snake_case:						Union[str, Any]         =   number % 2
        # right shift the bits by unity
        snake_case:						Union[str, Any]         =   number >> 1
        # add that bit to our ans
        snake_case:						Tuple         =   result | end_bit
    return get_reverse_bit_string(_lowerCAmelCase       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


