"""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 					a					:



  def __init__(		self   :    Tuple						,  lowerCamelCase_   :    List[str]						,  lowerCamelCase_   :    Optional[int]=13						,  lowerCamelCase_   :    Tuple=32						,  lowerCamelCase_   :    Optional[int]=2						,  lowerCamelCase_   :    Union[str, Any]=3						,  lowerCamelCase_   :    Tuple=16						,  lowerCamelCase_   :    List[Any]=[1, 2, 1]						,  lowerCamelCase_   :    int=[2, 2, 4]						,  lowerCamelCase_   :    Tuple=2						,  lowerCamelCase_   :    Any=2.0						,  lowerCamelCase_   :    Tuple=True						,  lowerCamelCase_   :    int=0.0						,  lowerCamelCase_   :    Any=0.0						,  lowerCamelCase_   :    Any=0.1						,  lowerCamelCase_   :    Optional[int]="gelu"						,  lowerCamelCase_   :    Optional[Any]=False						,  lowerCamelCase_   :    Optional[Any]=True						,  lowerCamelCase_   :    Optional[Any]=0.02						,  lowerCamelCase_   :    Union[str, Any]=1E-5						,  lowerCamelCase_   :    str=True						,  lowerCamelCase_   :    Optional[int]=None						,  lowerCamelCase_   :    List[Any]=True						,  lowerCamelCase_   :    Optional[int]=10						,  lowerCamelCase_   :    List[str]=8						,  )      -> Any:
      __a			      =						parent
      __a			      =						batch_size
      __a			      =						image_size
      __a			      =						patch_size
      __a			      =						num_channels
      __a			      =						embed_dim
      __a			      =						depths
      __a			      =						num_heads
      __a			      =						window_size
      __a			      =						mlp_ratio
      __a			      =						qkv_bias
      __a			      =						hidden_dropout_prob
      __a			      =						attention_probs_dropout_prob
      __a			      =						drop_path_rate
      __a			      =						hidden_act
      __a			      =						use_absolute_embeddings
      __a			      =						patch_norm
      __a			      =						layer_norm_eps
      __a			      =						initializer_range
      __a			      =						is_training
      __a			      =						scope
      __a			      =						use_labels
      __a			      =						type_sequence_label_size
      __a			      =						encoder_stride



  def        lowerCAmelCase_				(		self   :    str     )      -> Any:
      __a			      =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

      __a			      =						None
      if self.use_labels:
          __a			      =						ids_tensor([self.batch_size]						,  self.type_sequence_label_size     )

      __a			      =						self.get_config()

      return config, pixel_values, labels



  def        lowerCAmelCase_				(		self   :    List[str]     )      -> List[Any]:
      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   :    Union[str, Any]						,  lowerCamelCase_   :    Any						,  lowerCamelCase_   :    Union[str, Any]						,  lowerCamelCase_   :    Optional[int]     )      -> Optional[Any]:
      __a			      =						DonutSwinModel(config=lowerCamelCase_     )
      model.to(lowerCamelCase_     )
      model.eval()
      __a			      =						model(lowerCamelCase_     )

      __a			      =						((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths     ) - 1))
      __a			      =						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   :    Union[str, Any]     )      -> int:
      __a			      =						self.prepare_config_and_inputs()
      (
          (
          __a
      )      ,  (
          __a
      )      ,  (
          __a
      )      ,  
      )			      =						config_and_inputs
      __a			      =						{"""pixel_values""": pixel_values}
      return config, inputs_dict






@require_torch
class 					a					(				A_   ,		A_   ,		unittest.TestCase							):
  A_ :				List[str] 			=							(DonutSwinModel,) if is_torch_available() else ()
  A_ :				Optional[Any] 			=							{'''feature-extraction''': DonutSwinModel} if is_torch_available() else {}
  A_ :				int 			=							True

  A_ :				List[Any] 			=							False
  A_ :				List[str] 			=							False
  A_ :				Optional[Any] 			=							False



  def        lowerCAmelCase_				(		self   :    Optional[Any]     )      -> Tuple:
      __a			      =						DonutSwinModelTester(self     )
      __a			      =						ConfigTester(self						,  config_class=lowerCamelCase_						,  embed_dim=37     )



  def        lowerCAmelCase_				(		self   :    Optional[int]     )      -> str:
      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   :    Tuple     )      -> Union[str, Any]:
      return



  def        lowerCAmelCase_				(		self   :    Optional[Any]     )      -> Optional[Any]:
      __a			      =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Tuple     )      -> Tuple:
      # DonutSwin does not use inputs_embeds
      pass



  def        lowerCAmelCase_				(		self   :    str     )      -> Any:
      __a      ,  __a			      =						self.model_tester.prepare_config_and_inputs_for_common()

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



  def        lowerCAmelCase_				(		self   :    int     )      -> List[str]:
      __a      ,  __a			      =						self.model_tester.prepare_config_and_inputs_for_common()

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

          __a			      =						["""pixel_values"""]
          self.assertListEqual(arg_names[:1]						,  lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Any     )      -> Optional[int]:
      __a      ,  __a			      =						self.model_tester.prepare_config_and_inputs_for_common()
      __a			      =						True

      for model_class in self.all_model_classes:
          __a			      =						True
          __a			      =						False
          __a			      =						True
          __a			      =						model_class(lowerCamelCase_     )
          model.to(lowerCamelCase_     )
          model.eval()
          with torch.no_grad():
              __a			      =						model(**self._prepare_for_class(lowerCamelCase_						,  lowerCamelCase_     )     )
          __a			      =						outputs.attentions
          __a			      =						len(self.model_tester.depths     )
          self.assertEqual(len(lowerCamelCase_     )						,  lowerCamelCase_     )

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          __a			      =						True
          __a			      =						config.window_size**2
          __a			      =						model_class(lowerCamelCase_     )
          model.to(lowerCamelCase_     )
          model.eval()
          with torch.no_grad():
              __a			      =						model(**self._prepare_for_class(lowerCamelCase_						,  lowerCamelCase_     )     )
          __a			      =						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]						,  )
          __a			      =						len(lowerCamelCase_     )

          # Check attention is always last and order is fine
          __a			      =						True
          __a			      =						True
          __a			      =						model_class(lowerCamelCase_     )
          model.to(lowerCamelCase_     )
          model.eval()
          with torch.no_grad():
              __a			      =						model(**self._prepare_for_class(lowerCamelCase_						,  lowerCamelCase_     )     )

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

          __a			      =						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   :    str						,  lowerCamelCase_   :    Any						,  lowerCamelCase_   :    str						,  lowerCamelCase_   :    List[str]						,  lowerCamelCase_   :    Optional[int]     )      -> List[str]:
      __a			      =						model_class(lowerCamelCase_     )
      model.to(lowerCamelCase_     )
      model.eval()

      with torch.no_grad():
          __a			      =						model(**self._prepare_for_class(lowerCamelCase_						,  lowerCamelCase_     )     )

      __a			      =						outputs.hidden_states

      __a			      =						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
      __a			      =						(
          config.patch_size
          if isinstance(config.patch_size						,  collections.abc.Iterable     )
          else (config.patch_size, config.patch_size)
      )

      __a			      =						(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			      =						outputs.reshaped_hidden_states
      self.assertEqual(len(lowerCamelCase_     )						,  lowerCamelCase_     )

      __a      ,  __a      ,  __a      ,  __a			      =						reshaped_hidden_states[0].shape
      __a			      =						(
          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   :    Union[str, Any]     )      -> Optional[int]:
      __a      ,  __a			      =						self.model_tester.prepare_config_and_inputs_for_common()

      __a			      =						(
          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			      =						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"]
          __a			      =						True

          self.check_hidden_states_output(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Dict     )      -> Any:
      __a      ,  __a			      =						self.model_tester.prepare_config_and_inputs_for_common()
      __a			      =						3

      __a			      =						(
          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			      =						(
          config.patch_size
          if isinstance(config.patch_size						,  collections.abc.Iterable     )
          else (config.patch_size, config.patch_size)
      )

      __a			      =						image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
      __a			      =						image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

      for model_class in self.all_model_classes:
          __a			      =						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"]
          __a			      =						True
          self.check_hidden_states_output(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  (padded_height, padded_width)     )



  @slow
  def        lowerCAmelCase_				(		self   :    Tuple     )      -> Optional[Any]:
      for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          __a			      =						DonutSwinModel.from_pretrained(lowerCamelCase_     )
          self.assertIsNotNone(lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Union[str, Any]     )      -> Dict:
      __a      ,  __a			      =						self.model_tester.prepare_config_and_inputs_for_common()

      __a			      =						_config_zero_init(lowerCamelCase_     )
      for model_class in self.all_model_classes:
          __a			      =						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"""						,  )

"""simple docstring"""


from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    """cross_attention""",
    """0.20.0""",
    """Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.""",
    standard_warn=False,
)


__A       			=      AttentionProcessor






class 					a					(				A_							):



  def __init__(		self   :    Optional[int]						,  *lowerCamelCase_   :    Union[str, Any]						,  **lowerCamelCase_   :    str     )      -> int:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Optional[int]						,  *lowerCamelCase_   :    Dict						,  **lowerCamelCase_   :    Union[str, Any]     )      -> List[str]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Tuple						,  *lowerCamelCase_   :    Tuple						,  **lowerCamelCase_   :    List[str]     )      -> Optional[Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    str						,  *lowerCamelCase_   :    Tuple						,  **lowerCamelCase_   :    Any     )      -> List[Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Union[str, Any]						,  *lowerCamelCase_   :    Optional[Any]						,  **lowerCamelCase_   :    Tuple     )      -> Union[str, Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    List[str]						,  *lowerCamelCase_   :    Dict						,  **lowerCamelCase_   :    List[Any]     )      -> Any:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Tuple						,  *lowerCamelCase_   :    List[str]						,  **lowerCamelCase_   :    Dict     )      -> Any:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    List[Any]						,  *lowerCamelCase_   :    Tuple						,  **lowerCamelCase_   :    Optional[Any]     )      -> List[Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )
