


import collections
import inspect
import unittest

from transformers import SwinConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
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 SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
  from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor

class      lowerCamelCase	:

       def __init__(			self	,						a__	,						a__=13	,						a__=32	,						a__=2	,						a__=3	,						a__=16	,						a__=[1, 2, 1]	,						a__=[2, 2, 4]	,						a__=2	,						a__=2.0	,						a__=True	,						a__=0.0	,						a__=0.0	,						a__=0.1	,						a__="gelu"	,						a__=False	,						a__=True	,						a__=0.02	,						a__=1e-5	,						a__=True	,						a__=None	,						a__=True	,						a__=10	,						a__=8	,						a__=["stage1", "stage2"]	,						a__=[1, 2]	,						):






        '''simple docstring'''






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

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






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

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

        snake_case   : Dict	  =      self.get_config()

        return config, pixel_values, labels

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        return SwinConfig(
            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	,						out_features=self.out_features	,						out_indices=self.out_indices	,						)

       def 				UpperCAmelCase				(			self	,						a__	,						a__	,						a__					):






        '''simple docstring'''






        snake_case   : int	  =      SwinModel(config=a__					)
        model.to(a__					)
        model.eval()
        snake_case   : Dict	  =      model(a__					)

        snake_case   : Union[str, Any]	  =      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths					) - 1))
        snake_case   : Any	  =      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 				UpperCAmelCase				(			self	,						a__	,						a__	,						a__					):






        '''simple docstring'''






        snake_case   : Tuple	  =      SwinBackbone(config=a__					)
        model.to(a__					)
        model.eval()
        snake_case   : List[Any]	  =      model(a__					)

        # verify hidden states
        self.parent.assertEqual(len(result.feature_maps					)	,						len(config.out_features					)					)
        self.parent.assertListEqual(list(result.feature_maps[0].shape					)	,						[self.batch_size, model.channels[0], 16, 16]					)

        # verify channels
        self.parent.assertEqual(len(model.channels					)	,						len(config.out_features					)					)

        # verify backbone works with out_features=None
        snake_case   : Any	  =      None
        snake_case   : Optional[Any]	  =      SwinBackbone(config=a__					)
        model.to(a__					)
        model.eval()
        snake_case   : Optional[Any]	  =      model(a__					)

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps					)	,						1					)
        self.parent.assertListEqual(list(result.feature_maps[0].shape					)	,						[self.batch_size, model.channels[-1], 4, 4]					)

        # verify channels
        self.parent.assertEqual(len(model.channels					)	,						1					)

       def 				UpperCAmelCase				(			self	,						a__	,						a__	,						a__					):






        '''simple docstring'''






        snake_case   : List[str]	  =      SwinForMaskedImageModeling(config=a__					)
        model.to(a__					)
        model.eval()
        snake_case   : List[Any]	  =      model(a__					)
        self.parent.assertEqual(
            result.logits.shape	,						(self.batch_size, self.num_channels, self.image_size, self.image_size)					)

        # test greyscale images
        snake_case   : int	  =      1
        snake_case   : Union[str, Any]	  =      SwinForMaskedImageModeling(a__					)
        model.to(a__					)
        model.eval()

        snake_case   : List[str]	  =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
        snake_case   : int	  =      model(a__					)
        self.parent.assertEqual(result.logits.shape	,						(self.batch_size, 1, self.image_size, self.image_size)					)

       def 				UpperCAmelCase				(			self	,						a__	,						a__	,						a__					):






        '''simple docstring'''






        snake_case   : str	  =      self.type_sequence_label_size
        snake_case   : List[str]	  =      SwinForImageClassification(a__					)
        model.to(a__					)
        model.eval()
        snake_case   : Any	  =      model(a__	,						labels=a__					)
        self.parent.assertEqual(result.logits.shape	,						(self.batch_size, self.type_sequence_label_size)					)

        # test greyscale images
        snake_case   : Dict	  =      1
        snake_case   : Optional[Any]	  =      SwinForImageClassification(a__					)
        model.to(a__					)
        model.eval()

        snake_case   : List[Any]	  =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
        snake_case   : Dict	  =      model(a__					)
        self.parent.assertEqual(result.logits.shape	,						(self.batch_size, self.type_sequence_label_size)					)



       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Tuple	  =      self.prepare_config_and_inputs()
        (
            (
            snake_case
        )  ,					(
            snake_case
        )  ,					(
            snake_case
        )  ,					
        )   : Tuple	  =      config_and_inputs
        snake_case   : Dict	  =      {'''pixel_values''': pixel_values}
        return config, inputs_dict







@require_torch
class      lowerCamelCase	(  SCREAMING_SNAKE_CASE,			SCREAMING_SNAKE_CASE,			unittest.TestCase     ):
       _UpperCamelCase    		=      (
           (
               SwinModel,
               SwinBackbone,
               SwinForImageClassification,
               SwinForMaskedImageModeling,
           )
           if is_torch_available()
           else ()
       )
       _UpperCamelCase    		=      (
           {"""feature-extraction""": SwinModel, """image-classification""": SwinForImageClassification}
           if is_torch_available()
           else {}
       )
       _UpperCamelCase    		=      True

       _UpperCamelCase    		=      False
       _UpperCamelCase    		=      False
       _UpperCamelCase    		=      False

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : int	  =      SwinModelTester(self					)
        snake_case   : Tuple	  =      ConfigTester(self	,						config_class=a__	,						embed_dim=37					)

       def 				UpperCAmelCase				(			self					):






        '''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 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        return

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : str	  =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*a__					)

       @unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.'''					)
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        pass

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        super().test_training_gradient_checkpointing()

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Any	  =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*a__					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : List[str]	  =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*a__					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Optional[Any]	  =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*a__					)

       @unittest.skip(reason='''Swin does not use inputs_embeds'''					)
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        pass

       @unittest.skip(reason='''Swin Transformer does not use feedforward chunking'''					)
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        pass

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






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

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

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






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

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

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

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






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

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

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

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

         # also another +1 for reshaped_hidden_states
         snake_case   : int	  =      1 if model_class.__name__ == '''SwinBackbone''' else 2
         self.assertEqual(out_len + added_hidden_states	,						len(a__					)					)

         snake_case   : 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 				UpperCAmelCase				(			self	,						a__	,						a__	,						a__	,						a__					):






        '''simple docstring'''






        snake_case   : Any	  =      model_class(a__					)
        model.to(a__					)
        model.eval()

        with torch.no_grad():
         snake_case   : List[Any]	  =      model(**self._prepare_for_class(a__	,						a__					)					)

        snake_case   : int	  =      outputs.hidden_states

        snake_case   : int	  =      getattr(
            self.model_tester	,						'''expected_num_hidden_layers'''	,						len(self.model_tester.depths					) + 1					)
        self.assertEqual(len(a__					)	,						a__					)

        # Swin has a different seq_length
        snake_case   : Optional[Any]	  =      (
            config.patch_size
            if isinstance(config.patch_size	,						collections.abc.Iterable					)
            else (config.patch_size, config.patch_size)
        )

        snake_case   : Tuple	  =      (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]	,						)

        if not model_class.__name__ == "SwinBackbone":
         snake_case   : Any	  =      outputs.reshaped_hidden_states
         self.assertEqual(len(a__					)	,						a__					)

         snake_case  ,					snake_case  ,					snake_case  ,					snake_case   : List[Any]	  =      reshaped_hidden_states[0].shape
         snake_case   : Tuple	  =      (
             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 				UpperCAmelCase				(			self					):






        '''simple docstring'''






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

        snake_case   : List[Any]	  =      (
            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[Any]	  =      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"]
         snake_case   : Optional[int]	  =      True

         self.check_hidden_states_output(a__	,						a__	,						a__	,						a__					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case  ,					snake_case   : Union[str, Any]	  =      self.model_tester.prepare_config_and_inputs_for_common()
        snake_case   : List[Any]	  =      3

        snake_case   : List[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)
        )
        snake_case   : Union[str, Any]	  =      (
            config.patch_size
            if isinstance(config.patch_size	,						collections.abc.Iterable					)
            else (config.patch_size, config.patch_size)
        )

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

        for model_class in self.all_model_classes:
         snake_case   : str	  =      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"]
         snake_case   : str	  =      True
         self.check_hidden_states_output(a__	,						a__	,						a__	,						(padded_height, padded_width)					)

       @slow
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case   : List[str]	  =      SwinModel.from_pretrained(a__					)
         self.assertIsNotNone(a__					)



       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






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

        snake_case   : List[Any]	  =      _config_zero_init(a__					)
        for model_class in self.all_model_classes:
         snake_case   : Union[str, Any]	  =      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'''	,						)







@require_vision
@require_torch
class      lowerCamelCase	(  unittest.TestCase     ):

       @cached_property
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        return (
            AutoImageProcessor.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''					)
            if is_vision_available()
            else None
        )



       @slow
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Union[str, Any]	  =      SwinForImageClassification.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''					).to(a__					)
        snake_case   : Any	  =      self.default_image_processor

        snake_case   : List[Any]	  =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
        snake_case   : Dict	  =      image_processor(images=a__	,						return_tensors='''pt'''					).to(a__					)

        # forward pass
        with torch.no_grad():
         snake_case   : str	  =      model(**a__					)

        # verify the logits
        snake_case   : Union[str, Any]	  =      torch.Size((1, 10_00)					)
        self.assertEqual(outputs.logits.shape	,						a__					)
        snake_case   : Optional[int]	  =      torch.tensor([-0.0948, -0.6454, -0.0921]					).to(a__					)
        self.assertTrue(torch.allclose(outputs.logits[0, :3]	,						a__	,						atol=1e-4					)					)







@require_torch
class      lowerCamelCase	(  unittest.TestCase,			SCREAMING_SNAKE_CASE     ):
       _UpperCamelCase    		=      (SwinBackbone,) if is_torch_available() else ()
       _UpperCamelCase    		=      SwinConfig

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : List[str]	  =      SwinModelTester(self					)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a				:							List[str]					=			{
    'configuration_donut_swin': ['DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DonutSwinConfig'],
    'processing_donut': ['DonutProcessor'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a				:							Dict					=			[
      'DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DonutSwinModel',
      'DonutSwinPreTrainedModel',
  ]

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a				:							Any					=			['DonutFeatureExtractor']
  a				:							Dict					=			['DonutImageProcessor']


if TYPE_CHECKING:
  from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
  from .processing_donut import DonutProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_donut_swin import (
        DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
        DonutSwinModel,
        DonutSwinPreTrainedModel,
    )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_donut import DonutFeatureExtractor
    from .image_processing_donut import DonutImageProcessor

else:
  import sys

  a				:							Union[str, Any]					=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






