






"""simple docstring"""


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 						snake_case  :





    def __init__(							self	:					List[str]				,		A	:					int				,		A	:					List[Any]=1_3				,		A	:					Tuple=3_2				,		A	:					Optional[int]=2				,		A	:					Dict=3				,		A	:					int=1_6				,		A	:					Any=[1, 2, 1]				,		A	:					int=[2, 2, 4]				,		A	:					int=2				,		A	:					Tuple=2.0				,		A	:					int=True				,		A	:					str=0.0				,		A	:					Union[str, Any]=0.0				,		A	:					List[str]=0.1				,		A	:					Any="gelu"				,		A	:					str=False				,		A	:					Tuple=True				,		A	:					Union[str, Any]=0.02				,		A	:					int=1E-5				,		A	:					Optional[int]=True				,		A	:					List[Any]=None				,		A	:					Dict=True				,		A	:					List[Any]=1_0				,		A	:					Dict=8				,		A	:					Dict=["stage1", "stage2"]				,		A	:					str=[1, 2]				,		):


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''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     :			Dict				=   ids_tensor([self.batch_size]				,		self.type_sequence_label_size			)

      a     :			int				=   self.get_config()

      return config, pixel_values, labels





    def 				lowerCamelCase__      (							self	:					str			):


      '''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 				lowerCamelCase__      (							self	:					Tuple				,		A	:					Union[str, Any]				,		A	:					List[str]				,		A	:					List[Any]			):


      '''simple docstring'''




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

      a     :			Tuple				=   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths			) - 1))
      a     :			List[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	:					int				,		A	:					int				,		A	:					Dict				,		A	:					int			):


      '''simple docstring'''




      a     :			int				=   SwinBackbone(config=A			)
      model.to(A			)
      model.eval()
      a     :			Union[str, 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], 1_6, 1_6]			)

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

      # verify backbone works with out_features=None
      a     :			Dict				=   None
      a     :			Tuple				=   SwinBackbone(config=A			)
      model.to(A			)
      model.eval()
      a     :			List[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 				lowerCamelCase__      (							self	:					List[str]				,		A	:					Optional[int]				,		A	:					Optional[int]				,		A	:					str			):


      '''simple docstring'''




      a     :			Any				=   SwinForMaskedImageModeling(config=A			)
      model.to(A			)
      model.eval()
      a     :			Tuple				=   model(A			)
      self.parent.assertEqual(
          result.logits.shape				,		(self.batch_size, self.num_channels, self.image_size, self.image_size)			)

      # test greyscale images
      a     :			int				=   1
      a     :			Optional[int]				=   SwinForMaskedImageModeling(A			)
      model.to(A			)
      model.eval()

      a     :			Dict				=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
      a     :			Optional[Any]				=   model(A			)
      self.parent.assertEqual(result.logits.shape				,		(self.batch_size, 1, self.image_size, self.image_size)			)





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


      '''simple docstring'''




      a     :			Union[str, Any]				=   self.type_sequence_label_size
      a     :			int				=   SwinForImageClassification(A			)
      model.to(A			)
      model.eval()
      a     :			Union[str, Any]				=   model(A				,		labels=A			)
      self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.type_sequence_label_size)			)

      # test greyscale images
      a     :			Optional[int]				=   1
      a     :			Tuple				=   SwinForImageClassification(A			)
      model.to(A			)
      model.eval()

      a     :			Union[str, Any]				=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
      a     :			Any				=   model(A			)
      self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.type_sequence_label_size)			)





    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




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






@require_torch
class 						snake_case  (  UpperCAmelCase   , UpperCAmelCase   , unittest.TestCase       ):
    __magic_name__         =     (
        (
            SwinModel,
            SwinBackbone,
            SwinForImageClassification,
            SwinForMaskedImageModeling,
        )
        if is_torch_available()
        else ()
    )
    __magic_name__         =     (
        {'''feature-extraction''': SwinModel, '''image-classification''': SwinForImageClassification}
        if is_torch_available()
        else {}
    )
    __magic_name__         =     True

    __magic_name__         =     False
    __magic_name__         =     False
    __magic_name__         =     False





    def 				lowerCamelCase__      (							self	:					List[str]			):


      '''simple docstring'''




      a     :			Dict				=   SwinModelTester(self			)
      a     :			Any				=   ConfigTester(self				,		config_class=A				,		embed_dim=3_7			)





    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''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[int]			):


      '''simple docstring'''




      return





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


      '''simple docstring'''




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


      '''simple docstring'''




      pass





    def 				lowerCamelCase__      (							self	:					Tuple			):


      '''simple docstring'''




      super().test_training_gradient_checkpointing()





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


      '''simple docstring'''




      a     :			Dict				=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_backbone(*A			)





    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




      a     :			List[Any]				=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*A			)





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      a     :			int				=   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 				lowerCamelCase__      (							self	:					Tuple			):


      '''simple docstring'''




      pass





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


      '''simple docstring'''




      pass





    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




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

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





    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''simple docstring'''




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

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

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





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




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

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

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

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

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

        a     :			List[str]				=   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	:					Any				,		A	:					Dict				,		A	:					Dict				,		A	:					List[str]				,		A	:					Optional[Any]			):


      '''simple docstring'''




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

      with torch.no_grad():
        a     :			Optional[int]				=   model(**self._prepare_for_class(A				,		A			)			)

      a     :			Optional[Any]				=   outputs.hidden_states

      a     :			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
      a     :			Dict				=   (
          config.patch_size
          if isinstance(config.patch_size				,		collections.abc.Iterable			)
          else (config.patch_size, config.patch_size)
      )

      a     :			List[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]				,		)

      if not model_class.__name__ == "SwinBackbone":
        a     :			str				=   outputs.reshaped_hidden_states
        self.assertEqual(len(A			)				,		A			)

        a,     a,     a,     a     :			Optional[Any]				=   reshaped_hidden_states[0].shape
        a     :			List[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	:					int			):


      '''simple docstring'''




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

      a     :			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:
        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     :			int				=   True

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





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




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

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

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

      for model_class in self.all_model_classes:
        a     :			Tuple				=   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     :			Dict				=   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 SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a     :			int				=   SwinModel.from_pretrained(A			)
        self.assertIsNotNone(A			)





    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''simple docstring'''




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

      a     :			Union[str, Any]				=   _config_zero_init(A			)
      for model_class in self.all_model_classes:
        a     :			List[str]				=   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 						snake_case  (  unittest.TestCase       ):





    @cached_property
    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




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





    @slow
    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''simple docstring'''




      a     :			int				=   SwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'			).to(A			)
      a     :			Optional[int]				=   self.default_image_processor

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

      # forward pass
      with torch.no_grad():
        a     :			List[Any]				=   model(**A			)

      # verify the logits
      a     :			Optional[Any]				=   torch.Size((1, 1_0_0_0)			)
      self.assertEqual(outputs.logits.shape				,		A			)
      a     :			Union[str, Any]				=   torch.tensor([-0.09_48, -0.64_54, -0.09_21]			).to(A			)
      self.assertTrue(torch.allclose(outputs.logits[0, :3]				,		A				,		atol=1E-4			)			)






@require_torch
class 						snake_case  (  unittest.TestCase   , UpperCAmelCase       ):
    __magic_name__         =     (SwinBackbone,) if is_torch_available() else ()
    __magic_name__         =     SwinConfig





    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




      a     :			Dict				=   SwinModelTester(self			)












"""simple docstring"""


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   import torch

   from transformers import CamembertModel






@require_torch
@require_sentencepiece
@require_tokenizers
class 						snake_case  (  unittest.TestCase       ):





    @slow
    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




      a     :			int				=   CamembertModel.from_pretrained('camembert-base'			)
      model.to(A			)

      a     :			List[Any]				=   torch.tensor(
          [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]				,		device=A				,		dtype=torch.long				,		)  # J'aime le camembert !
      with torch.no_grad():
        a     :			int				=   model(A			)['last_hidden_state']
      a     :			Union[str, Any]				=   torch.Size((1, 1_0, 7_6_8)			)
      self.assertEqual(output.shape				,		A			)
      # compare the actual values for a slice.
      a     :			List[str]				=   torch.tensor(
          [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]				,		device=A				,		dtype=torch.float				,		)
      # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
      # camembert.eval()
      # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

      self.assertTrue(torch.allclose(output[:, :3, :3]				,		A				,		atol=1E-4			)			)




