

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				,    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				,    lowerCamelCase=["stage1", "stage2"]				,    lowerCamelCase=[1, 2]				,    )		->			Optional[Any]:
        snake_case_					= parent
        snake_case_					= batch_size
        snake_case_					= image_size
        snake_case_					= patch_size
        snake_case_					= num_channels
        snake_case_					= embed_dim
        snake_case_					= depths
        snake_case_					= num_heads
        snake_case_					= window_size
        snake_case_					= mlp_ratio
        snake_case_					= qkv_bias
        snake_case_					= hidden_dropout_prob
        snake_case_					= attention_probs_dropout_prob
        snake_case_					= drop_path_rate
        snake_case_					= hidden_act
        snake_case_					= use_absolute_embeddings
        snake_case_					= patch_norm
        snake_case_					= layer_norm_eps
        snake_case_					= initializer_range
        snake_case_					= is_training
        snake_case_					= scope
        snake_case_					= use_labels
        snake_case_					= type_sequence_label_size
        snake_case_					= encoder_stride
        snake_case_					= out_features
        snake_case_					= out_indices







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

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

        snake_case_					= self.get_config()

        return config, pixel_values, labels







      def       lowerCAmelCase_       (	self  )		->			Dict:
        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				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			Any:
        snake_case_					= SwinModel(config=lowerCamelCase  )
        model.to(lowerCamelCase  )
        model.eval()
        snake_case_					= model(lowerCamelCase  )

        snake_case_					= ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths  ) - 1))
        snake_case_					= 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				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			Tuple:
        snake_case_					= SwinBackbone(config=lowerCamelCase  )
        model.to(lowerCamelCase  )
        model.eval()
        snake_case_					= model(lowerCamelCase  )

        # 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_					= None
        snake_case_					= SwinBackbone(config=lowerCamelCase  )
        model.to(lowerCamelCase  )
        model.eval()
        snake_case_					= model(lowerCamelCase  )

        # 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				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			List[str]:
        snake_case_					= SwinForMaskedImageModeling(config=lowerCamelCase  )
        model.to(lowerCamelCase  )
        model.eval()
        snake_case_					= model(lowerCamelCase  )
        self.parent.assertEqual(
            result.logits.shape				,    (self.batch_size, self.num_channels, self.image_size, self.image_size)  )

        # test greyscale images
        snake_case_					= 1
        snake_case_					= SwinForMaskedImageModeling(lowerCamelCase  )
        model.to(lowerCamelCase  )
        model.eval()

        snake_case_					= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
        snake_case_					= model(lowerCamelCase  )
        self.parent.assertEqual(result.logits.shape				,    (self.batch_size, 1, self.image_size, self.image_size)  )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			Tuple:
        snake_case_					= self.type_sequence_label_size
        snake_case_					= SwinForImageClassification(lowerCamelCase  )
        model.to(lowerCamelCase  )
        model.eval()
        snake_case_					= model(lowerCamelCase				,    labels=lowerCamelCase  )
        self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.type_sequence_label_size)  )

        # test greyscale images
        snake_case_					= 1
        snake_case_					= SwinForImageClassification(lowerCamelCase  )
        model.to(lowerCamelCase  )
        model.eval()

        snake_case_					= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
        snake_case_					= model(lowerCamelCase  )
        self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.type_sequence_label_size)  )







      def       lowerCAmelCase_       (	self  )		->			str:
        snake_case_					= self.prepare_config_and_inputs()
        (
            (
            snake_case_
        )		,							(
            snake_case_
        )		,							(
            snake_case_
        )		,							
        )					= config_and_inputs
        snake_case_					= {"""pixel_values""": pixel_values}
        return config, inputs_dict







@require_torch
class 					__lowerCamelCase							(      __snake_case    ,				__snake_case    ,				unittest.TestCase      ):
      lowerCamelCase_      : Optional[Any]  			=   (
          (
              SwinModel,
              SwinBackbone,
              SwinForImageClassification,
              SwinForMaskedImageModeling,
          )
          if is_torch_available()
          else ()
      )
      lowerCamelCase_      : Dict  			=   (
          {'feature-extraction': SwinModel, 'image-classification': SwinForImageClassification}
          if is_torch_available()
          else {}
      )
      lowerCamelCase_      : Tuple  			=   True

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







      def       lowerCAmelCase_       (	self  )		->			Union[str, Any]:
        snake_case_					= SwinModelTester(self  )
        snake_case_					= ConfigTester(self				,    config_class=lowerCamelCase				,    embed_dim=37  )







      def       lowerCAmelCase_       (	self  )		->			List[Any]:
        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  )		->			Dict:
        return







      def       lowerCAmelCase_       (	self  )		->			str:
        snake_case_					= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase  )







      @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""  )
      def       lowerCAmelCase_       (	self  )		->			Dict:
        pass







      def       lowerCAmelCase_       (	self  )		->			int:
        super().test_training_gradient_checkpointing()







      def       lowerCAmelCase_       (	self  )		->			List[str]:
        snake_case_					= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			Union[str, Any]:
        snake_case_					= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			Optional[Any]:
        snake_case_					= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCamelCase  )







      @unittest.skip(reason="""Swin does not use inputs_embeds"""  )
      def       lowerCAmelCase_       (	self  )		->			Dict:
        pass







      @unittest.skip(reason="""Swin Transformer does not use feedforward chunking"""  )
      def       lowerCAmelCase_       (	self  )		->			Any:
        pass







      def       lowerCAmelCase_       (	self  )		->			List[Any]:
        snake_case_		,							snake_case_					= self.model_tester.prepare_config_and_inputs_for_common()

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







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

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

          snake_case_					= ["""pixel_values"""]
          self.assertListEqual(arg_names[:1]				,    lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			Any:
        snake_case_		,							snake_case_					= self.model_tester.prepare_config_and_inputs_for_common()
        snake_case_					= True

        for model_class in self.all_model_classes:
          snake_case_					= True
          snake_case_					= False
          snake_case_					= True
          snake_case_					= model_class(lowerCamelCase  )
          model.to(lowerCamelCase  )
          model.eval()
          with torch.no_grad():
            snake_case_					= model(**self._prepare_for_class(lowerCamelCase				,    lowerCamelCase  )  )
          snake_case_					= outputs.attentions
          snake_case_					= 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_					= True
          snake_case_					= config.window_size**2
          snake_case_					= model_class(lowerCamelCase  )
          model.to(lowerCamelCase  )
          model.eval()
          with torch.no_grad():
            snake_case_					= model(**self._prepare_for_class(lowerCamelCase				,    lowerCamelCase  )  )
          snake_case_					= 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_					= len(lowerCamelCase  )

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

          # also another +1 for reshaped_hidden_states
          snake_case_					= 1 if model_class.__name__ == """SwinBackbone""" else 2
          self.assertEqual(out_len + added_hidden_states				,    len(lowerCamelCase  )  )

          snake_case_					= 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  )		->			str:
        snake_case_					= model_class(lowerCamelCase  )
        model.to(lowerCamelCase  )
        model.eval()

        with torch.no_grad():
          snake_case_					= model(**self._prepare_for_class(lowerCamelCase				,    lowerCamelCase  )  )

        snake_case_					= outputs.hidden_states

        snake_case_					= getattr(
            self.model_tester				,    """expected_num_hidden_layers"""				,    len(self.model_tester.depths  ) + 1  )
        self.assertEqual(len(lowerCamelCase  )				,    lowerCamelCase  )

        # Swin has a different seq_length
        snake_case_					= (
            config.patch_size
            if isinstance(config.patch_size				,    collections.abc.Iterable  )
            else (config.patch_size, config.patch_size)
        )

        snake_case_					= (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_					= outputs.reshaped_hidden_states
          self.assertEqual(len(lowerCamelCase  )				,    lowerCamelCase  )

          snake_case_		,							snake_case_		,							snake_case_		,							snake_case_					= reshaped_hidden_states[0].shape
          snake_case_					= (
              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  )		->			Any:
        snake_case_		,							snake_case_					= self.model_tester.prepare_config_and_inputs_for_common()

        snake_case_					= (
            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_					= 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_					= True

          self.check_hidden_states_output(lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			str:
        snake_case_		,							snake_case_					= self.model_tester.prepare_config_and_inputs_for_common()
        snake_case_					= 3

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

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

        for model_class in self.all_model_classes:
          snake_case_					= 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_					= True
          self.check_hidden_states_output(lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    (padded_height, padded_width)  )







      @slow
      def       lowerCAmelCase_       (	self  )		->			Dict:
        for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          snake_case_					= SwinModel.from_pretrained(lowerCamelCase  )
          self.assertIsNotNone(lowerCamelCase  )







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

        snake_case_					= _config_zero_init(lowerCamelCase  )
        for model_class in self.all_model_classes:
          snake_case_					= 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'''				,    )







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







      @cached_property
      def       lowerCAmelCase_       (	self  )		->			Union[str, Any]:
        return (
            AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""  )
            if is_vision_available()
            else None
        )







      @slow
      def       lowerCAmelCase_       (	self  )		->			Optional[int]:
        snake_case_					= SwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""  ).to(lowerCamelCase  )
        snake_case_					= self.default_image_processor

        snake_case_					= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
        snake_case_					= image_processor(images=lowerCamelCase				,    return_tensors="""pt"""  ).to(lowerCamelCase  )

        # forward pass
        with torch.no_grad():
          snake_case_					= model(**lowerCamelCase  )

        # verify the logits
        snake_case_					= torch.Size((1, 1000)  )
        self.assertEqual(outputs.logits.shape				,    lowerCamelCase  )
        snake_case_					= torch.tensor([-0.0948, -0.6454, -0.0921]  ).to(lowerCamelCase  )
        self.assertTrue(torch.allclose(outputs.logits[0, :3]				,    lowerCamelCase				,    atol=1e-4  )  )







@require_torch
class 					__lowerCamelCase							(      unittest.TestCase    ,				__snake_case      ):
      lowerCamelCase_      : Tuple  			=   (SwinBackbone,) if is_torch_available() else ()
      lowerCamelCase_      : int  			=   SwinConfig







      def       lowerCAmelCase_       (	self  )		->			int:
        snake_case_					= SwinModelTester(self  )


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 					__lowerCamelCase							(      __snake_case      ):
      lowerCamelCase_      : Dict  			=   'AutoFeatureExtractor'
      lowerCamelCase_      : Optional[int]  			=   'Speech2Text2Tokenizer'







      def __init__(	self				,    lowerCamelCase				,    lowerCamelCase  )		->			List[str]:
        super().__init__(lowerCamelCase				,    lowerCamelCase  )
        snake_case_					= self.feature_extractor
        snake_case_					= False







      def __call__(	self				,    *lowerCamelCase				,    **lowerCamelCase  )		->			Dict:
        # For backward compatibility
        if self._in_target_context_manager:
          return self.current_processor(*lowerCamelCase				,    **lowerCamelCase  )

        if "raw_speech" in kwargs:
          warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""  )
          snake_case_					= kwargs.pop("""raw_speech"""  )
        else:
          snake_case_					= kwargs.pop("""audio"""				,    lowerCamelCase  )
        snake_case_					= kwargs.pop("""sampling_rate"""				,    lowerCamelCase  )
        snake_case_					= kwargs.pop("""text"""				,    lowerCamelCase  )
        if len(lowerCamelCase  ) > 0:
          snake_case_					= args[0]
          snake_case_					= args[1:]

        if audio is None and text is None:
          raise ValueError("""You need to specify either an `audio` or `text` input to process."""  )

        if audio is not None:
          snake_case_					= self.feature_extractor(lowerCamelCase				,    *lowerCamelCase				,    sampling_rate=lowerCamelCase				,    **lowerCamelCase  )
        if text is not None:
          snake_case_					= self.tokenizer(lowerCamelCase				,    **lowerCamelCase  )

        if text is None:
          return inputs
        elif audio is None:
          return encodings
        else:
          snake_case_					= encodings["""input_ids"""]
          return inputs







      def       lowerCAmelCase_       (	self				,    *lowerCamelCase				,    **lowerCamelCase  )		->			int:
        return self.tokenizer.batch_decode(*lowerCamelCase				,    **lowerCamelCase  )







      def       lowerCAmelCase_       (	self				,    *lowerCamelCase				,    **lowerCamelCase  )		->			Union[str, Any]:
        return self.tokenizer.decode(*lowerCamelCase				,    **lowerCamelCase  )







      @contextmanager
      def       lowerCAmelCase_       (	self  )		->			Any:
        warnings.warn(
            """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
            """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
            """your audio inputs, or in a separate call."""  )
        snake_case_					= True
        snake_case_					= self.tokenizer
        yield
        snake_case_					= self.feature_extractor
        snake_case_					= False