
"""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   _UpperCAmelCase					:
    def __init__(     self    ,     snake_case_    ,     snake_case_=13    ,     snake_case_=32    ,     snake_case_=2    ,     snake_case_=3    ,     snake_case_=16    ,     snake_case_=[1, 2, 1]    ,     snake_case_=[2, 2, 4]    ,     snake_case_=2    ,     snake_case_=2.0    ,     snake_case_=True    ,     snake_case_=0.0    ,     snake_case_=0.0    ,     snake_case_=0.1    ,     snake_case_="gelu"    ,     snake_case_=False    ,     snake_case_=True    ,     snake_case_=0.02    ,     snake_case_=1E-5    ,     snake_case_=True    ,     snake_case_=None    ,     snake_case_=True    ,     snake_case_=10    ,     snake_case_=8    ,     snake_case_=["stage1", "stage2"]    ,     snake_case_=[1, 2]    ,     ):
      _snake_case  :    Optional[Any]            = parent
      _snake_case  :    List[Any]            = batch_size
      _snake_case  :    List[str]            = image_size
      _snake_case  :    str            = patch_size
      _snake_case  :    List[str]            = num_channels
      _snake_case  :    Optional[Any]            = embed_dim
      _snake_case  :    List[str]            = depths
      _snake_case  :    Dict            = num_heads
      _snake_case  :    List[str]            = window_size
      _snake_case  :    Optional[int]            = mlp_ratio
      _snake_case  :    Any            = qkv_bias
      _snake_case  :    List[str]            = hidden_dropout_prob
      _snake_case  :    List[Any]            = attention_probs_dropout_prob
      _snake_case  :    int            = drop_path_rate
      _snake_case  :    Any            = hidden_act
      _snake_case  :    List[str]            = use_absolute_embeddings
      _snake_case  :    List[str]            = patch_norm
      _snake_case  :    Any            = layer_norm_eps
      _snake_case  :    Dict            = initializer_range
      _snake_case  :    int            = is_training
      _snake_case  :    Tuple            = scope
      _snake_case  :    Tuple            = use_labels
      _snake_case  :    Dict            = type_sequence_label_size
      _snake_case  :    int            = encoder_stride
      _snake_case  :    Dict            = out_features
      _snake_case  :    Union[str, Any]            = out_indices
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Any            = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

      _snake_case  :    Optional[int]            = self.get_config()

      return config, pixel_values, labels
    def        lowerCamelCase__						(     self  ):
      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    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    int            = SwinModel(config=snake_case_  )
      model.to(snake_case_  )
      model.eval()
      _snake_case  :    Tuple            = model(snake_case_  )

      _snake_case  :    int            = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths  ) - 1))
      _snake_case  :    int            = 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    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    List[str]            = SwinBackbone(config=snake_case_  )
      model.to(snake_case_  )
      model.eval()
      _snake_case  :    int            = model(snake_case_  )

      # 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[int]            = SwinBackbone(config=snake_case_  )
      model.to(snake_case_  )
      model.eval()
      _snake_case  :    int            = model(snake_case_  )

      # 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    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    str            = SwinForMaskedImageModeling(config=snake_case_  )
      model.to(snake_case_  )
      model.eval()
      _snake_case  :    Any            = model(snake_case_  )
      self.parent.assertEqual(
          result.logits.shape    ,     (self.batch_size, self.num_channels, self.image_size, self.image_size)  )

      # test greyscale images
      _snake_case  :    List[Any]            = 1
      _snake_case  :    Optional[Any]            = SwinForMaskedImageModeling(snake_case_  )
      model.to(snake_case_  )
      model.eval()

      _snake_case  :    Dict            = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
      _snake_case  :    Tuple            = model(snake_case_  )
      self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, 1, self.image_size, self.image_size)  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    List[Any]            = self.type_sequence_label_size
      _snake_case  :    Dict            = SwinForImageClassification(snake_case_  )
      model.to(snake_case_  )
      model.eval()
      _snake_case  :    List[str]            = model(snake_case_    ,     labels=snake_case_  )
      self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.type_sequence_label_size)  )

      # test greyscale images
      _snake_case  :    Union[str, Any]            = 1
      _snake_case  :    List[str]            = SwinForImageClassification(snake_case_  )
      model.to(snake_case_  )
      model.eval()

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


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






@require_torch
class   _UpperCAmelCase					(       _snake_case		,       _snake_case		,       unittest.TestCase):
    __lowercase				:			Tuple	=       (
        (
            SwinModel,
            SwinBackbone,
            SwinForImageClassification,
            SwinForMaskedImageModeling,
        )
        if is_torch_available()
        else ()
    )
    __lowercase				:			Tuple	=       (
        {"""feature-extraction""": SwinModel, """image-classification""": SwinForImageClassification}
        if is_torch_available()
        else {}
    )
    __lowercase				:			Any	=       True

    __lowercase				:			Dict	=       False
    __lowercase				:			int	=       False
    __lowercase				:			Any	=       False
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Tuple            = SwinModelTester(self  )
      _snake_case  :    List[str]            = ConfigTester(self    ,     config_class=snake_case_    ,     embed_dim=37  )
    def        lowerCamelCase__						(     self  ):
      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  ):
      return
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Dict            = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*snake_case_  )
    @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."  )
    def        lowerCamelCase__						(     self  ):
      pass
    def        lowerCamelCase__						(     self  ):
      super().test_training_gradient_checkpointing()
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Optional[Any]            = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_backbone(*snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Tuple            = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*snake_case_  )
    @unittest.skip(reason="Swin does not use inputs_embeds"  )
    def        lowerCamelCase__						(     self  ):
      pass
    @unittest.skip(reason="Swin Transformer does not use feedforward chunking"  )
    def        lowerCamelCase__						(     self  ):
      pass
    def        lowerCamelCase__						(     self  ):
      _snake_case      ,       _snake_case  :    Dict            = self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        _snake_case  :    List[Any]            = model_class(snake_case_  )
        self.assertIsInstance(model.get_input_embeddings()    ,     (nn.Module)  )
        _snake_case  :    Any            = model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(snake_case_    ,     nn.Linear  )  )
    def        lowerCamelCase__						(     self  ):
      _snake_case      ,       _snake_case  :    str            = self.model_tester.prepare_config_and_inputs_for_common()

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

        _snake_case  :    Optional[int]            = ["pixel_values"]
        self.assertListEqual(arg_names[:1]    ,     snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case      ,       _snake_case  :    Any            = self.model_tester.prepare_config_and_inputs_for_common()
      _snake_case  :    Dict            = True

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

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

        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(snake_case_  )

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

        # also another +1 for reshaped_hidden_states
        _snake_case  :    Optional[Any]            = 1 if model_class.__name__ == "SwinBackbone" else 2
        self.assertEqual(out_len + added_hidden_states    ,     len(snake_case_  )  )

        _snake_case  :    List[str]            = outputs.attentions

        self.assertEqual(len(snake_case_  )    ,     snake_case_  )

        self.assertListEqual(
            list(self_attentions[0].shape[-3:]  )    ,     [self.model_tester.num_heads[0], window_size_squared, window_size_squared]    ,     )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    int            = model_class(snake_case_  )
      model.to(snake_case_  )
      model.eval()

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

      _snake_case  :    Union[str, Any]            = outputs.hidden_states

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

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

      _snake_case  :    int            = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

      self.assertListEqual(
          list(hidden_states[0].shape[-2:]  )    ,     [num_patches, self.model_tester.embed_dim]    ,     )

      if not model_class.__name__ == "SwinBackbone":
        _snake_case  :    Union[str, Any]            = outputs.reshaped_hidden_states
        self.assertEqual(len(snake_case_  )    ,     snake_case_  )

        _snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case  :    Union[str, Any]            = reshaped_hidden_states[0].shape
        _snake_case  :    Any            = (
            reshaped_hidden_states[0].view(snake_case_    ,     snake_case_    ,     height * width  ).permute(0    ,     2    ,     1  )
        )
        self.assertListEqual(
            list(reshaped_hidden_states.shape[-2:]  )    ,     [num_patches, self.model_tester.embed_dim]    ,     )
    def        lowerCamelCase__						(     self  ):
      _snake_case      ,       _snake_case  :    Any            = self.model_tester.prepare_config_and_inputs_for_common()

      _snake_case  :    Optional[int]            = (
          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  :    str            = True
        self.check_hidden_states_output(snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  )

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        _snake_case  :    Union[str, Any]            = True

        self.check_hidden_states_output(snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case      ,       _snake_case  :    str            = self.model_tester.prepare_config_and_inputs_for_common()
      _snake_case  :    Union[str, Any]            = 3

      _snake_case  :    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)
      )
      _snake_case  :    Any            = (
          config.patch_size
          if isinstance(config.patch_size    ,     collections.abc.Iterable  )
          else (config.patch_size, config.patch_size)
      )

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

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

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        _snake_case  :    List[Any]            = True
        self.check_hidden_states_output(snake_case_    ,     snake_case_    ,     snake_case_    ,     (padded_height, padded_width)  )
    @slow
    def        lowerCamelCase__						(     self  ):
      for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        _snake_case  :    str            = SwinModel.from_pretrained(snake_case_  )
        self.assertIsNotNone(snake_case_  )


    def        lowerCamelCase__						(     self  ):
      _snake_case      ,       _snake_case  :    Tuple            = self.model_tester.prepare_config_and_inputs_for_common()

      _snake_case  :    List[str]            = _config_zero_init(snake_case_  )
      for model_class in self.all_model_classes:
        _snake_case  :    Optional[int]            = model_class(config=snake_case_  )
        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   _UpperCAmelCase					(       unittest.TestCase):
    @cached_property
    def        lowerCamelCase__						(     self  ):
      return (
          AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"  )
          if is_vision_available()
          else None
      )


    @slow
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[str]            = SwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"  ).to(snake_case_  )
      _snake_case  :    Optional[int]            = self.default_image_processor

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

      # forward pass
      with torch.no_grad():
        _snake_case  :    Optional[Any]            = model(**snake_case_  )

      # verify the logits
      _snake_case  :    Tuple            = torch.Size((1, 10_00)  )
      self.assertEqual(outputs.logits.shape    ,     snake_case_  )
      _snake_case  :    List[str]            = torch.tensor([-0.0948, -0.6454, -0.0921]  ).to(snake_case_  )
      self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,     snake_case_    ,     atol=1E-4  )  )






@require_torch
class   _UpperCAmelCase					(       unittest.TestCase		,       _snake_case):
    __lowercase				:			Optional[int]	=       (SwinBackbone,) if is_torch_available() else ()
    __lowercase				:			Optional[Any]	=       SwinConfig
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = SwinModelTester(self  )



_A       =					{
    "+": lambda x, y: x + y,
    "-": lambda x, y: x - y,
    "*": lambda x, y: x * y,
    "/": lambda x, y: x / y,
}



def 		lowercase_     (  A__						)				->				Union[str, Any]:
    """simple docstring"""

    return c.isdigit()



def 		lowercase_     (  A__						)				->				int:
    """simple docstring"""

    snake_case						=   []

    # iterate over the string in reverse order
    for c in expression.split()[::-1]:
        # push operand to stack
        if is_operand(A__						):
            stack.append(int(A__						)						)

        else:
            # pop values from stack can calculate the result
            # push the result onto the stack again
            snake_case						=   stack.pop()
            snake_case						=   stack.pop()
            stack.append(calc[c](A__					,						A__						)						)

    return stack.pop()


# Driver code
if __name__ == "__main__":
   _A       =					"+ 9 * 2 6"
   print(evaluate(test_expression))

   _A       =					"/ * 10 2 + 4 1 "
   print(evaluate(test_expression))

