




"""simple docstring"""



import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import SwiftFormerForImageClassification, SwiftFormerModel
      from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import ViTImageProcessor




class      UpperCAmelCase_  :




    def __init__(   self						:	Optional[int]     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	int=13     ,			snake_case_						:	str=3     ,			snake_case_						:	Dict=True     ,			snake_case_						:	Optional[Any]=True     ,			snake_case_						:	Any=0.1     ,			snake_case_						:	Dict=0.1     ,			snake_case_						:	str=224     ,			snake_case_						:	int=1_000     ,			snake_case_						:	Tuple=[3, 3, 6, 4]     ,			snake_case_						:	Dict=[48, 56, 112, 220]     ,			)				->							Any:




     '''simple docstring'''

     A__	=    parent
     A__	=    batch_size
     A__	=    num_channels
     A__	=    is_training
     A__	=    use_labels
     A__	=    hidden_dropout_prob
     A__	=    attention_probs_dropout_prob
     A__	=    num_labels
     A__	=    image_size
     A__	=    layer_depths
     A__	=    embed_dims




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							int:




     '''simple docstring'''

     A__	=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

     A__	=    None
     if self.use_labels:
      A__	=    ids_tensor([self.batch_size]     ,			self.num_labels				)

     A__	=    self.get_config()

     return config, pixel_values, labels




    def 							__magic_name__							(   self						:	List[Any]				)				->							Optional[int]:




     '''simple docstring'''

     return SwiftFormerConfig(
         depths=self.layer_depths     ,			embed_dims=self.embed_dims     ,			mlp_ratio=4     ,			downsamples=[True, True, True, True]     ,			hidden_act="gelu"     ,			num_labels=self.num_labels     ,			down_patch_size=3     ,			down_stride=2     ,			down_pad=1     ,			drop_rate=0.0     ,			drop_path_rate=0.0     ,			use_layer_scale=a__     ,			layer_scale_init_value=1e-5     ,			)




    def 							__magic_name__							(   self						:	Tuple     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	List[Any]     ,			snake_case_						:	int				)				->							Tuple:




     '''simple docstring'''

     A__	=    SwiftFormerModel(config=a__				)
     model.to(a__				)
     model.eval()
     A__	=    model(a__				)
     self.parent.assertEqual(result.last_hidden_state.shape     ,			(self.batch_size, self.embed_dims[-1], 7, 7)				)




    def 							__magic_name__							(   self						:	Any     ,			snake_case_						:	Tuple     ,			snake_case_						:	str     ,			snake_case_						:	Dict				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    self.num_labels
     A__	=    SwiftFormerForImageClassification(a__				)
     model.to(a__				)
     model.eval()
     A__	=    model(a__     ,			labels=a__				)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.num_labels)				)

     A__	=    SwiftFormerForImageClassification(a__				)
     model.to(a__				)
     model.eval()

     A__	=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)
     A__	=    model(a__				)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.num_labels)				)




    def 							__magic_name__							(   self						:	Union[str, Any]				)				->							Tuple:




     '''simple docstring'''

     ((A__),				(A__),				(A__))	=    self.prepare_config_and_inputs()
     A__	=    {"pixel_values": pixel_values}
     return config, inputs_dict






@require_torch
class      UpperCAmelCase_  (						__a,       __a,       unittest.TestCase							):
    lowercase__						 =				(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
    lowercase__						 =				(
        {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
        if is_torch_available()
        else {}
    )

    lowercase__						 =				False
    lowercase__						 =				False
    lowercase__						 =				False
    lowercase__						 =				False
    lowercase__						 =				False




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Dict:




     '''simple docstring'''

     A__	=    SwiftFormerModelTester(self				)
     A__	=    ConfigTester(
         self     ,			config_class=a__     ,			has_text_modality=a__     ,			hidden_size=37     ,			num_attention_heads=12     ,			num_hidden_layers=12     ,			)




    def 							__magic_name__							(   self						:	Union[str, Any]				)				->							str:




     '''simple docstring'''

     self.config_tester.run_common_tests()




    @unittest.skip(reason="SwiftFormer does not use inputs_embeds"				)
    def 							__magic_name__							(   self						:	Tuple				)				->							Optional[Any]:




     '''simple docstring'''

     pass




    def 							__magic_name__							(   self						:	List[str]				)				->							Optional[Any]:




     '''simple docstring'''

     A__,				A__	=    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
      A__	=    model_class(a__				)
      A__	=    model.get_output_embeddings()
      self.assertTrue(x is None or isinstance(a__     ,			nn.Linear				)				)




    def 							__magic_name__							(   self						:	str				)				->							Optional[Any]:




     '''simple docstring'''

     A__,				A__	=    self.model_tester.prepare_config_and_inputs_for_common()

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

      A__	=    ["pixel_values"]
      self.assertListEqual(arg_names[:1]     ,			a__				)




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*a__				)




    def 							__magic_name__							(   self						:	Any				)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*a__				)




    @slow
    def 							__magic_name__							(   self						:	List[str]				)				->							Any:




     '''simple docstring'''

     for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      A__	=    SwiftFormerModel.from_pretrained(a__				)
      self.assertIsNotNone(a__				)




    @unittest.skip(reason="SwiftFormer does not output attentions"				)
    def 							__magic_name__							(   self						:	Optional[int]				)				->							Union[str, Any]:




     '''simple docstring'''

     pass




    def 							__magic_name__							(   self						:	Dict				)				->							Union[str, Any]:




     '''simple docstring'''

     def check_hidden_states_output(snake_case_						:	List[Any]     ,			snake_case_						:	int     ,			snake_case_						:	List[Any]				):
      A__	=    model_class(a__				)
      model.to(a__				)
      model.eval()

      with torch.no_grad():
       A__	=    model(**self._prepare_for_class(a__     ,			a__				)				)

      A__	=    outputs.hidden_states

      A__	=    8
      self.assertEqual(len(a__				)     ,			a__				)  # TODO

      # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
      # with the width and height being successively divided by 2, after every 2 blocks
      for i in range(len(a__				)				):
       self.assertEqual(
           hidden_states[i].shape     ,			torch.Size(
               [
                   self.model_tester.batch_size,
                   self.model_tester.embed_dims[i // 2],
                   (self.model_tester.image_size // 4) // 2 ** (i // 2),
                   (self.model_tester.image_size // 4) // 2 ** (i // 2),
               ]				)     ,			)

     A__,				A__	=    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
      A__	=    True
      check_hidden_states_output(a__     ,			a__     ,			a__				)

      # check that output_hidden_states also work using config
      del inputs_dict["output_hidden_states"]
      A__	=    True

      check_hidden_states_output(a__     ,			a__     ,			a__				)




    def 							__magic_name__							(   self						:	Union[str, Any]				)				->							Tuple:




     '''simple docstring'''

     def _config_zero_init(snake_case_						:	Tuple				):
      A__	=    copy.deepcopy(a__				)
      for key in configs_no_init.__dict__.keys():
       if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
        setattr(a__     ,			a__     ,			1e-10				)
       if isinstance(getattr(a__     ,			a__     ,			a__				)     ,			a__				):
        A__	=    _config_zero_init(getattr(a__     ,			a__				)				)
        setattr(a__     ,			a__     ,			a__				)
      return configs_no_init

     A__,				A__	=    self.model_tester.prepare_config_and_inputs_for_common()

     A__	=    _config_zero_init(a__				)
     for model_class in self.all_model_classes:
      A__	=    model_class(config=a__				)
      for name, param in model.named_parameters():
       if param.requires_grad:
        self.assertIn(
            ((param.data.mean() * 1e9) / 1e9).round().item()     ,			[0.0, 1.0]     ,			msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""     ,			)




    @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."				)
    def 							__magic_name__							(   self						:	int				)				->							List[str]:




     '''simple docstring'''

     pass







def   _SCREAMING_SNAKE_CASE       (							):
 A__	=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
 return image






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




    @cached_property
    def 							__magic_name__							(   self						:	List[str]				)				->							int:




     '''simple docstring'''

     return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs"				) if is_vision_available() else None




    @slow
    def 							__magic_name__							(   self						:	Dict				)				->							Dict:




     '''simple docstring'''

     A__	=    SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"				).to(a__				)

     A__	=    self.default_image_processor
     A__	=    prepare_img()
     A__	=    image_processor(images=a__     ,			return_tensors="pt"				).to(a__				)

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

     # verify the logits
     A__	=    torch.Size((1, 1_000)				)
     self.assertEqual(outputs.logits.shape     ,			a__				)

     A__	=    torch.tensor([[-2.1_703e00, 2.1_107e00, -2.0_811e00]]				).to(a__				)
     self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,			a__     ,			atol=1e-4				)				)











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE    =	{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	["MobileViTFeatureExtractor"]
      SCREAMING_SNAKE_CASE    =	["MobileViTImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	[
          "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "MobileViTForImageClassification",
          "MobileViTForSemanticSegmentation",
          "MobileViTModel",
          "MobileViTPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	[
          "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFMobileViTForImageClassification",
          "TFMobileViTForSemanticSegmentation",
          "TFMobileViTModel",
          "TFMobileViTPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_mobilevit import MobileViTFeatureExtractor
            from .image_processing_mobilevit import MobileViTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilevit import (
                MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileViTForImageClassification,
                MobileViTForSemanticSegmentation,
                MobileViTModel,
                MobileViTPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mobilevit import (
                TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMobileViTForImageClassification,
                TFMobileViTForSemanticSegmentation,
                TFMobileViTModel,
                TFMobileViTPreTrainedModel,
            )


else:
      import sys

      SCREAMING_SNAKE_CASE    =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





