



"""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       SCREAMING_SNAKE_CASE__  :

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=13      ,     _SCREAMING_SNAKE_CASE=3      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=0.1      ,     _SCREAMING_SNAKE_CASE=0.1      ,     _SCREAMING_SNAKE_CASE=224      ,     _SCREAMING_SNAKE_CASE=1000      ,     _SCREAMING_SNAKE_CASE=[3, 3, 6, 4]      ,     _SCREAMING_SNAKE_CASE=[48, 56, 112, 220]      ,     )  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	:					str	=	parent
    UpperCAmelCase	:					List[Any]	=	batch_size
    UpperCAmelCase	:					Dict	=	num_channels
    UpperCAmelCase	:					int	=	is_training
    UpperCAmelCase	:					List[str]	=	use_labels
    UpperCAmelCase	:					Tuple	=	hidden_dropout_prob
    UpperCAmelCase	:					Optional[Any]	=	attention_probs_dropout_prob
    UpperCAmelCase	:					Optional[Any]	=	num_labels
    UpperCAmelCase	:					str	=	image_size
    UpperCAmelCase	:					List[Any]	=	layer_depths
    UpperCAmelCase	:					Tuple	=	embed_dims

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

    UpperCAmelCase	:					Tuple	=	self.get_config()

    return config, pixel_values, labels

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''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=_SCREAMING_SNAKE_CASE      ,     layer_scale_init_value=1E-5      ,     )

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      List[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	SwiftFormerModel(config=_SCREAMING_SNAKE_CASE						)
    model.to(_SCREAMING_SNAKE_CASE						)
    model.eval()
    UpperCAmelCase	:					int	=	model(_SCREAMING_SNAKE_CASE						)
    self.parent.assertEqual(result.last_hidden_state.shape      ,     (self.batch_size, self.embed_dims[-1], 7, 7)						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      int:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	self.num_labels
    UpperCAmelCase	:					List[Any]	=	SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE						)
    model.to(_SCREAMING_SNAKE_CASE						)
    model.eval()
    UpperCAmelCase	:					Union[str, Any]	=	model(_SCREAMING_SNAKE_CASE      ,     labels=_SCREAMING_SNAKE_CASE						)
    self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, self.num_labels)						)

    UpperCAmelCase	:					Union[str, Any]	=	SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE						)
    model.to(_SCREAMING_SNAKE_CASE						)
    model.eval()

    UpperCAmelCase	:					int	=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)
    UpperCAmelCase	:					Dict	=	model(_SCREAMING_SNAKE_CASE						)
    self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, self.num_labels)						)




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





    '''simple docstring'''




    ((UpperCAmelCase)	,		(UpperCAmelCase)	,		(UpperCAmelCase))	:					Union[str, Any]	=	self.prepare_config_and_inputs()
    UpperCAmelCase	:					Union[str, Any]	=	{"""pixel_values""": pixel_values}
    return config, inputs_dict



@require_torch
class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__      ,			UpperCAmelCase__      ,			unittest.TestCase ):
   __lowerCAmelCase  :   Union[str, Any]						=						(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
   __lowerCAmelCase  :   Optional[int]						=						(
       {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
       if is_torch_available()
       else {}
   )

   __lowerCAmelCase  :   Any						=						False
   __lowerCAmelCase  :   Optional[Any]						=						False
   __lowerCAmelCase  :   Any						=						False
   __lowerCAmelCase  :   Optional[Any]						=						False
   __lowerCAmelCase  :   Any						=						False

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[int]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	SwiftFormerModelTester(self						)
    UpperCAmelCase	:					int	=	ConfigTester(
        self      ,     config_class=_SCREAMING_SNAKE_CASE      ,     has_text_modality=_SCREAMING_SNAKE_CASE      ,     hidden_size=37      ,     num_attention_heads=12      ,     num_hidden_layers=12      ,     )

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    self.config_tester.run_common_tests()

   @unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""						)
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    pass

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[Any]:





    '''simple docstring'''




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

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

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




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

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

     UpperCAmelCase	:					Dict	=	["""pixel_values"""]
     self.assertListEqual(arg_names[:1]      ,     _SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE						)

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





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE						)

   @slow
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Union[str, Any]:





    '''simple docstring'''




    for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     UpperCAmelCase	:					Any	=	SwiftFormerModel.from_pretrained(_SCREAMING_SNAKE_CASE						)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE						)

   @unittest.skip(reason="""SwiftFormer does not output attentions"""						)
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    pass

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[Any]:





    '''simple docstring'''




    def check_hidden_states_output(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						):
     UpperCAmelCase	:					Dict	=	model_class(_SCREAMING_SNAKE_CASE						)
     model.to(_SCREAMING_SNAKE_CASE						)
     model.eval()

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

     UpperCAmelCase	:					int	=	outputs.hidden_states

     UpperCAmelCase	:					Optional[Any]	=	8
     self.assertEqual(len(_SCREAMING_SNAKE_CASE						)      ,     _SCREAMING_SNAKE_CASE						)  # 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(_SCREAMING_SNAKE_CASE						)						):
      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),
              ]						)      ,     )

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

    for model_class in self.all_model_classes:
     UpperCAmelCase	:					Optional[Any]	=	True
     check_hidden_states_output(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     UpperCAmelCase	:					List[Any]	=	True

     check_hidden_states_output(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[int]:





    '''simple docstring'''




    def _config_zero_init(_SCREAMING_SNAKE_CASE						):
     UpperCAmelCase	:					Optional[int]	=	copy.deepcopy(_SCREAMING_SNAKE_CASE						)
     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(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     1E-10						)
      if isinstance(getattr(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)      ,     _SCREAMING_SNAKE_CASE						):
       UpperCAmelCase	:					Any	=	_config_zero_init(getattr(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)						)
       setattr(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
     return configs_no_init

    UpperCAmelCase	,		UpperCAmelCase	:					Any	=	self.model_tester.prepare_config_and_inputs_for_common()

    UpperCAmelCase	:					List[Any]	=	_config_zero_init(_SCREAMING_SNAKE_CASE						)
    for model_class in self.all_model_classes:
     UpperCAmelCase	:					List[Any]	=	model_class(config=_SCREAMING_SNAKE_CASE						)
     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 	SCREAMING_SNAKE_CASE     (   self						)  ->      Union[str, Any]:





    '''simple docstring'''




    pass

def       _snake_case						(						):
 UpperCAmelCase	:					int	=	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
 return image



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

   @cached_property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[Any]:





    '''simple docstring'''




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




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





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""						).to(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					List[Any]	=	self.default_image_processor
    UpperCAmelCase	:					Tuple	=	prepare_img()
    UpperCAmelCase	:					Tuple	=	image_processor(images=_SCREAMING_SNAKE_CASE      ,     return_tensors="""pt"""						).to(_SCREAMING_SNAKE_CASE						)

    # forward pass
    with torch.no_grad():
     UpperCAmelCase	:					Tuple	=	model(**_SCREAMING_SNAKE_CASE						)

    # verify the logits
    UpperCAmelCase	:					str	=	torch.Size((1, 1000)						)
    self.assertEqual(outputs.logits.shape      ,     _SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Union[str, Any]	=	torch.tensor([[-2.1703E00, 2.1107E00, -2.0811E00]]						).to(_SCREAMING_SNAKE_CASE						)
    self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,     _SCREAMING_SNAKE_CASE      ,     atol=1E-4						)						)





"""simple docstring"""
from typing import TYPE_CHECKING

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


A:   List[str]								=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Tuple								=		["DPTFeatureExtractor"]
      A:   int								=		["DPTImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[Any]								=		[
          "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPTForDepthEstimation",
          "DPTForSemanticSegmentation",
          "DPTModel",
          "DPTPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_dpt import DPTFeatureExtractor
            from .image_processing_dpt import DPTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dpt import (
                DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPTForDepthEstimation,
                DPTForSemanticSegmentation,
                DPTModel,
                DPTPreTrainedModel,
            )


else:
      import sys

      A:   str								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
