



"""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__   :




      """simple docstring"""




      def __init__(      self				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      str=1_3    ,			SCREAMING_SNAKE_CASE_				:      str=3    ,			SCREAMING_SNAKE_CASE_				:      str=True    ,			SCREAMING_SNAKE_CASE_				:      Dict=True    ,			SCREAMING_SNAKE_CASE_				:      Dict=0.1    ,			SCREAMING_SNAKE_CASE_				:      Dict=0.1    ,			SCREAMING_SNAKE_CASE_				:      List[str]=2_2_4    ,			SCREAMING_SNAKE_CASE_				:      Tuple=1_0_0_0    ,			SCREAMING_SNAKE_CASE_				:      Dict=[3, 3, 6, 4]    ,			SCREAMING_SNAKE_CASE_				:      Tuple=[4_8, 5_6, 1_1_2, 2_2_0]    ,			):
        lowerCAmelCase_   :				str								= parent
        lowerCAmelCase_   :				Dict								= batch_size
        lowerCAmelCase_   :				Optional[Any]								= num_channels
        lowerCAmelCase_   :				Optional[int]								= is_training
        lowerCAmelCase_   :				Any								= use_labels
        lowerCAmelCase_   :				List[str]								= hidden_dropout_prob
        lowerCAmelCase_   :				Optional[int]								= attention_probs_dropout_prob
        lowerCAmelCase_   :				Tuple								= num_labels
        lowerCAmelCase_   :				Any								= image_size
        lowerCAmelCase_   :				Tuple								= layer_depths
        lowerCAmelCase_   :				Union[str, Any]								= embed_dims



      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_   :				List[Any]								= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

        lowerCAmelCase_   :				Optional[int]								= None
        if self.use_labels:
          lowerCAmelCase_   :				List[Any]								= ids_tensor([self.batch_size]    ,			self.num_labels       )

        lowerCAmelCase_   :				Tuple								= self.get_config()

        return config, pixel_values, labels



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        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				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      List[str]       ):
        lowerCAmelCase_   :				Optional[int]								= SwiftFormerModel(config=SCREAMING_SNAKE_CASE_       )
        model.to(SCREAMING_SNAKE_CASE_       )
        model.eval()
        lowerCAmelCase_   :				List[Any]								= 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				:      Any    ,			SCREAMING_SNAKE_CASE_				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      List[str]       ):
        lowerCAmelCase_   :				Tuple								= self.num_labels
        lowerCAmelCase_   :				Optional[Any]								= SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE_       )
        model.to(SCREAMING_SNAKE_CASE_       )
        model.eval()
        lowerCAmelCase_   :				Dict								= model(SCREAMING_SNAKE_CASE_    ,			labels=SCREAMING_SNAKE_CASE_       )
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.num_labels)       )

        lowerCAmelCase_   :				List[str]								= SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE_       )
        model.to(SCREAMING_SNAKE_CASE_       )
        model.eval()

        lowerCAmelCase_   :				Dict								= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )
        lowerCAmelCase_   :				Optional[Any]								= model(SCREAMING_SNAKE_CASE_       )
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.num_labels)       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        ((lowerCAmelCase_)						,(lowerCAmelCase_)						,(lowerCAmelCase_))   :				Any								= self.prepare_config_and_inputs()
        lowerCAmelCase_   :				Union[str, Any]								= {'pixel_values': pixel_values}
        return config, inputs_dict







@require_torch
class       UpperCamelCase__   (	lowercase_,					lowercase_,					unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
      _SCREAMING_SNAKE_CASE  				=     (
          {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
          if is_torch_available()
          else {}
      )

      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False



      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_   :				Optional[int]								= SwiftFormerModelTester(self       )
        lowerCAmelCase_   :				Optional[int]								= ConfigTester(
            self    ,			config_class=SCREAMING_SNAKE_CASE_    ,			has_text_modality=SCREAMING_SNAKE_CASE_    ,			hidden_size=3_7    ,			num_attention_heads=1_2    ,			num_hidden_layers=1_2    ,			)



      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        self.config_tester.run_common_tests()



      @unittest.skip(reason='SwiftFormer does not use inputs_embeds'       )
      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        pass



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        lowerCAmelCase_						,lowerCAmelCase_   :				int								= self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          lowerCAmelCase_   :				List[str]								= model_class(SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				Optional[int]								= model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_    ,			nn.Linear       )       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_						,lowerCAmelCase_   :				str								= self.model_tester.prepare_config_and_inputs_for_common()

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

          lowerCAmelCase_   :				Optional[Any]								= ['pixel_values']
          self.assertListEqual(arg_names[:1]    ,			SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				List[Any]								= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple       ):
        lowerCAmelCase_   :				Tuple								= 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				:      str       ):
        for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase_   :				Dict								= SwiftFormerModel.from_pretrained(SCREAMING_SNAKE_CASE_       )
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_       )



      @unittest.skip(reason='SwiftFormer does not output attentions'       )
      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        pass



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        def check_hidden_states_output(SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]       ):
          lowerCAmelCase_   :				int								= model_class(SCREAMING_SNAKE_CASE_       )
          model.to(SCREAMING_SNAKE_CASE_       )
          model.eval()

          with torch.no_grad():
            lowerCAmelCase_   :				Tuple								= model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )       )

          lowerCAmelCase_   :				List[str]								= outputs.hidden_states

          lowerCAmelCase_   :				List[str]								= 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),
                    ]       )    ,			)

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

        for model_class in self.all_model_classes:
          lowerCAmelCase_   :				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"]
          lowerCAmelCase_   :				List[str]								= True

          check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        def _config_zero_init(SCREAMING_SNAKE_CASE_				:      Optional[int]       ):
          lowerCAmelCase_   :				Tuple								= 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_       ):
              lowerCAmelCase_   :				Optional[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

        lowerCAmelCase_						,lowerCAmelCase_   :				Tuple								= self.model_tester.prepare_config_and_inputs_for_common()

        lowerCAmelCase_   :				Optional[Any]								= _config_zero_init(SCREAMING_SNAKE_CASE_       )
        for model_class in self.all_model_classes:
          lowerCAmelCase_   :				List[str]								= 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				:      List[str]       ):
        pass




def 				UpperCamelCase_    (    )						->   Optional[int]:

  """simple docstring"""




  lowerCAmelCase_   :				List[str]								= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
  return image







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




      """simple docstring"""




      @cached_property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict       ):
        return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs'       ) if is_vision_available() else None





      @slow
      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple       ):
        lowerCAmelCase_   :				List[str]								= SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'       ).to(SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				int								= self.default_image_processor
        lowerCAmelCase_   :				int								= prepare_img()
        lowerCAmelCase_   :				Tuple								= image_processor(images=SCREAMING_SNAKE_CASE_    ,			return_tensors='pt'       ).to(SCREAMING_SNAKE_CASE_       )

        # forward pass
        with torch.no_grad():
          lowerCAmelCase_   :				int								= model(**SCREAMING_SNAKE_CASE_       )

        # verify the logits
        lowerCAmelCase_   :				Tuple								= torch.Size((1, 1_0_0_0)       )
        self.assertEqual(outputs.logits.shape    ,			SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				Optional[Any]								= torch.tensor([[-2.17_03E00, 2.11_07E00, -2.08_11E00]]       ).to(SCREAMING_SNAKE_CASE_       )
        self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,			SCREAMING_SNAKE_CASE_    ,			atol=1E-4       )       )










"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__		:				Tuple          =	logging.get_logger(__name__)

lowercase__		:				Any          =	{
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     """dpr"""



      def __init__(      self				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      Tuple=3_0_5_2_2    ,			SCREAMING_SNAKE_CASE_				:      Any=7_6_8    ,			SCREAMING_SNAKE_CASE_				:      Dict=1_2    ,			SCREAMING_SNAKE_CASE_				:      List[str]=1_2    ,			SCREAMING_SNAKE_CASE_				:      int=3_0_7_2    ,			SCREAMING_SNAKE_CASE_				:      List[Any]="gelu"    ,			SCREAMING_SNAKE_CASE_				:      int=0.1    ,			SCREAMING_SNAKE_CASE_				:      int=0.1    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=5_1_2    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=2    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=0.02    ,			SCREAMING_SNAKE_CASE_				:      List[str]=1E-12    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=0    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]="absolute"    ,			SCREAMING_SNAKE_CASE_				:      int = 0    ,			**SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			):
        super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				Optional[int]								= vocab_size
        lowerCAmelCase_   :				Tuple								= hidden_size
        lowerCAmelCase_   :				int								= num_hidden_layers
        lowerCAmelCase_   :				str								= num_attention_heads
        lowerCAmelCase_   :				Any								= hidden_act
        lowerCAmelCase_   :				List[str]								= intermediate_size
        lowerCAmelCase_   :				int								= hidden_dropout_prob
        lowerCAmelCase_   :				Union[str, Any]								= attention_probs_dropout_prob
        lowerCAmelCase_   :				Tuple								= max_position_embeddings
        lowerCAmelCase_   :				List[str]								= type_vocab_size
        lowerCAmelCase_   :				str								= initializer_range
        lowerCAmelCase_   :				str								= layer_norm_eps
        lowerCAmelCase_   :				List[str]								= projection_dim
        lowerCAmelCase_   :				List[str]								= position_embedding_type





