



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 			_snake_case     :




    def __init__(  self   :  Optional[Any],   __lowercase   :  Optional[Any],   __lowercase   :  Any=13,   __lowercase   :  Optional[Any]=3,   __lowercase   :  str=True,   __lowercase   :  Union[str, Any]=True,   __lowercase   :  int=0.1,   __lowercase   :  Any=0.1,   __lowercase   :  Optional[int]=224,   __lowercase   :  Tuple=1000,   __lowercase   :  Dict=[3, 3, 6, 4],   __lowercase   :  str=[48, 56, 112, 220],   ):
       lowercase__   					=			parent
       lowercase__   					=			batch_size
       lowercase__   					=			num_channels
       lowercase__   					=			is_training
       lowercase__   					=			use_labels
       lowercase__   					=			hidden_dropout_prob
       lowercase__   					=			attention_probs_dropout_prob
       lowercase__   					=			num_labels
       lowercase__   					=			image_size
       lowercase__   					=			layer_depths
       lowercase__   					=			embed_dims




    def 							A__ (  self   :  Tuple  ):
       lowercase__   					=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

       lowercase__   					=			self.get_config()

       return config, pixel_values, labels




    def 							A__ (  self   :  List[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=__lowercase,   layer_scale_init_value=1e-5,   )




    def 							A__ (  self   :  str,   __lowercase   :  Optional[Any],   __lowercase   :  Tuple,   __lowercase   :  Union[str, Any]  ):
       lowercase__   					=			SwiftFormerModel(config=__lowercase  )
       model.to(__lowercase  )
       model.eval()
       lowercase__   					=			model(__lowercase  )
       self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.embed_dims[-1], 7, 7)  )




    def 							A__ (  self   :  Optional[int],   __lowercase   :  Optional[Any],   __lowercase   :  Dict,   __lowercase   :  Dict  ):
       lowercase__   					=			self.num_labels
       lowercase__   					=			SwiftFormerForImageClassification(__lowercase  )
       model.to(__lowercase  )
       model.eval()
       lowercase__   					=			model(__lowercase,   labels=__lowercase  )
       self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_labels)  )

       lowercase__   					=			SwiftFormerForImageClassification(__lowercase  )
       model.to(__lowercase  )
       model.eval()

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




    def 							A__ (  self   :  Optional[Any]  ):
       ((lowercase__)					,    (lowercase__)					,    (lowercase__))   					=			self.prepare_config_and_inputs()
       lowercase__   					=			{"pixel_values": pixel_values}
       return config, inputs_dict
@require_torch
class 			_snake_case     (   lowercase__      ,		lowercase__      ,		unittest.TestCase):
    UpperCamelCase__			:   int       						=(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
    UpperCamelCase__			:   Optional[int]       						=(
        {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
        if is_torch_available()
        else {}
    )

    UpperCamelCase__			:   Dict       						=False
    UpperCamelCase__			:   List[Any]       						=False
    UpperCamelCase__			:   Optional[Any]       						=False
    UpperCamelCase__			:   int       						=False
    UpperCamelCase__			:   Union[str, Any]       						=False




    def 							A__ (  self   :  Dict  ):
       lowercase__   					=			SwiftFormerModelTester(self  )
       lowercase__   					=			ConfigTester(
           self,   config_class=__lowercase,   has_text_modality=__lowercase,   hidden_size=37,   num_attention_heads=12,   num_hidden_layers=12,   )




    def 							A__ (  self   :  List[Any]  ):
       self.config_tester.run_common_tests()




    @unittest.skip(reason="SwiftFormer does not use inputs_embeds"  )
    def 							A__ (  self   :  Dict  ):
       pass




    def 							A__ (  self   :  Optional[Any]  ):
       lowercase__					,    lowercase__   					=			self.model_tester.prepare_config_and_inputs_for_common()

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




    def 							A__ (  self   :  List[Any]  ):
       lowercase__					,    lowercase__   					=			self.model_tester.prepare_config_and_inputs_for_common()

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

          lowercase__   					=			["pixel_values"]
          self.assertListEqual(arg_names[:1],   __lowercase  )




    def 							A__ (  self   :  Tuple  ):
       lowercase__   					=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowercase  )




    def 							A__ (  self   :  List[str]  ):
       lowercase__   					=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__lowercase  )




    @slow
    def 							A__ (  self   :  str  ):
       for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowercase__   					=			SwiftFormerModel.from_pretrained(__lowercase  )
          self.assertIsNotNone(__lowercase  )




    @unittest.skip(reason="SwiftFormer does not output attentions"  )
    def 							A__ (  self   :  List[str]  ):
       pass




    def 							A__ (  self   :  Optional[Any]  ):
       def check_hidden_states_output(__lowercase   :  Any,   __lowercase   :  Optional[int],   __lowercase   :  Optional[int]  ):
          lowercase__   					=			model_class(__lowercase  )
          model.to(__lowercase  )
          model.eval()

          with torch.no_grad():
             lowercase__   					=			model(**self._prepare_for_class(__lowercase,   __lowercase  )  )

          lowercase__   					=			outputs.hidden_states

          lowercase__   					=			8
          self.assertEqual(len(__lowercase  ),   __lowercase  )  # 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(__lowercase  )  ):
             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),
                     ]  ),   )

       lowercase__					,    lowercase__   					=			self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          lowercase__   					=			True
          check_hidden_states_output(__lowercase,   __lowercase,   __lowercase  )

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

          check_hidden_states_output(__lowercase,   __lowercase,   __lowercase  )




    def 							A__ (  self   :  Any  ):
       def _config_zero_init(__lowercase   :  List[str]  ):
          lowercase__   					=			copy.deepcopy(__lowercase  )
          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(__lowercase,   __lowercase,   1e-1_0  )
             if isinstance(getattr(__lowercase,   __lowercase,   __lowercase  ),   __lowercase  ):
                lowercase__   					=			_config_zero_init(getattr(__lowercase,   __lowercase  )  )
                setattr(__lowercase,   __lowercase,   __lowercase  )
          return configs_no_init

       lowercase__					,    lowercase__   					=			self.model_tester.prepare_config_and_inputs_for_common()

       lowercase__   					=			_config_zero_init(__lowercase  )
       for model_class in self.all_model_classes:
          lowercase__   					=			model_class(config=__lowercase  )
          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 							A__ (  self   :  Optional[Any]  ):
       pass



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




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




    @cached_property
    def 							A__ (  self   :  List[str]  ):
       return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs"  ) if is_vision_available() else None




    @slow
    def 							A__ (  self   :  Dict  ):
       lowercase__   					=			SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"  ).to(__lowercase  )

       lowercase__   					=			self.default_image_processor
       lowercase__   					=			prepare_img()
       lowercase__   					=			image_processor(images=__lowercase,   return_tensors="pt"  ).to(__lowercase  )

       # forward pass
       with torch.no_grad():
          lowercase__   					=			model(**__lowercase  )

       # verify the logits
       lowercase__   					=			torch.Size((1, 1000)  )
       self.assertEqual(outputs.logits.shape,   __lowercase  )

       lowercase__   					=			torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]]  ).to(__lowercase  )
       self.assertTrue(torch.allclose(outputs.logits[0, :3],   __lowercase,   atol=1e-4  )  )









def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			int(SCREAMING_SNAKE_CASE_					)
			if decimal in (0, 1):  # Exit cases for the recursion
						return str(SCREAMING_SNAKE_CASE_					)
			lowercase__					,    lowercase__   					=			divmod(SCREAMING_SNAKE_CASE_  ,					2					)
			return binary_recursive(SCREAMING_SNAKE_CASE_					) + str(SCREAMING_SNAKE_CASE_					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			str(SCREAMING_SNAKE_CASE_					).strip()
			if not number:
						raise ValueError("No input value was provided"					)
			lowercase__   					=			"-" if number.startswith("-"					) else ""
			lowercase__   					=			number.lstrip("-"					)
			if not number.isnumeric():
						raise ValueError("Input value is not an integer"					)
			return f'''{negative}0b{binary_recursive(int(SCREAMING_SNAKE_CASE_					)					)}'''


if __name__ == "__main__":
							from doctest import testmod

							testmod()




