





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  __A:

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





            """simple docstring"""

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

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

            _UpperCamelCase              =						self.get_config()

            return config, pixel_values, labels

     def 	_UpperCamelCase     (      self      ):





            """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 	_UpperCamelCase     (      self,       A,       A,       A      ):





            """simple docstring"""

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

     def 	_UpperCamelCase     (      self,       A,       A,       A      ):





            """simple docstring"""

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

            _UpperCamelCase              =						SwiftFormerForImageClassification(A      )
            model.to(A      )
            model.eval()

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



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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






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

     __A	  =       False
     __A	  =       False
     __A	  =       False
     __A	  =       False
     __A	  =       False

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						SwiftFormerModelTester(self      )
            _UpperCamelCase              =						ConfigTester(
                self,       config_class=A,       has_text_modality=A,       hidden_size=37,       num_attention_heads=12,       num_hidden_layers=12,       )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            self.config_tester.run_common_tests()

     @unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''      )
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            pass

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.model_tester.prepare_config_and_inputs_for_common()

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.model_tester.prepare_config_and_inputs_for_common()

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

                   _UpperCamelCase              =						["pixel_values"]
                   self.assertListEqual(arg_names[:1],       A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*A      )

     @slow
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

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





            """simple docstring"""

            pass

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            def check_hidden_states_output(A,       A,       A      ):
                   _UpperCamelCase              =						model_class(A      )
                   model.to(A      )
                   model.eval()

                   with torch.no_grad():
                          _UpperCamelCase              =						model(**self._prepare_for_class(A,       A      )      )

                   _UpperCamelCase              =						outputs.hidden_states

                   _UpperCamelCase              =						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),
                                  ]      ),       )

            _UpperCamelCase              =						self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _UpperCamelCase              =						True
                   check_hidden_states_output(A,       A,       A      )

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

                   check_hidden_states_output(A,       A,       A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            def _config_zero_init(A      ):
                   _UpperCamelCase              =						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-1_0      )
                          if isinstance(getattr(A,       A,       A      ),       A      ):
                                 _UpperCamelCase              =						_config_zero_init(getattr(A,       A      )      )
                                 setattr(A,       A,       A      )
                   return configs_no_init

            _UpperCamelCase              =						self.model_tester.prepare_config_and_inputs_for_common()

            _UpperCamelCase              =						_config_zero_init(A      )
            for model_class in self.all_model_classes:
                   _UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            pass






def     SCREAMING_SNAKE_CASE			(      ):
       _UpperCamelCase              =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
       return image






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

     @cached_property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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



     @slow
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''      ).to(A      )

            _UpperCamelCase              =						self.default_image_processor
            _UpperCamelCase              =						prepare_img()
            _UpperCamelCase              =						image_processor(images=A,       return_tensors='''pt'''      ).to(A      )

            # forward pass
            with torch.no_grad():
                   _UpperCamelCase              =						model(**A      )

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

            _UpperCamelCase              =						torch.tensor([[-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0]]      ).to(A      )
            self.assertTrue(torch.allclose(outputs.logits[0, :3],       A,       atol=1E-4      )      )













def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase = 1_000					):
       _UpperCamelCase              =						3
       _UpperCamelCase              =						0
       while a < n:
              if a % 3 == 0 or a % 5 == 0:
                     result += a
              elif a % 15 == 0:
                     result -= a
              a += 1
       return result


if __name__ == "__main__":
       print(F'''{solution() = }''')






