






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							:






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase=13		,		_UpperCAmelCase=3		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=224		,		_UpperCAmelCase=1000		,		_UpperCAmelCase=[3, 3, 6, 4]		,		_UpperCAmelCase=[48, 56, 112, 220]		,		)      ->			Optional[int]:
     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			)      ->			Optional[Any]:
     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			)      ->			str:
     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=_UpperCAmelCase		,		layer_scale_init_value=1e-5		,		)




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




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Optional[int]:
     UpperCamelCase_						=  self.num_labels
     UpperCamelCase_						=  SwiftFormerForImageClassification(_UpperCAmelCase			)
     model.to(_UpperCAmelCase			)
     model.eval()
     UpperCamelCase_						=  model(_UpperCAmelCase		,		labels=_UpperCAmelCase			)
     self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.num_labels)			)

     UpperCamelCase_						=  SwiftFormerForImageClassification(_UpperCAmelCase			)
     model.to(_UpperCAmelCase			)
     model.eval()

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




  def  _UpperCAmelCase  (  self			)      ->			str:
     ((UpperCamelCase_)		,   (UpperCamelCase_)		,   (UpperCamelCase_))						=  self.prepare_config_and_inputs()
     UpperCamelCase_						=  {'pixel_values': pixel_values}
     return config, inputs_dict




@require_torch
class     _a							(	UpperCAmelCase__    ,							UpperCAmelCase__    ,							unittest.TestCase     ):






  """simple docstring"""





  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			)      ->			List[Any]:
     UpperCamelCase_						=  SwiftFormerModelTester(self			)
     UpperCamelCase_						=  ConfigTester(
         self		,		config_class=_UpperCAmelCase		,		has_text_modality=_UpperCAmelCase		,		hidden_size=37		,		num_attention_heads=12		,		num_hidden_layers=12		,		)




  def  _UpperCAmelCase  (  self			)      ->			str:
     self.config_tester.run_common_tests()




  @unittest.skip(reason='SwiftFormer does not use inputs_embeds'			)
  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     pass




  def  _UpperCAmelCase  (  self			)      ->			int:
     UpperCamelCase_		,   UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs_for_common()

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




  def  _UpperCAmelCase  (  self			)      ->			str:
     UpperCamelCase_		,   UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        UpperCamelCase_						=  model_class(_UpperCAmelCase			)
        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]		,		_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			str:
     UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			Any:
     UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase			)




  @slow
  def  _UpperCAmelCase  (  self			)      ->			Optional[Any]:
     for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCamelCase_						=  SwiftFormerModel.from_pretrained(_UpperCAmelCase			)
        self.assertIsNotNone(_UpperCAmelCase			)




  @unittest.skip(reason='SwiftFormer does not output attentions'			)
  def  _UpperCAmelCase  (  self			)      ->			Union[str, Any]:
     pass




  def  _UpperCAmelCase  (  self			)      ->			int:
     def check_hidden_states_output(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			):
        UpperCamelCase_						=  model_class(_UpperCAmelCase			)
        model.to(_UpperCAmelCase			)
        model.eval()

        with torch.no_grad():
           UpperCamelCase_						=  model(**self._prepare_for_class(_UpperCAmelCase		,		_UpperCAmelCase			)			)

        UpperCamelCase_						=  outputs.hidden_states

        UpperCamelCase_						=  8
        self.assertEqual(len(_UpperCAmelCase			)		,		_UpperCAmelCase			)  # 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(_UpperCAmelCase			)			):
           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_						=  self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        UpperCamelCase_						=  True
        check_hidden_states_output(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)

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

        check_hidden_states_output(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			Tuple:
     def _config_zero_init(_UpperCAmelCase			):
        UpperCamelCase_						=  copy.deepcopy(_UpperCAmelCase			)
        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(_UpperCAmelCase		,		_UpperCAmelCase		,		1e-10			)
           if isinstance(getattr(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)		,		_UpperCAmelCase			):
              UpperCamelCase_						=  _config_zero_init(getattr(_UpperCAmelCase		,		_UpperCAmelCase			)			)
              setattr(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)
        return configs_no_init

     UpperCamelCase_		,   UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs_for_common()

     UpperCamelCase_						=  _config_zero_init(_UpperCAmelCase			)
     for model_class in self.all_model_classes:
        UpperCamelCase_						=  model_class(config=_UpperCAmelCase			)
        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			)      ->			Union[str, Any]:
     pass






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


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






  """simple docstring"""





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




  @slow
  def  _UpperCAmelCase  (  self			)      ->			str:
     UpperCamelCase_						=  SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'			).to(_UpperCAmelCase			)

     UpperCamelCase_						=  self.default_image_processor
     UpperCamelCase_						=  prepare_img()
     UpperCamelCase_						=  image_processor(images=_UpperCAmelCase		,		return_tensors='pt'			).to(_UpperCAmelCase			)

     # forward pass
     with torch.no_grad():
        UpperCamelCase_						=  model(**_UpperCAmelCase			)

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

     UpperCamelCase_						=  torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]]			).to(_UpperCAmelCase			)
     self.assertTrue(torch.allclose(outputs.logits[0, :3]		,		_UpperCAmelCase		,		atol=1e-4			)			)













from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

snake_case__   :						Dict													=   TypeVar("""T""")


class     _a							(	Generic[T]     ):






  """simple docstring"""





  A_						       = 42  # Cache store of keys
  A_						       = 42  # References of the keys in cache
  A_						       = 10  # Maximum capacity of cache




  def __init__(  self		,		_UpperCAmelCase			)      ->			None:
     UpperCamelCase_						=  deque()
     UpperCamelCase_						=  set()
     if not n:
        UpperCamelCase_						=  sys.maxsize
     elif n < 0:
        raise ValueError('n should be an integer greater than 0.'			)
     else:
        UpperCamelCase_						=  n




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			None:
     if x not in self.key_reference:
        if len(self.dq_store			) == LRUCache._MAX_CAPACITY:
           UpperCamelCase_						=  self.dq_store.pop()
           self.key_reference.remove(_UpperCAmelCase			)
     else:
        self.dq_store.remove(_UpperCAmelCase			)

     self.dq_store.appendleft(_UpperCAmelCase			)
     self.key_reference.add(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			None:
     for k in self.dq_store:
        print(_UpperCAmelCase			)




  def __repr__(  self			)      ->			str:
     return f"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store			)}"""


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 snake_case__   :						LRUCache[str | int]													=   LRUCache(4)
 lru_cache.refer("""A""")
 lru_cache.refer(2)
 lru_cache.refer(3)
 lru_cache.refer("""A""")
 lru_cache.refer(4)
 lru_cache.refer(5)
 lru_cache.display()

 print(lru_cache)
 assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"





