





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:						Optional[Any]		,  _lowerCAmelCase:						Dict		,  _lowerCAmelCase:						str=13		,  _lowerCAmelCase:						Any=3		,  _lowerCAmelCase:						Any=True		,  _lowerCAmelCase:						Optional[Any]=True		,  _lowerCAmelCase:						Union[str, Any]=0.1		,  _lowerCAmelCase:						Tuple=0.1		,  _lowerCAmelCase:						str=224		,  _lowerCAmelCase:						Tuple=1000		,  _lowerCAmelCase:						Optional[int]=[3, 3, 6, 4]		,  _lowerCAmelCase:						Any=[48, 56, 112, 220]		,  )						->    Tuple:
         '''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 	lowerCAmelCase__    (	self:						Tuple       )						->    Union[str, Any]:
         '''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 	lowerCAmelCase__    (	self:						int       )						->    Optional[int]:
         '''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=_lowerCAmelCase		,  layer_scale_init_value=1e-5		,  )



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						List[str]		,  _lowerCAmelCase:						Optional[int]		,  _lowerCAmelCase:						Optional[int]       )						->    Dict:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Any		,  _lowerCAmelCase:						Optional[Any]		,  _lowerCAmelCase:						Union[str, Any]		,  _lowerCAmelCase:						List[str]       )						->    Union[str, Any]:
         '''simple docstring'''

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

         UpperCAmelCase_											=SwiftFormerForImageClassification(_lowerCAmelCase       )
         model.to(_lowerCAmelCase       )
         model.eval()

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






      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Optional[Any]:
         '''simple docstring'''

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

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

      _snake_case							     =False
      _snake_case							     =False
      _snake_case							     =False
      _snake_case							     =False
      _snake_case							     =False



      def 	lowerCAmelCase__    (	self:						Tuple       )						->    Any:
         '''simple docstring'''

         UpperCAmelCase_											=SwiftFormerModelTester(self       )
         UpperCAmelCase_											=ConfigTester(
             self		,  config_class=_lowerCAmelCase		,  has_text_modality=_lowerCAmelCase		,  hidden_size=37		,  num_attention_heads=12		,  num_hidden_layers=12		,  )



      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    Optional[int]:
         '''simple docstring'''

         self.config_tester.run_common_tests()



      @unittest.skip(reason="SwiftFormer does not use inputs_embeds"       )
      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    Dict:
         '''simple docstring'''

         pass



      def 	lowerCAmelCase__    (	self:						str       )						->    List[str]:
         '''simple docstring'''

         UpperCAmelCase_						,       UpperCAmelCase_											=self.model_tester.prepare_config_and_inputs_for_common()

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



      def 	lowerCAmelCase__    (	self:						Tuple       )						->    List[Any]:
         '''simple docstring'''

         UpperCAmelCase_						,       UpperCAmelCase_											=self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            UpperCAmelCase_											=model_class(_lowerCAmelCase       )
            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]		,  _lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_											=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						str       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_											=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase       )



      @slow
      def 	lowerCAmelCase__    (	self:						Tuple       )						->    int:
         '''simple docstring'''

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



      @unittest.skip(reason="SwiftFormer does not output attentions"       )
      def 	lowerCAmelCase__    (	self:						int       )						->    int:
         '''simple docstring'''

         pass



      def 	lowerCAmelCase__    (	self:						List[str]       )						->    Optional[Any]:
         '''simple docstring'''

         def check_hidden_states_output(_lowerCAmelCase:						List[str]		,  _lowerCAmelCase:						Union[str, Any]		,  _lowerCAmelCase:						List[str]       ):
            UpperCAmelCase_											=model_class(_lowerCAmelCase       )
            model.to(_lowerCAmelCase       )
            model.eval()

            with torch.no_grad():
               UpperCAmelCase_											=model(**self._prepare_for_class(_lowerCAmelCase		,  _lowerCAmelCase       )       )

            UpperCAmelCase_											=outputs.hidden_states

            UpperCAmelCase_											=8
            self.assertEqual(len(_lowerCAmelCase       )		,  _lowerCAmelCase       )  # 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(_lowerCAmelCase       )       ):
               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(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       )

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

            check_hidden_states_output(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    List[Any]:
         '''simple docstring'''

         def _config_zero_init(_lowerCAmelCase:						Optional[Any]       ):
            UpperCAmelCase_											=copy.deepcopy(_lowerCAmelCase       )
            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(_lowerCAmelCase		,  _lowerCAmelCase		,  1e-10       )
               if isinstance(getattr(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       )		,  _lowerCAmelCase       ):
                  UpperCAmelCase_											=_config_zero_init(getattr(_lowerCAmelCase		,  _lowerCAmelCase       )       )
                  setattr(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       )
            return configs_no_init

         UpperCAmelCase_						,       UpperCAmelCase_											=self.model_tester.prepare_config_and_inputs_for_common()

         UpperCAmelCase_											=_config_zero_init(_lowerCAmelCase       )
         for model_class in self.all_model_classes:
            UpperCAmelCase_											=model_class(config=_lowerCAmelCase       )
            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 	lowerCAmelCase__    (	self:						List[str]       )						->    Union[str, Any]:
         '''simple docstring'''

         pass




def     a__ (					):






   '''simple docstring'''






   UpperCAmelCase_											=Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
   return image





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



      @cached_property
      def 	lowerCAmelCase__    (	self:						str       )						->    int:
         '''simple docstring'''

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






      @slow
      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    int:
         '''simple docstring'''

         UpperCAmelCase_											=SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"       ).to(_lowerCAmelCase       )

         UpperCAmelCase_											=self.default_image_processor
         UpperCAmelCase_											=prepare_img()
         UpperCAmelCase_											=image_processor(images=_lowerCAmelCase		,  return_tensors="pt"       ).to(_lowerCAmelCase       )

         # forward pass
         with torch.no_grad():
            UpperCAmelCase_											=model(**_lowerCAmelCase       )

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

         UpperCAmelCase_											=torch.tensor([[-2.1_703e00, 2.1_107e00, -2.0_811e00]]       ).to(_lowerCAmelCase       )
         self.assertTrue(torch.allclose(outputs.logits[0, :3]		,  _lowerCAmelCase		,  atol=1e-4       )       )











from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

__lowercase  :   Optional[int]						      =TypeVar("""T""")




def     a__ (					lowercase__  ):






   '''simple docstring'''






   return (position - 1) // 2




def     a__ (					lowercase__  ):






   '''simple docstring'''






   return (2 * position) + 1




def     a__ (					lowercase__  ):






   '''simple docstring'''






   return (2 * position) + 2

class      A       (	Generic[T]						):



      def __init__(	self:						List[str]       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											=[]
         UpperCAmelCase_											={}
         UpperCAmelCase_											=0



      def __len__(	self:						Union[str, Any]       )						->    int:
         '''simple docstring'''

         return self.elements



      def __repr__(	self:						Dict       )						->    str:
         '''simple docstring'''

         return str(self.heap       )



      def 	lowerCAmelCase__    (	self:						Any       )						->    bool:
         '''simple docstring'''

         return self.elements == 0



      def 	lowerCAmelCase__    (	self:						Optional[int]		,  _lowerCAmelCase:						T		,  _lowerCAmelCase:						int       )						->    None:
         '''simple docstring'''

         self.heap.append((elem, weight)       )
         UpperCAmelCase_											=self.elements
         self.elements += 1
         self._bubble_up(_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Tuple       )						->    T:
         '''simple docstring'''

         if self.elements > 1:
            self._swap_nodes(0		,  self.elements - 1       )
         UpperCAmelCase_						,       UpperCAmelCase_											=self.heap.pop()
         del self.position_map[elem]
         self.elements -= 1
         if self.elements > 0:
            UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[0]
            self._bubble_down(_lowerCAmelCase       )
         return elem



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						T		,  _lowerCAmelCase:						int       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											=self.position_map[elem]
         UpperCAmelCase_											=(elem, weight)
         if position > 0:
            UpperCAmelCase_											=get_parent_position(_lowerCAmelCase       )
            UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[parent_position]
            if parent_weight > weight:
               self._bubble_up(_lowerCAmelCase       )
            else:
               self._bubble_down(_lowerCAmelCase       )
         else:
            self._bubble_down(_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Any		,  _lowerCAmelCase:						T       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											=self.position_map[elem]
         if curr_pos == 0:
            return None
         UpperCAmelCase_											=get_parent_position(_lowerCAmelCase       )
         UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[curr_pos]
         UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[parent_position]
         if parent_weight > weight:
            self._swap_nodes(_lowerCAmelCase		,  _lowerCAmelCase       )
            return self._bubble_up(_lowerCAmelCase       )
         return None



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						T       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											=self.position_map[elem]
         UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[curr_pos]
         UpperCAmelCase_											=get_child_left_position(_lowerCAmelCase       )
         UpperCAmelCase_											=get_child_right_position(_lowerCAmelCase       )
         if child_left_position < self.elements and child_right_position < self.elements:
            UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[child_left_position]
            UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[child_right_position]
            if child_right_weight < child_left_weight and child_right_weight < weight:
               self._swap_nodes(_lowerCAmelCase		,  _lowerCAmelCase       )
               return self._bubble_down(_lowerCAmelCase       )
         if child_left_position < self.elements:
            UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[child_left_position]
            if child_left_weight < weight:
               self._swap_nodes(_lowerCAmelCase		,  _lowerCAmelCase       )
               return self._bubble_down(_lowerCAmelCase       )
         else:
            return None
         if child_right_position < self.elements:
            UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[child_right_position]
            if child_right_weight < weight:
               self._swap_nodes(_lowerCAmelCase		,  _lowerCAmelCase       )
               return self._bubble_down(_lowerCAmelCase       )
         return None






      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						int       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											=self.heap[nodea_pos][0]
         UpperCAmelCase_											=self.heap[nodea_pos][0]
         UpperCAmelCase_						,       UpperCAmelCase_											=(
             self.heap[nodea_pos],
             self.heap[nodea_pos],
         )
         UpperCAmelCase_											=nodea_pos
         UpperCAmelCase_											=nodea_pos

class      A       (	Generic[T]						):



      def __init__(	self:						Tuple       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											={}
         UpperCAmelCase_											=0



      def __repr__(	self:						List[str]       )						->    str:
         '''simple docstring'''

         return str(self.connections       )



      def __len__(	self:						Optional[Any]       )						->    int:
         '''simple docstring'''

         return self.nodes



      def 	lowerCAmelCase__    (	self:						Union[str, Any]		,  _lowerCAmelCase:						T       )						->    None:
         '''simple docstring'''

         if node not in self.connections:
            UpperCAmelCase_											={}
            self.nodes += 1






      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						T		,  _lowerCAmelCase:						T		,  _lowerCAmelCase:						int       )						->    None:
         '''simple docstring'''

         self.add_node(_lowerCAmelCase       )
         self.add_node(_lowerCAmelCase       )
         UpperCAmelCase_											=weight
         UpperCAmelCase_											=weight




def     a__ (					lowercase__  ,    ):






   '''simple docstring'''






   UpperCAmelCase_											={node: maxsize for node in graph.connections}
   UpperCAmelCase_											={node: None for node in graph.connections}

   UpperCAmelCase_											=MinPriorityQueue()
   for node, weight in dist.items():
      priority_queue.push(lowercase__  ,    lowercase__  )

   if priority_queue.is_empty():
      return dist, parent

   # initialization
   UpperCAmelCase_											=priority_queue.extract_min()
   UpperCAmelCase_											=0
   for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
         UpperCAmelCase_											=dist[node] + graph.connections[node][neighbour]
         priority_queue.update_key(lowercase__  ,    dist[neighbour]  )
         UpperCAmelCase_											=node

    # running prim's algorithm
   while not priority_queue.is_empty():
      UpperCAmelCase_											=priority_queue.extract_min()
      for neighbour in graph.connections[node]:
         if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
            UpperCAmelCase_											=dist[node] + graph.connections[node][neighbour]
            priority_queue.update_key(lowercase__  ,    dist[neighbour]  )
            UpperCAmelCase_											=node
   return dist, parent




