
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						:



   def __init__(self						:   int       ,		snake_case__						:   List[Any]       ,		snake_case__						:   Optional[int]=13       ,		snake_case__						:   Optional[Any]=3       ,		snake_case__						:   Tuple=True       ,		snake_case__						:   Dict=True       ,		snake_case__						:   Dict=0.1       ,		snake_case__						:   Optional[int]=0.1       ,		snake_case__						:   Union[str, Any]=2_24       ,		snake_case__						:   Tuple=10_00       ,		snake_case__						:   Optional[Any]=[3, 3, 6, 4]       ,		snake_case__						:   str=[48, 56, 1_12, 2_20]       ,		)						->      Optional[Any]:


          '''simple docstring'''


          snake_case			:  Tuple     							= parent
          snake_case			:  Dict     							= batch_size
          snake_case			:  Tuple     							= num_channels
          snake_case			:  Tuple     							= is_training
          snake_case			:  str     							= use_labels
          snake_case			:  Any     							= hidden_dropout_prob
          snake_case			:  Dict     							= attention_probs_dropout_prob
          snake_case			:  Optional[Any]     							= num_labels
          snake_case			:  Optional[int]     							= image_size
          snake_case			:  Tuple     							= layer_depths
          snake_case			:  int     							= embed_dims



   def 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      List[Any]:


          '''simple docstring'''


          snake_case			:  Tuple     							= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

          snake_case			:  List[str]     							= None
          if self.use_labels:
                 snake_case			:  List[str]     							= ids_tensor([self.batch_size]       ,		self.num_labels					)

          snake_case			:  Tuple     							= self.get_config()

          return config, pixel_values, labels



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]					)						->      str:


          '''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=snake_case__       ,		layer_scale_init_value=1e-5       ,		)



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]       ,		snake_case__						:   List[Any]       ,		snake_case__						:   List[str]       ,		snake_case__						:   Dict					)						->      Union[str, Any]:


          '''simple docstring'''


          snake_case			:  List[Any]     							= SwiftFormerModel(config=snake_case__					)
          model.to(snake_case__					)
          model.eval()
          snake_case			:  List[Any]     							= model(snake_case__					)
          self.parent.assertEqual(result.last_hidden_state.shape       ,		(self.batch_size, self.embed_dims[-1], 7, 7)					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]       ,		snake_case__						:   str       ,		snake_case__						:   Union[str, Any]       ,		snake_case__						:   List[Any]					)						->      Optional[int]:


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= self.num_labels
          snake_case			:  Optional[int]     							= SwiftFormerForImageClassification(snake_case__					)
          model.to(snake_case__					)
          model.eval()
          snake_case			:  str     							= model(snake_case__       ,		labels=snake_case__					)
          self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_labels)					)

          snake_case			:  List[str]     							= SwiftFormerForImageClassification(snake_case__					)
          model.to(snake_case__					)
          model.eval()

          snake_case			:  str     							= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)
          snake_case			:  int     							= model(snake_case__					)
          self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_labels)					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]					)						->      Dict:


          '''simple docstring'''


          ((snake_case)							,   (snake_case)							,   (snake_case))			:  str     							= self.prepare_config_and_inputs()
          snake_case			:  Union[str, Any]     							= {"pixel_values": pixel_values}
          return config, inputs_dict






@require_torch
class     UpperCAmelCase						(		A_			,A_			,unittest.TestCase					):
   A__   :						Any			=	(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
   A__   :						List[str]			=	(
       {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
       if is_torch_available()
       else {}
   )

   A__   :						str			=	False
   A__   :						Any			=	False
   A__   :						int			=	False
   A__   :						int			=	False
   A__   :						List[Any]			=	False



   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict					)						->      List[str]:


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= SwiftFormerModelTester(self					)
          snake_case			:  Optional[Any]     							= ConfigTester(
              self       ,		config_class=snake_case__       ,		has_text_modality=snake_case__       ,		hidden_size=37       ,		num_attention_heads=12       ,		num_hidden_layers=12       ,		)



   def 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      List[str]:


          '''simple docstring'''


          self.config_tester.run_common_tests()



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


          '''simple docstring'''


          pass



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]					)						->      Dict:


          '''simple docstring'''


          snake_case							,   snake_case			:  int     							= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 snake_case			:  Dict     							= model_class(snake_case__					)
                 snake_case			:  str     							= model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(snake_case__       ,		nn.Linear					)					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      Union[str, Any]:


          '''simple docstring'''


          snake_case							,   snake_case			:  List[str]     							= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 snake_case			:  Optional[int]     							= model_class(snake_case__					)
                 snake_case			:  List[str]     							= inspect.signature(model.forward					)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 snake_case			:  Optional[int]     							= [*signature.parameters.keys()]

                 snake_case			:  Any     							= ["pixel_values"]
                 self.assertListEqual(arg_names[:1]       ,		snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]					)						->      str:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]					)						->      Tuple:


          '''simple docstring'''


          snake_case			:  List[Any]     							= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case__					)



   @slow
   def 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      Tuple:


          '''simple docstring'''


          for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 snake_case			:  Dict     							= SwiftFormerModel.from_pretrained(snake_case__					)
                 self.assertIsNotNone(snake_case__					)



   @unittest.skip(reason="SwiftFormer does not output attentions"					)
   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]					)						->      List[Any]:


          '''simple docstring'''


          pass



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      List[str]:


          '''simple docstring'''


          def check_hidden_states_output(snake_case__						:   Any       ,		snake_case__						:   Optional[int]       ,		snake_case__						:   str					):
                 snake_case			:  List[Any]     							= model_class(snake_case__					)
                 model.to(snake_case__					)
                 model.eval()

                 with torch.no_grad():
                        snake_case			:  Tuple     							= model(**self._prepare_for_class(snake_case__       ,		snake_case__					)					)

                 snake_case			:  Union[str, Any]     							= outputs.hidden_states

                 snake_case			:  Optional[int]     							= 8
                 self.assertEqual(len(snake_case__					)       ,		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(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),
                                ]					)       ,		)

          snake_case							,   snake_case			:  Optional[Any]     							= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 snake_case			:  str     							= True
                 check_hidden_states_output(snake_case__       ,		snake_case__       ,		snake_case__					)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 snake_case			:  List[str]     							= True

                 check_hidden_states_output(snake_case__       ,		snake_case__       ,		snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Any					)						->      Optional[int]:


          '''simple docstring'''


          def _config_zero_init(snake_case__						:   Tuple					):
                 snake_case			:  List[str]     							= copy.deepcopy(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(snake_case__       ,		snake_case__       ,		1e-10					)
                        if isinstance(getattr(snake_case__       ,		snake_case__       ,		snake_case__					)       ,		snake_case__					):
                               snake_case			:  List[Any]     							= _config_zero_init(getattr(snake_case__       ,		snake_case__					)					)
                               setattr(snake_case__       ,		snake_case__       ,		snake_case__					)
                 return configs_no_init

          snake_case							,   snake_case			:  List[str]     							= self.model_tester.prepare_config_and_inputs_for_common()

          snake_case			:  Union[str, Any]     							= _config_zero_init(snake_case__					)
          for model_class in self.all_model_classes:
                 snake_case			:  Optional[Any]     							= model_class(config=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[Any]					)						->      int:


          '''simple docstring'''


          pass



def      UpperCamelCase   (       ):
       snake_case			:  int     							= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
       return image






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



   @cached_property
   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]					)						->      Any:


          '''simple docstring'''


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



   @slow
   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]					)						->      int:


          '''simple docstring'''


          snake_case			:  List[Any]     							= SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"					).to(snake_case__					)

          snake_case			:  Union[str, Any]     							= self.default_image_processor
          snake_case			:  List[Any]     							= prepare_img()
          snake_case			:  List[str]     							= image_processor(images=snake_case__       ,		return_tensors="pt"					).to(snake_case__					)

          # forward pass
          with torch.no_grad():
                 snake_case			:  Dict     							= model(**snake_case__					)

          # verify the logits
          snake_case			:  List[Any]     							= torch.Size((1, 10_00)					)
          self.assertEqual(outputs.logits.shape       ,		snake_case__					)

          snake_case			:  Any     							= torch.tensor([[-2.17_03e00, 2.11_07e00, -2.08_11e00]]					).to(snake_case__					)
          self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,		snake_case__       ,		atol=1e-4					)					)







class     UpperCAmelCase						:



   def __init__(self						:   Optional[Any]       ,		snake_case__						:   str = ""       ,		snake_case__						:   bool = False					)						->      None:


          '''simple docstring'''


          snake_case			:  dict[str, RadixNode]     							= {}

          # A node will be a leaf if the tree contains its word
          snake_case			:  int     							= is_leaf

          snake_case			:  Dict     							= prefix



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]       ,		snake_case__						:   str					)						->      tuple[str, str, str]:


          '''simple docstring'''


          snake_case			:  Any     							= 0
          for q, w in zip(self.prefix       ,		snake_case__					):
                 if q != w:
                        break

                 x += 1

          return self.prefix[:x], self.prefix[x:], word[x:]



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple       ,		snake_case__						:   list[str]					)						->      None:


          '''simple docstring'''


          for word in words:
                 self.insert(snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   int       ,		snake_case__						:   str					)						->      None:


          '''simple docstring'''


          if self.prefix == word:
                 snake_case			:  str     							= True

          # Case 2: The node has no edges that have a prefix to the word
          # Solution: We create an edge from the current node to a new one
          # containing the word
          elif word[0] not in self.nodes:
                 snake_case			:  Optional[int]     							= RadixNode(prefix=snake_case__       ,		is_leaf=snake_case__					)

          else:
                 snake_case			:  Dict     							= self.nodes[word[0]]
                 snake_case							,   snake_case							,   snake_case			:  int     							= incoming_node.match(
                     snake_case__					)

                 # Case 3: The node prefix is equal to the matching
                 # Solution: We insert remaining word on the next node
                 if remaining_prefix == "":
                        self.nodes[matching_string[0]].insert(snake_case__					)

                 # Case 4: The word is greater equal to the matching
                 # Solution: Create a node in between both nodes, change
                 # prefixes and add the new node for the remaining word
                 else:
                        snake_case			:  Tuple     							= remaining_prefix

                        snake_case			:  Dict     							= self.nodes[matching_string[0]]
                        snake_case			:  Tuple     							= RadixNode(snake_case__       ,		snake_case__					)
                        snake_case			:  List[str]     							= aux_node

                        if remaining_word == "":
                               snake_case			:  Tuple     							= True
                        else:
                               self.nodes[matching_string[0]].insert(snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple       ,		snake_case__						:   str					)						->      bool:


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= self.nodes.get(word[0]       ,		snake_case__					)
          if not incoming_node:
                 return False
          else:
                 snake_case							,   snake_case							,   snake_case			:  Optional[int]     							= incoming_node.match(
                     snake_case__					)
                 # If there is remaining prefix, the word can't be on the tree
                 if remaining_prefix != "":
                        return False
                 # This applies when the word and the prefix are equal
                 elif remaining_word == "":
                        return incoming_node.is_leaf
                 # We have word remaining so we check the next node
                 else:
                        return incoming_node.find(snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]       ,		snake_case__						:   str					)						->      bool:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= self.nodes.get(word[0]       ,		snake_case__					)
          if not incoming_node:
                 return False
          else:
                 snake_case							,   snake_case							,   snake_case			:  Optional[Any]     							= incoming_node.match(
                     snake_case__					)
                 # If there is remaining prefix, the word can't be on the tree
                 if remaining_prefix != "":
                        return False
                 # We have word remaining so we check the next node
                 elif remaining_word != "":
                        return incoming_node.delete(snake_case__					)
                 else:
                        # If it is not a leaf, we don't have to delete
                        if not incoming_node.is_leaf:
                               return False
                        else:
                               # We delete the nodes if no edges go from it
                               if len(incoming_node.nodes					) == 0:
                                      del self.nodes[word[0]]
                                      # We merge the current node with its only child
                                      if len(self.nodes					) == 1 and not self.is_leaf:
                                             snake_case			:  List[Any]     							= list(self.nodes.values()					)[0]
                                             snake_case			:  Optional[int]     							= merging_node.is_leaf
                                             self.prefix += merging_node.prefix
                                             snake_case			:  str     							= merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
                               elif len(incoming_node.nodes					) > 1:
                                      snake_case			:  List[Any]     							= False
                               # If there is 1 edge, we merge it with its child
                               else:
                                      snake_case			:  Any     							= list(incoming_node.nodes.values()					)[0]
                                      snake_case			:  Any     							= merging_node.is_leaf
                                      incoming_node.prefix += merging_node.prefix
                                      snake_case			:  Union[str, Any]     							= merging_node.nodes

                               return True



   def 							_SCREAMING_SNAKE_CASE  (self						:   Any       ,		snake_case__						:   int = 0					)						->      None:


          '''simple docstring'''


          if self.prefix != "":
                 print("-" * height       ,		self.prefix       ,		"  (leaf)" if self.is_leaf else ""					)

          for value in self.nodes.values():
                 value.print_tree(height + 1					)



def      UpperCamelCase   (       ):
       snake_case			:  Dict     							= "banana bananas bandana band apple all beast".split()
       snake_case			:  Tuple     							= RadixNode()
       root.insert_many(__lowerCamelCase       )

       assert all(root.find(__lowerCamelCase       ) for word in words       )
       assert not root.find("bandanas"       )
       assert not root.find("apps"       )
       root.delete("all"       )
       assert not root.find("all"       )
       root.delete("banana"       )
       assert not root.find("banana"       )
       assert root.find("bananas"       )

       return True


def      UpperCamelCase   (       ):
       assert test_trie()


def      UpperCamelCase   (       ):
       snake_case			:  Union[str, Any]     							= RadixNode()
       snake_case			:  List[str]     							= "banana bananas bandanas bandana band apple all beast".split()
       root.insert_many(__lowerCamelCase       )

       print("Words:"							,   __lowerCamelCase       )
       print("Tree:"       )
       root.print_tree()


if __name__ == "__main__":
   main()





