import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 transformers import (
     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
     MODEL_MAPPING,
     LevitForImageClassification,
     LevitForImageClassificationWithTeacher,
     LevitModel,
 )
 from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import LevitImageProcessor

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	self.config_class(**self.inputs_dict )
       self.parent.assertTrue(hasattr(lowerCamelCase__       ,						"hidden_sizes" ) )
       self.parent.assertTrue(hasattr(lowerCamelCase__       ,						"num_attention_heads" ) )





class 					_lowercase    :






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__=13       ,						lowerCamelCase__=64       ,						lowerCamelCase__=3       ,						lowerCamelCase__=3       ,						lowerCamelCase__=2       ,						lowerCamelCase__=1       ,						lowerCamelCase__=16       ,						lowerCamelCase__=[128, 256, 384]       ,						lowerCamelCase__=[4, 6, 8]       ,						lowerCamelCase__=[2, 3, 4]       ,						lowerCamelCase__=[16, 16, 16]       ,						lowerCamelCase__=0       ,						lowerCamelCase__=[2, 2, 2]       ,						lowerCamelCase__=[2, 2, 2]       ,						lowerCamelCase__=0.0_2       ,						lowerCamelCase__=True       ,						lowerCamelCase__=True       ,						lowerCamelCase__=2       ,						):
       lowerCAmelCase_:							int       =	parent
       lowerCAmelCase_:							Tuple       =	batch_size
       lowerCAmelCase_:							List[str]       =	image_size
       lowerCAmelCase_:							Tuple       =	num_channels
       lowerCAmelCase_:							Optional[int]       =	kernel_size
       lowerCAmelCase_:							int       =	stride
       lowerCAmelCase_:							Optional[int]       =	padding
       lowerCAmelCase_:							Tuple       =	hidden_sizes
       lowerCAmelCase_:							Union[str, Any]       =	num_attention_heads
       lowerCAmelCase_:							Tuple       =	depths
       lowerCAmelCase_:							Optional[int]       =	key_dim
       lowerCAmelCase_:							Optional[Any]       =	drop_path_rate
       lowerCAmelCase_:							List[str]       =	patch_size
       lowerCAmelCase_:							Any       =	attention_ratio
       lowerCAmelCase_:							Tuple       =	mlp_ratio
       lowerCAmelCase_:							Any       =	initializer_range
       lowerCAmelCase_:							List[str]       =	[
           ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
           ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
       ]
       lowerCAmelCase_:							Any       =	is_training
       lowerCAmelCase_:							Optional[int]       =	use_labels
       lowerCAmelCase_:							Dict       =	num_labels
       lowerCAmelCase_:							Any       =	initializer_range




    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, Any]       =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

       lowerCAmelCase_:							Tuple       =	None
       if self.use_labels:
          lowerCAmelCase_:							int       =	ids_tensor([self.batch_size]       ,						self.num_labels )

       lowerCAmelCase_:							Tuple       =	self.get_config()
       return config, pixel_values, labels




    def      _a    (    self ):
       return LevitConfig(
           image_size=self.image_size       ,						num_channels=self.num_channels       ,						kernel_size=self.kernel_size       ,						stride=self.stride       ,						padding=self.padding       ,						patch_size=self.patch_size       ,						hidden_sizes=self.hidden_sizes       ,						num_attention_heads=self.num_attention_heads       ,						depths=self.depths       ,						key_dim=self.key_dim       ,						drop_path_rate=self.drop_path_rate       ,						mlp_ratio=self.mlp_ratio       ,						attention_ratio=self.attention_ratio       ,						initializer_range=self.initializer_range       ,						down_ops=self.down_ops       ,						)




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							int       =	LevitModel(config=lowerCamelCase__ )
       model.to(lowerCamelCase__ )
       model.eval()
       lowerCAmelCase_:							Optional[Any]       =	model(lowerCamelCase__ )
       lowerCAmelCase_:							Tuple       =	(self.image_size, self.image_size)
       lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[int]       =	image_size[0], image_size[1]
       for _ in range(4 ):
          lowerCAmelCase_:							Union[str, Any]       =	floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
          lowerCAmelCase_:							str       =	floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
       self.parent.assertEqual(
           result.last_hidden_state.shape       ,						(self.batch_size, ceil(height / 4 ) * ceil(width / 4 ), self.hidden_sizes[-1])       ,						)




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Tuple       =	self.num_labels
       lowerCAmelCase_:							str       =	LevitForImageClassification(lowerCamelCase__ )
       model.to(lowerCamelCase__ )
       model.eval()
       lowerCAmelCase_:							Optional[Any]       =	model(lowerCamelCase__       ,						labels=lowerCamelCase__ )
       self.parent.assertEqual(result.logits.shape       ,						(self.batch_size, self.num_labels) )




    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, Any]       =	self.prepare_config_and_inputs()
       lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							Dict       =	config_and_inputs
       lowerCAmelCase_:							Tuple       =	{"pixel_values": pixel_values}
       return config, inputs_dict





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






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Any          =    (
        (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
        if is_torch_available()
        else ()
    )
    SCREAMING_SNAKE_CASE:				int          =    (
        {
            'feature-extraction': LevitModel,
            'image-classification': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
        }
        if is_torch_available()
        else {}
    )

    SCREAMING_SNAKE_CASE:				Optional[int]          =    False
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    False
    SCREAMING_SNAKE_CASE:				str          =    False
    SCREAMING_SNAKE_CASE:				str          =    False
    SCREAMING_SNAKE_CASE:				List[Any]          =    False




    def      _a    (    self ):
       lowerCAmelCase_:							List[str]       =	LevitModelTester(self )
       lowerCAmelCase_:							Union[str, Any]       =	ConfigTester(self       ,						config_class=lowerCamelCase__       ,						has_text_modality=lowerCamelCase__       ,						hidden_size=37 )




    def      _a    (    self ):
       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()




    def      _a    (    self ):
       return




    @unittest.skip(reason="Levit does not use inputs_embeds" )
    def      _a    (    self ):
       pass




    @unittest.skip(reason="Levit does not support input and output embeddings" )
    def      _a    (    self ):
       pass




    @unittest.skip(reason="Levit does not output attentions" )
    def      _a    (    self ):
       pass




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[Any]       =	self.model_tester.prepare_config_and_inputs_for_common()

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

          lowerCAmelCase_:							str       =	["pixel_values"]
          self.assertListEqual(arg_names[:1]       ,						lowerCamelCase__ )




    def      _a    (    self ):
       def check_hidden_states_output(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
          lowerCAmelCase_:							int       =	model_class(lowerCamelCase__ )
          model.to(lowerCamelCase__ )
          model.eval()

          with torch.no_grad():
             lowerCAmelCase_:							Dict       =	model(**self._prepare_for_class(lowerCamelCase__       ,						lowerCamelCase__ ) )

          lowerCAmelCase_:							str       =	outputs.hidden_states

          lowerCAmelCase_:							List[Any]       =	len(self.model_tester.depths ) + 1
          self.assertEqual(len(lowerCamelCase__ )       ,						lowerCamelCase__ )

          lowerCAmelCase_:							List[Any]       =	(self.model_tester.image_size, self.model_tester.image_size)
          lowerCAmelCase_  ,	lowerCAmelCase_:							int       =	image_size[0], image_size[1]
          for _ in range(4 ):
             lowerCAmelCase_:							Tuple       =	floor(
                 (
                     (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                     / self.model_tester.stride
                 )
                 + 1 )
             lowerCAmelCase_:							Optional[int]       =	floor(
                 (
                     (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                     / self.model_tester.stride
                 )
                 + 1 )
          # verify the first hidden states (first block)
          self.assertListEqual(
              list(hidden_states[0].shape[-2:] )       ,						[
                  height * width,
                  self.model_tester.hidden_sizes[0],
              ]       ,						)

       lowerCAmelCase_  ,	lowerCAmelCase_:							str       =	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          lowerCAmelCase_:							int       =	True
          check_hidden_states_output(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          lowerCAmelCase_:							Union[str, Any]       =	True

          check_hidden_states_output(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )




    @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
    def      _a    (    self ):
       pass




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__=False ):
       lowerCAmelCase_:							List[str]       =	super()._prepare_for_class(lowerCamelCase__       ,						lowerCamelCase__       ,						return_labels=lowerCamelCase__ )

       if return_labels:
          if model_class.__name__ == "LevitForImageClassificationWithTeacher":
             del inputs_dict["labels"]

       return inputs_dict




    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, Any]       =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_:							str       =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )




    def      _a    (    self ):
       if not self.model_tester.is_training:
          return

       lowerCAmelCase_  ,	lowerCAmelCase_:							Tuple       =	self.model_tester.prepare_config_and_inputs_for_common()
       lowerCAmelCase_:							int       =	True

       for model_class in self.all_model_classes:
          # LevitForImageClassificationWithTeacher supports inference-only
          if (
              model_class in get_values(lowerCamelCase__ )
              or model_class.__name__ == "LevitForImageClassificationWithTeacher"
          ):
             continue
          lowerCAmelCase_:							Tuple       =	model_class(lowerCamelCase__ )
          model.to(lowerCamelCase__ )
          model.train()
          lowerCAmelCase_:							Optional[int]       =	self._prepare_for_class(lowerCamelCase__       ,						lowerCamelCase__       ,						return_labels=lowerCamelCase__ )
          lowerCAmelCase_:							Tuple       =	model(**lowerCamelCase__ ).loss
          loss.backward()




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							List[str]       =	self.model_tester.prepare_config_and_inputs_for_common()
       if not self.model_tester.is_training:
          return

       lowerCAmelCase_:							Any       =	False
       lowerCAmelCase_:							Tuple       =	True

       for model_class in self.all_model_classes:
          if model_class in get_values(lowerCamelCase__ ) or not model_class.supports_gradient_checkpointing:
             continue
          # LevitForImageClassificationWithTeacher supports inference-only
          if model_class.__name__ == "LevitForImageClassificationWithTeacher":
             continue
          lowerCAmelCase_:							Any       =	model_class(lowerCamelCase__ )
          model.gradient_checkpointing_enable()
          model.to(lowerCamelCase__ )
          model.train()
          lowerCAmelCase_:							str       =	self._prepare_for_class(lowerCamelCase__       ,						lowerCamelCase__       ,						return_labels=lowerCamelCase__ )
          lowerCAmelCase_:							List[str]       =	model(**lowerCamelCase__ ).loss
          loss.backward()




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							int       =	self.model_tester.prepare_config_and_inputs_for_common()

       lowerCAmelCase_:							Optional[int]       =	[
           {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
           {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
           {"title": "regression", "num_labels": 1, "dtype": torch.float},
       ]

       for model_class in self.all_model_classes:
          if (
              model_class
              not in [
                  *get_values(lowerCamelCase__ ),
              ]
              or model_class.__name__ == "LevitForImageClassificationWithTeacher"
          ):
             continue

          for problem_type in problem_types:
             with self.subTest(msg=F'''Testing {model_class} with {problem_type["title"]}''' ):
                lowerCAmelCase_:							Tuple       =	problem_type["title"]
                lowerCAmelCase_:							Optional[Any]       =	problem_type["num_labels"]

                lowerCAmelCase_:							List[str]       =	model_class(lowerCamelCase__ )
                model.to(lowerCamelCase__ )
                model.train()

                lowerCAmelCase_:							List[Any]       =	self._prepare_for_class(lowerCamelCase__       ,						lowerCamelCase__       ,						return_labels=lowerCamelCase__ )

                if problem_type["num_labels"] > 1:
                   lowerCAmelCase_:							List[str]       =	inputs["labels"].unsqueeze(1 ).repeat(1       ,						problem_type["num_labels"] )

                lowerCAmelCase_:							List[Any]       =	inputs["labels"].to(problem_type["dtype"] )

                # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                # they have the same size." which is a symptom something in wrong for the regression problem.
                # See https://github.com/huggingface/transformers/issues/11780
                with warnings.catch_warnings(record=lowerCamelCase__ ) as warning_list:
                   lowerCAmelCase_:							Dict       =	model(**lowerCamelCase__ ).loss
                for w in warning_list:
                   if "Using a target size that is different to the input size" in str(w.message ):
                      raise ValueError(
                          F'''Something is going wrong in the regression problem: intercepted {w.message}''' )

                loss.backward()




    @slow
    def      _a    (    self ):
       for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase_:							Optional[int]       =	LevitModel.from_pretrained(lowerCamelCase__ )
          self.assertIsNotNone(lowerCamelCase__ )





def 		snake_case__	(		):
   lowerCAmelCase_:							Optional[int]       =	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
   return image







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






    '''simple docstring'''




    @cached_property
    def      _a    (    self ):
       return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )




    @slow
    def      _a    (    self ):
       lowerCAmelCase_:							Any       =	LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
           lowerCamelCase__ )

       lowerCAmelCase_:							str       =	self.default_image_processor
       lowerCAmelCase_:							int       =	prepare_img()
       lowerCAmelCase_:							Optional[int]       =	image_processor(images=lowerCamelCase__       ,						return_tensors="pt" ).to(lowerCamelCase__ )

       # forward pass
       with torch.no_grad():
          lowerCAmelCase_:							Tuple       =	model(**lowerCamelCase__ )

       # verify the logits
       lowerCAmelCase_:							str       =	torch.Size((1, 1_000) )
       self.assertEqual(outputs.logits.shape       ,						lowerCamelCase__ )

       lowerCAmelCase_:							Union[str, Any]       =	torch.tensor([1.0_4_4_8, -0.3_7_4_5, -1.8_3_1_7] ).to(lowerCamelCase__ )

       self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,						lowerCamelCase__       ,						atol=1E-4 ) )
class 					_lowercase    :






    '''simple docstring'''




    def __init__(    self ):
       lowerCAmelCase_:							dict[str, TrieNode]       =	{}  # Mapping from char to TrieNode
       lowerCAmelCase_:							str       =	False




    def      _a    (    self       ,						lowerCamelCase__ ):
       for word in words:
          self.insert(lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Union[str, Any]       =	self
       for char in word:
          if char not in curr.nodes:
             lowerCAmelCase_:							List[Any]       =	TrieNode()
          lowerCAmelCase_:							Optional[Any]       =	curr.nodes[char]
       lowerCAmelCase_:							Dict       =	True




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Optional[Any]       =	self
       for char in word:
          if char not in curr.nodes:
             return False
          lowerCAmelCase_:							Dict       =	curr.nodes[char]
       return curr.is_leaf




    def      _a    (    self       ,						lowerCamelCase__ ):

       def _delete(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ) -> bool:
          if index == len(lowerCamelCase__ ):
             # If word does not exist
             if not curr.is_leaf:
                return False
             lowerCAmelCase_:							List[str]       =	False
             return len(curr.nodes ) == 0
          lowerCAmelCase_:							Union[str, Any]       =	word[index]
          lowerCAmelCase_:							Optional[int]       =	curr.nodes.get(lowerCamelCase__ )
          # If char not in current trie node
          if not char_node:
             return False
          # Flag to check if node can be deleted
          lowerCAmelCase_:							List[Any]       =	_delete(lowerCamelCase__       ,						lowerCamelCase__       ,						index + 1 )
          if delete_curr:
             del curr.nodes[char]
             return len(curr.nodes ) == 0
          return delete_curr

       _delete(self       ,						lowerCamelCase__       ,						0 )





def 		snake_case__	(		lowercase							,				lowercase					):
   if node.is_leaf:
      print(lowercase							,				end=" "					)

   for key, value in node.nodes.items():
      print_words(lowercase							,				word + key					)
def 		snake_case__	(		):
   lowerCAmelCase_:							Any       =	"banana bananas bandana band apple all beast".split()
   lowerCAmelCase_:							Optional[Any]       =	TrieNode()
   root.insert_many(lowercase					)
   # print_words(root, "")
   assert all(root.find(lowercase					) for word in words					)
   assert root.find("banana"					)
   assert not root.find("bandanas"					)
   assert not root.find("apps"					)
   assert root.find("apple"					)
   assert root.find("all"					)
   root.delete("all"					)
   assert not root.find("all"					)
   root.delete("banana"					)
   assert not root.find("banana"					)
   assert root.find("bananas"					)
   return True
def 		snake_case__	(		lowercase							,				lowercase					):
   print(str(lowercase					)							,				"works!" if passes else "doesn't work :("					)
def 		snake_case__	(		):
   assert test_trie()
def 		snake_case__	(		):
   print_results("Testing trie functionality"							,				test_trie()					)


if __name__ == "__main__":
 main()