



"""simple docstring"""
import collections
import inspect
import unittest

from transformers import DinatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import DinatBackbone, DinatForImageClassification, DinatModel
   from transformers.models.dinat.modeling_dinat import DINAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor
class    a		:



  def __init__(  self	:   Dict    ,						lowerCAmelCase	:   int    ,						lowerCAmelCase	:   Dict=13    ,						lowerCAmelCase	:   Any=64    ,						lowerCAmelCase	:   Optional[int]=4    ,						lowerCAmelCase	:   int=3    ,						lowerCAmelCase	:   List[str]=16    ,						lowerCAmelCase	:   Tuple=[1, 2, 1]    ,						lowerCAmelCase	:   Tuple=[2, 4, 8]    ,						lowerCAmelCase	:   str=3    ,						lowerCAmelCase	:   List[Any]=[[3], [1, 2], [1]]    ,						lowerCAmelCase	:   List[str]=2.0    ,						lowerCAmelCase	:   Optional[Any]=True    ,						lowerCAmelCase	:   Any=0.0    ,						lowerCAmelCase	:   List[str]=0.0    ,						lowerCAmelCase	:   Union[str, Any]=0.1    ,						lowerCAmelCase	:   Optional[Any]="gelu"    ,						lowerCAmelCase	:   int=True    ,						lowerCAmelCase	:   Any=0.0_2    ,						lowerCAmelCase	:   Optional[int]=1E-5    ,						lowerCAmelCase	:   Any=True    ,						lowerCAmelCase	:   Any=None    ,						lowerCAmelCase	:   List[Any]=True    ,						lowerCAmelCase	:   Tuple=10    ,						lowerCAmelCase	:   Tuple=["stage1", "stage2"]    ,						lowerCAmelCase	:   Dict=[1, 2]    ,						)       ->   Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[Any]            =parent
     SCREAMING_SNAKE_CASE_:	List[Any]            =batch_size
     SCREAMING_SNAKE_CASE_:	int            =image_size
     SCREAMING_SNAKE_CASE_:	Optional[int]            =patch_size
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =num_channels
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =embed_dim
     SCREAMING_SNAKE_CASE_:	List[str]            =depths
     SCREAMING_SNAKE_CASE_:	int            =num_heads
     SCREAMING_SNAKE_CASE_:	Dict            =kernel_size
     SCREAMING_SNAKE_CASE_:	List[str]            =dilations
     SCREAMING_SNAKE_CASE_:	Any            =mlp_ratio
     SCREAMING_SNAKE_CASE_:	Dict            =qkv_bias
     SCREAMING_SNAKE_CASE_:	int            =hidden_dropout_prob
     SCREAMING_SNAKE_CASE_:	Dict            =attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE_:	str            =drop_path_rate
     SCREAMING_SNAKE_CASE_:	Tuple            =hidden_act
     SCREAMING_SNAKE_CASE_:	int            =patch_norm
     SCREAMING_SNAKE_CASE_:	List[Any]            =layer_norm_eps
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =initializer_range
     SCREAMING_SNAKE_CASE_:	int            =is_training
     SCREAMING_SNAKE_CASE_:	List[Any]            =scope
     SCREAMING_SNAKE_CASE_:	List[Any]            =use_labels
     SCREAMING_SNAKE_CASE_:	Any            =num_labels
     SCREAMING_SNAKE_CASE_:	Any            =out_features
     SCREAMING_SNAKE_CASE_:	Any            =out_indices



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Any            =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =None
     if self.use_labels:
        SCREAMING_SNAKE_CASE_:	str            =ids_tensor([self.batch_size]    ,						self.num_labels						)

     SCREAMING_SNAKE_CASE_:	Any            =self.get_config()

     return config, pixel_values, labels



  def 		lowerCamelCase__    (  self	:   str						)       ->   Dict:


     '''simple docstring'''




     return DinatConfig(
         num_labels=self.num_labels    ,						image_size=self.image_size    ,						patch_size=self.patch_size    ,						num_channels=self.num_channels    ,						embed_dim=self.embed_dim    ,						depths=self.depths    ,						num_heads=self.num_heads    ,						kernel_size=self.kernel_size    ,						dilations=self.dilations    ,						mlp_ratio=self.mlp_ratio    ,						qkv_bias=self.qkv_bias    ,						hidden_dropout_prob=self.hidden_dropout_prob    ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,						drop_path_rate=self.drop_path_rate    ,						hidden_act=self.hidden_act    ,						patch_norm=self.patch_norm    ,						layer_norm_eps=self.layer_norm_eps    ,						initializer_range=self.initializer_range    ,						out_features=self.out_features    ,						out_indices=self.out_indices    ,						)



  def 		lowerCamelCase__    (  self	:   Dict    ,						lowerCAmelCase	:   Union[str, Any]    ,						lowerCAmelCase	:   str    ,						lowerCAmelCase	:   Any						)       ->   int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Any            =DinatModel(config=lowerCAmelCase						)
     model.to(lowerCAmelCase						)
     model.eval()
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =model(lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	int            =(config.image_size // config.patch_size) // (2 ** (len(config.depths						) - 1))
     SCREAMING_SNAKE_CASE_:	List[str]            =int(config.embed_dim * 2 ** (len(config.depths						) - 1)						)

     self.parent.assertEqual(
         result.last_hidden_state.shape    ,						(self.batch_size, expected_height, expected_width, expected_dim)						)



  def 		lowerCamelCase__    (  self	:   List[Any]    ,						lowerCAmelCase	:   Optional[Any]    ,						lowerCAmelCase	:   List[Any]    ,						lowerCAmelCase	:   Any						)       ->   int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	str            =DinatForImageClassification(lowerCAmelCase						)
     model.to(lowerCAmelCase						)
     model.eval()
     SCREAMING_SNAKE_CASE_:	Tuple            =model(lowerCAmelCase    ,						labels=lowerCAmelCase						)
     self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_labels)						)

     # test greyscale images
     SCREAMING_SNAKE_CASE_:	Optional[int]            =1
     SCREAMING_SNAKE_CASE_:	List[Any]            =DinatForImageClassification(lowerCAmelCase						)
     model.to(lowerCAmelCase						)
     model.eval()

     SCREAMING_SNAKE_CASE_:	Optional[int]            =floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =model(lowerCAmelCase						)
     self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_labels)						)



  def 		lowerCamelCase__    (  self	:   List[str]    ,						lowerCAmelCase	:   List[str]    ,						lowerCAmelCase	:   List[Any]    ,						lowerCAmelCase	:   Dict						)       ->   List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[int]            =DinatBackbone(config=lowerCAmelCase						)
     model.to(lowerCAmelCase						)
     model.eval()
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =model(lowerCAmelCase						)

     # verify hidden states
     self.parent.assertEqual(len(result.feature_maps						)    ,						len(config.out_features						)						)
     self.parent.assertListEqual(list(result.feature_maps[0].shape						)    ,						[self.batch_size, model.channels[0], 16, 16]						)

     # verify channels
     self.parent.assertEqual(len(model.channels						)    ,						len(config.out_features						)						)

     # verify backbone works with out_features=None
     SCREAMING_SNAKE_CASE_:	int            =None
     SCREAMING_SNAKE_CASE_:	Optional[int]            =DinatBackbone(config=lowerCAmelCase						)
     model.to(lowerCAmelCase						)
     model.eval()
     SCREAMING_SNAKE_CASE_:	Optional[int]            =model(lowerCAmelCase						)

     # verify feature maps
     self.parent.assertEqual(len(result.feature_maps						)    ,						1						)
     self.parent.assertListEqual(list(result.feature_maps[0].shape						)    ,						[self.batch_size, model.channels[-1], 4, 4]						)

     # verify channels
     self.parent.assertEqual(len(model.channels						)    ,						1						)





  def 		lowerCamelCase__    (  self	:   Tuple						)       ->   Optional[Any]:


     '''simple docstring'''




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







@require_natten
@require_torch
class    a		(					UpperCAmelCase__		, UpperCAmelCase__		, unittest.TestCase    ):
  UpperCamelCase		:			int         =   (
      (
          DinatModel,
          DinatForImageClassification,
          DinatBackbone,
      )
      if is_torch_available()
      else ()
  )
  UpperCamelCase		:			Any         =   (
      {'feature-extraction': DinatModel, 'image-classification': DinatForImageClassification}
      if is_torch_available()
      else {}
  )
  UpperCamelCase		:			str         =   False

  UpperCamelCase		:			str         =   False
  UpperCamelCase		:			int         =   False
  UpperCamelCase		:			Tuple         =   False
  UpperCamelCase		:			List[Any]         =   False



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Any            =DinatModelTester(self						)
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =ConfigTester(self    ,						config_class=lowerCAmelCase    ,						embed_dim=37						)



  def 		lowerCamelCase__    (  self	:   Tuple						)       ->   str:


     '''simple docstring'''




     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 		lowerCamelCase__    (  self	:   str						)       ->   Optional[int]:


     '''simple docstring'''




     return



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[int]            =self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCAmelCase						)



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Dict            =self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase						)



  def 		lowerCamelCase__    (  self	:   Any						)       ->   Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_backbone(*lowerCAmelCase						)



  @unittest.skip(reason="""Dinat does not use inputs_embeds"""						)
  def 		lowerCamelCase__    (  self	:   Optional[Any]						)       ->   Union[str, Any]:


     '''simple docstring'''




     pass



  @unittest.skip(reason="""Dinat does not use feedforward chunking"""						)
  def 		lowerCamelCase__    (  self	:   Any						)       ->   Union[str, Any]:


     '''simple docstring'''




     pass



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Tuple            =self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        SCREAMING_SNAKE_CASE_:	Tuple            =model_class(lowerCAmelCase						)
        self.assertIsInstance(model.get_input_embeddings()    ,						(nn.Module)						)
        SCREAMING_SNAKE_CASE_:	List[str]            =model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(lowerCAmelCase    ,						nn.Linear						)						)



  def 		lowerCamelCase__    (  self	:   Dict						)       ->   str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[int]            =self.model_tester.prepare_config_and_inputs_for_common()

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

        SCREAMING_SNAKE_CASE_:	Dict            =["""pixel_values"""]
        self.assertListEqual(arg_names[:1]    ,						lowerCAmelCase						)



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


     '''simple docstring'''




     self.skipTest("""Dinat's attention operation is handled entirely by NATTEN."""						)



  def 		lowerCamelCase__    (  self	:   Optional[Any]    ,						lowerCAmelCase	:   Dict    ,						lowerCAmelCase	:   Any    ,						lowerCAmelCase	:   Union[str, Any]    ,						lowerCAmelCase	:   str						)       ->   Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =model_class(lowerCAmelCase						)
     model.to(lowerCAmelCase						)
     model.eval()

     with torch.no_grad():
        SCREAMING_SNAKE_CASE_:	List[str]            =model(**self._prepare_for_class(lowerCAmelCase    ,						lowerCAmelCase						)						)

     SCREAMING_SNAKE_CASE_:	List[str]            =outputs.hidden_states

     SCREAMING_SNAKE_CASE_:	str            =getattr(
         self.model_tester    ,						"""expected_num_hidden_layers"""    ,						len(self.model_tester.depths						) + 1						)
     self.assertEqual(len(lowerCAmelCase						)    ,						lowerCAmelCase						)

     # Dinat has a different seq_length
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =(
         config.patch_size
         if isinstance(config.patch_size    ,						collections.abc.Iterable						)
         else (config.patch_size, config.patch_size)
     )

     SCREAMING_SNAKE_CASE_:	Tuple            =image_size[0] // patch_size[0]
     SCREAMING_SNAKE_CASE_:	Tuple            =image_size[1] // patch_size[1]

     self.assertListEqual(
         list(hidden_states[0].shape[-3:]						)    ,						[height, width, self.model_tester.embed_dim]    ,						)

     if model_class.__name__ != "DinatBackbone":
        SCREAMING_SNAKE_CASE_:	Optional[int]            =outputs.reshaped_hidden_states
        self.assertEqual(len(lowerCAmelCase						)    ,						lowerCAmelCase						)

        SCREAMING_SNAKE_CASE_:	Optional[int]            =reshaped_hidden_states[0].shape
        SCREAMING_SNAKE_CASE_:	Optional[Any]            =(
            reshaped_hidden_states[0].view(lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase						).permute(0    ,						2    ,						3    ,						1						)
        )
        self.assertListEqual(
            list(reshaped_hidden_states.shape[-3:]						)    ,						[height, width, self.model_tester.embed_dim]    ,						)



  def 		lowerCamelCase__    (  self	:   Any						)       ->   str:


     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE_:	Tuple            =(
         self.model_tester.image_size
         if isinstance(self.model_tester.image_size    ,						collections.abc.Iterable						)
         else (self.model_tester.image_size, self.model_tester.image_size)
     )

     for model_class in self.all_model_classes:
        SCREAMING_SNAKE_CASE_:	Dict            =True
        self.check_hidden_states_output(lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase						)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        SCREAMING_SNAKE_CASE_:	Tuple            =True

        self.check_hidden_states_output(lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase						)



  @slow
  def 		lowerCamelCase__    (  self	:   Any						)       ->   Dict:


     '''simple docstring'''




     for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        SCREAMING_SNAKE_CASE_:	Union[str, Any]            =DinatModel.from_pretrained(lowerCAmelCase						)
        self.assertIsNotNone(lowerCAmelCase						)





  def 		lowerCamelCase__    (  self	:   Dict						)       ->   str:


     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE_:	Dict            =_config_zero_init(lowerCAmelCase						)
     for model_class in self.all_model_classes:
        SCREAMING_SNAKE_CASE_:	Tuple            =model_class(config=lowerCAmelCase						)
        for name, param in model.named_parameters():
           if "embeddings" not in name and param.requires_grad:
              self.assertIn(
                  ((param.data.mean() * 1E9).round() / 1E9).item()    ,						[0.0, 1.0]    ,						msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''    ,						)







@require_natten
@require_vision
@require_torch
class    a		(					unittest.TestCase    ):



  @cached_property
  def 		lowerCamelCase__    (  self	:   Dict						)       ->   int:


     '''simple docstring'''




     return AutoImageProcessor.from_pretrained("""shi-labs/dinat-mini-in1k-224"""						) if is_vision_available() else None





  @slow
  def 		lowerCamelCase__    (  self	:   Union[str, Any]						)       ->   str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[Any]            =DinatForImageClassification.from_pretrained("""shi-labs/dinat-mini-in1k-224"""						).to(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	List[Any]            =self.default_image_processor

     SCREAMING_SNAKE_CASE_:	List[Any]            =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
     SCREAMING_SNAKE_CASE_:	int            =image_processor(images=lowerCAmelCase    ,						return_tensors="""pt"""						).to(lowerCAmelCase						)

     # forward pass
     with torch.no_grad():
        SCREAMING_SNAKE_CASE_:	Dict            =model(**lowerCAmelCase						)

     # verify the logits
     SCREAMING_SNAKE_CASE_:	List[str]            =torch.Size((1, 1000)						)
     self.assertEqual(outputs.logits.shape    ,						lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =torch.tensor([-0.1_5_4_5, -0.7_6_6_7, 0.4_6_4_2]						).to(lowerCAmelCase						)
     self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,						lowerCAmelCase    ,						atol=1E-4						)						)







@require_torch
@require_natten
class    a		(					unittest.TestCase		, UpperCAmelCase__    ):
  UpperCamelCase		:			List[str]         =   (DinatBackbone,) if is_torch_available() else ()
  UpperCamelCase		:			Optional[int]         =   DinatConfig



  def 		lowerCamelCase__    (  self	:   str						)       ->   Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[int]            =DinatModelTester(self						)








"""simple docstring"""
from __future__ import annotations

import sys
class    a		:



  def __init__(  self	:   Optional[Any]    ,						lowerCAmelCase	:   str    ,						lowerCAmelCase	:   int						)       ->   List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	str            =letter
     SCREAMING_SNAKE_CASE_:	int            =freq
     SCREAMING_SNAKE_CASE_:	dict[str, str]            ={}





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


     '''simple docstring'''




     return f'''{self.letter}:{self.freq}'''







class    a		:



  def __init__(  self	:   Optional[Any]    ,						lowerCAmelCase	:   int    ,						lowerCAmelCase	:   Letter | TreeNode    ,						lowerCAmelCase	:   Letter | TreeNode						)       ->   List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =freq
     SCREAMING_SNAKE_CASE_:	Letter | TreeNode            =left
     SCREAMING_SNAKE_CASE_:	Letter | TreeNode            =right







def        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	dict[str, int]            ={}
   with open(lowercase	) as f:
      while True:
         SCREAMING_SNAKE_CASE_:	List[Any]            =f.read(1	)
         if not c:
            break
         SCREAMING_SNAKE_CASE_:	Any            =chars[c] + 1 if c in chars else 1
   return sorted((Letter(lowercase							,     lowercase	) for c, f in chars.items())							,     key=lambda lowercase	: x.freq	)





def        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	list[Letter | TreeNode]            =letters  # type: ignore
   while len(lowercase	) > 1:
      SCREAMING_SNAKE_CASE_:	Any            =response.pop(0	)
      SCREAMING_SNAKE_CASE_:	List[Any]            =response.pop(0	)
      SCREAMING_SNAKE_CASE_:	List[Any]            =left.freq + right.freq
      SCREAMING_SNAKE_CASE_:	Any            =TreeNode(lowercase							,     lowercase							,     lowercase	)
      response.append(lowercase	)
      response.sort(key=lambda lowercase	: x.freq	)
   return response[0]





def        __magic_name__			(							lowercase							,     lowercase	):
   if isinstance(lowercase							,     lowercase	):
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =bitstring
      return [root]
   SCREAMING_SNAKE_CASE_:	TreeNode            =root  # type: ignore
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[]
   letters += traverse_tree(treenode.left							,     bitstring + """0"""	)
   letters += traverse_tree(treenode.right							,     bitstring + """1"""	)
   return letters





def        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =parse_file(lowercase	)
   SCREAMING_SNAKE_CASE_:	str            =build_tree(lowercase	)
   SCREAMING_SNAKE_CASE_:	List[Any]            ={
       k: v for letter in traverse_tree(lowercase							,     """"""	) for k, v in letter.bitstring.items()
   }
   print(f'''Huffman Coding  of {file_path}: '''	)
   with open(lowercase	) as f:
      while True:
         SCREAMING_SNAKE_CASE_:	Any            =f.read(1	)
         if not c:
            break
         print(letters[c]							,     end=""" """	)
   print()


if __name__ == "__main__":
   # pass the file path to the huffman function
   huffman(sys.argv[1])



