





import unittest

from transformers import ElectraConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      MODEL_FOR_PRETRAINING_MAPPING,
      ElectraForCausalLM,
      ElectraForMaskedLM,
      ElectraForMultipleChoice,
      ElectraForPreTraining,
      ElectraForQuestionAnswering,
      ElectraForSequenceClassification,
      ElectraForTokenClassification,
      ElectraModel,
  )
  from transformers.models.electra.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST

class 		__a							:

       '''simple docstring'''





       def __init__(     self	:   Any					,			UpperCAmelCase	:   Optional[int]					,			UpperCAmelCase	:   int=13					,			UpperCAmelCase	:   List[str]=7					,			UpperCAmelCase	:   Tuple=True					,			UpperCAmelCase	:   int=True					,			UpperCAmelCase	:   Optional[Any]=True					,			UpperCAmelCase	:   Optional[int]=True					,			UpperCAmelCase	:   Any=99					,			UpperCAmelCase	:   List[str]=32					,			UpperCAmelCase	:   Dict=5					,			UpperCAmelCase	:   Optional[int]=4					,			UpperCAmelCase	:   List[Any]=37					,			UpperCAmelCase	:   List[str]="gelu"					,			UpperCAmelCase	:   List[Any]=0.1					,			UpperCAmelCase	:   Any=0.1					,			UpperCAmelCase	:   Union[str, Any]=5_12					,			UpperCAmelCase	:   Any=16					,			UpperCAmelCase	:   Optional[Any]=2					,			UpperCAmelCase	:   Optional[int]=0.02					,			UpperCAmelCase	:   int=3					,			UpperCAmelCase	:   Dict=4					,			UpperCAmelCase	:   Optional[Any]=None					,			):
           lowerCAmelCase_					:	Union[str, Any]						=   parent
           lowerCAmelCase_					:	List[Any]						=   batch_size
           lowerCAmelCase_					:	Tuple						=   seq_length
           lowerCAmelCase_					:	str						=   is_training
           lowerCAmelCase_					:	str						=   use_input_mask
           lowerCAmelCase_					:	Optional[int]						=   use_token_type_ids
           lowerCAmelCase_					:	List[Any]						=   use_labels
           lowerCAmelCase_					:	Optional[Any]						=   vocab_size
           lowerCAmelCase_					:	List[str]						=   hidden_size
           lowerCAmelCase_					:	Tuple						=   num_hidden_layers
           lowerCAmelCase_					:	Optional[int]						=   num_attention_heads
           lowerCAmelCase_					:	str						=   intermediate_size
           lowerCAmelCase_					:	Tuple						=   hidden_act
           lowerCAmelCase_					:	Union[str, Any]						=   hidden_dropout_prob
           lowerCAmelCase_					:	Optional[Any]						=   attention_probs_dropout_prob
           lowerCAmelCase_					:	Optional[int]						=   max_position_embeddings
           lowerCAmelCase_					:	Optional[int]						=   type_vocab_size
           lowerCAmelCase_					:	Union[str, Any]						=   type_sequence_label_size
           lowerCAmelCase_					:	Dict						=   initializer_range
           lowerCAmelCase_					:	Any						=   num_labels
           lowerCAmelCase_					:	Any						=   num_choices
           lowerCAmelCase_					:	Union[str, Any]						=   scope



       def  A							(     self	:   Optional[Any]			):
           lowerCAmelCase_					:	List[str]						=   ids_tensor([self.batch_size, self.seq_length]					,			self.vocab_size			)

           lowerCAmelCase_					:	Any						=   None
           if self.use_input_mask:
               lowerCAmelCase_					:	str						=   random_attention_mask([self.batch_size, self.seq_length]			)

           lowerCAmelCase_					:	Dict						=   None
           if self.use_token_type_ids:
               lowerCAmelCase_					:	Union[str, Any]						=   ids_tensor([self.batch_size, self.seq_length]					,			self.type_vocab_size			)

           lowerCAmelCase_					:	List[Any]						=   None
           lowerCAmelCase_					:	int						=   None
           lowerCAmelCase_					:	Any						=   None
           if self.use_labels:
               lowerCAmelCase_					:	List[Any]						=   ids_tensor([self.batch_size]					,			self.type_sequence_label_size			)
               lowerCAmelCase_					:	Tuple						=   ids_tensor([self.batch_size, self.seq_length]					,			self.num_labels			)
               lowerCAmelCase_					:	str						=   ids_tensor([self.batch_size]					,			self.num_choices			)
               lowerCAmelCase_					:	List[Any]						=   ids_tensor([self.batch_size, self.seq_length]					,			1			)

           lowerCAmelCase_					:	Tuple						=   self.get_config()

           return (
               config,
               input_ids,
               token_type_ids,
               input_mask,
               sequence_labels,
               token_labels,
               choice_labels,
               fake_token_labels,
           )



       def  A							(     self	:   Optional[int]			):
           return ElectraConfig(
               vocab_size=self.vocab_size					,			hidden_size=self.hidden_size					,			num_hidden_layers=self.num_hidden_layers					,			num_attention_heads=self.num_attention_heads					,			intermediate_size=self.intermediate_size					,			hidden_act=self.hidden_act					,			hidden_dropout_prob=self.hidden_dropout_prob					,			attention_probs_dropout_prob=self.attention_probs_dropout_prob					,			max_position_embeddings=self.max_position_embeddings					,			type_vocab_size=self.type_vocab_size					,			is_decoder=UpperCAmelCase					,			initializer_range=self.initializer_range					,			)



       def  A							(     self	:   Optional[Any]			):
           (
               lowerCAmelCase_		
           )					:	str						=   self.prepare_config_and_inputs()

           lowerCAmelCase_					:	Optional[int]						=   True
           lowerCAmelCase_					:	Optional[Any]						=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
           lowerCAmelCase_					:	List[str]						=   ids_tensor([self.batch_size, self.seq_length]					,			vocab_size=2			)

           return (
               config,
               input_ids,
               token_type_ids,
               input_mask,
               sequence_labels,
               token_labels,
               choice_labels,
               encoder_hidden_states,
               encoder_attention_mask,
           )



       def  A							(     self	:   Union[str, Any]					,			UpperCAmelCase	:   List[str]					,			UpperCAmelCase	:   Any					,			UpperCAmelCase	:   str					,			UpperCAmelCase	:   Tuple					,			UpperCAmelCase	:   List[str]					,			UpperCAmelCase	:   str					,			UpperCAmelCase	:   Tuple					,			UpperCAmelCase	:   Union[str, Any]					,			):
           lowerCAmelCase_					:	Dict						=   ElectraModel(config=UpperCAmelCase			)
           model.to(UpperCAmelCase			)
           model.eval()
           lowerCAmelCase_					:	List[Any]						=   model(UpperCAmelCase					,			attention_mask=UpperCAmelCase					,			token_type_ids=UpperCAmelCase			)
           lowerCAmelCase_					:	List[Any]						=   model(UpperCAmelCase					,			token_type_ids=UpperCAmelCase			)
           lowerCAmelCase_					:	Optional[Any]						=   model(UpperCAmelCase			)
           self.parent.assertEqual(result.last_hidden_state.shape					,			(self.batch_size, self.seq_length, self.hidden_size)			)



       def  A							(     self	:   Tuple					,			UpperCAmelCase	:   int					,			UpperCAmelCase	:   Optional[Any]					,			UpperCAmelCase	:   List[str]					,			UpperCAmelCase	:   Optional[int]					,			UpperCAmelCase	:   Dict					,			UpperCAmelCase	:   List[Any]					,			UpperCAmelCase	:   Optional[Any]					,			UpperCAmelCase	:   Tuple					,			UpperCAmelCase	:   int					,			):
           lowerCAmelCase_					:	Any						=   True
           lowerCAmelCase_					:	Dict						=   ElectraModel(UpperCAmelCase			)
           model.to(UpperCAmelCase			)
           model.eval()
           lowerCAmelCase_					:	str						=   model(
               UpperCAmelCase					,			attention_mask=UpperCAmelCase					,			token_type_ids=UpperCAmelCase					,			encoder_hidden_states=UpperCAmelCase					,			encoder_attention_mask=UpperCAmelCase					,			)
           lowerCAmelCase_					:	Union[str, Any]						=   model(
               UpperCAmelCase					,			attention_mask=UpperCAmelCase					,			token_type_ids=UpperCAmelCase					,			encoder_hidden_states=UpperCAmelCase					,			)
           lowerCAmelCase_					:	Dict						=   model(UpperCAmelCase					,			attention_mask=UpperCAmelCase					,			token_type_ids=UpperCAmelCase			)
           self.parent.assertEqual(result.last_hidden_state.shape					,			(self.batch_size, self.seq_length, self.hidden_size)			)



       def  A							(     self	:   Optional[int]					,			UpperCAmelCase	:   Any					,			UpperCAmelCase	:   List[str]					,			UpperCAmelCase	:   Any					,			UpperCAmelCase	:   List[str]					,			UpperCAmelCase	:   Any					,			UpperCAmelCase	:   str					,			UpperCAmelCase	:   Union[str, Any]					,			UpperCAmelCase	:   Optional[int]					,			):
           lowerCAmelCase_					:	Any						=   ElectraForMaskedLM(config=UpperCAmelCase			)
           model.to(UpperCAmelCase			)
           model.eval()
           lowerCAmelCase_					:	List[str]						=   model(UpperCAmelCase					,			attention_mask=UpperCAmelCase					,			token_type_ids=UpperCAmelCase					,			labels=UpperCAmelCase			)
           self.parent.assertEqual(result.logits.shape					,			(self.batch_size, self.seq_length, self.vocab_size)			)



       def  A							(     self	:   Dict					,			UpperCAmelCase	:   Tuple					,			UpperCAmelCase	:   int					,			UpperCAmelCase	:   str					,			UpperCAmelCase	:   Optional[int]					,			UpperCAmelCase	:   Union[str, Any]					,			UpperCAmelCase	:   Dict					,			UpperCAmelCase	:   int					,			UpperCAmelCase	:   Any					,			UpperCAmelCase	:   List[Any]					,			):
           lowerCAmelCase_					:	Union[str, Any]						=   ElectraForCausalLM(config=UpperCAmelCase			)
           model.to(UpperCAmelCase			)
           model.eval()
           lowerCAmelCase_					:	Tuple						=   model(UpperCAmelCase					,			attention_mask=UpperCAmelCase					,			token_type_ids=UpperCAmelCase					,			labels=UpperCAmelCase			)
           self.parent.assertEqual(result.logits.shape					,			(self.batch_size, self.seq_length, self.vocab_size)			)



       def  A							(     self	:   Union[str, Any]					,			UpperCAmelCase	:   int					,			UpperCAmelCase	:   str					,			UpperCAmelCase	:   List[str]					,			UpperCAmelCase	:   Optional[Any]					,			UpperCAmelCase	:   Optional[Any]					,			UpperCAmelCase	:   Tuple					,			UpperCAmelCase	:   int					,			UpperCAmelCase	:   Tuple					,			):
           lowerCAmelCase_					:	Tuple						=   self.num_labels
           lowerCAmelCase_					:	int						=   ElectraForTokenClassification(config=UpperCAmelCase			)
           model.to(UpperCAmelCase			)
           model.eval()
           lowerCAmelCase_					:	Union[str, Any]						=   model(UpperCAmelCase					,			attention_mask=UpperCAmelCase					,			token_type_ids=UpperCAmelCase					,			labels=UpperCAmelCase			)
           self.parent.assertEqual(result.logits.shape					,			(self.batch_size, self.seq_length, self.num_labels)			)



       def  A							(     self	:   Optional[Any]					,			UpperCAmelCase	:   Union[str, Any]					,			UpperCAmelCase	:   Any					,			UpperCAmelCase	:   int					,			UpperCAmelCase	:   int					,			UpperCAmelCase	:   Dict					,			UpperCAmelCase	:   Optional[int]					,			UpperCAmelCase	:   Optional[int]					,			UpperCAmelCase	:   int					,			):
           lowerCAmelCase_					:	Optional[int]						=   self.num_labels
           lowerCAmelCase_					:	Optional[Any]						=   ElectraForPreTraining(config=UpperCAmelCase			)
           model.to(UpperCAmelCase			)
           model.eval()
           lowerCAmelCase_					:	List[Any]						=   model(UpperCAmelCase					,			attention_mask=UpperCAmelCase					,			token_type_ids=UpperCAmelCase					,			labels=UpperCAmelCase			)
           self.parent.assertEqual(result.logits.shape					,			(self.batch_size, self.seq_length)			)



       def  A							(     self	:   List[str]					,			UpperCAmelCase	:   List[str]					,			UpperCAmelCase	:   Union[str, Any]					,			UpperCAmelCase	:   Union[str, Any]					,			UpperCAmelCase	:   List[Any]					,			UpperCAmelCase	:   int					,			UpperCAmelCase	:   Any					,			UpperCAmelCase	:   str					,			UpperCAmelCase	:   str					,			):
           lowerCAmelCase_					:	Optional[int]						=   self.num_labels
           lowerCAmelCase_					:	Optional[int]						=   ElectraForSequenceClassification(UpperCAmelCase			)
           model.to(UpperCAmelCase			)
           model.eval()
           lowerCAmelCase_					:	str						=   model(UpperCAmelCase					,			attention_mask=UpperCAmelCase					,			token_type_ids=UpperCAmelCase					,			labels=UpperCAmelCase			)
           self.parent.assertEqual(result.logits.shape					,			(self.batch_size, self.num_labels)			)



       def  A							(     self	:   str					,			UpperCAmelCase	:   Union[str, Any]					,			UpperCAmelCase	:   Tuple					,			UpperCAmelCase	:   List[str]					,			UpperCAmelCase	:   Tuple					,			UpperCAmelCase	:   List[str]					,			UpperCAmelCase	:   List[str]					,			UpperCAmelCase	:   Dict					,			UpperCAmelCase	:   List[Any]					,			):
           lowerCAmelCase_					:	Tuple						=   ElectraForQuestionAnswering(config=UpperCAmelCase			)
           model.to(UpperCAmelCase			)
           model.eval()
           lowerCAmelCase_					:	List[Any]						=   model(
               UpperCAmelCase					,			attention_mask=UpperCAmelCase					,			token_type_ids=UpperCAmelCase					,			start_positions=UpperCAmelCase					,			end_positions=UpperCAmelCase					,			)
           self.parent.assertEqual(result.start_logits.shape					,			(self.batch_size, self.seq_length)			)
           self.parent.assertEqual(result.end_logits.shape					,			(self.batch_size, self.seq_length)			)



       def  A							(     self	:   Optional[int]					,			UpperCAmelCase	:   List[str]					,			UpperCAmelCase	:   Tuple					,			UpperCAmelCase	:   Tuple					,			UpperCAmelCase	:   List[str]					,			UpperCAmelCase	:   Tuple					,			UpperCAmelCase	:   Tuple					,			UpperCAmelCase	:   Tuple					,			UpperCAmelCase	:   Any					,			):
           lowerCAmelCase_					:	List[Any]						=   self.num_choices
           lowerCAmelCase_					:	Tuple						=   ElectraForMultipleChoice(config=UpperCAmelCase			)
           model.to(UpperCAmelCase			)
           model.eval()
           lowerCAmelCase_					:	int						=   input_ids.unsqueeze(1			).expand(-1					,			self.num_choices					,			-1			).contiguous()
           lowerCAmelCase_					:	List[Any]						=   token_type_ids.unsqueeze(1			).expand(-1					,			self.num_choices					,			-1			).contiguous()
           lowerCAmelCase_					:	List[Any]						=   input_mask.unsqueeze(1			).expand(-1					,			self.num_choices					,			-1			).contiguous()
           lowerCAmelCase_					:	Tuple						=   model(
               UpperCAmelCase					,			attention_mask=UpperCAmelCase					,			token_type_ids=UpperCAmelCase					,			labels=UpperCAmelCase					,			)
           self.parent.assertEqual(result.logits.shape					,			(self.batch_size, self.num_choices)			)



       def  A							(     self	:   List[Any]			):
           lowerCAmelCase_					:	List[str]						=   self.prepare_config_and_inputs()
           (
               lowerCAmelCase_		
           )					:	Union[str, Any]						=   config_and_inputs
           lowerCAmelCase_					:	Union[str, Any]						=   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
           return config, inputs_dict






@require_torch
class 		__a							(	__UpperCamelCase			,__UpperCamelCase			,unittest.TestCase      ):

       '''simple docstring'''





       __snake_case						:   Optional[Any]      =		(
           (
               ElectraModel,
               ElectraForPreTraining,
               ElectraForMaskedLM,
               ElectraForCausalLM,
               ElectraForMultipleChoice,
               ElectraForTokenClassification,
               ElectraForSequenceClassification,
               ElectraForQuestionAnswering,
           )
           if is_torch_available()
           else ()
       )
       __snake_case						:   Tuple      =		(
           {
               """feature-extraction""": ElectraModel,
               """fill-mask""": ElectraForMaskedLM,
               """question-answering""": ElectraForQuestionAnswering,
               """text-classification""": ElectraForSequenceClassification,
               """text-generation""": ElectraForCausalLM,
               """token-classification""": ElectraForTokenClassification,
               """zero-shot""": ElectraForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       __snake_case						:   List[str]      =		True



       def  A							(     self	:   Tuple					,			UpperCAmelCase	:   int					,			UpperCAmelCase	:   Dict					,			UpperCAmelCase	:   int=False			):
           lowerCAmelCase_					:	Union[str, Any]						=   super()._prepare_for_class(UpperCAmelCase					,			UpperCAmelCase					,			return_labels=UpperCAmelCase			)

           if return_labels:
               if model_class in get_values(UpperCAmelCase			):
                   lowerCAmelCase_					:	List[Any]						=   torch.zeros(
                       (self.model_tester.batch_size, self.model_tester.seq_length)					,			dtype=torch.long					,			device=UpperCAmelCase			)
           return inputs_dict



       def  A							(     self	:   str			):
           lowerCAmelCase_					:	Tuple						=   ElectraModelTester(self			)
           lowerCAmelCase_					:	List[str]						=   ConfigTester(self					,			config_class=UpperCAmelCase					,			hidden_size=37			)



       def  A							(     self	:   str			):
           self.config_tester.run_common_tests()



       def  A							(     self	:   int			):
           lowerCAmelCase_					:	List[str]						=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_electra_model(*UpperCAmelCase			)



       def  A							(     self	:   Optional[Any]			):
           lowerCAmelCase_					:	List[str]						=   self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_electra_model_as_decoder(*UpperCAmelCase			)



       def  A							(     self	:   int			):
           lowerCAmelCase_					:	str						=   self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
               lowerCAmelCase_					:	List[str]						=   type
               self.model_tester.create_and_check_electra_model(*UpperCAmelCase			)



       def  A							(     self	:   str			):
           lowerCAmelCase_					:	int						=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_electra_for_masked_lm(*UpperCAmelCase			)



       def  A							(     self	:   Union[str, Any]			):
           lowerCAmelCase_					:	Tuple						=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_electra_for_token_classification(*UpperCAmelCase			)



       def  A							(     self	:   List[Any]			):
           lowerCAmelCase_					:	List[str]						=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_electra_for_pretraining(*UpperCAmelCase			)



       def  A							(     self	:   str			):
           lowerCAmelCase_					:	Optional[Any]						=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_electra_for_sequence_classification(*UpperCAmelCase			)



       def  A							(     self	:   Optional[int]			):
           lowerCAmelCase_					:	Any						=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_electra_for_question_answering(*UpperCAmelCase			)



       def  A							(     self	:   Optional[Any]			):
           lowerCAmelCase_					:	Any						=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_electra_for_multiple_choice(*UpperCAmelCase			)



       @slow
       def  A							(     self	:   List[str]			):
           for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowerCAmelCase_					:	Dict						=   ElectraModel.from_pretrained(UpperCAmelCase			)
               self.assertIsNotNone(UpperCAmelCase			)



       def  A							(     self	:   Optional[Any]			):
           lowerCAmelCase_					:	Union[str, Any]						=   self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_electra_for_causal_lm(*UpperCAmelCase			)






@require_torch
class 		__a							(	unittest.TestCase      ):

       '''simple docstring'''





       @slow
       def  A							(     self	:   Union[str, Any]			):
           lowerCAmelCase_					:	int						=   ElectraModel.from_pretrained("""google/electra-small-discriminator"""			)
           lowerCAmelCase_					:	Optional[int]						=   torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]			)
           lowerCAmelCase_					:	Union[str, Any]						=   torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
           lowerCAmelCase_					:	Optional[Any]						=   model(UpperCAmelCase					,			attention_mask=UpperCAmelCase			)[0]
           lowerCAmelCase_					:	List[Any]						=   torch.Size((1, 11, 2_56)			)
           self.assertEqual(output.shape					,			UpperCAmelCase			)
           lowerCAmelCase_					:	Any						=   torch.tensor(
               [[[0.4471, 0.6821, -0.3265], [0.4627, 0.5255, -0.3668], [0.4532, 0.3313, -0.4344]]]			)

           self.assertTrue(torch.allclose(output[:, 1:4, 1:4]					,			UpperCAmelCase					,			atol=1e-4			)			)









from __future__ import annotations

import sys

class 		__a							:



       def __init__(     self	:   Any					,			UpperCAmelCase	:   str					,			UpperCAmelCase	:   int			):
           lowerCAmelCase_					:	str						=   letter
           lowerCAmelCase_					:	int						=   freq
           lowerCAmelCase_					:	dict[str, str]						=   {}



       def __repr__(     self	:   Optional[int]			):
           return F'{self.letter}:{self.freq}'






class 		__a							:



       def __init__(     self	:   Any					,			UpperCAmelCase	:   int					,			UpperCAmelCase	:   Letter | TreeNode					,			UpperCAmelCase	:   Letter | TreeNode			):
           lowerCAmelCase_					:	int						=   freq
           lowerCAmelCase_					:	Letter | TreeNode						=   left
           lowerCAmelCase_					:	Letter | TreeNode						=   right






def 			__UpperCamelCase				(			lowercase__	:       str		)	->      list[Letter]:
    '''simple docstring'''





    lowerCAmelCase_					:	dict[str, int]						=   {}
    with open(lowercase__		) as f:
        while True:
            lowerCAmelCase_					:	int						=   f.read(1		)
            if not c:
                break
            lowerCAmelCase_					:	Optional[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 			__UpperCamelCase				(			lowercase__	:       list[Letter]		)	->      Letter | TreeNode:
    '''simple docstring'''





    lowerCAmelCase_					:	list[Letter | TreeNode]						=   letters  # type: ignore
    while len(lowercase__		) > 1:
        lowerCAmelCase_					:	str						=   response.pop(0		)
        lowerCAmelCase_					:	Any						=   response.pop(0		)
        lowerCAmelCase_					:	Union[str, Any]						=   left.freq + right.freq
        lowerCAmelCase_					:	Union[str, Any]						=   TreeNode(lowercase__		,    lowercase__		,    lowercase__		)
        response.append(lowercase__		)
        response.sort(key=lambda lowercase__		: x.freq		)
    return response[0]
def 			__UpperCamelCase				(			lowercase__	:       Letter | TreeNode		,    lowercase__	:       str		)	->      list[Letter]:
    '''simple docstring'''





    if isinstance(lowercase__		,    lowercase__		):
        lowerCAmelCase_					:	Dict						=   bitstring
        return [root]
    lowerCAmelCase_					:	TreeNode						=   root  # type: ignore
    lowerCAmelCase_					:	Union[str, Any]						=   []
    letters += traverse_tree(treenode.left		,    bitstring + """0"""		)
    letters += traverse_tree(treenode.right		,    bitstring + """1"""		)
    return letters
def 			__UpperCamelCase				(			lowercase__	:       str		)	->      None:
    '''simple docstring'''





    lowerCAmelCase_					:	Any						=   parse_file(lowercase__		)
    lowerCAmelCase_					:	int						=   build_tree(lowercase__		)
    lowerCAmelCase_					:	Tuple						=   {
        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:
            lowerCAmelCase_					:	List[str]						=   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])


