code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  from collections import OrderedDict
  from typing import Any, Mapping, Optional
  
  from ... import PreTrainedTokenizer
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
  from ...onnx.utils import compute_effective_axis_dimension
  from ...utils import TensorType, is_torch_available, logging
  
  
  lowercase__			=	logging.get_logger(__name__)
  
  lowercase__			=	{
      '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json''',
      # See all MBART models at https://huggingface.co/models?filter=mbart
  }
  
  
  
  
  
  
  class 						lowercase     (   __UpperCAmelCase):
        __lowercase 		=  """mbart"""
        __lowercase 		=  ["""past_key_values"""]
        __lowercase 		=  {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
  
  
  
  
  
  
        def __init__(self ,  a__=5_0_2_6_5 ,  a__=1_0_2_4 ,  a__=1_2 ,  a__=4_0_9_6 ,  a__=1_6 ,  a__=1_2 ,  a__=4_0_9_6 ,  a__=1_6 ,  a__=0.0 ,  a__=0.0 ,  a__=True ,  a__=True ,  a__="gelu" ,  a__=1_0_2_4 ,  a__=0.1 ,  a__=0.0 ,  a__=0.0 ,  a__=0.02 ,  a__=0.0 ,  a__=False ,  a__=1 ,  a__=0 ,  a__=2 ,  a__=2 ,  **a__ ,  )       ->   Any:
           SCREAMING_SNAKE_CASE__			:				Tuple       						= vocab_size
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= max_position_embeddings
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= d_model
           SCREAMING_SNAKE_CASE__			:				Any       						= encoder_ffn_dim
           SCREAMING_SNAKE_CASE__			:				Tuple       						= encoder_layers
           SCREAMING_SNAKE_CASE__			:				Any       						= encoder_attention_heads
           SCREAMING_SNAKE_CASE__			:				List[str]       						= decoder_ffn_dim
           SCREAMING_SNAKE_CASE__			:				Tuple       						= decoder_layers
           SCREAMING_SNAKE_CASE__			:				int       						= decoder_attention_heads
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= dropout
           SCREAMING_SNAKE_CASE__			:				str       						= attention_dropout
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= activation_dropout
           SCREAMING_SNAKE_CASE__			:				Any       						= activation_function
           SCREAMING_SNAKE_CASE__			:				List[str]       						= init_std
           SCREAMING_SNAKE_CASE__			:				Tuple       						= encoder_layerdrop
           SCREAMING_SNAKE_CASE__			:				Any       						= decoder_layerdrop
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= classifier_dropout
           SCREAMING_SNAKE_CASE__			:				Any       						= use_cache
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= encoder_layers
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= scale_embedding  # scale factor will be sqrt(d_model) if True
           super().__init__(
               pad_token_id=lowerCamelCase__ ,  bos_token_id=lowerCamelCase__ ,  eos_token_id=lowerCamelCase__ ,  is_encoder_decoder=lowerCamelCase__ ,  forced_eos_token_id=lowerCamelCase__ ,  **lowerCamelCase__ ,  )
  
  
  
  
  
  
  class 						lowercase     (   __UpperCAmelCase):
  
  
  
  
  
  
        @property
        def    A__(self					)       ->   Mapping[str, Mapping[int, str]]:
  
  
  
           if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE__			:				Tuple       						= OrderedDict(
                  [
                      ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                      ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                  ]					)
  
              if self.use_past:
                 SCREAMING_SNAKE_CASE__			:				str       						= {0: '''batch'''}
                 SCREAMING_SNAKE_CASE__			:				List[str]       						= {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
              else:
                 SCREAMING_SNAKE_CASE__			:				List[str]       						= {0: '''batch''', 1: '''decoder_sequence'''}
                 SCREAMING_SNAKE_CASE__			:				Any       						= {0: '''batch''', 1: '''decoder_sequence'''}
  
              if self.use_past:
                 self.fill_with_past_key_values_(lowerCamelCase__ ,  direction='''inputs'''					)
           elif self.task == "causal-lm":
              # TODO: figure this case out.
              SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= OrderedDict(
                  [
                      ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                      ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                  ]					)
              if self.use_past:
                 SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				List[str]       						= self.num_layers
                 for i in range(lowerCamelCase__					):
                    SCREAMING_SNAKE_CASE__			:				str       						= {0: '''batch''', 2: '''past_sequence + sequence'''}
                    SCREAMING_SNAKE_CASE__			:				List[Any]       						= {0: '''batch''', 2: '''past_sequence + sequence'''}
           else:
              SCREAMING_SNAKE_CASE__			:				int       						= OrderedDict(
                  [
                      ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                      ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                      ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                      ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
                  ]					)
  
           return common_inputs
  
  
  
  
  
  
        @property
        def    A__(self					)       ->   Mapping[str, Mapping[int, str]]:
  
  
  
           if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE__			:				List[Any]       						= super().outputs
           else:
              SCREAMING_SNAKE_CASE__			:				int       						= super(lowerCamelCase__ ,  self					).outputs
              if self.use_past:
                 SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				Dict       						= self.num_layers
                 for i in range(lowerCamelCase__					):
                    SCREAMING_SNAKE_CASE__			:				str       						= {0: '''batch''', 2: '''past_sequence + sequence'''}
                    SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= {0: '''batch''', 2: '''past_sequence + sequence'''}
           return common_outputs
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ = -1 ,  a__ = -1 ,  a__ = False ,  a__ = None ,  )       ->   Mapping[str, Any]:
           SCREAMING_SNAKE_CASE__			:				str       						= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               lowerCamelCase__ ,  lowerCamelCase__ ,  lowerCamelCase__ ,  lowerCamelCase__ ,  lowerCamelCase__					)
  
           # Generate decoder inputs
           SCREAMING_SNAKE_CASE__			:				str       						= seq_length if not self.use_past else 1
           SCREAMING_SNAKE_CASE__			:				int       						= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               lowerCamelCase__ ,  lowerCamelCase__ ,  lowerCamelCase__ ,  lowerCamelCase__ ,  lowerCamelCase__					)
           SCREAMING_SNAKE_CASE__			:				Tuple       						= {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
           SCREAMING_SNAKE_CASE__			:				str       						= dict(**lowerCamelCase__ ,  **lowerCamelCase__					)
  
           if self.use_past:
              if not is_torch_available():
                 raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''					)
              else:
                 import torch
              SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				Dict       						= common_inputs['''input_ids'''].shape
              SCREAMING_SNAKE_CASE__			:				int       						= common_inputs['''decoder_input_ids'''].shape[1]
              SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				List[Any]       						= self.num_attention_heads
              SCREAMING_SNAKE_CASE__			:				List[Any]       						= (
                  batch,
                  num_encoder_attention_heads,
                  encoder_seq_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              SCREAMING_SNAKE_CASE__			:				int       						= decoder_seq_length + 3
              SCREAMING_SNAKE_CASE__			:				Tuple       						= (
                  batch,
                  num_decoder_attention_heads,
                  decoder_past_length,
                  self._config.hidden_size // num_decoder_attention_heads,
              )
  
              SCREAMING_SNAKE_CASE__			:				Optional[int]       						= torch.cat(
                  [common_inputs['''decoder_attention_mask'''], torch.ones(lowerCamelCase__ ,  lowerCamelCase__					)] ,  dim=1					)
  
              SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= []
              # If the number of encoder and decoder layers are present in the model configuration, both are considered
              SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				List[str]       						= self.num_layers
              SCREAMING_SNAKE_CASE__			:				Optional[int]       						= min(lowerCamelCase__ ,  lowerCamelCase__					)
              SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= max(lowerCamelCase__ ,  lowerCamelCase__					) - min_num_layers
              SCREAMING_SNAKE_CASE__			:				str       						= '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
  
              for _ in range(lowerCamelCase__					):
                 common_inputs["past_key_values"].append(
                     (
                         torch.zeros(lowerCamelCase__					),
                         torch.zeros(lowerCamelCase__					),
                         torch.zeros(lowerCamelCase__					),
                         torch.zeros(lowerCamelCase__					),
                     )					)
              # TODO: test this.
              SCREAMING_SNAKE_CASE__			:				List[str]       						= encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
              for _ in range(lowerCamelCase__ ,  lowerCamelCase__					):
                 common_inputs["past_key_values"].append((torch.zeros(lowerCamelCase__					), torch.zeros(lowerCamelCase__					))					)
           return common_inputs
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ = -1 ,  a__ = -1 ,  a__ = False ,  a__ = None ,  )       ->   Mapping[str, Any]:
           SCREAMING_SNAKE_CASE__			:				List[str]       						= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               lowerCamelCase__ ,  lowerCamelCase__ ,  lowerCamelCase__ ,  lowerCamelCase__ ,  lowerCamelCase__					)
  
           if self.use_past:
              if not is_torch_available():
                 raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''					)
              else:
                 import torch
              SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				List[Any]       						= common_inputs['''input_ids'''].shape
              # Not using the same length for past_key_values
              SCREAMING_SNAKE_CASE__			:				List[str]       						= seqlen + 2
              SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				Tuple       						= self.num_layers
              SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				List[Any]       						= self.num_attention_heads
              SCREAMING_SNAKE_CASE__			:				List[str]       						= (
                  batch,
                  num_encoder_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
  
              SCREAMING_SNAKE_CASE__			:				Any       						= common_inputs['''attention_mask'''].dtype
              SCREAMING_SNAKE_CASE__			:				List[str]       						= torch.cat(
                  [common_inputs['''attention_mask'''], torch.ones(lowerCamelCase__ ,  lowerCamelCase__ ,  dtype=lowerCamelCase__					)] ,  dim=1					)
              SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= [
                  (torch.zeros(lowerCamelCase__					), torch.zeros(lowerCamelCase__					)) for _ in range(lowerCamelCase__					)
              ]
           return common_inputs
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ = -1 ,  a__ = -1 ,  a__ = False ,  a__ = None ,  )       ->   Mapping[str, Any]:
           SCREAMING_SNAKE_CASE__			:				Any       						= compute_effective_axis_dimension(
               lowerCamelCase__ ,  fixed_dimension=OnnxConfig.default_fixed_batch ,  num_token_to_add=0					)
  
           # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
           SCREAMING_SNAKE_CASE__			:				Dict       						= tokenizer.num_special_tokens_to_add(lowerCamelCase__					)
           SCREAMING_SNAKE_CASE__			:				List[str]       						= compute_effective_axis_dimension(
               lowerCamelCase__ ,  fixed_dimension=OnnxConfig.default_fixed_sequence ,  num_token_to_add=lowerCamelCase__					)
  
           # Generate dummy inputs according to compute batch and sequence
           SCREAMING_SNAKE_CASE__			:				str       						= [''' '''.join([tokenizer.unk_token]					) * seq_length] * batch_size
           SCREAMING_SNAKE_CASE__			:				str       						= dict(tokenizer(lowerCamelCase__ ,  return_tensors=lowerCamelCase__					)					)
           return common_inputs
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ = -1 ,  a__ = -1 ,  a__ = False ,  a__ = None ,  )       ->   Mapping[str, Any]:
  
  
  
           if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                  lowerCamelCase__ ,  batch_size=lowerCamelCase__ ,  seq_length=lowerCamelCase__ ,  is_pair=lowerCamelCase__ ,  framework=lowerCamelCase__					)
  
           elif self.task == "causal-lm":
              SCREAMING_SNAKE_CASE__			:				List[str]       						= self._generate_dummy_inputs_for_causal_lm(
                  lowerCamelCase__ ,  batch_size=lowerCamelCase__ ,  seq_length=lowerCamelCase__ ,  is_pair=lowerCamelCase__ ,  framework=lowerCamelCase__					)
           else:
              SCREAMING_SNAKE_CASE__			:				Optional[int]       						= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  lowerCamelCase__ ,  batch_size=lowerCamelCase__ ,  seq_length=lowerCamelCase__ ,  is_pair=lowerCamelCase__ ,  framework=lowerCamelCase__					)
  
           return common_inputs
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ ,  a__ ,  a__					)       ->   Optional[Any]:
  
  
  
           if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE__			:				int       						= super()._flatten_past_key_values_(lowerCamelCase__ ,  lowerCamelCase__ ,  lowerCamelCase__ ,  lowerCamelCase__					)
           else:
              SCREAMING_SNAKE_CASE__			:				str       						= super(lowerCamelCase__ ,  self					)._flatten_past_key_values_(
                  lowerCamelCase__ ,  lowerCamelCase__ ,  lowerCamelCase__ ,  lowerCamelCase__					)
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  from __future__ import annotations
  
  import sys
  
  
  
  
  
  
  class 						lowercase     :
  
  
  
  
  
  
        def __init__(self ,  a__ ,  a__					)       ->   List[str]:
           SCREAMING_SNAKE_CASE__			:				str       						= letter
           SCREAMING_SNAKE_CASE__			:				int       						= freq
           SCREAMING_SNAKE_CASE__			:				dict[str, str]       						= {}
  
  
  
  
  
  
        def __repr__(self					)       ->   str:
           return f"""{self.letter}:{self.freq}"""
  
  
  
  
  
  
  class 						lowercase     :
  
  
  
  
  
  
        def __init__(self ,  a__ ,  a__ ,  a__					)       ->   Union[str, Any]:
           SCREAMING_SNAKE_CASE__			:				int       						= freq
           SCREAMING_SNAKE_CASE__			:				Letter | TreeNode       						= left
           SCREAMING_SNAKE_CASE__			:				Letter | TreeNode       						= right
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				dict[str, int]       						= {}
     with open(snake_case   ) as f:
        while True:
           SCREAMING_SNAKE_CASE__			:				List[str]       						= f.read(1   )
           if not c:
              break
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= chars[c] + 1 if c in chars else 1
     return sorted((Letter(snake_case	,     snake_case   ) for c, f in chars.items())	,     key=lambda snake_case   : x.freq   )
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				list[Letter | TreeNode]       						= letters  # type: ignore
     while len(snake_case   ) > 1:
        SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= response.pop(0   )
        SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= response.pop(0   )
        SCREAMING_SNAKE_CASE__			:				Dict       						= left.freq + right.freq
        SCREAMING_SNAKE_CASE__			:				Any       						= TreeNode(snake_case	,     snake_case	,     snake_case   )
        response.append(snake_case   )
        response.sort(key=lambda snake_case   : x.freq   )
     return response[0]
  
  
  def 				UpperCamelCase	(  snake_case	,     snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     if isinstance(snake_case	,     snake_case   ):
        SCREAMING_SNAKE_CASE__			:				Optional[int]       						= bitstring
        return [root]
     SCREAMING_SNAKE_CASE__			:				TreeNode       						= root  # type: ignore
     SCREAMING_SNAKE_CASE__			:				int       						= []
     letters += traverse_tree(treenode.left	,     bitstring + '''0'''   )
     letters += traverse_tree(treenode.right	,     bitstring + '''1'''   )
     return letters
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Optional[int]       						= parse_file(snake_case   )
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= build_tree(snake_case   )
     SCREAMING_SNAKE_CASE__			:				List[str]       						= {
         k: v for letter in traverse_tree(snake_case	,     ''''''   ) for k, v in letter.bitstring.items()
     }
     print(f"""Huffman Coding  of {file_path}: """   )
     with open(snake_case   ) as f:
        while True:
           SCREAMING_SNAKE_CASE__			:				Tuple       						= 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])
  
  