





from __future__ import annotations

import sys




class 			_lowerCamelCase	:



      """simple docstring"""






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






        '''simple docstring'''






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







      def __repr__(	self				)    ->				str:






        '''simple docstring'''






        return F"""{self.letter}:{self.freq}"""




class 			_lowerCamelCase	:



      """simple docstring"""






      def __init__(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				Any:






        '''simple docstring'''






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







def  lowerCAmelCase__(		lowercase    :      str			)	->      list[Letter]:
  __snake_case				:			dict[str, int]            =    {}
  with open(lowercase			) as f:
    while True:
      __snake_case				:			List[Any]            =    f.read(1			)
      if not c:
        break
      __snake_case				:			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  lowerCAmelCase__(		lowercase    :      list[Letter]			)	->      Letter | TreeNode:
  __snake_case				:			list[Letter | TreeNode]            =    letters  # type: ignore
  while len(lowercase			) > 1:
    __snake_case				:			Any            =    response.pop(0			)
    __snake_case				:			Optional[int]            =    response.pop(0			)
    __snake_case				:			str            =    left.freq + right.freq
    __snake_case				:			Dict            =    TreeNode(lowercase				,       lowercase				,       lowercase			)
    response.append(lowercase			)
    response.sort(key=lambda lowercase			: x.freq			)
  return response[0]







def  lowerCAmelCase__(		lowercase    :      Letter | TreeNode				,       lowercase    :      str			)	->      list[Letter]:
  if isinstance(lowercase				,       lowercase			):
    __snake_case				:			List[str]            =    bitstring
    return [root]
  __snake_case				:			TreeNode            =    root  # type: ignore
  __snake_case				:			Union[str, Any]            =    []
  letters += traverse_tree(treenode.left				,       bitstring + "0"			)
  letters += traverse_tree(treenode.right				,       bitstring + "1"			)
  return letters







def  lowerCAmelCase__(		lowercase    :      str			)	->      None:
  __snake_case				:			List[Any]            =    parse_file(lowercase			)
  __snake_case				:			List[str]            =    build_tree(lowercase			)
  __snake_case				:			Optional[int]            =    {
      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:
      __snake_case				:			int            =    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])








from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


_UpperCamelCase    =	logging.get_logger(__name__)

_UpperCamelCase    =	{
    '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json''',
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}




class 			_lowerCamelCase	(      a      ):



      """simple docstring"""






      UpperCAmelCase_  : Tuple				      ="blenderbot"
      UpperCAmelCase_  : Any				      =["past_key_values"]
      UpperCAmelCase_  : Optional[int]				      ={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







      def __init__(	self			,  UpperCAmelCase=8008			,  UpperCAmelCase=128			,  UpperCAmelCase=2			,  UpperCAmelCase=10240			,  UpperCAmelCase=32			,  UpperCAmelCase=24			,  UpperCAmelCase=10240			,  UpperCAmelCase=32			,  UpperCAmelCase=0.0			,  UpperCAmelCase=0.0			,  UpperCAmelCase=True			,  UpperCAmelCase=True			,  UpperCAmelCase="gelu"			,  UpperCAmelCase=2560			,  UpperCAmelCase=0.1			,  UpperCAmelCase=0.0			,  UpperCAmelCase=0.0			,  UpperCAmelCase=0.02			,  UpperCAmelCase=1			,  UpperCAmelCase=False			,  UpperCAmelCase=0			,  UpperCAmelCase=1			,  UpperCAmelCase=2			,  UpperCAmelCase=3			,  UpperCAmelCase=2			,  **UpperCAmelCase			,  )    ->				List[Any]:






        '''simple docstring'''






        __snake_case				:			List[Any]            =    vocab_size
        __snake_case				:			List[str]            =    max_position_embeddings
        __snake_case				:			str            =    d_model
        __snake_case				:			int            =    encoder_ffn_dim
        __snake_case				:			Dict            =    encoder_layers
        __snake_case				:			Any            =    encoder_attention_heads
        __snake_case				:			Optional[int]            =    decoder_ffn_dim
        __snake_case				:			int            =    decoder_layers
        __snake_case				:			Dict            =    decoder_attention_heads
        __snake_case				:			Optional[int]            =    dropout
        __snake_case				:			Optional[Any]            =    attention_dropout
        __snake_case				:			Dict            =    activation_dropout
        __snake_case				:			Any            =    activation_function
        __snake_case				:			List[Any]            =    init_std
        __snake_case				:			Any            =    encoder_layerdrop
        __snake_case				:			List[Any]            =    decoder_layerdrop
        __snake_case				:			Any            =    use_cache
        __snake_case				:			Optional[int]            =    encoder_layers
        __snake_case				:			Optional[int]            =    scale_embedding  # scale factor will be sqrt(d_model) if True

        super().__init__(
            pad_token_id=UpperCAmelCase			,  bos_token_id=UpperCAmelCase			,  eos_token_id=UpperCAmelCase			,  is_encoder_decoder=UpperCAmelCase			,  decoder_start_token_id=UpperCAmelCase			,  encoder_no_repeat_ngram_size=UpperCAmelCase			,  forced_eos_token_id=UpperCAmelCase			,  **UpperCAmelCase			,  )




class 			_lowerCamelCase	(      a      ):



      """simple docstring"""






      @property
      def   UpperCAmelCase						(	self				)    ->				Mapping[str, Mapping[int, str]]:






        '''simple docstring'''






        if self.task in ["default", "seq2seq-lm"]:
          __snake_case				:			Any            =    OrderedDict(
              [
                  ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                  ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
              ]				)
          if self.use_past:
            __snake_case				:			List[str]            =    {0: "batch"}
            __snake_case				:			Optional[Any]            =    {0: "batch", 1: "past_decoder_sequence + sequence"}
          else:
            __snake_case				:			int            =    {0: "batch", 1: "decoder_sequence"}
            __snake_case				:			Optional[int]            =    {0: "batch", 1: "decoder_sequence"}
          if self.use_past:
            self.fill_with_past_key_values_(UpperCAmelCase			,  direction="inputs"				)
        elif self.task == "causal-lm":
          __snake_case				:			List[str]            =    OrderedDict(
              [
                  ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                  ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
              ]				)
          if self.use_past:
            __snake_case      ,				__snake_case				:			List[Any]            =    self.num_layers
            for i in range(UpperCAmelCase				):
              __snake_case				:			int            =    {0: "batch", 2: "past_sequence + sequence"}
              __snake_case				:			Tuple            =    {0: "batch", 2: "past_sequence + sequence"}
        else:
          __snake_case				:			Dict            =    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
      # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
      def   UpperCAmelCase						(	self				)    ->				Mapping[str, Mapping[int, str]]:






        '''simple docstring'''






        if self.task in ["default", "seq2seq-lm"]:
          __snake_case				:			Union[str, Any]            =    super().outputs
        else:
          __snake_case				:			Dict            =    super(UpperCAmelCase			,  self				).outputs
          if self.use_past:
            __snake_case      ,				__snake_case				:			Any            =    self.num_layers
            for i in range(UpperCAmelCase				):
              __snake_case				:			Optional[int]            =    {0: "batch", 2: "past_sequence + sequence"}
              __snake_case				:			Tuple            =    {0: "batch", 2: "past_sequence + sequence"}
        return common_outputs







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase = -1			,  UpperCAmelCase = -1			,  UpperCAmelCase = False			,  UpperCAmelCase = None			,  )    ->				Mapping[str, Any]:






        '''simple docstring'''






        __snake_case				:			int            =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)
        # Generate decoder inputs
        __snake_case				:			List[Any]            =    seq_length if not self.use_past else 1
        __snake_case				:			Tuple            =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)
        __snake_case				:			Tuple            =    {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
        __snake_case				:			Dict            =    dict(**UpperCAmelCase			,  **UpperCAmelCase				)

        if self.use_past:
          if not is_torch_available():
            raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."				)
          else:
            import torch
          __snake_case      ,				__snake_case				:			int            =    common_inputs["input_ids"].shape
          __snake_case				:			Union[str, Any]            =    common_inputs["decoder_input_ids"].shape[1]
          __snake_case      ,				__snake_case				:			Optional[Any]            =    self.num_attention_heads
          __snake_case				:			Tuple            =    (
              batch,
              num_encoder_attention_heads,
              encoder_seq_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )
          __snake_case				:			Any            =    decoder_seq_length
          __snake_case				:			List[Any]            =    (
              batch,
              num_decoder_attention_heads,
              decoder_past_length,
              self._config.hidden_size // num_decoder_attention_heads,
          )
          __snake_case				:			Any            =    torch.cat(
              [common_inputs["decoder_attention_mask"], torch.ones(UpperCAmelCase			,  UpperCAmelCase				)]			,  dim=1				)
          __snake_case				:			Any            =    []
          __snake_case      ,				__snake_case				:			Tuple            =    self.num_layers

          for _ in range(UpperCAmelCase				):
            common_inputs["past_key_values"].append(
                (
                    torch.zeros(UpperCAmelCase				),
                    torch.zeros(UpperCAmelCase				),
                    torch.zeros(UpperCAmelCase				),
                    torch.zeros(UpperCAmelCase				),
                )				)
        return common_inputs







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase = -1			,  UpperCAmelCase = -1			,  UpperCAmelCase = False			,  UpperCAmelCase = None			,  )    ->				Mapping[str, Any]:






        '''simple docstring'''






        __snake_case				:			List[Any]            =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)

        if self.use_past:
          if not is_torch_available():
            raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."				)
          else:
            import torch
          __snake_case      ,				__snake_case				:			List[Any]            =    common_inputs["input_ids"].shape
          __snake_case				:			Any            =    seqlen
          __snake_case      ,				__snake_case				:			Dict            =    self.num_layers
          __snake_case      ,				__snake_case				:			int            =    self.num_attention_heads
          __snake_case				:			str            =    (
              batch,
              num_encoder_attention_heads,
              past_key_values_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )
          __snake_case				:			Dict            =    common_inputs["attention_mask"].dtype
          __snake_case				:			Optional[Any]            =    torch.cat(
              [common_inputs["attention_mask"], torch.ones(UpperCAmelCase			,  UpperCAmelCase			,  dtype=UpperCAmelCase				)]			,  dim=1				)
          __snake_case				:			str            =    [
              (torch.zeros(UpperCAmelCase				), torch.zeros(UpperCAmelCase				)) for _ in range(UpperCAmelCase				)
          ]
        return common_inputs







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase = -1			,  UpperCAmelCase = -1			,  UpperCAmelCase = False			,  UpperCAmelCase = None			,  )    ->				Mapping[str, Any]:






        '''simple docstring'''






        __snake_case				:			Tuple            =    compute_effective_axis_dimension(
            UpperCAmelCase			,  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
        __snake_case				:			str            =    tokenizer.num_special_tokens_to_add(UpperCAmelCase				)
        __snake_case				:			Any            =    compute_effective_axis_dimension(
            UpperCAmelCase			,  fixed_dimension=OnnxConfig.default_fixed_sequence			,  num_token_to_add=UpperCAmelCase				)

        # Generate dummy inputs according to compute batch and sequence
        __snake_case				:			Tuple            =    [" ".join([tokenizer.unk_token]				) * seq_length] * batch_size
        __snake_case				:			str            =    dict(tokenizer(UpperCAmelCase			,  return_tensors=UpperCAmelCase				)				)
        return common_inputs







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase = -1			,  UpperCAmelCase = -1			,  UpperCAmelCase = False			,  UpperCAmelCase = None			,  )    ->				Mapping[str, Any]:






        '''simple docstring'''






        if self.task in ["default", "seq2seq-lm"]:
          __snake_case				:			Any            =    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
              UpperCAmelCase			,  batch_size=UpperCAmelCase			,  seq_length=UpperCAmelCase			,  is_pair=UpperCAmelCase			,  framework=UpperCAmelCase				)

        elif self.task == "causal-lm":
          __snake_case				:			Union[str, Any]            =    self._generate_dummy_inputs_for_causal_lm(
              UpperCAmelCase			,  batch_size=UpperCAmelCase			,  seq_length=UpperCAmelCase			,  is_pair=UpperCAmelCase			,  framework=UpperCAmelCase				)
        else:
          __snake_case				:			Dict            =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              UpperCAmelCase			,  batch_size=UpperCAmelCase			,  seq_length=UpperCAmelCase			,  is_pair=UpperCAmelCase			,  framework=UpperCAmelCase				)

        return common_inputs







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				int:






        '''simple docstring'''






        if self.task in ["default", "seq2seq-lm"]:
          __snake_case				:			str            =    super()._flatten_past_key_values_(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)
        else:
          __snake_case				:			int            =    super(UpperCAmelCase			,  self				)._flatten_past_key_values_(
              UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				Any:






        '''simple docstring'''






        if direction not in ["inputs", "outputs"]:
          raise ValueError(F"""direction must either be \"inputs\" or \"outputs\", but {direction} was given"""				)

        __snake_case				:			Tuple            =    "past_key_values" if direction == "inputs" else "present"
        __snake_case      ,				__snake_case				:			Any            =    self.num_layers

        __snake_case				:			int            =    "past_encoder_sequence"
        __snake_case				:			int            =    "past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"

        for i in range(UpperCAmelCase				):
          __snake_case				:			Any            =    {0: "batch", 2: decoder_sequence}
          __snake_case				:			List[Any]            =    {0: "batch", 2: decoder_sequence}
          __snake_case				:			Tuple            =    {0: "batch", 2: encoder_sequence}
          __snake_case				:			int            =    {0: "batch", 2: encoder_sequence}

