





from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


__UpperCAmelCase						=      {
    'configuration_utils': ['GenerationConfig'],
    'streamers': ['TextIteratorStreamer', 'TextStreamer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase						=      [
      'Constraint',
      'ConstraintListState',
      'DisjunctiveConstraint',
      'PhrasalConstraint',
  ]
  __UpperCAmelCase						=      [
      'BeamHypotheses',
      'BeamScorer',
      'BeamSearchScorer',
      'ConstrainedBeamSearchScorer',
  ]
  __UpperCAmelCase						=      [
      'EpsilonLogitsWarper',
      'EtaLogitsWarper',
      'ForcedBOSTokenLogitsProcessor',
      'ForcedEOSTokenLogitsProcessor',
      'HammingDiversityLogitsProcessor',
      'InfNanRemoveLogitsProcessor',
      'LogitsProcessor',
      'LogitsProcessorList',
      'LogitsWarper',
      'MinLengthLogitsProcessor',
      'MinNewTokensLengthLogitsProcessor',
      'NoBadWordsLogitsProcessor',
      'NoRepeatNGramLogitsProcessor',
      'PrefixConstrainedLogitsProcessor',
      'RepetitionPenaltyLogitsProcessor',
      'SequenceBiasLogitsProcessor',
      'EncoderRepetitionPenaltyLogitsProcessor',
      'TemperatureLogitsWarper',
      'TopKLogitsWarper',
      'TopPLogitsWarper',
      'TypicalLogitsWarper',
      'EncoderNoRepeatNGramLogitsProcessor',
      'ExponentialDecayLengthPenalty',
      'LogitNormalization',
  ]
  __UpperCAmelCase						=      [
      'MaxNewTokensCriteria',
      'MaxLengthCriteria',
      'MaxTimeCriteria',
      'StoppingCriteria',
      'StoppingCriteriaList',
      'validate_stopping_criteria',
  ]
  __UpperCAmelCase						=      [
      'GenerationMixin',
      'top_k_top_p_filtering',
      'GreedySearchEncoderDecoderOutput',
      'GreedySearchDecoderOnlyOutput',
      'SampleEncoderDecoderOutput',
      'SampleDecoderOnlyOutput',
      'BeamSearchEncoderDecoderOutput',
      'BeamSearchDecoderOnlyOutput',
      'BeamSampleEncoderDecoderOutput',
      'BeamSampleDecoderOnlyOutput',
      'ContrastiveSearchEncoderDecoderOutput',
      'ContrastiveSearchDecoderOnlyOutput',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase						=      [
      'TFForcedBOSTokenLogitsProcessor',
      'TFForcedEOSTokenLogitsProcessor',
      'TFLogitsProcessor',
      'TFLogitsProcessorList',
      'TFLogitsWarper',
      'TFMinLengthLogitsProcessor',
      'TFNoBadWordsLogitsProcessor',
      'TFNoRepeatNGramLogitsProcessor',
      'TFRepetitionPenaltyLogitsProcessor',
      'TFTemperatureLogitsWarper',
      'TFTopKLogitsWarper',
      'TFTopPLogitsWarper',
      'TFForceTokensLogitsProcessor',
      'TFSuppressTokensAtBeginLogitsProcessor',
      'TFSuppressTokensLogitsProcessor',
  ]
  __UpperCAmelCase						=      [
      'TFGenerationMixin',
      'tf_top_k_top_p_filtering',
      'TFGreedySearchDecoderOnlyOutput',
      'TFGreedySearchEncoderDecoderOutput',
      'TFSampleEncoderDecoderOutput',
      'TFSampleDecoderOnlyOutput',
      'TFBeamSearchEncoderDecoderOutput',
      'TFBeamSearchDecoderOnlyOutput',
      'TFBeamSampleEncoderDecoderOutput',
      'TFBeamSampleDecoderOnlyOutput',
      'TFContrastiveSearchEncoderDecoderOutput',
      'TFContrastiveSearchDecoderOnlyOutput',
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase						=      [
      'FlaxForcedBOSTokenLogitsProcessor',
      'FlaxForcedEOSTokenLogitsProcessor',
      'FlaxLogitsProcessor',
      'FlaxLogitsProcessorList',
      'FlaxLogitsWarper',
      'FlaxMinLengthLogitsProcessor',
      'FlaxTemperatureLogitsWarper',
      'FlaxTopKLogitsWarper',
      'FlaxTopPLogitsWarper',
  ]
  __UpperCAmelCase						=      [
      'FlaxGenerationMixin',
      'FlaxGreedySearchOutput',
      'FlaxSampleOutput',
      'FlaxBeamSearchOutput',
  ]

if TYPE_CHECKING:
  from .configuration_utils import GenerationConfig
  from .streamers import TextIteratorStreamer, TextStreamer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
    from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
    from .logits_process import (
        EncoderNoRepeatNGramLogitsProcessor,
        EncoderRepetitionPenaltyLogitsProcessor,
        EpsilonLogitsWarper,
        EtaLogitsWarper,
        ExponentialDecayLengthPenalty,
        ForcedBOSTokenLogitsProcessor,
        ForcedEOSTokenLogitsProcessor,
        HammingDiversityLogitsProcessor,
        InfNanRemoveLogitsProcessor,
        LogitNormalization,
        LogitsProcessor,
        LogitsProcessorList,
        LogitsWarper,
        MinLengthLogitsProcessor,
        MinNewTokensLengthLogitsProcessor,
        NoBadWordsLogitsProcessor,
        NoRepeatNGramLogitsProcessor,
        PrefixConstrainedLogitsProcessor,
        RepetitionPenaltyLogitsProcessor,
        SequenceBiasLogitsProcessor,
        TemperatureLogitsWarper,
        TopKLogitsWarper,
        TopPLogitsWarper,
        TypicalLogitsWarper,
    )
    from .stopping_criteria import (
        MaxLengthCriteria,
        MaxNewTokensCriteria,
        MaxTimeCriteria,
        StoppingCriteria,
        StoppingCriteriaList,
        validate_stopping_criteria,
    )
    from .utils import (
        BeamSampleDecoderOnlyOutput,
        BeamSampleEncoderDecoderOutput,
        BeamSearchDecoderOnlyOutput,
        BeamSearchEncoderDecoderOutput,
        ContrastiveSearchDecoderOnlyOutput,
        ContrastiveSearchEncoderDecoderOutput,
        GenerationMixin,
        GreedySearchDecoderOnlyOutput,
        GreedySearchEncoderDecoderOutput,
        SampleDecoderOnlyOutput,
        SampleEncoderDecoderOutput,
        top_k_top_p_filtering,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tf_logits_process import (
        TFForcedBOSTokenLogitsProcessor,
        TFForcedEOSTokenLogitsProcessor,
        TFForceTokensLogitsProcessor,
        TFLogitsProcessor,
        TFLogitsProcessorList,
        TFLogitsWarper,
        TFMinLengthLogitsProcessor,
        TFNoBadWordsLogitsProcessor,
        TFNoRepeatNGramLogitsProcessor,
        TFRepetitionPenaltyLogitsProcessor,
        TFSuppressTokensAtBeginLogitsProcessor,
        TFSuppressTokensLogitsProcessor,
        TFTemperatureLogitsWarper,
        TFTopKLogitsWarper,
        TFTopPLogitsWarper,
    )
    from .tf_utils import (
        TFBeamSampleDecoderOnlyOutput,
        TFBeamSampleEncoderDecoderOutput,
        TFBeamSearchDecoderOnlyOutput,
        TFBeamSearchEncoderDecoderOutput,
        TFContrastiveSearchDecoderOnlyOutput,
        TFContrastiveSearchEncoderDecoderOutput,
        TFGenerationMixin,
        TFGreedySearchDecoderOnlyOutput,
        TFGreedySearchEncoderDecoderOutput,
        TFSampleDecoderOnlyOutput,
        TFSampleEncoderDecoderOutput,
        tf_top_k_top_p_filtering,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .flax_logits_process import (
        FlaxForcedBOSTokenLogitsProcessor,
        FlaxForcedEOSTokenLogitsProcessor,
        FlaxLogitsProcessor,
        FlaxLogitsProcessorList,
        FlaxLogitsWarper,
        FlaxMinLengthLogitsProcessor,
        FlaxTemperatureLogitsWarper,
        FlaxTopKLogitsWarper,
        FlaxTopPLogitsWarper,
    )
    from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
  import sys

  __UpperCAmelCase						=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









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])


