
'''simple docstring'''

from __future__ import annotations

import sys


class 		_a     :







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Any								=		letter
    SCREAMING_SNAKE_CASE  :  Dict								=		freq
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		{}






   def __repr__(       self      ):
    return f"""{self.letter}:{self.freq}"""



class 		_a     :







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		freq
    SCREAMING_SNAKE_CASE  :  List[str]								=		left
    SCREAMING_SNAKE_CASE  :  Dict								=		right



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	List[Any]							)       ->	list[Letter]:
 SCREAMING_SNAKE_CASE  :  List[str]								=		{}
 with open(__UpperCAmelCase							) as f:
  while True:
   SCREAMING_SNAKE_CASE  :  Optional[Any]								=		f.read(1							)
   if not c:
    break
   SCREAMING_SNAKE_CASE  :  Optional[Any]								=		chars[c] + 1 if c in chars else 1
 return sorted((Letter(__UpperCAmelCase   ,     __UpperCAmelCase							) for c, f in chars.items())   ,     key=lambda snake_case_							: x.freq							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Tuple							)       ->	Letter | TreeNode:
 SCREAMING_SNAKE_CASE  :  List[Any]								=		letters  # type: ignore
 while len(__UpperCAmelCase							) > 1:
  SCREAMING_SNAKE_CASE  :  List[str]								=		response.pop(0							)
  SCREAMING_SNAKE_CASE  :  Optional[int]								=		response.pop(0							)
  SCREAMING_SNAKE_CASE  :  List[Any]								=		left.freq + right.freq
  SCREAMING_SNAKE_CASE  :  Optional[int]								=		TreeNode(__UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase							)
  response.append(__UpperCAmelCase							)
  response.sort(key=lambda snake_case_							: x.freq							)
 return response[0]



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Optional[int]   ,     snake_case_				:	Any							)       ->	list[Letter]:

 if isinstance(__UpperCAmelCase   ,     __UpperCAmelCase							):
  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		bitstring
  return [root]
 SCREAMING_SNAKE_CASE  :  Optional[int]								=		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        SCREAMING_SNAKE_CASE_  (	snake_case_				:	List[str]							)       ->	None:
 SCREAMING_SNAKE_CASE  :  Optional[int]								=		parse_file(__UpperCAmelCase							)
 SCREAMING_SNAKE_CASE  :  str								=		build_tree(__UpperCAmelCase							)
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		{
     k: v for letter in traverse_tree(__UpperCAmelCase   ,     ''							) for k, v in letter.bitstring.items()
 }
 print(f"""Huffman Coding  of {file_path}: """							)
 with open(__UpperCAmelCase							) as f:
  while True:
   SCREAMING_SNAKE_CASE  :  Optional[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])



import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


snake_case__						      =			logging.get_logger(__name__)

snake_case__						      =			OrderedDict(
    [
        ('align', 'AlignProcessor'),
        ('altclip', 'AltCLIPProcessor'),
        ('bark', 'BarkProcessor'),
        ('blip', 'BlipProcessor'),
        ('blip-2', 'Blip2Processor'),
        ('bridgetower', 'BridgeTowerProcessor'),
        ('chinese_clip', 'ChineseCLIPProcessor'),
        ('clap', 'ClapProcessor'),
        ('clip', 'CLIPProcessor'),
        ('clipseg', 'CLIPSegProcessor'),
        ('flava', 'FlavaProcessor'),
        ('git', 'GitProcessor'),
        ('groupvit', 'CLIPProcessor'),
        ('hubert', 'Wav2Vec2Processor'),
        ('instructblip', 'InstructBlipProcessor'),
        ('layoutlmv2', 'LayoutLMv2Processor'),
        ('layoutlmv3', 'LayoutLMv3Processor'),
        ('markuplm', 'MarkupLMProcessor'),
        ('mctct', 'MCTCTProcessor'),
        ('mgp-str', 'MgpstrProcessor'),
        ('oneformer', 'OneFormerProcessor'),
        ('owlvit', 'OwlViTProcessor'),
        ('pix2struct', 'Pix2StructProcessor'),
        ('sam', 'SamProcessor'),
        ('sew', 'Wav2Vec2Processor'),
        ('sew-d', 'Wav2Vec2Processor'),
        ('speech_to_text', 'Speech2TextProcessor'),
        ('speech_to_text_2', 'Speech2Text2Processor'),
        ('speecht5', 'SpeechT5Processor'),
        ('trocr', 'TrOCRProcessor'),
        ('tvlt', 'TvltProcessor'),
        ('unispeech', 'Wav2Vec2Processor'),
        ('unispeech-sat', 'Wav2Vec2Processor'),
        ('vilt', 'ViltProcessor'),
        ('vision-text-dual-encoder', 'VisionTextDualEncoderProcessor'),
        ('wav2vec2', 'Wav2Vec2Processor'),
        ('wav2vec2-conformer', 'Wav2Vec2Processor'),
        ('wavlm', 'Wav2Vec2Processor'),
        ('whisper', 'WhisperProcessor'),
        ('xclip', 'XCLIPProcessor'),
    ]
)

snake_case__						      =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)
def    __magic_name__(	__UpperCAmelCase		)    ->     List[str]:



      '''simple docstring'''

      for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
            if class_name in processors:
                  _lowerCamelCase           =					model_type_to_module_name(__UpperCAmelCase		)

                  _lowerCamelCase           =					importlib.import_module(F'.{module_name}'			,    '''transformers.models'''		)
                  try:
                        return getattr(__UpperCAmelCase			,    __UpperCAmelCase		)
                  except AttributeError:
                        continue

      for processor in PROCESSOR_MAPPING._extra_content.values():
            if getattr(__UpperCAmelCase			,    '''__name__'''			,    __UpperCAmelCase		) == class_name:
                  return processor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
      _lowerCamelCase           =					importlib.import_module('''transformers'''		)
      if hasattr(__UpperCAmelCase			,    __UpperCAmelCase		):
            return getattr(__UpperCAmelCase			,    __UpperCAmelCase		)

      return None




class 			UpperCamelCase					:

     '''simple docstring'''

     def __init__(       self    )			->					List[str]:






           """simple docstring"""
           raise EnvironmentError(
               '''AutoProcessor is designed to be instantiated '''
               '''using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method.'''    )



     @classmethod
     @replace_list_option_in_docstrings(A_    )
     def 		UpperCamelCase_    (       cls					,					A_					,					**A_    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					kwargs.pop('''config'''					,					A_    )
           _lowerCamelCase           =					kwargs.pop('''trust_remote_code'''					,					A_    )
           _lowerCamelCase           =					True

           _lowerCamelCase           =					None
           _lowerCamelCase           =					None

           # First, let's see if we have a preprocessor config.
           # Filter the kwargs for `get_file_from_repo`.
           _lowerCamelCase           =					{
               key: kwargs[key] for key in inspect.signature(A_    ).parameters.keys() if key in kwargs
           }
           # Let's start by checking whether the processor class is saved in an image processor
           _lowerCamelCase           =					get_file_from_repo(
               A_					,					A_					,					**A_    )
           if preprocessor_config_file is not None:
                 _lowerCamelCase     ,       _lowerCamelCase           =					ImageProcessingMixin.get_image_processor_dict(A_					,					**A_    )
                 _lowerCamelCase           =					config_dict.get('''processor_class'''					,					A_    )
                 if "AutoProcessor" in config_dict.get('''auto_map'''					,					{}    ):
                       _lowerCamelCase           =					config_dict['''auto_map''']['''AutoProcessor''']

        # If not found, let's check whether the processor class is saved in a feature extractor config
           if preprocessor_config_file is not None and processor_class is None:
                 _lowerCamelCase     ,       _lowerCamelCase           =					FeatureExtractionMixin.get_feature_extractor_dict(A_					,					**A_    )
                 _lowerCamelCase           =					config_dict.get('''processor_class'''					,					A_    )
                 if "AutoProcessor" in config_dict.get('''auto_map'''					,					{}    ):
                       _lowerCamelCase           =					config_dict['''auto_map''']['''AutoProcessor''']

           if processor_class is None:
                 # Next, let's check whether the processor class is saved in a tokenizer
                 _lowerCamelCase           =					get_file_from_repo(
                     A_					,					A_					,					**A_    )
                 if tokenizer_config_file is not None:
                       with open(A_					,					encoding='''utf-8'''    ) as reader:
                             _lowerCamelCase           =					json.load(A_    )

                       _lowerCamelCase           =					config_dict.get('''processor_class'''					,					A_    )
                       if "AutoProcessor" in config_dict.get('''auto_map'''					,					{}    ):
                             _lowerCamelCase           =					config_dict['''auto_map''']['''AutoProcessor''']

           if processor_class is None:
                 # Otherwise, load config, if it can be loaded.
                 if not isinstance(A_					,					A_    ):
                       _lowerCamelCase           =					AutoConfig.from_pretrained(
                           A_					,					trust_remote_code=A_					,					**A_    )

                 # And check if the config contains the processor class.
                 _lowerCamelCase           =					getattr(A_					,					'''processor_class'''					,					A_    )
                 if hasattr(A_					,					'''auto_map'''    ) and "AutoProcessor" in config.auto_map:
                       _lowerCamelCase           =					config.auto_map['''AutoProcessor''']

           if processor_class is not None:
                 _lowerCamelCase           =					processor_class_from_name(A_    )

           _lowerCamelCase           =					processor_auto_map is not None
           _lowerCamelCase           =					processor_class is not None or type(A_    ) in PROCESSOR_MAPPING
           _lowerCamelCase           =					resolve_trust_remote_code(
               A_					,					A_					,					A_					,					A_    )

           if has_remote_code and trust_remote_code:
                 _lowerCamelCase           =					get_class_from_dynamic_module(
                     A_					,					A_					,					**A_    )
                 _lowerCamelCase           =					kwargs.pop('''code_revision'''					,					A_    )
                 if os.path.isdir(A_    ):
                       processor_class.register_for_auto_class()
                 return processor_class.from_pretrained(
                     A_					,					trust_remote_code=A_					,					**A_    )
           elif processor_class is not None:
                 return processor_class.from_pretrained(
                     A_					,					trust_remote_code=A_					,					**A_    )
           # Last try: we use the PROCESSOR_MAPPING.
           elif type(A_    ) in PROCESSOR_MAPPING:
                 return PROCESSOR_MAPPING[type(A_    )].from_pretrained(A_					,					**A_    )

           # At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
           # tokenizer.
           try:
                 return AutoTokenizer.from_pretrained(
                     A_					,					trust_remote_code=A_					,					**A_    )
           except Exception:
                 try:
                       return AutoImageProcessor.from_pretrained(
                           A_					,					trust_remote_code=A_					,					**A_    )
                 except Exception:
                       pass

                 try:
                       return AutoFeatureExtractor.from_pretrained(
                           A_					,					trust_remote_code=A_					,					**A_    )
                 except Exception:
                       pass

           raise ValueError(
               F'Unrecognized processing class in {pretrained_model_name_or_path}. Can\'t instantiate a processor, a '
               '''tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains'''
               '''the files of at least one of those processing classes.'''    )





     @staticmethod
     def 		UpperCamelCase_    (       A_					,					A_    )			->					List[Any]:






           """simple docstring"""
           PROCESSOR_MAPPING.register(A_					,					A_    )