
import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor



def 	_a  (					):






  """simple docstring"""




  _lowerCAmelCase						=		set()
  # Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
  for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
    _lowerCAmelCase						=		getattr(transformers.models.auto		,				lowerCamelCase_		,				lowerCamelCase_    )
    if module is None:
      continue
    # all mappings in a single auto modeling file
    _lowerCAmelCase						=		[
        x
        for x in dir(lowerCamelCase_    )
        if x.endswith('_MAPPING_NAMES'    )
        and (x.startswith('MODEL_'    ) or x.startswith('TF_MODEL_'    ) or x.startswith('FLAX_MODEL_'    ))
    ]
    for name in mapping_names:
      _lowerCAmelCase						=		getattr(lowerCamelCase_		,				lowerCamelCase_    )
      if mapping is not None:
        for v in mapping.values():
          if isinstance(lowerCamelCase_		,				(list, tuple)    ):
            model_names.update(lowerCamelCase_    )
          elif isinstance(lowerCamelCase_		,				lowerCamelCase_    ):
            model_names.add(lowerCamelCase_    )

  return sorted(lowerCamelCase_    )



def 	_a  (					):






  """simple docstring"""




  _lowerCAmelCase						=		set(get_all_model_names()    )

  with open('tests/utils/tiny_model_summary.json'    ) as fp:
    _lowerCAmelCase						=		json.load(lowerCamelCase_    )
  _lowerCAmelCase						=		set()
  for model_base_name in tiny_model_info:
    tiny_models_names.update(tiny_model_info[model_base_name]['model_classes']    )

  # Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
  _lowerCAmelCase						=		model_names.difference(lowerCamelCase_    )
  for model_name in copy.copy(lowerCamelCase_    ):
    if not model_name.startswith('TF'    ) and f'''TF{model_name}''' in not_on_hub:
      tiny_models_names.remove(lowerCamelCase_    )
    elif model_name.startswith('TF'    ) and model_name[2:] in not_on_hub:
      tiny_models_names.remove(lowerCamelCase_    )

  return sorted(lowerCamelCase_    )



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[str]    ):






  """simple docstring"""




  _lowerCAmelCase						=		COMPOSITE_MODELS.values()

  # All tiny model base names on Hub
  _lowerCAmelCase						=		get_all_model_names()
  _lowerCAmelCase						=		hf_api.list_models(
      filter=ModelFilter(
          author='hf-internal-testing'		,				)    )
  _lowerCAmelCase						=		set()
  for x in models:
    _lowerCAmelCase						=		x.modelId
    _lowerCAmelCase	,       _lowerCAmelCase						=		model.split('/'    )
    if not model.startswith('tiny-random-'    ):
      continue
    _lowerCAmelCase						=		model.replace('tiny-random-'		,				''    )
    if not model[0].isupper():
      continue
    if model not in model_names and model not in special_models:
      continue
    _models.add(lowerCamelCase_    )

  _lowerCAmelCase						=		sorted(_models    )
  # All tiny model names on Hub
  _lowerCAmelCase						=		{}
  for model in models:
    _lowerCAmelCase						=		f'''hf-internal-testing/tiny-random-{model}'''
    _lowerCAmelCase						=		model.split('-'    )[0]
    try:
      _lowerCAmelCase						=		hf_api.repo_info(lowerCamelCase_    )
      _lowerCAmelCase						=		{
          'tokenizer_classes': set(),
          'processor_classes': set(),
          'model_classes': set(),
          'sha': repo_info.sha,
      }
    except Exception:
      continue
    try:
      time.sleep(1    )
      _lowerCAmelCase						=		AutoTokenizer.from_pretrained(lowerCamelCase_    )
      content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__    )
    except Exception:
      pass
    try:
      time.sleep(1    )
      _lowerCAmelCase						=		AutoTokenizer.from_pretrained(lowerCamelCase_		,				use_fast=lowerCamelCase_    )
      content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__    )
    except Exception:
      pass
    try:
      time.sleep(1    )
      _lowerCAmelCase						=		AutoImageProcessor.from_pretrained(lowerCamelCase_    )
      content["processor_classes"].add(img_p.__class__.__name__    )
    except Exception:
      pass
    try:
      time.sleep(1    )
      _lowerCAmelCase						=		AutoFeatureExtractor.from_pretrained(lowerCamelCase_    )
      if not isinstance(lowerCamelCase_		,				lowerCamelCase_    ):
        content["processor_classes"].add(feat_p.__class__.__name__    )
    except Exception:
      pass
    try:
      time.sleep(1    )
      _lowerCAmelCase						=		getattr(lowerCamelCase_		,				lowerCamelCase_    )
      _lowerCAmelCase						=		model_class.from_pretrained(lowerCamelCase_    )
      content["model_classes"].add(m.__class__.__name__    )
    except Exception:
      pass
    try:
      time.sleep(1    )
      _lowerCAmelCase						=		getattr(lowerCamelCase_		,				f'''TF{model}'''    )
      _lowerCAmelCase						=		model_class.from_pretrained(lowerCamelCase_    )
      content["model_classes"].add(m.__class__.__name__    )
    except Exception:
      pass

    _lowerCAmelCase						=		sorted(content['tokenizer_classes']    )
    _lowerCAmelCase						=		sorted(content['processor_classes']    )
    _lowerCAmelCase						=		sorted(content['model_classes']    )

    _lowerCAmelCase						=		content
    with open(os.path.join(lowerCamelCase_		,				'hub_tiny_model_summary.json'    )		,				'w'    ) as fp:
      json.dump(lowerCamelCase_		,				lowerCamelCase_		,				ensure_ascii=lowerCamelCase_		,				indent=4    )


if __name__ == "__main__":
       _UpperCamelCase:					Optional[int]     =argparse.ArgumentParser()
       parser.add_argument('--num_workers', default=1, type=int, help='The number of workers to run.')
       _UpperCamelCase:					Optional[Any]     =parser.parse_args()

       # This has to be `spawn` to avoid hanging forever!
       multiprocessing.set_start_method('spawn')

       _UpperCamelCase:					Union[str, Any]     ='tiny_models'
       _UpperCamelCase:					Union[str, Any]     =True
       _UpperCamelCase:					Optional[Any]     =None
       _UpperCamelCase:					Dict     =get_tiny_model_names_from_repo()
       _UpperCamelCase:					Dict     =True
       _UpperCamelCase:					str     =True
       _UpperCamelCase:					Union[str, Any]     ='hf-internal-testing'

       create_tiny_models(
           output_path,
           all,
           model_types,
           models_to_skip,
           no_check,
           upload,
           organization,
           token=os.environ.get('TOKEN', None),
           num_workers=args.num_workers,
       )




"""simple docstring"""






from __future__ import annotations

import sys


class       lowerCamelCase_		:





				"""simple docstring"""







				def __init__(    self							,		UpperCAmelCase__							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					letter
								SCREAMING_SNAKE_CASE__  				=					freq
								SCREAMING_SNAKE_CASE__  				=					{}






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


class       lowerCamelCase_		:





				"""simple docstring"""







				def __init__(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					freq
								SCREAMING_SNAKE_CASE__  				=					left
								SCREAMING_SNAKE_CASE__  				=					right


def __lowercase  (   lowerCamelCase_	:   str	):
				SCREAMING_SNAKE_CASE__  				=					{}
				with open(lowerCamelCase_	) as f:
								while True:
												SCREAMING_SNAKE_CASE__  				=					f.read(1	)
												if not c:
																break
												SCREAMING_SNAKE_CASE__  				=					chars[c] + 1 if c in chars else 1
				return sorted((Letter(lowerCamelCase_    ,       lowerCamelCase_	) for c, f in chars.items())    ,       key=lambda lowerCamelCase_	: x.freq	)


def __lowercase  (   lowerCamelCase_	:   list[Letter]	):
				SCREAMING_SNAKE_CASE__  				=					letters  # type: ignore
				while len(lowerCamelCase_	) > 1:
								SCREAMING_SNAKE_CASE__  				=					response.pop(0	)
								SCREAMING_SNAKE_CASE__  				=					response.pop(0	)
								SCREAMING_SNAKE_CASE__  				=					left.freq + right.freq
								SCREAMING_SNAKE_CASE__  				=					TreeNode(lowerCamelCase_    ,       lowerCamelCase_    ,       lowerCamelCase_	)
								response.append(lowerCamelCase_	)
								response.sort(key=lambda lowerCamelCase_	: x.freq	)
				return response[0]


def __lowercase  (   lowerCamelCase_	:   Letter | TreeNode    ,       lowerCamelCase_	:   str	):
				if isinstance(lowerCamelCase_    ,       lowerCamelCase_	):
								SCREAMING_SNAKE_CASE__  				=					bitstring
								return [root]
				SCREAMING_SNAKE_CASE__  				=					root  # type: ignore
				SCREAMING_SNAKE_CASE__  				=					[]
				letters += traverse_tree(treenode.left    ,       bitstring + "0"	)
				letters += traverse_tree(treenode.right    ,       bitstring + "1"	)
				return letters


def __lowercase  (   lowerCamelCase_	:   str	):
				SCREAMING_SNAKE_CASE__  				=					parse_file(lowerCamelCase_	)
				SCREAMING_SNAKE_CASE__  				=					build_tree(lowerCamelCase_	)
				SCREAMING_SNAKE_CASE__  				=					{
				    k: v for letter in traverse_tree(lowerCamelCase_    ,       ""	) for k, v in letter.bitstring.items()
				}
				print(F'''Huffman Coding  of {file_path}: '''	)
				with open(lowerCamelCase_	) as f:
								while True:
												SCREAMING_SNAKE_CASE__  				=					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])
