code: 
  
  
  
  
  
  
  """simple docstring"""
  
  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 	__lowerCamelCase			(						)		->					Union[str, Any]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							Optional[int]             =    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_     :							Any             =    getattr(transformers.models.auto   , __UpperCamelCase   , __UpperCamelCase )
  				if module is None:
  						continue
  				# all mappings in a single auto modeling file
  				lowerCAmelCase_     :							Dict             =    [
  				    x
  				    for x in dir(__UpperCamelCase )
  				    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_     :							Optional[int]             =    getattr(__UpperCamelCase   , __UpperCamelCase )
  						if mapping is not None:
  								for v in mapping.values():
  										if isinstance(__UpperCamelCase   , (list, tuple) ):
  												model_names.update(__UpperCamelCase )
  										elif isinstance(__UpperCamelCase   , __UpperCamelCase ):
  												model_names.add(__UpperCamelCase )
  
  		return sorted(__UpperCamelCase )
  
  def 	__lowerCamelCase			(						)		->					Optional[int]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							Optional[int]             =    set(get_all_model_names() )
  
  		with open("tests/utils/tiny_model_summary.json" ) as fp:
  				lowerCAmelCase_     :							Dict             =    json.load(__UpperCamelCase )
  		lowerCAmelCase_     :							Dict             =    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_     :							Any             =    model_names.difference(__UpperCamelCase )
  		for model_name in copy.copy(__UpperCamelCase ):
  				if not model_name.startswith("TF" ) and f'''TF{model_name}''' in not_on_hub:
  						tiny_models_names.remove(__UpperCamelCase )
  				elif model_name.startswith("TF" ) and model_name[2:] in not_on_hub:
  						tiny_models_names.remove(__UpperCamelCase )
  
  		return sorted(__UpperCamelCase )
  
  def 	__lowerCamelCase			(						__UpperCamelCase )		->					Tuple:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							List[str]             =    COMPOSITE_MODELS.values()
  
  		# All tiny model base names on Hub
  		lowerCAmelCase_     :							Dict             =    get_all_model_names()
  		lowerCAmelCase_     :							Tuple             =    hf_api.list_models(
  		    filter=ModelFilter(
  		        author="hf-internal-testing"   , ) )
  		lowerCAmelCase_     :							Union[str, Any]             =    set()
  		for x in models:
  				lowerCAmelCase_     :							int             =    x.modelId
  				lowerCAmelCase_       ,			lowerCAmelCase_     :							Union[str, Any]             =    model.split("/" )
  				if not model.startswith("tiny-random-" ):
  						continue
  				lowerCAmelCase_     :							Tuple             =    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(__UpperCamelCase )
  
  		lowerCAmelCase_     :							Any             =    sorted(_models )
  		# All tiny model names on Hub
  		lowerCAmelCase_     :							Dict             =    {}
  		for model in models:
  				lowerCAmelCase_     :							Optional[Any]             =    f'''hf-internal-testing/tiny-random-{model}'''
  				lowerCAmelCase_     :							Dict             =    model.split("-" )[0]
  				try:
  						lowerCAmelCase_     :							str             =    hf_api.repo_info(__UpperCamelCase )
  						lowerCAmelCase_     :							Any             =    {
  						    "tokenizer_classes": set(),
  						    "processor_classes": set(),
  						    "model_classes": set(),
  						    "sha": repo_info.sha,
  						}
  				except Exception:
  						continue
  				try:
  						time.sleep(1 )
  						lowerCAmelCase_     :							List[str]             =    AutoTokenizer.from_pretrained(__UpperCamelCase )
  						content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__ )
  				except Exception:
  						pass
  				try:
  						time.sleep(1 )
  						lowerCAmelCase_     :							Union[str, Any]             =    AutoTokenizer.from_pretrained(__UpperCamelCase   , use_fast=__UpperCamelCase )
  						content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__ )
  				except Exception:
  						pass
  				try:
  						time.sleep(1 )
  						lowerCAmelCase_     :							Any             =    AutoImageProcessor.from_pretrained(__UpperCamelCase )
  						content["processor_classes"].add(img_p.__class__.__name__ )
  				except Exception:
  						pass
  				try:
  						time.sleep(1 )
  						lowerCAmelCase_     :							Optional[int]             =    AutoFeatureExtractor.from_pretrained(__UpperCamelCase )
  						if not isinstance(__UpperCamelCase   , __UpperCamelCase ):
  								content["processor_classes"].add(feat_p.__class__.__name__ )
  				except Exception:
  						pass
  				try:
  						time.sleep(1 )
  						lowerCAmelCase_     :							Any             =    getattr(__UpperCamelCase   , __UpperCamelCase )
  						lowerCAmelCase_     :							Optional[Any]             =    model_class.from_pretrained(__UpperCamelCase )
  						content["model_classes"].add(m.__class__.__name__ )
  				except Exception:
  						pass
  				try:
  						time.sleep(1 )
  						lowerCAmelCase_     :							Optional[int]             =    getattr(__UpperCamelCase   , f'''TF{model}''' )
  						lowerCAmelCase_     :							Optional[int]             =    model_class.from_pretrained(__UpperCamelCase )
  						content["model_classes"].add(m.__class__.__name__ )
  				except Exception:
  						pass
  
  				lowerCAmelCase_     :							Dict             =    sorted(content["tokenizer_classes"] )
  				lowerCAmelCase_     :							Optional[int]             =    sorted(content["processor_classes"] )
  				lowerCAmelCase_     :							Dict             =    sorted(content["model_classes"] )
  
  				lowerCAmelCase_     :							str             =    content
  				with open(os.path.join(__UpperCamelCase   , "hub_tiny_model_summary.json" )   , "w" ) as fp:
  						json.dump(__UpperCamelCase   , __UpperCamelCase   , ensure_ascii=__UpperCamelCase   , indent=4 )
  
  
  if __name__ == "__main__":
  					lowercase__      =				argparse.ArgumentParser()
  					parser.add_argument("""--num_workers""", default=1, type=int, help="""The number of workers to run.""")
  					lowercase__      =				parser.parse_args()
  
  					# This has to be `spawn` to avoid hanging forever!
  					multiprocessing.set_start_method("""spawn""")
  
  					lowercase__      =				"""tiny_models"""
  					lowercase__      =				True
  					lowercase__      =				None
  					lowercase__      =				get_tiny_model_names_from_repo()
  					lowercase__      =				True
  					lowercase__      =				True
  					lowercase__      =				"""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,
  					)
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  from __future__ import annotations
  
  import random
  
  # Adjacency list representation of this graph:
  # https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
  lowercase__      =				{
      """1""": ["""2""", """3""", """4""", """5"""],
      """2""": ["""1""", """3""", """4""", """5"""],
      """3""": ["""1""", """2""", """4""", """5""", """10"""],
      """4""": ["""1""", """2""", """3""", """5""", """6"""],
      """5""": ["""1""", """2""", """3""", """4""", """7"""],
      """6""": ["""7""", """8""", """9""", """10""", """4"""],
      """7""": ["""6""", """8""", """9""", """10""", """5"""],
      """8""": ["""6""", """7""", """9""", """10"""],
      """9""": ["""6""", """7""", """8""", """10"""],
      """10""": ["""6""", """7""", """8""", """9""", """3"""],
  }
  
  def 	__lowerCamelCase			(						__UpperCamelCase )		->					set[tuple[str, str]]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							Union[str, Any]             =    {node: {node} for node in graph}
  
  		lowerCAmelCase_     :							List[str]             =    {node: graph[node][:] for node in graph}
  
  		while len(__UpperCamelCase ) > 2:
  				# Choose a random edge.
  				lowerCAmelCase_     :							int             =    random.choice(list(graph_copy.keys() ) )
  				lowerCAmelCase_     :							Any             =    random.choice(graph_copy[u] )
  
  				# Contract edge (u, v) to new node uv
  				lowerCAmelCase_     :							Union[str, Any]             =    u + v
  				lowerCAmelCase_     :							List[Any]             =    list(set(graph_copy[u] + graph_copy[v] ) )
  				uv_neighbors.remove(__UpperCamelCase )
  				uv_neighbors.remove(__UpperCamelCase )
  				lowerCAmelCase_     :							str             =    uv_neighbors
  				for neighbor in uv_neighbors:
  						graph_copy[neighbor].append(__UpperCamelCase )
  
  				lowerCAmelCase_     :							Optional[int]             =    set(contracted_nodes[u].union(contracted_nodes[v] ) )
  
  				# Remove nodes u and v.
  				del graph_copy[u]
  				del graph_copy[v]
  				for neighbor in uv_neighbors:
  						if u in graph_copy[neighbor]:
  								graph_copy[neighbor].remove(__UpperCamelCase )
  						if v in graph_copy[neighbor]:
  								graph_copy[neighbor].remove(__UpperCamelCase )
  
      # Find cutset.
  		lowerCAmelCase_     :							Union[str, Any]             =    [contracted_nodes[node] for node in graph_copy]
  		return {
  		    (node, neighbor)
  		    for node in groups[0]
  		    for neighbor in graph[node]
  		    if neighbor in groups[1]
  		}
  
  
  if __name__ == "__main__":
  					print(partition_graph(TEST_GRAPH))
  