code: 
  
  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        __SCREAMING_SNAKE_CASE    (      )				->							List[Any]:
  	__A   : 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"]:
  		__A   : int								= getattr(transformers.models.auto ,_UpperCamelCase ,_UpperCamelCase					)
  		if module is None:
  			continue
  		# all mappings in a single auto modeling file
  		__A   : str								= [
  		    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:
  			__A   : Dict								= 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        __SCREAMING_SNAKE_CASE    (      )				->							List[Any]:
  	__A   : Tuple								= set(get_all_model_names()					)
  
  	with open("""tests/utils/tiny_model_summary.json"""					) as fp:
  		__A   : str								= json.load(_UpperCamelCase					)
  	__A   : List[Any]								= 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.
  	__A   : Optional[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        __SCREAMING_SNAKE_CASE    (      a__						:      Optional[int]					)				->							Tuple:
  	__A   : Optional[int]								= COMPOSITE_MODELS.values()
  
  	# All tiny model base names on Hub
  	__A   : Dict								= get_all_model_names()
  	__A   : str								= hf_api.list_models(
  	    filter=ModelFilter(
  	        author="""hf-internal-testing""" ,)					)
  	__A   : Tuple								= set()
  	for x in models:
  		__A   : Optional[int]								= x.modelId
  		__A						,							__A   : Optional[int]								= model.split("""/"""					)
  		if not model.startswith("""tiny-random-"""					):
  			continue
  		__A   : int								= 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					)
  
  	__A   : List[Any]								= sorted(_models					)
  	# All tiny model names on Hub
  	__A   : Union[str, Any]								= {}
  	for model in models:
  		__A   : Optional[Any]								= f"""hf-internal-testing/tiny-random-{model}"""
  		__A   : Any								= model.split("""-"""					)[0]
  		try:
  			__A   : Dict								= hf_api.repo_info(_UpperCamelCase					)
  			__A   : Union[str, Any]								= {
  			    """tokenizer_classes""": set(),
  			    """processor_classes""": set(),
  			    """model_classes""": set(),
  			    """sha""": repo_info.sha,
  			}
  		except Exception:
  			continue
  		try:
  			time.sleep(1					)
  			__A   : int								= AutoTokenizer.from_pretrained(_UpperCamelCase					)
  			content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__					)
  		except Exception:
  			pass
  		try:
  			time.sleep(1					)
  			__A   : List[Any]								= AutoTokenizer.from_pretrained(_UpperCamelCase ,use_fast=_UpperCamelCase					)
  			content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__					)
  		except Exception:
  			pass
  		try:
  			time.sleep(1					)
  			__A   : Any								= AutoImageProcessor.from_pretrained(_UpperCamelCase					)
  			content["processor_classes"].add(img_p.__class__.__name__					)
  		except Exception:
  			pass
  		try:
  			time.sleep(1					)
  			__A   : 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					)
  			__A   : str								= getattr(_UpperCamelCase ,_UpperCamelCase					)
  			__A   : str								= model_class.from_pretrained(_UpperCamelCase					)
  			content["model_classes"].add(m.__class__.__name__					)
  		except Exception:
  			pass
  		try:
  			time.sleep(1					)
  			__A   : List[Any]								= getattr(_UpperCamelCase ,f"""TF{model}"""					)
  			__A   : List[str]								= model_class.from_pretrained(_UpperCamelCase					)
  			content["model_classes"].add(m.__class__.__name__					)
  		except Exception:
  			pass
  
  		__A   : Optional[Any]								= sorted(content["""tokenizer_classes"""]					)
  		__A   : str								= sorted(content["""processor_classes"""]					)
  		__A   : Tuple								= sorted(content["""model_classes"""]					)
  
  		__A   : Optional[Any]								= 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__":
  			UpperCAmelCase_       :				str		   =      argparse.ArgumentParser()
  			parser.add_argument('''--num_workers''', default=1, type=int, help='''The number of workers to run.''')
  			UpperCAmelCase_       :				Tuple		   =      parser.parse_args()
  
  			# This has to be `spawn` to avoid hanging forever!
  			multiprocessing.set_start_method('''spawn''')
  
  			UpperCAmelCase_       :				List[str]		   =      "tiny_models"
  			UpperCAmelCase_       :				Union[str, Any]		   =      True
  			UpperCAmelCase_       :				str		   =      None
  			UpperCAmelCase_       :				Union[str, Any]		   =      get_tiny_model_names_from_repo()
  			UpperCAmelCase_       :				Any		   =      True
  			UpperCAmelCase_       :				Tuple		   =      True
  			UpperCAmelCase_       :				Optional[int]		   =      "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: 
  
  UpperCAmelCase_       :				str		   =      [
      [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
      [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
      [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
      [0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0],
      [0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0],
      [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
      [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
      [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
  ]
  def        __SCREAMING_SNAKE_CASE    (      a__						:      int ,a__						:      int ,a__						:      int ,a__						:      int					)				->							bool:
  	return 0 <= row < rows and 0 <= col < cols
  def        __SCREAMING_SNAKE_CASE    (      a__						:      int ,a__						:      int ,a__						:      set ,a__						:      list[list[int]]					)				->							int:
  	__A   : Union[str, Any]								= len(a__					)
  	__A   : Any								= len(mat[0]					)
  	if is_safe(a__ ,a__ ,a__ ,a__					) and (row, col) not in seen and mat[row][col] == 1:
  		seen.add((row, col)					)
  		return (
  		    1
  		    + depth_first_search(row + 1 ,a__ ,a__ ,a__					)
  		    + depth_first_search(row - 1 ,a__ ,a__ ,a__					)
  		    + depth_first_search(a__ ,col + 1 ,a__ ,a__					)
  		    + depth_first_search(a__ ,col - 1 ,a__ ,a__					)
  		)
  	else:
  		return 0
  def        __SCREAMING_SNAKE_CASE    (      a__						:      list[list[int]]					)				->							int:
  	__A   : set								= set()
  
  	__A   : Union[str, Any]								= 0
  	for row, line in enumerate(a__					):
  		for col, item in enumerate(a__					):
  			if item == 1 and (row, col) not in seen:
  				# Maximizing the area
  				__A   : Optional[Any]								= max(a__ ,depth_first_search(a__ ,a__ ,a__ ,a__					)					)
  	return max_area
  
  
  if __name__ == "__main__":
  			import doctest
  
  			print(find_max_area(matrix))  # Output -> 6
  			doctest.testmod()
  
  
  
  
  
  