
'''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     snake_case							(						)->     str:







      """simple docstring"""
      __A    		=     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    		=     getattr(transformers.models.auto ,					UpperCAmelCase ,					UpperCAmelCase		)
            if module is None:
                  continue
            # all mappings in a single auto modeling file
            __A    		=     [
                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    		=     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     snake_case							(						)->     str:







      """simple docstring"""
      # All model names defined in auto mappings
      __A    		=     set(get_all_model_names()		)

      with open('tests/utils/tiny_model_summary.json'		) as fp:
            __A    		=     json.load(UpperCAmelCase		)
      __A    		=     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    		=     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     snake_case							(						UpperCAmelCase		)->     Optional[int]:







      """simple docstring"""
      __A    		=     COMPOSITE_MODELS.values()

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

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

            __A    		=     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__":
 a__						:				Union[str, Any]           =    argparse.ArgumentParser()
 parser.add_argument("--num_workers", default=1, type=int, help="The number of workers to run.")
 a__						:				Tuple           =    parser.parse_args()

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

 a__						:				int           =    "tiny_models"
 a__						:				Tuple           =    True
 a__						:				Optional[Any]           =    None
 a__						:				List[str]           =    get_tiny_model_names_from_repo()
 a__						:				Dict           =    True
 a__						:				Optional[int]           =    True
 a__						:				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,
 )








'''simple docstring'''





from __future__ import annotations





def     snake_case							(						UpperCAmelCase		)->     List[Any]:







      """simple docstring"""
      global graph, reversed_graph, scc, component, visit, stack
      if visit[u]:
            return
      __A    		=     True
      for v in graph[u]:
            dfs(UpperCAmelCase		)
      stack.append(UpperCAmelCase		)





def     snake_case							(						UpperCAmelCase		)->     List[Any]:







      """simple docstring"""
      global graph, reversed_graph, scc, component, visit, stack
      if visit[u]:
            return
      __A    		=     True
      component.append(UpperCAmelCase		)
      for v in reversed_graph[u]:
            dfsa(UpperCAmelCase		)





def     snake_case							(						)->     Any:







      """simple docstring"""
      global graph, reversed_graph, scc, component, visit, stack
      for i in range(UpperCAmelCase		):
            dfs(UpperCAmelCase		)
      __A    		=     [False] * n
      for i in stack[::-1]:
            if visit[i]:
                  continue
            __A    		=     []
            dfsa(UpperCAmelCase		)
            scc.append(UpperCAmelCase		)
      return scc


if __name__ == "__main__":
 # n - no of nodes, m - no of edges
 a__    ,						a__						:				Tuple           =    list(map(int, input().strip().split()))

 a__						:				list[list[int]]           =    [[] for _ in range(n)]  # graph
 a__						:				list[list[int]]           =    [[] for i in range(n)]  # reversed graph
 # input graph data (edges)
 for _ in range(m):
  a__    ,						a__						:				Tuple           =    list(map(int, input().strip().split()))
  graph[u].append(v)
  reversed_graph[v].append(u)

 a__						:				list[int]           =    []
 a__						:				list[bool]           =    [False] * n
 a__						:				list[int]           =    []
 a__						:				list[int]           =    []
 print(kosaraju())






