






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_					(						)				->Dict:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		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"]:
  SCREAMING_SNAKE_CASE				    =		getattr(transformers.models.auto	,  lowercase_	,  lowercase_			)
  if module is None:
   continue
  # all mappings in a single auto modeling file
  SCREAMING_SNAKE_CASE				    =		[
      x
      for x in dir(lowercase_			)
      if x.endswith('_MAPPING_NAMES'			)
      and (x.startswith('MODEL_'			) or x.startswith('TF_MODEL_'			) or x.startswith('FLAX_MODEL_'			))
  ]
  for name in mapping_names:
   SCREAMING_SNAKE_CASE				    =		getattr(lowercase_	,  lowercase_			)
   if mapping is not None:
    for v in mapping.values():
     if isinstance(lowercase_	,  (list, tuple)			):
      model_names.update(lowercase_			)
     elif isinstance(lowercase_	,  lowercase_			):
      model_names.add(lowercase_			)

 return sorted(lowercase_			)





def 						A_					(						)				->Any:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		set(get_all_model_names()			)

 with open('tests/utils/tiny_model_summary.json'			) as fp:
  SCREAMING_SNAKE_CASE				    =		json.load(lowercase_			)
 SCREAMING_SNAKE_CASE				    =		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.
 SCREAMING_SNAKE_CASE				    =		model_names.difference(lowercase_			)
 for model_name in copy.copy(lowercase_			):
  if not model_name.startswith('TF'			) and f'''TF{model_name}''' in not_on_hub:
   tiny_models_names.remove(lowercase_			)
  elif model_name.startswith('TF'			) and model_name[2:] in not_on_hub:
   tiny_models_names.remove(lowercase_			)

 return sorted(lowercase_			)





def 						A_					(						lowercase_			)				->Union[str, Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		COMPOSITE_MODELS.values()

 # All tiny model base names on Hub
 SCREAMING_SNAKE_CASE				    =		get_all_model_names()
 SCREAMING_SNAKE_CASE				    =		hf_api.list_models(
     filter=ModelFilter(
         author='hf-internal-testing'	,  )			)
 SCREAMING_SNAKE_CASE				    =		set()
 for x in models:
  SCREAMING_SNAKE_CASE				    =		x.modelId
  SCREAMING_SNAKE_CASE				    =		model.split('/'			)
  if not model.startswith('tiny-random-'			):
   continue
  SCREAMING_SNAKE_CASE				    =		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(lowercase_			)

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

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

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


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

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

      __UpperCAmelCase      =				"tiny_models"
      __UpperCAmelCase      =				True
      __UpperCAmelCase      =				None
      __UpperCAmelCase      =				get_tiny_model_names_from_repo()
      __UpperCAmelCase      =				True
      __UpperCAmelCase      =				True
      __UpperCAmelCase      =				"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'''





lowercase__								=						[[0, 1, 1], [0, 0, 1], [1, 0, 0]]






class     lowercase  :






      def __init__(self			,	a__       )							-> List[Any]:
         SCREAMING_SNAKE_CASE__      :					int							    =			name
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			[]
         SCREAMING_SNAKE_CASE__      :					Any							    =			[]






      def  A__(self			,	a__       )							-> Dict:
         self.inbound.append(a__       )






      def  A__(self			,	a__       )							-> Any:
         self.outbound.append(a__       )






      def __repr__(self       )							-> List[str]:
         return f"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""


def 					UpperCamelCase						(	snake_case						,  snake_case=3						,  snake_case=0.85 ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			{}
   for node in nodes:
      SCREAMING_SNAKE_CASE__      :					List[Any]							    =			1

   SCREAMING_SNAKE_CASE__      :					int							    =			{}
   for node in nodes:
      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			len(node.outbound )

   for i in range(snake_case ):
      print(f"""======= Iteration {i + 1} =======""" )
      for _, node in enumerate(snake_case ):
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			(1 - d) + d * sum(
             ranks[ib] / outbounds[ib] for ib in node.inbound )
      print(snake_case )


def 					UpperCamelCase						(	):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Any							    =			list(input('''Enter Names of the Nodes: ''' ).split() )

   SCREAMING_SNAKE_CASE__      :					str							    =			[Node(snake_case ) for name in names]

   for ri, row in enumerate(snake_case ):
      for ci, col in enumerate(snake_case ):
         if col == 1:
            nodes[ci].add_inbound(names[ri] )
            nodes[ri].add_outbound(names[ci] )

   print('''======= Nodes =======''' )
   for node in nodes:
      print(snake_case )

   page_rank(snake_case )


if __name__ == "__main__":
       main()

