




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    UpperCAmelCase__	(  ):
  lowercase     :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"]:
    lowercase     :Union[str, Any]   						=    getattr(transformers.models.auto,      lowerCamelCase,      lowerCamelCase )
    if module is None:
      continue
    # all mappings in a single auto modeling file
    lowercase     :Tuple   						=    [
        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:
      lowercase     :List[Any]   						=    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    UpperCAmelCase__	(  ):
  # All model names defined in auto mappings
  lowercase     :Union[str, Any]   						=    set(get_all_model_names() )

  with open("tests/utils/tiny_model_summary.json" ) as fp:
    lowercase     :str   						=    json.load(lowerCamelCase )
  lowercase     :Tuple   						=    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.
  lowercase     :Dict   						=    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    UpperCAmelCase__	(  lowerCamelCase ):
  lowercase     :str   						=    COMPOSITE_MODELS.values()

  # All tiny model base names on Hub
  lowercase     :str   						=    get_all_model_names()
  lowercase     :Any   						=    hf_api.list_models(
      filter=ModelFilter(
          author="hf-internal-testing",      ) )
  lowercase     :int   						=    set()
  for x in models:
    lowercase     :Optional[Any]   						=    x.modelId
    lowercase						,     lowercase     :str   						=    model.split("/" )
    if not model.startswith("tiny-random-" ):
      continue
    lowercase     :List[str]   						=    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 )

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

    lowercase     :str   						=    sorted(content["tokenizer_classes"] )
    lowercase     :Optional[int]   						=    sorted(content["processor_classes"] )
    lowercase     :Any   						=    sorted(content["model_classes"] )

    lowercase     :int   						=    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						:    List[Any]      =  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						:    List[Any]      =  "tiny_models"
     _UpperCAmelCase						:    Optional[Any]      =  True
     _UpperCAmelCase						:    Dict      =  None
     _UpperCAmelCase						:    int      =  get_tiny_model_names_from_repo()
     _UpperCAmelCase						:    Any      =  True
     _UpperCAmelCase						:    Any      =  True
     _UpperCAmelCase						:    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,
     )









import decimal



def    UpperCAmelCase__	(  ):
  lowercase     :int   						=    0
  lowercase     :str   						=    decimal.Context(prec=105 )
  for i in range(2,      100 ):
    lowercase     :int   						=    decimal.Decimal(lowerCamelCase )
    lowercase     :str   						=    number.sqrt(lowerCamelCase )
    if len(str(lowerCamelCase ) ) > 1:
      answer += int(str(lowerCamelCase )[0] )
      lowercase     :int   						=    str(lowerCamelCase )[2:101]
      answer += sum(int(lowerCamelCase ) for x in sqrt_number_str )
  return answer


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(f'''{solution() = }''')



