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 					__lowercase						(    )					->Tuple:

  '''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			,					lowerCamelCase__			,					lowerCamelCase__       )
    if module is None:
      continue
    # all mappings in a single auto modeling file
    a__  		=  [
        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:
      a__  		=  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 					__lowercase						(    )					->List[Any]:

  '''simple docstring'''




  a__  		=  set(get_all_model_names()       )

  with open("tests/utils/tiny_model_summary.json"       ) as fp:
    a__  		=  json.load(lowerCamelCase__       )
  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(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 					__lowercase						(    lowerCamelCase__       )					->List[Any]:

  '''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(lowerCamelCase__       )

  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(lowerCamelCase__       )
      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(lowerCamelCase__       )
      content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__       )
    except Exception:
      pass
    try:
      time.sleep(1       )
      a__  		=  AutoTokenizer.from_pretrained(lowerCamelCase__			,					use_fast=lowerCamelCase__       )
      content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__       )
    except Exception:
      pass
    try:
      time.sleep(1       )
      a__  		=  AutoImageProcessor.from_pretrained(lowerCamelCase__       )
      content["processor_classes"].add(img_p.__class__.__name__       )
    except Exception:
      pass
    try:
      time.sleep(1       )
      a__  		=  AutoFeatureExtractor.from_pretrained(lowerCamelCase__       )
      if not isinstance(lowerCamelCase__			,					lowerCamelCase__       ):
        content["processor_classes"].add(feat_p.__class__.__name__       )
    except Exception:
      pass
    try:
      time.sleep(1       )
      a__  		=  getattr(lowerCamelCase__			,					lowerCamelCase__       )
      a__  		=  model_class.from_pretrained(lowerCamelCase__       )
      content["model_classes"].add(m.__class__.__name__       )
    except Exception:
      pass
    try:
      time.sleep(1       )
      a__  		=  getattr(lowerCamelCase__			,					f"TF{model}"       )
      a__  		=  model_class.from_pretrained(lowerCamelCase__       )
      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(lowerCamelCase__			,					"hub_tiny_model_summary.json"       )			,					"w"       ) as fp:
      json.dump(lowerCamelCase__			,					lowerCamelCase__			,					ensure_ascii=lowerCamelCase__			,					indent=4       )


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

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

     _SCREAMING_SNAKE_CASE     =     """tiny_models"""
     _SCREAMING_SNAKE_CASE     =     True
     _SCREAMING_SNAKE_CASE     =     None
     _SCREAMING_SNAKE_CASE     =     get_tiny_model_names_from_repo()
     _SCREAMING_SNAKE_CASE     =     True
     _SCREAMING_SNAKE_CASE     =     True
     _SCREAMING_SNAKE_CASE     =     """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,
     )


from collections.abc import Callable

_SCREAMING_SNAKE_CASE     =     Callable[[float], float]  # type alias for a real -> real function
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					)					->float:

  '''simple docstring'''




  a__  		=  float(lowerCamelCase__       )
  while True:
    try:
      a__  		=  prev_guess - function(lowerCamelCase__       ) / derivative(lowerCamelCase__       )
    except ZeroDivisionError:
      raise ZeroDivisionError("Could not find root"       ) from None
    if abs(prev_guess - next_guess       ) < 10**-5:
      return next_guess
    a__  		=  next_guess
def 					__lowercase						(    lowerCamelCase__       )					->float:

  '''simple docstring'''




  return (x**3) - (2 * x) - 5
def 					__lowercase						(    lowerCamelCase__       )					->float:

  '''simple docstring'''




  return 3 * (x**2) - 2


if __name__ == "__main__":
     print(newton(f, fa, 3))

