code: 
  
  
  """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 						_UpperCamelCase			(  ):
     UpperCamelCase_     =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"]:
        UpperCamelCase_     =getattr(transformers.models.auto    ,       A    ,       A			)
        if module is None:
           continue
        # all mappings in a single auto modeling file
        UpperCamelCase_     =[
            x
            for x in dir(A			)
            if x.endswith("_MAPPING_NAMES"			)
            and (x.startswith("MODEL_"			) or x.startswith("TF_MODEL_"			) or x.startswith("FLAX_MODEL_"			))
        ]
        for name in mapping_names:
           UpperCamelCase_     =getattr(A    ,       A			)
           if mapping is not None:
              for v in mapping.values():
                 if isinstance(A    ,       (list, tuple)			):
                    model_names.update(A			)
                 elif isinstance(A    ,       A			):
                    model_names.add(A			)
  
     return sorted(A			)
  
  
  
  
  def 						_UpperCamelCase			(  ):
     # All model names defined in auto mappings
     UpperCamelCase_     =set(get_all_model_names()			)
  
     with open("tests/utils/tiny_model_summary.json"			) as fp:
        UpperCamelCase_     =json.load(A			)
     UpperCamelCase_     =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.
     UpperCamelCase_     =model_names.difference(A			)
     for model_name in copy.copy(A			):
        if not model_name.startswith("TF"			) and f"""TF{model_name}""" in not_on_hub:
           tiny_models_names.remove(A			)
        elif model_name.startswith("TF"			) and model_name[2:] in not_on_hub:
           tiny_models_names.remove(A			)
  
     return sorted(A			)
  
  
  
  
  def 						_UpperCamelCase			(  A			):
     UpperCamelCase_     =COMPOSITE_MODELS.values()
  
     # All tiny model base names on Hub
     UpperCamelCase_     =get_all_model_names()
     UpperCamelCase_     =hf_api.list_models(
         filter=ModelFilter(
             author="hf-internal-testing"    ,       )			)
     UpperCamelCase_     =set()
     for x in models:
        UpperCamelCase_     =x.modelId
        UpperCamelCase_    ,							UpperCamelCase_     =model.split("/"			)
        if not model.startswith("tiny-random-"			):
           continue
        UpperCamelCase_     =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(A			)
  
     UpperCamelCase_     =sorted(_models			)
     # All tiny model names on Hub
     UpperCamelCase_     ={}
     for model in models:
        UpperCamelCase_     =f"""hf-internal-testing/tiny-random-{model}"""
        UpperCamelCase_     =model.split("-"			)[0]
        try:
           UpperCamelCase_     =hf_api.repo_info(A			)
           UpperCamelCase_     ={
               "tokenizer_classes": set(),
               "processor_classes": set(),
               "model_classes": set(),
               "sha": repo_info.sha,
           }
        except Exception:
           continue
        try:
           time.sleep(1			)
           UpperCamelCase_     =AutoTokenizer.from_pretrained(A			)
           content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__			)
        except Exception:
           pass
        try:
           time.sleep(1			)
           UpperCamelCase_     =AutoTokenizer.from_pretrained(A    ,       use_fast=A			)
           content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__			)
        except Exception:
           pass
        try:
           time.sleep(1			)
           UpperCamelCase_     =AutoImageProcessor.from_pretrained(A			)
           content["processor_classes"].add(img_p.__class__.__name__			)
        except Exception:
           pass
        try:
           time.sleep(1			)
           UpperCamelCase_     =AutoFeatureExtractor.from_pretrained(A			)
           if not isinstance(A    ,       A			):
              content["processor_classes"].add(feat_p.__class__.__name__			)
        except Exception:
           pass
        try:
           time.sleep(1			)
           UpperCamelCase_     =getattr(A    ,       A			)
           UpperCamelCase_     =model_class.from_pretrained(A			)
           content["model_classes"].add(m.__class__.__name__			)
        except Exception:
           pass
        try:
           time.sleep(1			)
           UpperCamelCase_     =getattr(A    ,       f"""TF{model}"""			)
           UpperCamelCase_     =model_class.from_pretrained(A			)
           content["model_classes"].add(m.__class__.__name__			)
        except Exception:
           pass
  
        UpperCamelCase_     =sorted(content["tokenizer_classes"]			)
        UpperCamelCase_     =sorted(content["processor_classes"]			)
        UpperCamelCase_     =sorted(content["model_classes"]			)
  
        UpperCamelCase_     =content
        with open(os.path.join(A    ,       "hub_tiny_model_summary.json"			)    ,       "w"			) as fp:
           json.dump(A    ,       A    ,       ensure_ascii=A    ,       indent=4			)
  
  
  if __name__ == "__main__":
      A_			  =    argparse.ArgumentParser()
      parser.add_argument("--num_workers", default=1, type=int, help="The number of workers to run.")
      A_			  =    parser.parse_args()
  
      # This has to be `spawn` to avoid hanging forever!
      multiprocessing.set_start_method("spawn")
  
      A_			  =    "tiny_models"
      A_			  =    True
      A_			  =    None
      A_			  =    get_tiny_model_names_from_repo()
      A_			  =    True
      A_			  =    True
      A_			  =    "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: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  from __future__ import annotations
  
  from pathlib import Path
  
  
  
  
  def 						_UpperCamelCase			(  A    ,       A			):
     return pointa[0] * pointa[1] - pointa[1] * pointa[0]
  
  
  
  
  def 						_UpperCamelCase			(  A    ,       A    ,       A    ,       A    ,       A    ,       A			):
     UpperCamelCase_     =(xa, ya)
     UpperCamelCase_     =(xa - xa, ya - ya)
     UpperCamelCase_     =(xa - xa, ya - ya)
     UpperCamelCase_     =-vector_product(A    ,       A			) / vector_product(
         A    ,       A			)
     UpperCamelCase_     =+vector_product(A    ,       A			) / vector_product(
         A    ,       A			)
  
     return a > 0 and b > 0 and a + b < 1
  
  
  
  
  def 						_UpperCamelCase			(  A = "p102_triangles.txt"			):
     UpperCamelCase_     =Path(A			).parent.joinpath(A			).read_text(encoding="utf-8"			)
  
     UpperCamelCase_     =[]
     for line in data.strip().split("\n"			):
        triangles.append([int(A			) for number in line.split(","			)]			)
  
     UpperCamelCase_     =0
     UpperCamelCase_     =42
  
     for triangle in triangles:
        ret += contains_origin(*A			)
  
     return ret
  
  
  if __name__ == "__main__":
      print(f'''{solution() = }''')
  