


"""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,
    )








"""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() = }''')
