
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   snake_case_       (							):
      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"]:
            snake_case_						       =							getattr(transformers.models.auto ,					_a ,					_a			)
            if module is None:
                  continue
            # all mappings in a single auto modeling file
            snake_case_						       =							[
                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:
                  snake_case_						       =							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   snake_case_       (							):
      # All model names defined in auto mappings
      snake_case_						       =							set(get_all_model_names()			)

      with open("tests/utils/tiny_model_summary.json"			) as fp:
            snake_case_						       =							json.load(_a			)
      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.
      snake_case_						       =							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   snake_case_       (							_a     :							Union[str, Any]			):
      snake_case_						       =							COMPOSITE_MODELS.values()

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

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

            snake_case_						       =							sorted(content["tokenizer_classes"]			)
            snake_case_						       =							sorted(content["processor_classes"]			)
            snake_case_						       =							sorted(content["model_classes"]			)

            snake_case_						       =							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'''





def   snake_case_       (							_a     :							int = 1_000			):
      return sum(i for i in range(_a			) if i % 3 == 0 or i % 5 == 0			)


if __name__ == "__main__":
     print(F"""{solution() = }""")

