

'''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   lowerCamelCase__       (      )		->    int:



    """simple docstring"""







    _snake_case : List[str]												=		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 : int												=		getattr(transformers.models.auto			,      a__			,      a__)
        if module is None:
            continue
        # all mappings in a single auto modeling file
        _snake_case : List[Any]												=		[
            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 : str												=		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   lowerCamelCase__       (      )		->    List[str]:



    """simple docstring"""







    _snake_case : str												=		set(get_all_model_names())

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



    """simple docstring"""







    _snake_case : Optional[Any]												=		COMPOSITE_MODELS.values()

    # All tiny model base names on Hub
    _snake_case : Tuple												=		get_all_model_names()
    _snake_case : Optional[Any]												=		hf_api.list_models(
        filter=ModelFilter(
            author='hf-internal-testing'			,      ))
    _snake_case : Optional[Any]												=		set()
    for x in models:
        _snake_case : Union[str, Any]												=		x.modelId
        _snake_case					,							_snake_case : Union[str, Any]												=		model.split('/')
        if not model.startswith('tiny-random-'):
            continue
        _snake_case : Optional[int]												=		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 : Any												=		sorted(_models)
    # All tiny model names on Hub
    _snake_case : Tuple												=		{}
    for model in models:
        _snake_case : Optional[int]												=		F"""hf-internal-testing/tiny-random-{model}"""
        _snake_case : Tuple												=		model.split('-')[0]
        try:
            _snake_case : Union[str, Any]												=		hf_api.repo_info(a__)
            _snake_case : Optional[int]												=		{
                'tokenizer_classes': set(),
                'processor_classes': set(),
                'model_classes': set(),
                'sha': repo_info.sha,
            }
        except Exception:
            continue
        try:
            time.sleep(1)
            _snake_case : List[Any]												=		AutoTokenizer.from_pretrained(a__)
            content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            _snake_case : Any												=		AutoTokenizer.from_pretrained(a__			,      use_fast=a__)
            content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            _snake_case : Tuple												=		AutoImageProcessor.from_pretrained(a__)
            content["processor_classes"].add(img_p.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            _snake_case : Any												=		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 : List[str]												=		getattr(a__			,      a__)
            _snake_case : str												=		model_class.from_pretrained(a__)
            content["model_classes"].add(m.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            _snake_case : Optional[int]												=		getattr(a__			,      F"""TF{model}""")
            _snake_case : Optional[int]												=		model_class.from_pretrained(a__)
            content["model_classes"].add(m.__class__.__name__)
        except Exception:
            pass

        _snake_case : str												=		sorted(content['tokenizer_classes'])
        _snake_case : Tuple												=		sorted(content['processor_classes'])
        _snake_case : List[Any]												=		sorted(content['model_classes'])

        _snake_case : List[str]												=		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__":
   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,
   )






'''simple docstring'''



def   lowerCamelCase__       (      a__			,      a__)		->    int:



    """simple docstring"""







    _snake_case : Tuple												=		0
    for ele in arr:
        total_sum += product_sum(a__			,      depth + 1) if isinstance(a__			,      a__) else ele
    return total_sum * depth

def   lowerCamelCase__       (      a__)		->    int:



    """simple docstring"""







    return product_sum(a__			,      1)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



