
"""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_     (						)  ->      Optional[int]:


					'''simple docstring'''
					__a:    int		     =       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:    Union[str, Any]		     =       getattr(transformers.models.auto						,					lowerCamelCase_						,					lowerCamelCase_			)
										if module is None:
															continue
										# all mappings in a single auto modeling file
										__a:    List[Any]		     =       [
										    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:    Tuple		     =       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      lowerCamelCase_     (						)  ->      Dict:


					'''simple docstring'''
					# All model names defined in auto mappings
					__a:    List[Any]		     =       set(get_all_model_names()			)

					with open("""tests/utils/tiny_model_summary.json"""			) as fp:
										__a:    Optional[Any]		     =       json.load(lowerCamelCase_			)
					__a:    Union[str, 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.
					__a:    Optional[int]		     =       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      lowerCamelCase_     (						lowerCamelCase_			)  ->      Dict:


					'''simple docstring'''
					__a:    Optional[int]		     =       COMPOSITE_MODELS.values()

					# All tiny model base names on Hub
					__a:    Dict		     =       get_all_model_names()
					__a:    Optional[Any]		     =       hf_api.list_models(
					    filter=ModelFilter(
					        author="""hf-internal-testing"""						,					)			)
					__a:    Dict		     =       set()
					for x in models:
										__a:    Union[str, Any]		     =       x.modelId
										__a     ,					__a:    Optional[Any]		     =       model.split("""/"""			)
										if not model.startswith("""tiny-random-"""			):
															continue
										__a:    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(lowerCamelCase_			)

					__a:    Optional[Any]		     =       sorted(_models			)
					# All tiny model names on Hub
					__a:    Any		     =       {}
					for model in models:
										__a:    Tuple		     =       f"""hf-internal-testing/tiny-random-{model}"""
										__a:    Dict		     =       model.split("""-"""			)[0]
										try:
															__a:    str		     =       hf_api.repo_info(lowerCamelCase_			)
															__a:    List[Any]		     =       {
															    """tokenizer_classes""": set(),
															    """processor_classes""": set(),
															    """model_classes""": set(),
															    """sha""": repo_info.sha,
															}
										except Exception:
															continue
										try:
															time.sleep(1			)
															__a:    Optional[int]		     =       AutoTokenizer.from_pretrained(lowerCamelCase_			)
															content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__			)
										except Exception:
															pass
										try:
															time.sleep(1			)
															__a:    Optional[Any]		     =       AutoTokenizer.from_pretrained(lowerCamelCase_						,					use_fast=lowerCamelCase_			)
															content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__			)
										except Exception:
															pass
										try:
															time.sleep(1			)
															__a:    Union[str, Any]		     =       AutoImageProcessor.from_pretrained(lowerCamelCase_			)
															content["processor_classes"].add(img_p.__class__.__name__			)
										except Exception:
															pass
										try:
															time.sleep(1			)
															__a:    List[Any]		     =       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:    Union[str, Any]		     =       getattr(lowerCamelCase_						,					lowerCamelCase_			)
															__a:    List[str]		     =       model_class.from_pretrained(lowerCamelCase_			)
															content["model_classes"].add(m.__class__.__name__			)
										except Exception:
															pass
										try:
															time.sleep(1			)
															__a:    Optional[Any]		     =       getattr(lowerCamelCase_						,					f"""TF{model}"""			)
															__a:    Tuple		     =       model_class.from_pretrained(lowerCamelCase_			)
															content["model_classes"].add(m.__class__.__name__			)
										except Exception:
															pass

										__a:    Optional[Any]		     =       sorted(content["""tokenizer_classes"""]			)
										__a:    Optional[int]		     =       sorted(content["""processor_classes"""]			)
										__a:    int		     =       sorted(content["""model_classes"""]			)

										__a:    str		     =       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__":
			lowercase		:    Any			=       argparse.ArgumentParser()
			parser.add_argument('--num_workers', default=1, type=int, help='The number of workers to run.')
			lowercase		:    List[str]			=       parser.parse_args()

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

			lowercase		:    List[Any]			=       'tiny_models'
			lowercase		:    Tuple			=       True
			lowercase		:    Dict			=       None
			lowercase		:    Dict			=       get_tiny_model_names_from_repo()
			lowercase		:    List[str]			=       True
			lowercase		:    str			=       True
			lowercase		:    Dict			=       '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_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round((float(lowerCamelCase_			) * 9 / 5) + 32						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round(float(lowerCamelCase_			) + 273.15						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round((float(lowerCamelCase_			) * 9 / 5) + 491.67						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round((float(lowerCamelCase_			) - 32) * 5 / 9						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round(((float(lowerCamelCase_			) - 32) * 5 / 9) + 273.15						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round(float(lowerCamelCase_			) + 459.67						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round(float(lowerCamelCase_			) - 273.15						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round(((float(lowerCamelCase_			) - 273.15) * 9 / 5) + 32						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round((float(lowerCamelCase_			) * 9 / 5)						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round((float(lowerCamelCase_			) - 491.67) * 5 / 9						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round(float(lowerCamelCase_			) - 459.67						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round((float(lowerCamelCase_			) * 5 / 9)						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round((float(lowerCamelCase_			) * 1.25 + 273.15)						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round((float(lowerCamelCase_			) * 2.25 + 32)						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round((float(lowerCamelCase_			) * 1.25)						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round((float(lowerCamelCase_			) * 2.25 + 32 + 459.67)						,					lowerCamelCase_			)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
