



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_  ():





			"""simple docstring"""


			UpperCAmelCase_:		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"]:
						UpperCAmelCase_:		Optional[Any]       			=    getattr(transformers.models.auto		,	lowerCAmelCase__		,	lowerCAmelCase__     )
						if module is None:
									continue
						# all mappings in a single auto modeling file
						UpperCAmelCase_:		Tuple       			=    [
						    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:
									UpperCAmelCase_:		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_  ():





			"""simple docstring"""


			UpperCAmelCase_:		Tuple       			=    set(get_all_model_names()     )

			with open("""tests/utils/tiny_model_summary.json"""     ) as fp:
						UpperCAmelCase_:		List[str]       			=    json.load(lowerCAmelCase__     )
			UpperCAmelCase_:		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.
			UpperCAmelCase_:		Union[str, Any]       			=    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__:		Tuple     ):





			"""simple docstring"""


			UpperCAmelCase_:		int       			=    COMPOSITE_MODELS.values()

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

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

						UpperCAmelCase_:		str       			=    sorted(content["""tokenizer_classes"""]     )
						UpperCAmelCase_:		Any       			=    sorted(content["""processor_classes"""]     )
						UpperCAmelCase_:		Optional[Any]       			=    sorted(content["""model_classes"""]     )

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

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

	a			:     List[str]     = 'tiny_models'
	a			:     Dict     = True
	a			:     str     = None
	a			:     Tuple     = get_tiny_model_names_from_repo()
	a			:     List[str]     = True
	a			:     str     = True
	a			:     int     = '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,
	)











def    lowerCAmelCase_  (lowerCAmelCase__:		str     ):





			"""simple docstring"""


			return "".join(chr(ord(lowerCAmelCase__     ) + 3_2     ) if """A""" <= char <= """Z""" else char for char in word     )


if __name__ == "__main__":
	from doctest import testmod

	testmod()






