





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

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

				UpperCamelCase__ :			int       	=			"""tiny_models"""
				UpperCamelCase__ :			Optional[Any]       	=			True
				UpperCamelCase__ :			List[str]       	=			None
				UpperCamelCase__ :			List[Any]       	=			get_tiny_model_names_from_repo()
				UpperCamelCase__ :			Union[str, Any]       	=			True
				UpperCamelCase__ :			Dict       	=			True
				UpperCamelCase__ :			List[str]       	=			"""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  A_(      A ):
	if not isinstance(A					,   A ):
		UpperCAmelCase_		    =  f"""Input value of [number={number}] must be an integer"""
		raise TypeError(A )
	if number < 0 or number % 2 == 1:
		return False
	UpperCAmelCase_		    =  int(number**0.5 )
	return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
				import doctest

				doctest.testmod()


