




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       __magic_name__    (			):






			'''simple docstring'''







			a   =      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   =      getattr(transformers.models.auto,  A,  A       )
						if module is None:
									continue
						# all mappings in a single auto modeling file
						a   =      [
						    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:
									a   =      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       __magic_name__    (			):






			'''simple docstring'''







			a   =      set(get_all_model_names()       )

			with open("tests/utils/tiny_model_summary.json"       ) as fp:
						a   =      json.load(A       )
			a   =      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   =      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       __magic_name__    (			A		: List[Any]       ):






			'''simple docstring'''







			a   =      COMPOSITE_MODELS.values()

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

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

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

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

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

		__lowerCAmelCase		:    Optional[Any]	      =		'tiny_models'
		__lowerCAmelCase		:    Optional[int]	      =		True
		__lowerCAmelCase		:    str	      =		None
		__lowerCAmelCase		:    str	      =		get_tiny_model_names_from_repo()
		__lowerCAmelCase		:    Dict	      =		True
		__lowerCAmelCase		:    str	      =		True
		__lowerCAmelCase		:    Optional[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       __magic_name__    (			A		: float       ):






			'''simple docstring'''







			return -num if num < 0 else num




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_min() arg is an empty sequence"       )
			a   =      x[0]
			for i in x:
						if abs_val(A       ) < abs_val(A       ):
									a   =      i
			return j




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_max() arg is an empty sequence"       )
			a   =      x[0]
			for i in x:
						if abs(A       ) > abs(A       ):
									a   =      i
			return j




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_max_sort() arg is an empty sequence"       )
			return sorted(A,  key=A       )[-1]




def       __magic_name__    (			):






			'''simple docstring'''







			assert abs_val(0       ) == 0
			assert abs_val(34       ) == 34
			assert abs_val(-100000000000       ) == 100000000000

			a   =      [-3, -1, 2, -11]
			assert abs_max(A       ) == -11
			assert abs_max_sort(A       ) == -11
			assert abs_min(A       ) == -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		test_abs_val()
		print(abs_val(-34))  # --> 34






