




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  snake_case_					(		)      ->			List[Any]:
							lowercase__  :	Union[str, Any]        =							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"]:
														lowercase__  :	Optional[Any]        =							getattr(transformers.models.auto    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )
														if module is None:
																					continue
														# all mappings in a single auto modeling file
														lowercase__  :	Union[str, Any]        =							[
														    x
														    for x in dir(SCREAMING_SNAKE_CASE_    )
														    if x.endswith("_MAPPING_NAMES"    )
														    and (x.startswith("MODEL_"    ) or x.startswith("TF_MODEL_"    ) or x.startswith("FLAX_MODEL_"    ))
														]
														for name in mapping_names:
																					lowercase__  :	str        =							getattr(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )
																					if mapping is not None:
																												for v in mapping.values():
																																			if isinstance(SCREAMING_SNAKE_CASE_    ,(list, tuple)    ):
																																										model_names.update(SCREAMING_SNAKE_CASE_    )
																																			elif isinstance(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ):
																																										model_names.add(SCREAMING_SNAKE_CASE_    )

							return sorted(SCREAMING_SNAKE_CASE_    )
def  snake_case_					(		)      ->			List[str]:
							# All model names defined in auto mappings
							lowercase__  :	Union[str, Any]        =							set(get_all_model_names()    )

							with open("tests/utils/tiny_model_summary.json"    ) as fp:
														lowercase__  :	str        =							json.load(SCREAMING_SNAKE_CASE_    )
							lowercase__  :	List[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.
							lowercase__  :	Tuple        =							model_names.difference(SCREAMING_SNAKE_CASE_    )
							for model_name in copy.copy(SCREAMING_SNAKE_CASE_    ):
														if not model_name.startswith("TF"    ) and F"""TF{model_name}""" in not_on_hub:
																					tiny_models_names.remove(SCREAMING_SNAKE_CASE_    )
														elif model_name.startswith("TF"    ) and model_name[2:] in not_on_hub:
																					tiny_models_names.remove(SCREAMING_SNAKE_CASE_    )

							return sorted(SCREAMING_SNAKE_CASE_    )
def  snake_case_					(		SCREAMING_SNAKE_CASE_    )      ->			Tuple:
							lowercase__  :	Optional[Any]        =							COMPOSITE_MODELS.values()

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

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

														lowercase__  :	Dict        =							sorted(content["tokenizer_classes"]    )
														lowercase__  :	Any        =							sorted(content["processor_classes"]    )
														lowercase__  :	str        =							sorted(content["model_classes"]    )

														lowercase__  :	Optional[Any]        =							content
														with open(os.path.join(SCREAMING_SNAKE_CASE_    ,"hub_tiny_model_summary.json"    )    ,"w"    ) as fp:
																					json.dump(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,ensure_ascii=SCREAMING_SNAKE_CASE_    ,indent=4    )


if __name__ == "__main__":
						__a  :		Union[str, Any]		     =   argparse.ArgumentParser()
						parser.add_argument('''--num_workers''', default=1, type=int, help='''The number of workers to run.''')
						__a  :		Optional[Any]		     =   parser.parse_args()

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

						__a  :		str		     =   '''tiny_models'''
						__a  :		int		     =   True
						__a  :		Optional[int]		     =   None
						__a  :		Dict		     =   get_tiny_model_names_from_repo()
						__a  :		int		     =   True
						__a  :		str		     =   True
						__a  :		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,
						)





import numpy as np
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			int:
							lowercase__  :	str        =							n_draws * class_counts / class_counts.sum()
							# floored means we don't overshoot n_samples, but probably undershoot
							lowercase__  :	Union[str, Any]        =							np.floor(SCREAMING_SNAKE_CASE_    )
							# we add samples according to how much "left over" probability
							# they had, until we arrive at n_samples
							lowercase__  :	Tuple        =							int(n_draws - floored.sum()    )
							if need_to_add > 0:
														lowercase__  :	Union[str, Any]        =							continuous - floored
														lowercase__  :	Optional[Any]        =							np.sort(np.unique(SCREAMING_SNAKE_CASE_    )    )[::-1]
														# add according to remainder, but break ties
														# randomly to avoid biases
														for value in values:
																					((lowercase__) ,       )  :	Optional[Any]        =							np.where(remainder == value    )
																					# if we need_to_add less than what's in inds
																					# we draw randomly from them.
																					# if we need to add more, we add them all and
																					# go to the next value
																					lowercase__  :	Dict        =							min(len(SCREAMING_SNAKE_CASE_    )    ,SCREAMING_SNAKE_CASE_    )
																					lowercase__  :	Any        =							rng.choice(SCREAMING_SNAKE_CASE_    ,size=SCREAMING_SNAKE_CASE_    ,replace=SCREAMING_SNAKE_CASE_    )
																					floored[inds] += 1
																					need_to_add -= add_now
																					if need_to_add == 0:
																												break
							return floored.astype(np.intaa    )




def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_=10    )      ->			List[str]:
							lowercase__ ,       lowercase__  :	int        =							np.unique(SCREAMING_SNAKE_CASE_    ,return_inverse=SCREAMING_SNAKE_CASE_    )
							lowercase__  :	int        =							classes.shape[0]
							lowercase__  :	Union[str, Any]        =							np.bincount(SCREAMING_SNAKE_CASE_    )
							if np.min(SCREAMING_SNAKE_CASE_    ) < 2:
														raise ValueError("Minimum class count error"    )
							if n_train < n_classes:
														raise ValueError(
														    "The train_size = %d should be greater or " "equal to the number of classes = %d" % (n_train, n_classes)    )
							if n_test < n_classes:
														raise ValueError(
														    "The test_size = %d should be greater or " "equal to the number of classes = %d" % (n_test, n_classes)    )
							lowercase__  :	List[Any]        =							np.split(np.argsort(SCREAMING_SNAKE_CASE_    ,kind="mergesort"    )    ,np.cumsum(SCREAMING_SNAKE_CASE_    )[:-1]    )
							for _ in range(SCREAMING_SNAKE_CASE_    ):
														lowercase__  :	str        =							approximate_mode(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )
														lowercase__  :	int        =							class_counts - n_i
														lowercase__  :	int        =							approximate_mode(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )

														lowercase__  :	Tuple        =							[]
														lowercase__  :	int        =							[]

														for i in range(SCREAMING_SNAKE_CASE_    ):
																					lowercase__  :	Union[str, Any]        =							rng.permutation(class_counts[i]    )
																					lowercase__  :	List[Any]        =							class_indices[i].take(SCREAMING_SNAKE_CASE_    ,mode="clip"    )
																					train.extend(perm_indices_class_i[: n_i[i]]    )
																					test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]    )
														lowercase__  :	Tuple        =							rng.permutation(SCREAMING_SNAKE_CASE_    )
														lowercase__  :	Dict        =							rng.permutation(SCREAMING_SNAKE_CASE_    )

														yield train, test