



"""simple docstring"""
from __future__ import annotations
def   A_    (     _lowercase,   _lowercase      ):







						'''simple docstring'''


						snake_case_					:Optional[Any]		=     0
						snake_case_					:Any		=     len(_lowercase      ) - 1
						while left <= right:
												snake_case_					:Union[str, Any]		=     (left + right) // 2
												if left == right:
																		if sorted_list[middle] < item:
																								snake_case_					:Dict		=     middle + 1
																		break
												elif sorted_list[middle] < item:
																		snake_case_					:Any		=     middle + 1
												else:
																		snake_case_					:Dict		=     middle - 1
						sorted_list.insert(_lowercase,   _lowercase      )
						return sorted_list
def   A_    (     _lowercase,   _lowercase      ):







						'''simple docstring'''


						snake_case_					:List[Any]		=     []
						while left and right:
												if left[0][0] < right[0][0]:
																		result.append(left.pop(0      )      )
												else:
																		result.append(right.pop(0      )      )
						return result + left + right
def   A_    (     _lowercase      ):







						'''simple docstring'''


						snake_case_					:Union[str, Any]		=     len(_lowercase      )
						if length <= 1:
												return list_ad
						snake_case_					:Optional[int]		=     length // 2
						return merge(sortlist_ad(list_ad[:middle]      ),   sortlist_ad(list_ad[middle:]      )      )
def   A_    (     _lowercase      ):







						'''simple docstring'''


						if len(_lowercase      ) <= 1:
												return collection
						snake_case_					:Optional[Any]		=     []
						snake_case_					:Tuple		=     False
						for i in range(0,   len(_lowercase      ),   2      ):
												if i == len(_lowercase      ) - 1:
																		snake_case_					:Optional[int]		=     True
												else:
																		if collection[i] < collection[i + 1]:
																								two_paired_list.append([collection[i], collection[i + 1]]      )
																		else:
																								two_paired_list.append([collection[i + 1], collection[i]]      )
						snake_case_					:List[str]		=     sortlist_ad(_lowercase      )
						snake_case_					:Any		=     [i[0] for i in sorted_list_ad]
						result.append(sorted_list_ad[-1][1]      )
						if has_last_odd_item:
												snake_case_					:Union[str, Any]		=     collection[-1]
												snake_case_					:int		=     binary_search_insertion(_lowercase,   _lowercase      )
						snake_case_					:Union[str, Any]		=     False
						for i in range(len(_lowercase      ) - 1      ):
												if result[i] == collection[-1] and has_last_odd_item:
																		snake_case_					:str		=     True
												snake_case_					:Optional[int]		=     sorted_list_ad[i][1]
												# If last_odd_item is inserted before the item's index,
												# you should forward index one more.
												if is_last_odd_item_inserted_before_this_index:
																		snake_case_					:Union[str, Any]		=     result[: i + 2] + binary_search_insertion(result[i + 2 :],   _lowercase      )
												else:
																		snake_case_					:Optional[Any]		=     result[: i + 1] + binary_search_insertion(result[i + 1 :],   _lowercase      )

						return result


if __name__ == "__main__":
						__a           =							input("Enter numbers separated by a comma:\n").strip()
						__a           =							[int(item) for item in user_input.split(",")]
						print(merge_insertion_sort(unsorted))





"""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   A_    (     ):







						'''simple docstring'''


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

						return sorted(_lowercase      )
def   A_    (     ):







						'''simple docstring'''


						snake_case_					:List[str]		=     set(get_all_model_names()      )

						with open("""tests/utils/tiny_model_summary.json"""      ) as fp:
												snake_case_					:Any		=     json.load(_lowercase      )
						snake_case_					: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.
						snake_case_					:Any		=     model_names.difference(_lowercase      )
						for model_name in copy.copy(_lowercase      ):
												if not model_name.startswith("""TF"""      ) and f"""TF{model_name}""" in not_on_hub:
																		tiny_models_names.remove(_lowercase      )
												elif model_name.startswith("""TF"""      ) and model_name[2:] in not_on_hub:
																		tiny_models_names.remove(_lowercase      )

						return sorted(_lowercase      )
def   A_    (     _lowercase      ):







						'''simple docstring'''


						snake_case_					:Tuple		=     COMPOSITE_MODELS.values()

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

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

												snake_case_					:Union[str, Any]		=     sorted(content["""tokenizer_classes"""]      )
												snake_case_					:Dict		=     sorted(content["""processor_classes"""]      )
												snake_case_					:List[str]		=     sorted(content["""model_classes"""]      )

												snake_case_					:Tuple		=     content
												with open(os.path.join(_lowercase,   """hub_tiny_model_summary.json"""      ),   """w"""      ) as fp:
																		json.dump(_lowercase,   _lowercase,   ensure_ascii=_lowercase,   indent=4      )


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

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

						__a           =							"tiny_models"
						__a           =							True
						__a           =							None
						__a           =							get_tiny_model_names_from_repo()
						__a           =							True
						__a           =							True
						__a           =							"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,
						)
