code: 
  '''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   snake_case_       (							):
        snake_case_						       =							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_						       =							getattr(transformers.models.auto ,					_a ,					_a			)
              if module is None:
                    continue
              # all mappings in a single auto modeling file
              snake_case_						       =							[
                  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:
                    snake_case_						       =							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   snake_case_       (							):
        # All model names defined in auto mappings
        snake_case_						       =							set(get_all_model_names()			)
  
        with open("tests/utils/tiny_model_summary.json"			) as fp:
              snake_case_						       =							json.load(_a			)
        snake_case_						       =							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_						       =							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   snake_case_       (							_a     :							Union[str, Any]			):
        snake_case_						       =							COMPOSITE_MODELS.values()
  
        # All tiny model base names on Hub
        snake_case_						       =							get_all_model_names()
        snake_case_						       =							hf_api.list_models(
            filter=ModelFilter(
                author="hf-internal-testing" ,					)			)
        snake_case_						       =							set()
        for x in models:
              snake_case_						       =							x.modelId
              snake_case_       ,	snake_case_						       =							model.split("/"			)
              if not model.startswith("tiny-random-"			):
                    continue
              snake_case_						       =							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			)
  
        snake_case_						       =							sorted(_models			)
        # All tiny model names on Hub
        snake_case_						       =							{}
        for model in models:
              snake_case_						       =							F'''hf-internal-testing/tiny-random-{model}'''
              snake_case_						       =							model.split("-"			)[0]
              try:
                    snake_case_						       =							hf_api.repo_info(_a			)
                    snake_case_						       =							{
                        "tokenizer_classes": set(),
                        "processor_classes": set(),
                        "model_classes": set(),
                        "sha": repo_info.sha,
                    }
              except Exception:
                    continue
              try:
                    time.sleep(1			)
                    snake_case_						       =							AutoTokenizer.from_pretrained(_a			)
                    content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__			)
              except Exception:
                    pass
              try:
                    time.sleep(1			)
                    snake_case_						       =							AutoTokenizer.from_pretrained(_a ,					use_fast=_a			)
                    content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__			)
              except Exception:
                    pass
              try:
                    time.sleep(1			)
                    snake_case_						       =							AutoImageProcessor.from_pretrained(_a			)
                    content["processor_classes"].add(img_p.__class__.__name__			)
              except Exception:
                    pass
              try:
                    time.sleep(1			)
                    snake_case_						       =							AutoFeatureExtractor.from_pretrained(_a			)
                    if not isinstance(_a ,					_a			):
                          content["processor_classes"].add(feat_p.__class__.__name__			)
              except Exception:
                    pass
              try:
                    time.sleep(1			)
                    snake_case_						       =							getattr(_a ,					_a			)
                    snake_case_						       =							model_class.from_pretrained(_a			)
                    content["model_classes"].add(m.__class__.__name__			)
              except Exception:
                    pass
              try:
                    time.sleep(1			)
                    snake_case_						       =							getattr(_a ,					F'''TF{model}'''			)
                    snake_case_						       =							model_class.from_pretrained(_a			)
                    content["model_classes"].add(m.__class__.__name__			)
              except Exception:
                    pass
  
              snake_case_						       =							sorted(content["tokenizer_classes"]			)
              snake_case_						       =							sorted(content["processor_classes"]			)
              snake_case_						       =							sorted(content["model_classes"]			)
  
              snake_case_						       =							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__":
       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,
       )
  
  
style_context: 
  '''simple docstring'''
  
  
  
  
  
  from __future__ import annotations
  
  A           =   [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]
  
  
  
  class  lowerCAmelCase__   :
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    def __init__(self  : Optional[int]	, lowerCAmelCase_  : int    )   ->       None:
          snake_case_						       =							-1
          snake_case_						       =							-1
          snake_case_						       =							[-1] * length
          snake_case_						       =							None
          snake_case_						       =							None
  
  
    def __repr__(self  : List[Any]    )   ->       str:
          return f'''Node(min_value={self.minn} max_value={self.maxx})'''
  
  def   snake_case_       (							_a     :							list[int]			):
        snake_case_						       =							Node(len(_a			)			)
        snake_case_       ,	snake_case_						       =							min(_a			), max(_a			)
        # Leaf node case where the node contains only one unique value
        if root.minn == root.maxx:
              return root
        snake_case_						       =							(root.minn + root.maxx) // 2
  
        snake_case_						       =							[]
        snake_case_						       =							[]
  
        for index, num in enumerate(_a			):
              if num <= pivot:
                    left_arr.append(_a			)
              else:
                    right_arr.append(_a			)
              snake_case_						       =							len(_a			)
        snake_case_						       =							build_tree(_a			)
        snake_case_						       =							build_tree(_a			)
        return root
  def   snake_case_       (							_a     :							Node | None ,					_a     :							int ,					_a     :							int			):
        if index < 0 or node is None:
              return 0
        # Leaf node cases
        if node.minn == node.maxx:
              return index + 1 if node.minn == num else 0
        snake_case_						       =							(node.minn + node.maxx) // 2
        if num <= pivot:
              # go the left subtree and map index to the left subtree
              return rank_till_index(node.left ,					_a ,					node.map_left[index] - 1			)
        else:
              # go to the right subtree and map index to the right subtree
              return rank_till_index(node.right ,					_a ,					index - node.map_left[index]			)
  def   snake_case_       (							_a     :							Node | None ,					_a     :							int ,					_a     :							int ,					_a     :							int			):
        if start > end:
              return 0
        snake_case_						       =							rank_till_index(_a ,					_a ,					_a			)
        snake_case_						       =							rank_till_index(_a ,					_a ,					start - 1			)
        return rank_till_end - rank_before_start
  def   snake_case_       (							_a     :							Node | None ,					_a     :							int ,					_a     :							int ,					_a     :							int			):
        if index > (end - start) or start > end or node is None:
              return -1
        # Leaf node case
        if node.minn == node.maxx:
              return node.minn
        # Number of elements in the left subtree in interval [start, end]
        snake_case_						       =							node.map_left[end] - (
            node.map_left[start - 1] if start else 0
        )
        if num_elements_in_left_tree > index:
              return quantile(
                  node.left ,					_a ,					(node.map_left[start - 1] if start else 0) ,					node.map_left[end] - 1 ,					)
        else:
              return quantile(
                  node.right ,					index - num_elements_in_left_tree ,					start - (node.map_left[start - 1] if start else 0) ,					end - node.map_left[end] ,					)
  def   snake_case_       (							_a     :							Node | None ,					_a     :							int ,					_a     :							int ,					_a     :							int ,					_a     :							int			):
        if (
            start > end
            or node is None
            or start_num > end_num
            or node.minn > end_num
            or node.maxx < start_num
        ):
              return 0
        if start_num <= node.minn and node.maxx <= end_num:
              return end - start + 1
        snake_case_						       =							range_counting(
            node.left ,					(node.map_left[start - 1] if start else 0) ,					node.map_left[end] - 1 ,					_a ,					_a ,					)
        snake_case_						       =							range_counting(
            node.right ,					start - (node.map_left[start - 1] if start else 0) ,					end - node.map_left[end] ,					_a ,					_a ,					)
        return left + right
  
  
  if __name__ == "__main__":
       import doctest
  
       doctest.testmod()
  
  