


'''simple docstring'''







from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar


class 				__lowerCamelCase				( a_ ):
    """simple docstring"""


    @abstractmethod
    def __lt__(						self    :							T   ,							SCREAMING_SNAKE_CASE    :							T):
           pass


    @abstractmethod
    def __gt__(						self    :							T   ,							SCREAMING_SNAKE_CASE    :							T):
           pass


    @abstractmethod
    def __eq__(						self    :							T   ,							SCREAMING_SNAKE_CASE    :							object):
           pass




A   :  List[str]       		=				TypeVar('''T''', bound=Comparable)


class 				__lowerCamelCase				( Generic[T] ):
    """simple docstring"""


    def __init__(						self    :							List[Any]):
           _A  :			list[T]											=      []
           _A  :			int											=      0


    def __repr__(						self    :							Dict):
           return str(self.h)


    def      A					(						self    :							Dict   ,							SCREAMING_SNAKE_CASE    :							int):
           if child_idx > 0:
                  return (child_idx - 1) // 2
           return None


    def      A					(						self    :							str   ,							SCREAMING_SNAKE_CASE    :							int):
           _A  :			Tuple											=      2 * parent_idx + 1
           if left_child_index < self.heap_size:
                  return left_child_index
           return None


    def      A					(						self    :							Dict   ,							SCREAMING_SNAKE_CASE    :							int):
           _A  :			List[str]											=      2 * parent_idx + 2
           if right_child_index < self.heap_size:
                  return right_child_index
           return None


    def      A					(						self    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							int):
           if index < self.heap_size:
                  _A  :			int											=      index
                  _A  :			Tuple											=      self.left_child_idx(SCREAMING_SNAKE_CASE)
                  _A  :			Dict											=      self.right_child_idx(SCREAMING_SNAKE_CASE)
                  # check which child is larger than its parent
                  if left_child is not None and self.h[left_child] > self.h[violation]:
                         _A  :			List[str]											=      left_child
                  if right_child is not None and self.h[right_child] > self.h[violation]:
                         _A  :			Optional[Any]											=      right_child
                  # if violation indeed exists
                  if violation != index:
                         # swap to fix the violation
                         _A						,				_A  :			Optional[Any]											=      self.h[index], self.h[violation]
                         # fix the subsequent violation recursively if any
                         self.max_heapify(SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							Iterable[T]):
           _A  :			List[str]											=      list(SCREAMING_SNAKE_CASE)
           _A  :			Optional[int]											=      len(self.h)
           if self.heap_size > 1:
                  # max_heapify from right to left but exclude leaves (last level)
                  for i in range(self.heap_size // 2 - 1   ,							-1   ,							-1):
                         self.max_heapify(SCREAMING_SNAKE_CASE)


    def      A					(						self    :							int):
           if self.heap_size >= 2:
                  _A  :			Union[str, Any]											=      self.h[0]
                  _A  :			str											=      self.h.pop(-1)
                  self.heap_size -= 1
                  self.max_heapify(0)
                  return me
           elif self.heap_size == 1:
                  self.heap_size -= 1
                  return self.h.pop(-1)
           else:
                  raise Exception('Empty heap')


    def      A					(						self    :							int   ,							SCREAMING_SNAKE_CASE    :							T):
           self.h.append(SCREAMING_SNAKE_CASE)
           _A  :			Any											=      (self.heap_size - 1) // 2
           self.heap_size += 1
           while idx >= 0:
                  self.max_heapify(SCREAMING_SNAKE_CASE)
                  _A  :			Optional[Any]											=      (idx - 1) // 2


    def      A					(						self    :							int):
           _A  :			List[Any]											=      self.heap_size
           for j in range(size - 1   ,							0   ,							-1):
                  _A						,				_A  :			Any											=      self.h[j], self.h[0]
                  self.heap_size -= 1
                  self.max_heapify(0)
           _A  :			int											=      size


if __name__ == "__main__":
   import doctest

   # run doc test
   doctest.testmod()

   # demo
   for unsorted in [
       [0],
       [2],
       [3, 5],
       [5, 3],
       [5, 5],
       [0, 0, 0, 0],
       [1, 1, 1, 1],
       [2, 2, 3, 5],
       [0, 2, 2, 3, 5],
       [2, 5, 3, 0, 2, 3, 0, 3],
       [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
       [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5],
       [-45, -2, -5],
   ]:
      print(f"""unsorted array: {unsorted}""")

      A   :  Heap[int]       		=				Heap()
      heap.build_max_heap(unsorted)
      print(f"""after build heap: {heap}""")

      print(f"""max value: {heap.extract_max()}""")
      print(f"""after max value removed: {heap}""")

      heap.insert(100)
      print(f"""after new value 100 inserted: {heap}""")

      heap.heap_sort()
      print(f"""heap-sorted array: {heap}\n""")










'''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    lowerCAmelCase__ (       ):
       _A  :			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"]:
              _A  :			Optional[Any]											=      getattr(transformers.models.auto						,lowerCamelCase						,lowerCamelCase     )
              if module is None:
                     continue
              # all mappings in a single auto modeling file
              _A  :			Any											=      [
                  x
                  for x in dir(lowerCamelCase     )
                  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  :			Tuple											=      getattr(lowerCamelCase						,lowerCamelCase     )
                     if mapping is not None:
                            for v in mapping.values():
                                   if isinstance(lowerCamelCase						,(list, tuple)     ):
                                          model_names.update(lowerCamelCase     )
                                   elif isinstance(lowerCamelCase						,lowerCamelCase     ):
                                          model_names.add(lowerCamelCase     )

       return sorted(lowerCamelCase     )






def    lowerCAmelCase__ (       ):
       # All model names defined in auto mappings
       _A  :			Union[str, Any]											=      set(get_all_model_names()     )

       with open('tests/utils/tiny_model_summary.json'     ) as fp:
              _A  :			Any											=      json.load(lowerCamelCase     )
       _A  :			Union[str, 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.
       _A  :			List[Any]											=      model_names.difference(lowerCamelCase     )
       for model_name in copy.copy(lowerCamelCase     ):
              if not model_name.startswith('TF'     ) and F'TF{model_name}' in not_on_hub:
                     tiny_models_names.remove(lowerCamelCase     )
              elif model_name.startswith('TF'     ) and model_name[2:] in not_on_hub:
                     tiny_models_names.remove(lowerCamelCase     )

       return sorted(lowerCamelCase     )






def    lowerCAmelCase__ (       lowerCamelCase  :    Union[str, Any]     ):
       _A  :			Optional[int]											=      COMPOSITE_MODELS.values()

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

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

              _A  :			Any											=      sorted(content['tokenizer_classes']     )
              _A  :			Tuple											=      sorted(content['processor_classes']     )
              _A  :			List[str]											=      sorted(content['model_classes']     )

              _A  :			int											=      content
              with open(os.path.join(lowerCamelCase						,'hub_tiny_model_summary.json'     )						,'w'     ) as fp:
                     json.dump(lowerCamelCase						,lowerCamelCase						,ensure_ascii=lowerCamelCase						,indent=4     )


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

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

   A   :  Any       		=				'''tiny_models'''
   A   :  Union[str, Any]       		=				True
   A   :  List[Any]       		=				None
   A   :  Any       		=				get_tiny_model_names_from_repo()
   A   :  List[str]       		=				True
   A   :  Union[str, Any]       		=				True
   A   :  Tuple       		=				'''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,
   )






