





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     UpperCAmelCase_						(						)  ->							List[Any]:
     '''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     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)
          if module is None:
               continue
          # all mappings in a single auto modeling file
          A_					      =					[
              x
              for x in dir(_UpperCAmelCase							)
              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(_UpperCAmelCase     ,	_UpperCAmelCase							)
               if mapping is not None:
                    for v in mapping.values():
                         if isinstance(_UpperCAmelCase     ,	(list, tuple)							):
                              model_names.update(_UpperCAmelCase							)
                         elif isinstance(_UpperCAmelCase     ,	_UpperCAmelCase							):
                              model_names.add(_UpperCAmelCase							)

     return sorted(_UpperCAmelCase							)

def     UpperCAmelCase_						(						)  ->							Tuple:
     '''simple docstring'''


     A_					      =					set(get_all_model_names()							)

     with open('''tests/utils/tiny_model_summary.json'''							) as fp:
          A_					      =					json.load(_UpperCAmelCase							)
     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(_UpperCAmelCase							)
     for model_name in copy.copy(_UpperCAmelCase							):
          if not model_name.startswith('''TF'''							) and f'TF{model_name}' in not_on_hub:
               tiny_models_names.remove(_UpperCAmelCase							)
          elif model_name.startswith('''TF'''							) and model_name[2:] in not_on_hub:
               tiny_models_names.remove(_UpperCAmelCase							)

     return sorted(_UpperCAmelCase							)

def     UpperCAmelCase_						(						_UpperCAmelCase       :str							)  ->							int:
     '''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(_UpperCAmelCase							)

     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(_UpperCAmelCase							)
               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(_UpperCAmelCase							)
               content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__							)
          except Exception:
               pass
          try:
               time.sleep(1							)
               A_					      =					AutoTokenizer.from_pretrained(_UpperCAmelCase     ,	use_fast=_UpperCAmelCase							)
               content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__							)
          except Exception:
               pass
          try:
               time.sleep(1							)
               A_					      =					AutoImageProcessor.from_pretrained(_UpperCAmelCase							)
               content["processor_classes"].add(img_p.__class__.__name__							)
          except Exception:
               pass
          try:
               time.sleep(1							)
               A_					      =					AutoFeatureExtractor.from_pretrained(_UpperCAmelCase							)
               if not isinstance(_UpperCAmelCase     ,	_UpperCAmelCase							):
                    content["processor_classes"].add(feat_p.__class__.__name__							)
          except Exception:
               pass
          try:
               time.sleep(1							)
               A_					      =					getattr(_UpperCAmelCase     ,	_UpperCAmelCase							)
               A_					      =					model_class.from_pretrained(_UpperCAmelCase							)
               content["model_classes"].add(m.__class__.__name__							)
          except Exception:
               pass
          try:
               time.sleep(1							)
               A_					      =					getattr(_UpperCAmelCase     ,	f'TF{model}'							)
               A_					      =					model_class.from_pretrained(_UpperCAmelCase							)
               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(_UpperCAmelCase     ,	'''hub_tiny_model_summary.json'''							)     ,	'''w'''							) as fp:
               json.dump(_UpperCAmelCase     ,	_UpperCAmelCase     ,	ensure_ascii=_UpperCAmelCase     ,	indent=4							)


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

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

      a__			:    List[Any]			      =		'tiny_models'
      a__			:    List[Any]			      =		True
      a__			:    Optional[int]			      =		None
      a__			:    List[Any]			      =		get_tiny_model_names_from_repo()
      a__			:    int			      =		True
      a__			:    Optional[int]			      =		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,
      )










from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
a__			:    List[str]			      =		load_iris()
iris.keys()


print(f'''Target names: \n {iris.target_names} ''')
print(f'''\n Features: \n {iris.feature_names}''')

# Train set e Test set
a__      ,							a__      ,							a__      ,							a__			:    Any			      =		train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

a__			:    List[Any]			      =		KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
a__			:    Optional[Any]			      =		[[1, 2, 1, 4], [2, 3, 4, 5]]

a__			:    Union[str, Any]			      =		knn.predict(X_new)

print(
    f'''\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'''
    f''' {iris['target_names'][prediction]}'''
)



