


"""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"""

     _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,  _a,  _a							)
          if module is None:
               continue
          # all mappings in a single auto modeling file
          _a     						=					[
              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:
               _a     						=					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    A_ ( ):






     """simple docstring"""

     _a     						=					set(get_all_model_names()							)

     with open('''tests/utils/tiny_model_summary.json'''							) as fp:
          _a     						=					json.load(_a							)
     _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(_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    A_ ( _lowerCAmelCase  :       Optional[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     						=					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(_a							)

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


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

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

   __snake_case												=							'''tiny_models'''
   __snake_case												=							True
   __snake_case												=							None
   __snake_case												=							get_tiny_model_names_from_repo()
   __snake_case												=							True
   __snake_case												=							True
   __snake_case												=							'''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 math






def 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






		if 1 < number < 4:
				# 2 and 3 are primes
				return True
		elif number < 2 or number % 2 == 0 or number % 3 == 0:
				# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
				return False

		# All primes number are in format of 6k +/- 1
		for i in range(5      ,  int(math.sqrt(_a			) + 1			)      ,  6			):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True






def 	lowerCamelCase_       (			_a = 1_000      ,  _a = 1_000			):




		"""simple docstring"""






		lowerCAmelCase__  :     Union[str, Any]       	=   [0, 0, 0]  # length, a, b
		for a in range((a_limit * -1) + 1      ,  _a			):
				for b in range(2      ,  _a			):
						if is_prime(_a			):
								lowerCAmelCase__  :     str       	=   0
								lowerCAmelCase__  :     int       	=   0
								while is_prime((n**2) + (a * n) + b			):
										count += 1
										n += 1
								if count > longest[0]:
										lowerCAmelCase__  :     Dict       	=   [count, a, b]
		lowerCAmelCase__  :     int       	=   longest[1] * longest[2]
		return ans


if __name__ == "__main__":
		print(solution(1000, 1000))




