

"""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'''


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

			return sorted(snake_case__      )

def 		A    (       ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= set(get_all_model_names()      )

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

			return sorted(snake_case__      )

def 		A    (       snake_case__      ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= COMPOSITE_MODELS.values()

			# All tiny model base names on Hub
			SCREAMING_SNAKE_CASE__									= get_all_model_names()
			SCREAMING_SNAKE_CASE__									= hf_api.list_models(
			    filter=ModelFilter(
			        author="""hf-internal-testing"""						,			)      )
			SCREAMING_SNAKE_CASE__									= set()
			for x in models:
						SCREAMING_SNAKE_CASE__									= x.modelId
						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= model.split("""/"""      )
						if not model.startswith("""tiny-random-"""      ):
									continue
						SCREAMING_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(snake_case__      )

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

						SCREAMING_SNAKE_CASE__									= sorted(content["""tokenizer_classes"""]      )
						SCREAMING_SNAKE_CASE__									= sorted(content["""processor_classes"""]      )
						SCREAMING_SNAKE_CASE__									= sorted(content["""model_classes"""]      )

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


if __name__ == "__main__":
						A_							:     List[str]     =	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_							:     Tuple     =	True
						A_							:     Optional[Any]     =	None
						A_							:     int     =	get_tiny_model_names_from_repo()
						A_							:     int     =	True
						A_							:     List[str]     =	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,
						)





"""simple docstring"""




import math

def 		A    (       snake_case__      ):




			'''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(snake_case__      ) + 1      )						,			6      ):
						if number % i == 0 or number % (i + 2) == 0:
									return False
			return True

def 		A    (       snake_case__ = 10_00						,			snake_case__ = 10_00      ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= [0, 0, 0]  # length, a, b
			for a in range((a_limit * -1) + 1						,			snake_case__      ):
						for b in range(2						,			snake_case__      ):
									if is_prime(snake_case__      ):
												SCREAMING_SNAKE_CASE__									= 0
												SCREAMING_SNAKE_CASE__									= 0
												while is_prime((n**2) + (a * n) + b      ):
															count += 1
															n += 1
												if count > longest[0]:
															SCREAMING_SNAKE_CASE__									= [count, a, b]
			SCREAMING_SNAKE_CASE__									= longest[1] * longest[2]
			return ans


if __name__ == "__main__":
						print(solution(1_000, 1_000))


