





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    SCREAMING_SNAKE_CASE							(			)		->						List[Any]:
		__lowercase					=    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"]:
				__lowercase					=    getattr(transformers.models.auto	,      snake_case	,      snake_case       )
				if module is None:
						continue
				# all mappings in a single auto modeling file
				__lowercase					=    [
				    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:
						__lowercase					=    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    SCREAMING_SNAKE_CASE							(			)		->						Dict:
		# All model names defined in auto mappings
		__lowercase					=    set(get_all_model_names()       )

		with open('tests/utils/tiny_model_summary.json'       ) as fp:
				__lowercase					=    json.load(snake_case       )
		__lowercase					=    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.
		__lowercase					=    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    SCREAMING_SNAKE_CASE							(			snake_case       )		->						Any:
		__lowercase					=    COMPOSITE_MODELS.values()

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

		__lowercase					=    sorted(_models       )
		# All tiny model names on Hub
		__lowercase					=    {}
		for model in models:
				__lowercase					=    F"hf-internal-testing/tiny-random-{model}"
				__lowercase					=    model.split('-'       )[0]
				try:
						__lowercase					=    hf_api.repo_info(snake_case       )
						__lowercase					=    {
						    'tokenizer_classes': set(),
						    'processor_classes': set(),
						    'model_classes': set(),
						    'sha': repo_info.sha,
						}
				except Exception:
						continue
				try:
						time.sleep(1       )
						__lowercase					=    AutoTokenizer.from_pretrained(snake_case       )
						content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__       )
				except Exception:
						pass
				try:
						time.sleep(1       )
						__lowercase					=    AutoTokenizer.from_pretrained(snake_case	,      use_fast=snake_case       )
						content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__       )
				except Exception:
						pass
				try:
						time.sleep(1       )
						__lowercase					=    AutoImageProcessor.from_pretrained(snake_case       )
						content["processor_classes"].add(img_p.__class__.__name__       )
				except Exception:
						pass
				try:
						time.sleep(1       )
						__lowercase					=    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       )
						__lowercase					=    getattr(snake_case	,      snake_case       )
						__lowercase					=    model_class.from_pretrained(snake_case       )
						content["model_classes"].add(m.__class__.__name__       )
				except Exception:
						pass
				try:
						time.sleep(1       )
						__lowercase					=    getattr(snake_case	,      F"TF{model}"       )
						__lowercase					=    model_class.from_pretrained(snake_case       )
						content["model_classes"].add(m.__class__.__name__       )
				except Exception:
						pass

				__lowercase					=    sorted(content['tokenizer_classes']       )
				__lowercase					=    sorted(content['processor_classes']       )
				__lowercase					=    sorted(content['model_classes']       )

				__lowercase					=    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__":
				SCREAMING_SNAKE_CASE_      :			Optional[Any]		  =      argparse.ArgumentParser()
				parser.add_argument('''--num_workers''', default=1, type=int, help='''The number of workers to run.''')
				SCREAMING_SNAKE_CASE_      :			Tuple		  =      parser.parse_args()

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

				SCREAMING_SNAKE_CASE_      :			Optional[int]		  =      '''tiny_models'''
				SCREAMING_SNAKE_CASE_      :			Union[str, Any]		  =      True
				SCREAMING_SNAKE_CASE_      :			Dict		  =      None
				SCREAMING_SNAKE_CASE_      :			str		  =      get_tiny_model_names_from_repo()
				SCREAMING_SNAKE_CASE_      :			Tuple		  =      True
				SCREAMING_SNAKE_CASE_      :			List[Any]		  =      True
				SCREAMING_SNAKE_CASE_      :			List[str]		  =      '''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 math import floor, sqrt



def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						int:
		__lowercase					=    0.0
		__lowercase					=    1.0
		__lowercase					=    int(sqrt(snake_case       )       )
		__lowercase					=    root
		__lowercase					=    0
		while integer_part != 2 * root:
				__lowercase					=    denominator * integer_part - numerator
				__lowercase					=    (n - numerator**2) / denominator
				__lowercase					=    int((root + numerator) / denominator       )
				period += 1
		return period



def    SCREAMING_SNAKE_CASE							(			snake_case = 10_000       )		->						int:
		__lowercase					=    0
		for i in range(2	,      n + 1       ):
				__lowercase					=    sqrt(snake_case       )
				if sr - floor(snake_case       ) != 0 and continuous_fraction_period(snake_case       ) % 2 == 1:
						count_odd_periods += 1
		return count_odd_periods


if __name__ == "__main__":
		print(F"""{solution(int(input().strip()))}""")




