
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


A__   :		List[Any] 		=[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-classification',
        'language-modeling',
        'summarization',
        'token-classification',
        'question-answering',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
     import run_clm_flax
     import run_flax_glue
     import run_flax_ner
     import run_mlm_flax
     import run_qa
     import run_summarization_flax
     import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

A__   :		Dict 		=logging.getLogger()







def        A_     (						)				->       str:





  """simple docstring"""

  __A		:     Optional[Any]												=		argparse.ArgumentParser()
  parser.add_argument("""-f""" )
  __A		:     Tuple												=		parser.parse_args()
  return args.f







def        A_     (						__SCREAMING_SNAKE_CASE						:  str			,     __SCREAMING_SNAKE_CASE						:  int="eval" )				->       Any:





  """simple docstring"""

  __A		:     int												=		os.path.join(__SCREAMING_SNAKE_CASE			,     F"{split}_results.json" )
  if os.path.exists(__SCREAMING_SNAKE_CASE ):
    with open(__SCREAMING_SNAKE_CASE			,     """r""" ) as f:
      return json.load(__SCREAMING_SNAKE_CASE )
  raise ValueError(F"can't find {path}" )


A__   :		Optional[int] 		=logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class  __A				( _SCREAMING_SNAKE_CASE			):
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   __A		:     Tuple												=		self.get_auto_remove_tmp_dir()
   __A		:     Optional[Any]												=		f"\n            run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --eval_steps=2\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

   with patch.object(lowerCamelCase				,   """argv"""				,   lowerCamelCase       ):
     run_flax_glue.main()
     __A		:     List[Any]												=		get_results(lowerCamelCase       )
     self.assertGreaterEqual(result["""eval_accuracy"""]				,   0.75       )
 @slow
 def 		lowercase_(   self			:    int       ):



   """simple docstring"""





   __A		:     Optional[Any]												=		self.get_auto_remove_tmp_dir()
   __A		:     int												=		f"\n            run_clm_flax.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

   with patch.object(lowerCamelCase				,   """argv"""				,   lowerCamelCase       ):
     run_clm_flax.main()
     __A		:     str												=		get_results(lowerCamelCase       )
     self.assertLess(result["""eval_perplexity"""]				,   1_00       )
 @slow
 def 		lowercase_(   self			:    int       ):



   """simple docstring"""





   __A		:     Optional[int]												=		self.get_auto_remove_tmp_dir()
   __A		:     Optional[int]												=		f"\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --test_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --do_predict\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --predict_with_generate\n        ".split()

   with patch.object(lowerCamelCase				,   """argv"""				,   lowerCamelCase       ):
     run_summarization_flax.main()
     __A		:     List[str]												=		get_results(lowerCamelCase				,   split="""test"""       )
     self.assertGreaterEqual(result["""test_rouge1"""]				,   10       )
     self.assertGreaterEqual(result["""test_rouge2"""]				,   2       )
     self.assertGreaterEqual(result["""test_rougeL"""]				,   7       )
     self.assertGreaterEqual(result["""test_rougeLsum"""]				,   7       )
 @slow
 def 		lowercase_(   self			:    List[Any]       ):



   """simple docstring"""





   __A		:     int												=		self.get_auto_remove_tmp_dir()
   __A		:     Union[str, Any]												=		f"\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --logging_steps 2 --eval_steps 2\n            --do_train\n            --do_eval\n            --num_train_epochs=1\n        ".split()

   with patch.object(lowerCamelCase				,   """argv"""				,   lowerCamelCase       ):
     run_mlm_flax.main()
     __A		:     List[str]												=		get_results(lowerCamelCase       )
     self.assertLess(result["""eval_perplexity"""]				,   42       )
 @slow
 def 		lowercase_(   self			:    Union[str, Any]       ):



   """simple docstring"""





   __A		:     List[Any]												=		self.get_auto_remove_tmp_dir()
   __A		:     Optional[Any]												=		f"\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

   with patch.object(lowerCamelCase				,   """argv"""				,   lowerCamelCase       ):
     run_ta_mlm_flax.main()
     __A		:     Optional[Any]												=		get_results(lowerCamelCase       )
     self.assertGreaterEqual(result["""eval_accuracy"""]				,   0.42       )
 @slow
 def 		lowercase_(   self			:    Union[str, Any]       ):



   """simple docstring"""





   __A		:     Dict												=		7 if get_gpu_count() > 1 else 2

   __A		:     Dict												=		self.get_auto_remove_tmp_dir()
   __A		:     List[str]												=		f"\n            run_flax_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --logging_steps 2 --eval_steps 2\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        ".split()

   with patch.object(lowerCamelCase				,   """argv"""				,   lowerCamelCase       ):
     run_flax_ner.main()
     __A		:     str												=		get_results(lowerCamelCase       )
     self.assertGreaterEqual(result["""eval_accuracy"""]				,   0.75       )
     self.assertGreaterEqual(result["""eval_f1"""]				,   0.3       )






 @slow
 def 		lowercase_(   self			:    Optional[int]       ):



   """simple docstring"""





   __A		:     Dict												=		self.get_auto_remove_tmp_dir()
   __A		:     int												=		f"\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --logging_steps 2 --eval_steps 2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

   with patch.object(lowerCamelCase				,   """argv"""				,   lowerCamelCase       ):
     run_qa.main()
     __A		:     List[Any]												=		get_results(lowerCamelCase       )
     self.assertGreaterEqual(result["""eval_f1"""]				,   30       )
     self.assertGreaterEqual(result["""eval_exact"""]				,   30       )





'''simple docstring'''
def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       int:





  """simple docstring"""

  __A		:     Optional[int]												=		1
  for i in range(1			,     num + 1 ):
    fact *= i
  return fact







def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       int:





  """simple docstring"""

  __A		:     List[str]												=		0
  while number > 0:
    __A		:     Union[str, Any]												=		number % 10
    sum_of_digits += last_digit
    __A		:     int												=		number // 10  # Removing the last_digit from the given number
  return sum_of_digits







def        A_     (						__SCREAMING_SNAKE_CASE						:  int = 100 )				->       int:





  """simple docstring"""

  __A		:     Union[str, Any]												=		factorial(__SCREAMING_SNAKE_CASE )
  __A		:     Any												=		split_and_add(__SCREAMING_SNAKE_CASE )
  return result


if __name__ == "__main__":
     print(solution(int(input('Enter the Number: ').strip())))



