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


__lowerCamelCase      :			Optional[int]							       =		[
    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)

__lowerCamelCase      :			List[str]							       =		logging.getLogger()



def      __SCREAMING_SNAKE_CASE  ( )							-> Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				argparse.ArgumentParser()
  parser.add_argument("""-f"""     )
  SCREAMING_SNAKE_CASE__      							=				parser.parse_args()
  return args.f



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[Any]			,					__UpperCamelCase   :	Optional[int]="eval"     )							-> Union[str, Any]:







  """simple docstring"""





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


__lowerCamelCase      :			List[str]							       =		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class __snake_case    (						lowerCamelCase__		):




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__      							=				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(_lowercase	,     """argv"""	,     _lowercase				):
       run_flax_glue.main()
       SCREAMING_SNAKE_CASE__      							=				get_results(_lowercase				)
       self.assertGreaterEqual(result["""eval_accuracy"""]	,     0.75				)




   @slow
   def      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__      							=				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(_lowercase	,     """argv"""	,     _lowercase				):
       run_clm_flax.main()
       SCREAMING_SNAKE_CASE__      							=				get_results(_lowercase				)
       self.assertLess(result["""eval_perplexity"""]	,     1_00				)




   @slow
   def      __a						(   self      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__      							=				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(_lowercase	,     """argv"""	,     _lowercase				):
       run_summarization_flax.main()
       SCREAMING_SNAKE_CASE__      							=				get_results(_lowercase	,     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      __a						(   self      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__      							=				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(_lowercase	,     """argv"""	,     _lowercase				):
       run_mlm_flax.main()
       SCREAMING_SNAKE_CASE__      							=				get_results(_lowercase				)
       self.assertLess(result["""eval_perplexity"""]	,     42				)




   @slow
   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__      							=				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(_lowercase	,     """argv"""	,     _lowercase				):
       run_ta_mlm_flax.main()
       SCREAMING_SNAKE_CASE__      							=				get_results(_lowercase				)
       self.assertGreaterEqual(result["""eval_accuracy"""]	,     0.42				)




   @slow
   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				7 if get_gpu_count() > 1 else 2

     SCREAMING_SNAKE_CASE__      							=				self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__      							=				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(_lowercase	,     """argv"""	,     _lowercase				):
       run_flax_ner.main()
       SCREAMING_SNAKE_CASE__      							=				get_results(_lowercase				)
       self.assertGreaterEqual(result["""eval_accuracy"""]	,     0.75				)
       self.assertGreaterEqual(result["""eval_f1"""]	,     0.3				)




   @slow
   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__      							=				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(_lowercase	,     """argv"""	,     _lowercase				):
       run_qa.main()
       SCREAMING_SNAKE_CASE__      							=				get_results(_lowercase				)
       self.assertGreaterEqual(result["""eval_f1"""]	,     30				)
       self.assertGreaterEqual(result["""eval_exact"""]	,     30				)

from math import isqrt



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int     )							-> list[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[True] * max_number
  for i in range(2			,					isqrt(max_number - 1     ) + 1     ):
    if is_prime[i]:
      for j in range(i**2			,					__UpperCamelCase			,					__UpperCamelCase     ):
        SCREAMING_SNAKE_CASE__      							=				False

  return [i for i in range(2			,					__UpperCamelCase     ) if is_prime[i]]



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int = 10**8     )							-> int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				calculate_prime_numbers(max_number // 2     )

  SCREAMING_SNAKE_CASE__      							=				0
  SCREAMING_SNAKE_CASE__      							=				0
  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     ) - 1
  while left <= right:
    while prime_numbers[left] * prime_numbers[right] >= max_number:
      right -= 1
    semiprimes_count += right - left + 1
    left += 1

  return semiprimes_count


if __name__ == "__main__":
   print(F"""{solution() = }""")
