





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


UpperCAmelCase		:  List[str]         =	[
    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)

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







def 					_SCREAMING_SNAKE_CASE							(  ) -> Union[str, Any]:
					__A      :		Optional[Any]         =							argparse.ArgumentParser()
					parser.add_argument('-f'    )
					__A      :		List[Any]         =							parser.parse_args()
					return args.f







def 					_SCREAMING_SNAKE_CASE							(  a   ,		a="eval"    ) -> Any:
					__A      :		str         =							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}"""    )


UpperCAmelCase		:  int         =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class 		_A(   snake_case_  ):


				"""simple docstring"""


				def    UpperCAmelCase_						(     self	):
									__A      :		List[Any]         =							self.get_auto_remove_tmp_dir()
									__A      :		Union[str, 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(_A	,   'argv'	,   _A	):
														run_flax_glue.main()
														__A      :		List[Any]         =							get_results(_A	)
														self.assertGreaterEqual(result['eval_accuracy']	,   0.7_5	)



				@slow
				def    UpperCAmelCase_						(     self	):
									__A      :		str         =							self.get_auto_remove_tmp_dir()
									__A      :		Tuple         =							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(_A	,   'argv'	,   _A	):
														run_clm_flax.main()
														__A      :		str         =							get_results(_A	)
														self.assertLess(result['eval_perplexity']	,   100	)



				@slow
				def    UpperCAmelCase_						(     self	):
									__A      :		Dict         =							self.get_auto_remove_tmp_dir()
									__A      :		Dict         =							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(_A	,   'argv'	,   _A	):
														run_summarization_flax.main()
														__A      :		List[Any]         =							get_results(_A	,   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    UpperCAmelCase_						(     self	):
									__A      :		int         =							self.get_auto_remove_tmp_dir()
									__A      :		Tuple         =							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(_A	,   'argv'	,   _A	):
														run_mlm_flax.main()
														__A      :		Dict         =							get_results(_A	)
														self.assertLess(result['eval_perplexity']	,   42	)



				@slow
				def    UpperCAmelCase_						(     self	):
									__A      :		Optional[int]         =							self.get_auto_remove_tmp_dir()
									__A      :		Optional[int]         =							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(_A	,   'argv'	,   _A	):
														run_ta_mlm_flax.main()
														__A      :		str         =							get_results(_A	)
														self.assertGreaterEqual(result['eval_accuracy']	,   0.4_2	)



				@slow
				def    UpperCAmelCase_						(     self	):
									# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
									__A      :		Any         =							7 if get_gpu_count() > 1 else 2

									__A      :		Optional[int]         =							self.get_auto_remove_tmp_dir()
									__A      :		Union[str, Any]         =							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(_A	,   'argv'	,   _A	):
														run_flax_ner.main()
														__A      :		Union[str, Any]         =							get_results(_A	)
														self.assertGreaterEqual(result['eval_accuracy']	,   0.7_5	)
														self.assertGreaterEqual(result['eval_f1']	,   0.3	)





				@slow
				def    UpperCAmelCase_						(     self	):
									__A      :		Dict         =							self.get_auto_remove_tmp_dir()
									__A      :		Union[str, Any]         =							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(_A	,   'argv'	,   _A	):
														run_qa.main()
														__A      :		str         =							get_results(_A	)
														self.assertGreaterEqual(result['eval_f1']	,   30	)
														self.assertGreaterEqual(result['eval_exact']	,   30	)






from __future__ import annotations

import math





def      lowerCamelCase_					(							lowerCamelCase__       ):

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


__A							=[num for num in range(3, 1_0_0_0_0_1, 2) if not is_prime(num)]





def      lowerCamelCase_					(							lowerCamelCase__       ):
				if not isinstance(lowerCamelCase__      ,					lowerCamelCase__       ):
								raise ValueError("n must be an integer"       )
				if n <= 0:
								raise ValueError("n must be >= 0"       )

				lowerCamelCase_         =     []
				for num in range(len(lowerCamelCase__       )       ):
								lowerCamelCase_         =     0
								while 2 * i * i <= odd_composites[num]:
												lowerCamelCase_         =     odd_composites[num] - 2 * i * i
												if is_prime(lowerCamelCase__       ):
																break
												i += 1
								else:
												list_nums.append(odd_composites[num]       )
												if len(lowerCamelCase__       ) == n:
																return list_nums

				return []





def      lowerCamelCase_					(							):
				return compute_nums(1       )[0]


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


