


"""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


__snake_case             =   [
    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)

__snake_case             =   logging.getLogger()







def   __lowerCAmelCase						(						)     ->						Tuple:


				"""simple docstring"""





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







def   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  Any="eval"			)     ->						List[Any]:


				"""simple docstring"""





				snake_case   :				List[Any]				 =   os.path.join(lowercase						,							F'{split}_results.json'			)
				if os.path.exists(lowercase			):
								with open(lowercase						,							"r"			) as f:
												return json.load(lowercase			)
				raise ValueError(F'can\'t find {path}'			)


__snake_case             =   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class _lowerCAmelCase    (				snake_case_     ):







			def 			lowerCamelCase		(     self						)     ->		str:

							'''simple docstring'''




							snake_case   :				List[Any]				 =   self.get_auto_remove_tmp_dir()
							snake_case   :				Optional[int]				 =   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(UpperCamelCase__						,     "argv"						,     UpperCamelCase__						):
											run_flax_glue.main()
											snake_case   :				Optional[Any]				 =   get_results(UpperCamelCase__						)
											self.assertGreaterEqual(result["eval_accuracy"]						,     0.75						)







			@slow
			def 			lowerCamelCase		(     self						)     ->		str:

							'''simple docstring'''




							snake_case   :				Dict				 =   self.get_auto_remove_tmp_dir()
							snake_case   :				Dict				 =   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(UpperCamelCase__						,     "argv"						,     UpperCamelCase__						):
											run_clm_flax.main()
											snake_case   :				Optional[Any]				 =   get_results(UpperCamelCase__						)
											self.assertLess(result["eval_perplexity"]						,     100						)







			@slow
			def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

							'''simple docstring'''




							snake_case   :				Any				 =   self.get_auto_remove_tmp_dir()
							snake_case   :				Optional[Any]				 =   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(UpperCamelCase__						,     "argv"						,     UpperCamelCase__						):
											run_summarization_flax.main()
											snake_case   :				int				 =   get_results(UpperCamelCase__						,     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 			lowerCamelCase		(     self						)     ->		Optional[Any]:

							'''simple docstring'''




							snake_case   :				Dict				 =   self.get_auto_remove_tmp_dir()
							snake_case   :				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(UpperCamelCase__						,     "argv"						,     UpperCamelCase__						):
											run_mlm_flax.main()
											snake_case   :				Dict				 =   get_results(UpperCamelCase__						)
											self.assertLess(result["eval_perplexity"]						,     42						)







			@slow
			def 			lowerCamelCase		(     self						)     ->		List[Any]:

							'''simple docstring'''




							snake_case   :				List[str]				 =   self.get_auto_remove_tmp_dir()
							snake_case   :				Union[str, 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(UpperCamelCase__						,     "argv"						,     UpperCamelCase__						):
											run_ta_mlm_flax.main()
											snake_case   :				Optional[int]				 =   get_results(UpperCamelCase__						)
											self.assertGreaterEqual(result["eval_accuracy"]						,     0.42						)







			@slow
			def 			lowerCamelCase		(     self						)     ->		List[Any]:

							'''simple docstring'''




							snake_case   :				Optional[int]				 =   7 if get_gpu_count() > 1 else 2

							snake_case   :				List[str]				 =   self.get_auto_remove_tmp_dir()
							snake_case   :				Optional[int]				 =   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(UpperCamelCase__						,     "argv"						,     UpperCamelCase__						):
											run_flax_ner.main()
											snake_case   :				Tuple				 =   get_results(UpperCamelCase__						)
											self.assertGreaterEqual(result["eval_accuracy"]						,     0.75						)
											self.assertGreaterEqual(result["eval_f1"]						,     0.3						)







			@slow
			def 			lowerCamelCase		(     self						)     ->		Optional[int]:

							'''simple docstring'''




							snake_case   :				str				 =   self.get_auto_remove_tmp_dir()
							snake_case   :				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(UpperCamelCase__						,     "argv"						,     UpperCamelCase__						):
											run_qa.main()
											snake_case   :				Any				 =   get_results(UpperCamelCase__						)
											self.assertGreaterEqual(result["eval_f1"]						,     30						)
											self.assertGreaterEqual(result["eval_exact"]						,     30						)









"""simple docstring"""



def   __lowerCAmelCase						(						lowercase  :  int			)     ->						int:


    """simple docstring"""





    if not isinstance(lowercase						,							lowercase			):
        raise ValueError("Input must be an integer"			)
    if input_num <= 0:
        raise ValueError("Input must be positive"			)
    return sum(
        divisor for divisor in range(1						,							input_num // 2 + 1			) if input_num % divisor == 0			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()





