





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__       = [
    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__       = logging.getLogger()
def      _lowerCAmelCase(	):
  UpperCAmelCase					      =  argparse.ArgumentParser()
  parser.add_argument("-f"			)
  UpperCAmelCase					      =  parser.parse_args()
  return args.f
def      _lowerCAmelCase(	__A   ,       __A="eval"			):
  UpperCAmelCase					      =  os.path.join(__A   ,       F"{split}_results.json"			)
  if os.path.exists(__A			):
    with open(__A   ,       "r"			) as f:
      return json.load(__A			)
  raise ValueError(F"can't find {path}"			)


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






class 						__magic_name__    (       _snake_case    ):


 def _UpperCamelCase	(						self		: Any    )    ->      List[str]:
   UpperCAmelCase					      =  self.get_auto_remove_tmp_dir()
   UpperCAmelCase					      =  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()
     UpperCAmelCase					      =  get_results(lowerCAmelCase__    )
     self.assertGreaterEqual(result["eval_accuracy"]			,						0.75    )


 @slow
 def _UpperCamelCase	(						self		: Any    )    ->      List[str]:
   UpperCAmelCase					      =  self.get_auto_remove_tmp_dir()
   UpperCAmelCase					      =  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()
     UpperCAmelCase					      =  get_results(lowerCAmelCase__    )
     self.assertLess(result["eval_perplexity"]			,						1_0_0    )


 @slow
 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      Any:
   UpperCAmelCase					      =  self.get_auto_remove_tmp_dir()
   UpperCAmelCase					      =  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()
     UpperCAmelCase					      =  get_results(lowerCAmelCase__			,						split="test"    )
     self.assertGreaterEqual(result["test_rouge1"]			,						1_0    )
     self.assertGreaterEqual(result["test_rouge2"]			,						2    )
     self.assertGreaterEqual(result["test_rougeL"]			,						7    )
     self.assertGreaterEqual(result["test_rougeLsum"]			,						7    )


 @slow
 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      Union[str, Any]:
   UpperCAmelCase					      =  self.get_auto_remove_tmp_dir()
   UpperCAmelCase					      =  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()
     UpperCAmelCase					      =  get_results(lowerCAmelCase__    )
     self.assertLess(result["eval_perplexity"]			,						4_2    )


 @slow
 def _UpperCamelCase	(						self		: int    )    ->      Dict:
   UpperCAmelCase					      =  self.get_auto_remove_tmp_dir()
   UpperCAmelCase					      =  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()
     UpperCAmelCase					      =  get_results(lowerCAmelCase__    )
     self.assertGreaterEqual(result["eval_accuracy"]			,						0.42    )


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

   UpperCAmelCase					      =  self.get_auto_remove_tmp_dir()
   UpperCAmelCase					      =  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()
     UpperCAmelCase					      =  get_results(lowerCAmelCase__    )
     self.assertGreaterEqual(result["eval_accuracy"]			,						0.75    )
     self.assertGreaterEqual(result["eval_f1"]			,						0.3    )


 @slow
 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      int:
   UpperCAmelCase					      =  self.get_auto_remove_tmp_dir()
   UpperCAmelCase					      =  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()
     UpperCAmelCase					      =  get_results(lowerCAmelCase__    )
     self.assertGreaterEqual(result["eval_f1"]			,						3_0    )
     self.assertGreaterEqual(result["eval_exact"]			,						3_0    )






def      _lowerCAmelCase(	__A			):

  if not isinstance(__A   ,       __A			):
    raise TypeError("only integers accepted as input"			)
  else:
    UpperCAmelCase					      =  str(abs(__A			)			)
    UpperCAmelCase					      =  [list(__A			) for char in range(len(__A			)			)]
    for index in range(len(__A			)			):
      num_transpositions[index].pop(__A			)
    return max(
        int("".join(list(__A			)			)			) for transposition in num_transpositions			)


if __name__ == "__main__":
      __import__("doctest").testmod()



