


'''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_   :      Union[str, 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_   :      Tuple				    =      logging.getLogger()






def 		_A			()				->					Union[str, Any]:




     '''simple docstring'''



     _a      =							argparse.ArgumentParser()
     parser.add_argument('-f'						)
     _a      =							parser.parse_args()
     return args.f






def 		_A			(lowerCAmelCase__					:Tuple			,  lowerCAmelCase__					:List[Any]="eval"						)				->					List[str]:




     '''simple docstring'''



     _a      =							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}'						)


a_   :      Dict				    =      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 	a       (       _SCREAMING_SNAKE_CASE    ):
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      _a      =							self.get_auto_remove_tmp_dir()
      _a      =							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(__magic_name__ ,			'argv' ,			__magic_name__  ):
           run_flax_glue.main()
           _a      =							get_results(__magic_name__  )
           self.assertGreaterEqual(result['eval_accuracy'] ,			0.7_5  )
 @slow
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							self.get_auto_remove_tmp_dir()
      _a      =							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(__magic_name__ ,			'argv' ,			__magic_name__  ):
           run_clm_flax.main()
           _a      =							get_results(__magic_name__  )
           self.assertLess(result['eval_perplexity'] ,			1_00  )
 @slow
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      _a      =							self.get_auto_remove_tmp_dir()
      _a      =							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(__magic_name__ ,			'argv' ,			__magic_name__  ):
           run_summarization_flax.main()
           _a      =							get_results(__magic_name__ ,			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  )   ->							Optional[Any]:
      _a      =							self.get_auto_remove_tmp_dir()
      _a      =							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(__magic_name__ ,			'argv' ,			__magic_name__  ):
           run_mlm_flax.main()
           _a      =							get_results(__magic_name__  )
           self.assertLess(result['eval_perplexity'] ,			42  )
 @slow
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      _a      =							self.get_auto_remove_tmp_dir()
      _a      =							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(__magic_name__ ,			'argv' ,			__magic_name__  ):
           run_ta_mlm_flax.main()
           _a      =							get_results(__magic_name__  )
           self.assertGreaterEqual(result['eval_accuracy'] ,			0.4_2  )
 @slow
 def 							__UpperCAmelCase						(	self  )   ->							int:
      # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
      _a      =							7 if get_gpu_count() > 1 else 2

      _a      =							self.get_auto_remove_tmp_dir()
      _a      =							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(__magic_name__ ,			'argv' ,			__magic_name__  ):
           run_flax_ner.main()
           _a      =							get_results(__magic_name__  )
           self.assertGreaterEqual(result['eval_accuracy'] ,			0.7_5  )
           self.assertGreaterEqual(result['eval_f1'] ,			0.3  )




 @slow
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      _a      =							self.get_auto_remove_tmp_dir()
      _a      =							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(__magic_name__ ,			'argv' ,			__magic_name__  ):
           run_qa.main()
           _a      =							get_results(__magic_name__  )
           self.assertGreaterEqual(result['eval_f1'] ,			30  )
           self.assertGreaterEqual(result['eval_exact'] ,			30  )





'''simple docstring'''






def 		_A			(lowerCAmelCase__					:list[int]			,  lowerCAmelCase__					:list[int]						)				->					None:




     '''simple docstring'''



     _a      =							len(lowerCAmelCase__						)
     print('The following activities are selected:'						)

     # The first activity is always selected
     _a      =							0
     print(lowerCAmelCase__			,  end=','						)

     # Consider rest of the activities
     for j in range(lowerCAmelCase__						):
          # If this activity has start time greater than
          # or equal to the finish time of previously
          # selected activity, then select it
          if start[j] >= finish[i]:
               print(lowerCAmelCase__			,  end=','						)
               _a      =							j


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 a_   :      List[str]				    =      [1, 3, 0, 5, 8, 5]
 a_   :      str				    =      [2, 4, 6, 7, 9, 9]
 print_max_activities(start, finish)

