




'''simple docstring'''





def     _SCREAMING_SNAKE_CASE    (							lowercase			: int			,							lowercase			: list  ):


      '''simple docstring'''



      _enforce_args(snake_case__			,							snake_case__  )
      if n == 0:
            return 0
      lowerCamelCase_	  =					float('-inf'  )
      for i in range(1			,							n + 1  ):
            lowerCamelCase_	  =					max(
                snake_case__			,							prices[i - 1] + naive_cut_rod_recursive(n - i			,							snake_case__  )  )

      return max_revue

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int			,							lowercase			: list  ):


      '''simple docstring'''



      _enforce_args(snake_case__			,							snake_case__  )
      lowerCamelCase_	  =					[float('-inf'  ) for _ in range(n + 1  )]
      return _top_down_cut_rod_recursive(snake_case__			,							snake_case__			,							snake_case__  )

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int			,							lowercase			: list			,							lowercase			: list  ):


      '''simple docstring'''



      if max_rev[n] >= 0:
            return max_rev[n]
      elif n == 0:
            return 0
      else:
            lowerCamelCase_	  =					float('-inf'  )
            for i in range(1			,							n + 1  ):
                  lowerCamelCase_	  =					max(
                      snake_case__			,							prices[i - 1] + _top_down_cut_rod_recursive(n - i			,							snake_case__			,							snake_case__  )			,							)

            lowerCamelCase_	  =					max_revenue

      return max_rev[n]

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int			,							lowercase			: list  ):


      '''simple docstring'''



      _enforce_args(snake_case__			,							snake_case__  )

      # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
      # length 0.
      lowerCamelCase_	  =					[float('-inf'  ) for _ in range(n + 1  )]
      lowerCamelCase_	  =					0

      for i in range(1			,							n + 1  ):
            lowerCamelCase_	  =					max_rev[i]
            for j in range(1			,							i + 1  ):
                  lowerCamelCase_	  =					max(snake_case__			,							prices[j - 1] + max_rev[i - j]  )

            lowerCamelCase_	  =					max_revenue_i

      return max_rev[n]

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int			,							lowercase			: list  ):


      '''simple docstring'''



      if n < 0:
            lowerCamelCase_	  =					f"""n must be greater than or equal to 0. Got n = {n}"""
            raise ValueError(snake_case__  )

      if n > len(snake_case__  ):
            lowerCamelCase_	  =					(
                'Each integral piece of rod must have a corresponding price. '
                f"""Got n = {n} but length of prices = {len(snake_case__  )}"""
            )
            raise ValueError(snake_case__  )

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					[6, 10, 12, 15, 20, 23]
      lowerCamelCase_	  =					len(snake_case__  )

      # the best revenue comes from cutting the rod into 6 pieces, each
      # of length 1 resulting in a revenue of 6 * 6 = 36.
      lowerCamelCase_	  =					36

      lowerCamelCase_	  =					top_down_cut_rod(snake_case__			,							snake_case__  )
      lowerCamelCase_	  =					bottom_up_cut_rod(snake_case__			,							snake_case__  )
      lowerCamelCase_	  =					naive_cut_rod_recursive(snake_case__			,							snake_case__  )

      assert expected_max_revenue == max_rev_top_down
      assert max_rev_top_down == max_rev_bottom_up
      assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
 main()









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       :	List[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)

lowerCamelCase       :	Tuple									=     logging.getLogger()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[Any]			,							lowercase			: Dict="eval"  ):


      '''simple docstring'''



      lowerCamelCase_	  =					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}"""  )


lowerCamelCase       :	str									=     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 def        a__				(       self      :						Union[str, Any]						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					f"""
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

       with patch.object(A_		,							'argv'		,							A_						):
             run_flax_glue.main()
             lowerCamelCase_	  =					get_results(A_						)
             self.assertGreaterEqual(result['eval_accuracy']		,							0.75						)

 @slow
 def        a__				(       self      :						List[str]						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					f"""
            run_clm_flax.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

       with patch.object(A_		,							'argv'		,							A_						):
             run_clm_flax.main()
             lowerCamelCase_	  =					get_results(A_						)
             self.assertLess(result['eval_perplexity']		,							100						)

 @slow
 def        a__				(       self      :						str						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					f"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        """.split()

       with patch.object(A_		,							'argv'		,							A_						):
             run_summarization_flax.main()
             lowerCamelCase_	  =					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        a__				(       self      :						Optional[int]						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					f"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        """.split()

       with patch.object(A_		,							'argv'		,							A_						):
             run_mlm_flax.main()
             lowerCamelCase_	  =					get_results(A_						)
             self.assertLess(result['eval_perplexity']		,							42						)

 @slow
 def        a__				(       self      :						Optional[Any]						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					f"""
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

       with patch.object(A_		,							'argv'		,							A_						):
             run_ta_mlm_flax.main()
             lowerCamelCase_	  =					get_results(A_						)
             self.assertGreaterEqual(result['eval_accuracy']		,							0.42						)

 @slow
 def        a__				(       self      :						int						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					7 if get_gpu_count() > 1 else 2

       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					f"""
            run_flax_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

       with patch.object(A_		,							'argv'		,							A_						):
             run_flax_ner.main()
             lowerCamelCase_	  =					get_results(A_						)
             self.assertGreaterEqual(result['eval_accuracy']		,							0.75						)
             self.assertGreaterEqual(result['eval_f1']		,							0.3						)





 @slow
 def        a__				(       self      :						str						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_auto_remove_tmp_dir()
       lowerCamelCase_	  =					f"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

       with patch.object(A_		,							'argv'		,							A_						):
             run_qa.main()
             lowerCamelCase_	  =					get_results(A_						)
             self.assertGreaterEqual(result['eval_f1']		,							30						)
             self.assertGreaterEqual(result['eval_exact']		,							30						)



