




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								=	[
    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								=	logging.getLogger()



def 				_A  (		):
     """simple docstring"""

     lowerCAmelCase__            =					argparse.ArgumentParser()
     parser.add_argument("-f"     )
     lowerCAmelCase__            =					parser.parse_args()
     return args.f



def 				_A  (		lowerCAmelCase_	:   Optional[int]  ,							lowerCAmelCase_	:   Any="eval"     ):
     """simple docstring"""

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






class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    def   a (			self	:	str  ) ->			Tuple:
         lowerCAmelCase__            =					self.get_auto_remove_tmp_dir()
         lowerCAmelCase__            =					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(SCREAMING_SNAKE_CASE__  ,      "argv"  ,      SCREAMING_SNAKE_CASE__  ):
              run_flax_glue.main()
              lowerCAmelCase__            =					get_results(SCREAMING_SNAKE_CASE__  )
              self.assertGreaterEqual(result["eval_accuracy"]  ,      0.75  )


    @slow
    def   a (			self	:	int  ) ->			str:
         lowerCAmelCase__            =					self.get_auto_remove_tmp_dir()
         lowerCAmelCase__            =					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(SCREAMING_SNAKE_CASE__  ,      "argv"  ,      SCREAMING_SNAKE_CASE__  ):
              run_clm_flax.main()
              lowerCAmelCase__            =					get_results(SCREAMING_SNAKE_CASE__  )
              self.assertLess(result["eval_perplexity"]  ,      100  )


    @slow
    def   a (			self	:	Any  ) ->			Union[str, Any]:
         lowerCAmelCase__            =					self.get_auto_remove_tmp_dir()
         lowerCAmelCase__            =					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(SCREAMING_SNAKE_CASE__  ,      "argv"  ,      SCREAMING_SNAKE_CASE__  ):
              run_summarization_flax.main()
              lowerCAmelCase__            =					get_results(SCREAMING_SNAKE_CASE__  ,      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	:	int  ) ->			str:
         lowerCAmelCase__            =					self.get_auto_remove_tmp_dir()
         lowerCAmelCase__            =					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(SCREAMING_SNAKE_CASE__  ,      "argv"  ,      SCREAMING_SNAKE_CASE__  ):
              run_mlm_flax.main()
              lowerCAmelCase__            =					get_results(SCREAMING_SNAKE_CASE__  )
              self.assertLess(result["eval_perplexity"]  ,      42  )


    @slow
    def   a (			self	:	Optional[Any]  ) ->			Optional[int]:
         lowerCAmelCase__            =					self.get_auto_remove_tmp_dir()
         lowerCAmelCase__            =					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(SCREAMING_SNAKE_CASE__  ,      "argv"  ,      SCREAMING_SNAKE_CASE__  ):
              run_ta_mlm_flax.main()
              lowerCAmelCase__            =					get_results(SCREAMING_SNAKE_CASE__  )
              self.assertGreaterEqual(result["eval_accuracy"]  ,      0.42  )


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

         lowerCAmelCase__            =					self.get_auto_remove_tmp_dir()
         lowerCAmelCase__            =					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(SCREAMING_SNAKE_CASE__  ,      "argv"  ,      SCREAMING_SNAKE_CASE__  ):
              run_flax_ner.main()
              lowerCAmelCase__            =					get_results(SCREAMING_SNAKE_CASE__  )
              self.assertGreaterEqual(result["eval_accuracy"]  ,      0.75  )
              self.assertGreaterEqual(result["eval_f1"]  ,      0.3  )


    @slow
    def   a (			self	:	Optional[Any]  ) ->			Tuple:
         lowerCAmelCase__            =					self.get_auto_remove_tmp_dir()
         lowerCAmelCase__            =					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(SCREAMING_SNAKE_CASE__  ,      "argv"  ,      SCREAMING_SNAKE_CASE__  ):
              run_qa.main()
              lowerCAmelCase__            =					get_results(SCREAMING_SNAKE_CASE__  )
              self.assertGreaterEqual(result["eval_f1"]  ,      30  )
              self.assertGreaterEqual(result["eval_exact"]  ,      30  )





'''simple docstring'''





import functools

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			) -> int:

  # Validation
  if not isinstance(_lowerCAmelCase			,_lowerCAmelCase			) or not all(isinstance(_lowerCAmelCase			,_lowerCAmelCase			) for day in days			):
    raise ValueError('The parameter days should be a list of integers'			)

  if len(_lowerCAmelCase			) != 3 or not all(isinstance(_lowerCAmelCase			,_lowerCAmelCase			) for cost in costs			):
    raise ValueError('The parameter costs should be a list of three integers'			)

  if len(_lowerCAmelCase			) == 0:
    return 0

  if min(_lowerCAmelCase			) <= 0:
    raise ValueError('All days elements should be greater than 0'			)

  if max(_lowerCAmelCase			) >= 366:
    raise ValueError('All days elements should be less than 366'			)

  __lowerCamelCase			:			int       			=					set(_lowerCAmelCase			)

  @functools.cache
  def dynamic_programming(_lowerCAmelCase			) -> int:
    if index > 365:
      return 0

    if index not in days_set:
      return dynamic_programming(index + 1			)

    return min(
        costs[0] + dynamic_programming(index + 1			)			,costs[1] + dynamic_programming(index + 7			)			,costs[2] + dynamic_programming(index + 30			)			,)

  return dynamic_programming(1			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()






