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


lowercase_  : 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)

lowercase_  : Union[str, Any]								=					logging.getLogger()







def       A__   (						):
 SCREAMING_SNAKE_CASE__: int=   argparse.ArgumentParser()
 parser.add_argument('''-f'''   )
 SCREAMING_SNAKE_CASE__: str=   parser.parse_args()
 return args.f







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


lowercase_  : Optional[int]								=					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 		_lowerCamelCase		( UpperCamelCase_     ):
    def     UpperCamelCase_					(	self )							->       int:
     SCREAMING_SNAKE_CASE__: str=   self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__: Tuple=   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()
      SCREAMING_SNAKE_CASE__: List[str]=   get_results(lowerCAmelCase )
      self.assertGreaterEqual(result['''eval_accuracy''']			,			0.75 )
    @slow
    def     UpperCamelCase_					(	self )							->       List[Any]:
     SCREAMING_SNAKE_CASE__: Dict=   self.get_auto_remove_tmp_dir()
     SCREAMING_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(lowerCAmelCase			,			'''argv'''			,			lowerCAmelCase ):
      run_clm_flax.main()
      SCREAMING_SNAKE_CASE__: Optional[Any]=   get_results(lowerCAmelCase )
      self.assertLess(result['''eval_perplexity''']			,			100 )
    @slow
    def     UpperCamelCase_					(	self )							->       List[Any]:
     SCREAMING_SNAKE_CASE__: Optional[Any]=   self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__: Tuple=   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()
      SCREAMING_SNAKE_CASE__: Optional[int]=   get_results(lowerCAmelCase			,			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 )							->       List[Any]:
     SCREAMING_SNAKE_CASE__: str=   self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   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()
      SCREAMING_SNAKE_CASE__: List[Any]=   get_results(lowerCAmelCase )
      self.assertLess(result['''eval_perplexity''']			,			42 )
    @slow
    def     UpperCamelCase_					(	self )							->       Tuple:
     SCREAMING_SNAKE_CASE__: Dict=   self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__: Optional[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(lowerCAmelCase			,			'''argv'''			,			lowerCAmelCase ):
      run_ta_mlm_flax.main()
      SCREAMING_SNAKE_CASE__: Optional[int]=   get_results(lowerCAmelCase )
      self.assertGreaterEqual(result['''eval_accuracy''']			,			0.42 )
    @slow
    def     UpperCamelCase_					(	self )							->       List[str]:
     # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   7 if get_gpu_count() > 1 else 2

     SCREAMING_SNAKE_CASE__: Optional[int]=   self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__: 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(lowerCAmelCase			,			'''argv'''			,			lowerCAmelCase ):
      run_flax_ner.main()
      SCREAMING_SNAKE_CASE__: List[Any]=   get_results(lowerCAmelCase )
      self.assertGreaterEqual(result['''eval_accuracy''']			,			0.75 )
      self.assertGreaterEqual(result['''eval_f1''']			,			0.3 )



    @slow
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     SCREAMING_SNAKE_CASE__: Optional[int]=   self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__: str=   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()
      SCREAMING_SNAKE_CASE__: Optional[int]=   get_results(lowerCAmelCase )
      self.assertGreaterEqual(result['''eval_f1''']			,			30 )
      self.assertGreaterEqual(result['''eval_exact''']			,			30 )








from __future__ import annotations

def   __snake_case    (       _lowerCAmelCase			:			list[int]					,					_lowerCAmelCase			:			int						)			->    list[list[int]]:
   A_       :					list[list[int]]        =      []
   A_       :					list[int]        =      []
   A_       :					Dict        =      0
   A_       :					str        =      sum(_lowerCAmelCase						)
   create_state_space_tree(_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase						)
   return result

def   __snake_case    (       _lowerCAmelCase			:			list[int]					,					_lowerCAmelCase			:			int					,					_lowerCAmelCase			:			int					,					_lowerCAmelCase			:			list[int]					,					_lowerCAmelCase			:			list[list[int]]					,					_lowerCAmelCase			:			int					,					)			->    None:
   if sum(_lowerCAmelCase						) > max_sum or (remaining_nums_sum + sum(_lowerCAmelCase						)) < max_sum:
      return
   if sum(_lowerCAmelCase						) == max_sum:
      result.append(_lowerCAmelCase						)
      return
   for index in range(_lowerCAmelCase					,					len(_lowerCAmelCase						)						):
      create_state_space_tree(
          _lowerCAmelCase					,					_lowerCAmelCase					,					index + 1					,					[*path, nums[index]]					,					_lowerCAmelCase					,					remaining_nums_sum - nums[index]					,					)
_lowerCAmelCase				:   List[Any]						       =						[3, 34, 4, 12, 5, 2]
_lowerCAmelCase				:   Dict						       =						9
_lowerCAmelCase				:   Dict						       =						generate_sum_of_subsets_soln(nums, max_sum)





print(*result)
