





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       :   Tuple					= [
    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       :   Optional[int]					= logging.getLogger()







def   __lowerCamelCase   (				)						->							Tuple:






		"""simple docstring"""

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







def   __lowerCamelCase   (				__a  :Union[str, Any]       ,	__a  :Optional[int]="eval"       )						->							Dict:






		"""simple docstring"""

		A__     =  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}'       )


A       :   List[Any]					= logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			def   a_			(     self  :       Dict  )      ->      Union[str, Any]:







					"""simple docstring"""






					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(__lowerCAmelCase				,      """argv"""				,      __lowerCAmelCase  ):
							run_flax_glue.main()
							A__     =  get_results(__lowerCAmelCase  )
							self.assertGreaterEqual(result["""eval_accuracy"""]				,      0.7_5  )






			@slow
			def   a_			(     self  :       List[str]  )      ->      Any:







					"""simple docstring"""






					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(__lowerCAmelCase				,      """argv"""				,      __lowerCAmelCase  ):
							run_clm_flax.main()
							A__     =  get_results(__lowerCAmelCase  )
							self.assertLess(result["""eval_perplexity"""]				,      1_00  )






			@slow
			def   a_			(     self  :       Optional[Any]  )      ->      List[str]:







					"""simple docstring"""






					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(__lowerCAmelCase				,      """argv"""				,      __lowerCAmelCase  ):
							run_summarization_flax.main()
							A__     =  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   a_			(     self  :       int  )      ->      Dict:







					"""simple docstring"""






					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(__lowerCAmelCase				,      """argv"""				,      __lowerCAmelCase  ):
							run_mlm_flax.main()
							A__     =  get_results(__lowerCAmelCase  )
							self.assertLess(result["""eval_perplexity"""]				,      42  )






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







					"""simple docstring"""






					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(__lowerCAmelCase				,      """argv"""				,      __lowerCAmelCase  ):
							run_ta_mlm_flax.main()
							A__     =  get_results(__lowerCAmelCase  )
							self.assertGreaterEqual(result["""eval_accuracy"""]				,      0.4_2  )






			@slow
			def   a_			(     self  :       str  )      ->      List[str]:







					"""simple docstring"""






					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(__lowerCAmelCase				,      """argv"""				,      __lowerCAmelCase  ):
							run_flax_ner.main()
							A__     =  get_results(__lowerCAmelCase  )
							self.assertGreaterEqual(result["""eval_accuracy"""]				,      0.7_5  )
							self.assertGreaterEqual(result["""eval_f1"""]				,      0.3  )







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







					"""simple docstring"""






					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(__lowerCAmelCase				,      """argv"""				,      __lowerCAmelCase  ):
							run_qa.main()
							A__     =  get_results(__lowerCAmelCase  )
							self.assertGreaterEqual(result["""eval_f1"""]				,      30  )
							self.assertGreaterEqual(result["""eval_exact"""]				,      30  )










def   __lowerCamelCase   (				__a  :Optional[Any]       )						->							Tuple:






		"""simple docstring"""

		A__     =  len(__a       )
		while cur > 1:
				# Find the maximum number in arr
				A__     =  arr.index(max(arr[0:cur]       )       )
				# Reverse from 0 to mi
				A__     =  arr[mi::-1] + arr[mi + 1 : len(__a       )]
				# Reverse whole list
				A__     =  arr[cur - 1 :: -1] + arr[cur : len(__a       )]
				cur -= 1
		return arr


if __name__ == "__main__":
					A       :   List[str]					= input('''Enter numbers separated by a comma:\n''').strip()
					A       :   int					= [int(item) for item in user_input.split(''',''')]
					print(pancake_sort(unsorted))



