



'''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							   =				[
    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							   =				logging.getLogger()







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





			"""simple docstring"""







			snake_case:    Dict      							=argparse.ArgumentParser()
			parser.add_argument('-f'					)
			snake_case:    List[str]      							=parser.parse_args()
			return args.f







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase="eval"					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    str      							=os.path.join(__UpperCAmelCase				,							f'''{split}_results.json'''					)
			if os.path.exists(__UpperCAmelCase					):
						with open(__UpperCAmelCase				,							'r'					) as f:
									return json.load(__UpperCAmelCase					)
			raise ValueError(f'''can\'t find {path}'''					)


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







class 						a_		(     snake_case    ):


							def    UpperCamelCase     (				self   :						Any			)			->					Dict:
										snake_case:    str      							=self.get_auto_remove_tmp_dir()
										snake_case:    List[str]      							=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()
													snake_case:    str      							=get_results(a_			)
													self.assertGreaterEqual(result['eval_accuracy']      ,						0.7_5			)


							@slow
							def    UpperCamelCase     (				self   :						List[str]			)			->					List[str]:
										snake_case:    str      							=self.get_auto_remove_tmp_dir()
										snake_case:    str      							=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()
													snake_case:    str      							=get_results(a_			)
													self.assertLess(result['eval_perplexity']      ,						1_0_0			)


							@slow
							def    UpperCamelCase     (				self   :						List[str]			)			->					Optional[Any]:
										snake_case:    Dict      							=self.get_auto_remove_tmp_dir()
										snake_case:    Dict      							=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()
													snake_case:    Any      							=get_results(a_      ,						split='test'			)
													self.assertGreaterEqual(result['test_rouge1']      ,						1_0			)
													self.assertGreaterEqual(result['test_rouge2']      ,						2			)
													self.assertGreaterEqual(result['test_rougeL']      ,						7			)
													self.assertGreaterEqual(result['test_rougeLsum']      ,						7			)


							@slow
							def    UpperCamelCase     (				self   :						List[str]			)			->					List[Any]:
										snake_case:    Union[str, Any]      							=self.get_auto_remove_tmp_dir()
										snake_case:    Dict      							=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()
													snake_case:    Union[str, Any]      							=get_results(a_			)
													self.assertLess(result['eval_perplexity']      ,						4_2			)


							@slow
							def    UpperCamelCase     (				self   :						Tuple			)			->					List[str]:
										snake_case:    List[Any]      							=self.get_auto_remove_tmp_dir()
										snake_case:    Optional[int]      							=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()
													snake_case:    Any      							=get_results(a_			)
													self.assertGreaterEqual(result['eval_accuracy']      ,						0.4_2			)


							@slow
							def    UpperCamelCase     (				self   :						List[str]			)			->					Union[str, Any]:
										# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
										snake_case:    Any      							=7 if get_gpu_count() > 1 else 2

										snake_case:    List[Any]      							=self.get_auto_remove_tmp_dir()
										snake_case:    Dict      							=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()
													snake_case:    List[Any]      							=get_results(a_			)
													self.assertGreaterEqual(result['eval_accuracy']      ,						0.7_5			)
													self.assertGreaterEqual(result['eval_f1']      ,						0.3			)




							@slow
							def    UpperCamelCase     (				self   :						Optional[int]			)			->					Optional[Any]:
										snake_case:    Any      							=self.get_auto_remove_tmp_dir()
										snake_case:    str      							=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()
													snake_case:    List[Any]      							=get_results(a_			)
													self.assertGreaterEqual(result['eval_f1']      ,						3_0			)
													self.assertGreaterEqual(result['eval_exact']      ,						3_0			)









'''simple docstring'''

from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge






@total_ordering
class 						a_		(     snake_case    ):


							def __lt__(				self   :						List[Any]      ,						a_   :						Optional[int]			)			->					List[str]:
										return self[-1] < other[-1]




							def __eq__(				self   :						int      ,						a_   :						Union[str, Any]			)			->					List[str]:
										return self[-1] == other[-1]







def a_     (    __UpperCAmelCase					)				->					list:





			"""simple docstring"""







			snake_case:    list[Stack]      							=[]
			# sort into stacks
			for element in collection:
						snake_case:    int      							=Stack([element]					)
						snake_case:    Union[str, Any]      							=bisect_left(__UpperCAmelCase				,							__UpperCAmelCase					)
						if i != len(__UpperCAmelCase					):
									stacks[i].append(__UpperCAmelCase					)
						else:
									stacks.append(__UpperCAmelCase					)

    # use a heap-based merge to merge stack efficiently
			snake_case:    int      							=merge(*(reversed(__UpperCAmelCase					) for stack in stacks)					)
			return collection


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




