


'''simple docstring'''



import sys







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					List[str]        =     len(UpperCamelCase__					)
			UpperCAmelCase__  :					Tuple        =     [[0 for x in range(UpperCamelCase__					)] for x in range(UpperCamelCase__					)]
			UpperCAmelCase__  :					Union[str, Any]        =     [[0 for x in range(UpperCamelCase__					)] for x in range(UpperCamelCase__					)]

			for chain_length in range(2 ,      UpperCamelCase__					):
						for a in range(1 ,      n - chain_length + 1					):
									UpperCAmelCase__  :					Union[str, Any]        =     a + chain_length - 1

									UpperCAmelCase__  :					Optional[int]        =     sys.maxsize
									for c in range(UpperCamelCase__ ,      UpperCamelCase__					):
												UpperCAmelCase__  :					int        =     (
												    matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
												)
												if cost < matrix[a][b]:
															UpperCAmelCase__  :					Optional[Any]        =     cost
															UpperCAmelCase__  :					int        =     c
			return matrix, sol







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			if i == j:
						print("""A""" + str(UpperCamelCase__					) ,      end=""" """					)
			else:
						print("""(""" ,      end=""" """					)
						print_optiomal_solution(UpperCamelCase__ ,      UpperCamelCase__ ,      optimal_solution[i][j]					)
						print_optiomal_solution(UpperCamelCase__ ,      optimal_solution[i][j] + 1 ,      UpperCamelCase__					)
						print(""")""" ,      end=""" """					)







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					Optional[Any]        =     [3_0, 3_5, 1_5, 5, 1_0, 2_0, 2_5]
			UpperCAmelCase__  :					Optional[Any]        =     len(UpperCamelCase__					)
			# Size of matrix created from above array will be
			# 30*35 35*15 15*5 5*10 10*20 20*25
			UpperCAmelCase__   ,       UpperCAmelCase__  :					Dict        =     matrix_chain_order(UpperCamelCase__					)

			print("""No. of Operation required: """ + str(matrix[1][n - 1]					)					)
			print_optiomal_solution(UpperCamelCase__ ,      1 ,      n - 1					)


if __name__ == "__main__":
		main()



'''simple docstring'''



import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

__A	=logging.getLogger()







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					List[Any]        =     argparse.ArgumentParser()
			parser.add_argument("""-f"""					)
			UpperCAmelCase__  :					Optional[Any]        =     parser.parse_args()
			return args.f


class 		_snake_case					(     a__	):




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Tuple        =     logging.StreamHandler(sys.stdout)
								logger.addHandler(_lowerCamelCase)




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Any        =     get_gpu_count()

								if n_gpu > 1:
											pass
											# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
											# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
											# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
											# cmd = [sys.executable] + distributed_args + args
											# execute_subprocess_async(cmd, env=self.get_env())
											# XXX: test the results - need to save them first into .json file
								else:
											args.insert(0							,							"""run_glue_deebert.py""")
											with patch.object(_lowerCamelCase							,							"""argv"""							,							_lowerCamelCase):
														UpperCAmelCase__  :					Union[str, Any]        =     run_glue_deebert.main()
														for value in result.values():
																	self.assertGreaterEqual(_lowerCamelCase							,							0.666)




					@slow
					@require_torch_non_multi_gpu
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					List[str]        =     """
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            """.split()
								self.run_and_check(_lowerCamelCase)

								UpperCAmelCase__  :					Dict        =     """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
								self.run_and_check(_lowerCamelCase)

								UpperCAmelCase__  :					Any        =     """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
								self.run_and_check(_lowerCamelCase)