"""simple docstring"""






import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow

class lowerCamelCase		:






					'''simple docstring'''


					def 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				Tuple   , _snake_case			:				Dict   , _snake_case			:				int		)							->       str:
									return None



class lowerCamelCase		:






					'''simple docstring'''


					def 			lowerCAmelCase_					(						self			:				Optional[int]   , _snake_case			:				Optional[int]   , _snake_case			:				Optional[int]   , _snake_case			:				List[str]   , _snake_case			:				str		)							->       Tuple:
									return None



class lowerCamelCase		(unittest.TestCase      ):






					'''simple docstring'''


					a   	=		[
					    # (model_name, model_kwargs)
					    ("bert-base-cased", {}),
					    ("gpt2", {"use_cache": False}),  # We don't support exporting GPT2 past keys anymore
					]



					@require_tf
					@slow
					def 			lowerCAmelCase_					(						self			:				str		)							->       List[Any]:
									for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													self._test_export(_snake_case   , "tf"   , 12   , **_snake_case		)



					@require_torch
					@slow
					def 			lowerCAmelCase_					(						self			:				Dict		)							->       List[str]:
									for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													self._test_export(_snake_case   , "pt"   , 12   , **_snake_case		)



					@require_torch
					@slow
					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       Optional[int]:
									from transformers import BertModel

									SCREAMING_SNAKE_CASE__		  =				["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
									with NamedTemporaryFile(mode="w+t"		) as vocab_file:
													vocab_file.write("\n".join(_snake_case		)		)
													vocab_file.flush()
													SCREAMING_SNAKE_CASE__		  =				BertTokenizerFast(vocab_file.name		)

									with TemporaryDirectory() as bert_save_dir:
													SCREAMING_SNAKE_CASE__		  =				BertModel(BertConfig(vocab_size=len(_snake_case		)		)		)
													model.save_pretrained(_snake_case		)
													self._test_export(_snake_case   , "pt"   , 12   , _snake_case		)



					@require_tf
					@slow
					def 			lowerCAmelCase_					(						self			:				int		)							->       Optional[int]:
									for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													SCREAMING_SNAKE_CASE__		  =				self._test_export(_snake_case   , "tf"   , 12   , **_snake_case		)
													SCREAMING_SNAKE_CASE__		  =				quantize(Path(_snake_case		)		)

													# Ensure the actual quantized model is not bigger than the original one
													if quantized_path.stat().st_size >= Path(_snake_case		).stat().st_size:
																	self.fail("Quantized model is bigger than initial ONNX model"		)



					@require_torch
					@slow
					def 			lowerCAmelCase_					(						self			:				str		)							->       Tuple:
									for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													SCREAMING_SNAKE_CASE__		  =				self._test_export(_snake_case   , "pt"   , 12   , **_snake_case		)
													SCREAMING_SNAKE_CASE__		  =				quantize(_snake_case		)

													# Ensure the actual quantized model is not bigger than the original one
													if quantized_path.stat().st_size >= Path(_snake_case		).stat().st_size:
																	self.fail("Quantized model is bigger than initial ONNX model"		)



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				str   , _snake_case			:				Tuple   , _snake_case			:				List[str]   , _snake_case			:				Union[str, Any]=None   , **_snake_case			:				Union[str, Any]		)							->       Optional[int]:
									try:
													# Compute path
													with TemporaryDirectory() as tempdir:
																	SCREAMING_SNAKE_CASE__		  =				Path(_snake_case		).joinpath("model.onnx"		)

													# Remove folder if exists
													if path.parent.exists():
																	path.parent.rmdir()

													# Export
													convert(_snake_case   , _snake_case   , _snake_case   , _snake_case   , _snake_case   , **_snake_case		)

													return path
									except Exception as e:
													self.fail(_snake_case		)



					@require_torch
					@require_tokenizers
					@slow
					def 			lowerCAmelCase_					(						self			:				Dict		)							->       Any:
									from transformers import BertModel

									SCREAMING_SNAKE_CASE__		  =				BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"		)		)
									SCREAMING_SNAKE_CASE__		  =				BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"		)
									self._test_infer_dynamic_axis(_snake_case   , _snake_case   , "pt"		)



					@require_tf
					@require_tokenizers
					@slow
					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       Dict:
									from transformers import TFBertModel

									SCREAMING_SNAKE_CASE__		  =				TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"		)		)
									SCREAMING_SNAKE_CASE__		  =				BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"		)
									self._test_infer_dynamic_axis(_snake_case   , _snake_case   , "tf"		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				Dict   , _snake_case			:				str   , _snake_case			:				Optional[int]		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				FeatureExtractionPipeline(_snake_case   , _snake_case		)

									SCREAMING_SNAKE_CASE__		  =				["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				infer_shapes(_snake_case   , _snake_case		)

									# Assert all variables are present
									self.assertEqual(len(_snake_case		)   , len(_snake_case		)		)
									self.assertTrue(all(var_name in shapes for var_name in variable_names		)		)
									self.assertSequenceEqual(variable_names[:3]   , _snake_case		)
									self.assertSequenceEqual(variable_names[3:]   , _snake_case		)

									# Assert inputs are {0: batch, 1: sequence}
									for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
													self.assertDictEqual(shapes[var_name]   , {0: "batch", 1: "sequence"}		)

									# Assert outputs are {0: batch, 1: sequence} and {0: batch}
									self.assertDictEqual(shapes["output_0"]   , {0: "batch", 1: "sequence"}		)
									self.assertDictEqual(shapes["output_1"]   , {0: "batch"}		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				["input_ids", "attention_mask", "token_type_ids"]
									SCREAMING_SNAKE_CASE__		  =				{"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				ensure_valid_input(FuncContiguousArgs()   , _snake_case   , _snake_case		)

									# Should have exactly the same number of args (all are valid)
									self.assertEqual(len(_snake_case		)   , 3		)

									# Should have exactly the same input names
									self.assertEqual(set(_snake_case		)   , set(_snake_case		)		)

									# Parameter should be reordered according to their respective place in the function:
									# (input_ids, token_type_ids, attention_mask)
									self.assertEqual(_snake_case   , (tokens["input_ids"], tokens["token_type_ids"], tokens["attention_mask"])		)

									# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				ensure_valid_input(FuncNonContiguousArgs()   , _snake_case   , _snake_case		)

									# Should have exactly the one arg (all before the one not provided "some_other_args")
									self.assertEqual(len(_snake_case		)   , 1		)
									self.assertEqual(len(_snake_case		)   , 1		)

									# Should have only "input_ids"
									self.assertEqual(inputs_args[0]   , tokens["input_ids"]		)
									self.assertEqual(ordered_input_names[0]   , "input_ids"		)



					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				generate_identified_filename(Path("/home/something/my_fake_model.onnx"		)   , "-test"		)
									self.assertEqual("/home/something/my_fake_model-test.onnx"   , generated.as_posix()		)





"""simple docstring"""






import sys




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				len(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				[[0 for x in range(__UpperCAmelCase       )] for x in range(__UpperCAmelCase       )]
				SCREAMING_SNAKE_CASE__		  =				[[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       ):
												SCREAMING_SNAKE_CASE__		  =				a + chain_length - 1

												SCREAMING_SNAKE_CASE__		  =				sys.maxsize
												for c in range(__UpperCAmelCase  ,					__UpperCAmelCase       ):
																SCREAMING_SNAKE_CASE__		  =				(
																    matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
																)
																if cost < matrix[a][b]:
																				SCREAMING_SNAKE_CASE__		  =				cost
																				SCREAMING_SNAKE_CASE__		  =				c
				return matrix, sol




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Union[str, Any]:
				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 						SCREAMING_SNAKE_CASE					(  )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				[30, 35, 15, 5, 10, 20, 25]
				SCREAMING_SNAKE_CASE__		  =				len(__UpperCAmelCase       )
				# Size of matrix created from above array will be
				# 30*35 35*15 15*5 5*10 10*20 20*25
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				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()




