"""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    a__    :


			def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
								'''simple docstring'''



								return None






class    a__    :


			def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
								'''simple docstring'''



								return None






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


			@require_tf
			@slow
			def 	snake_case__	(     self  ):
								'''simple docstring'''



								for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													self._test_export(_UpperCAmelCase,				"tf",				12,				**_UpperCAmelCase  )


			@require_torch
			@slow
			def 	snake_case__	(     self  ):
								'''simple docstring'''



								for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													self._test_export(_UpperCAmelCase,				"pt",				12,				**_UpperCAmelCase  )


			@require_torch
			@slow
			def 	snake_case__	(     self  ):
								'''simple docstring'''



								from transformers import BertModel

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

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


			@require_tf
			@slow
			def 	snake_case__	(     self  ):
								'''simple docstring'''



								for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													lowercase__				 =							self._test_export(_UpperCAmelCase,				"tf",				12,				**_UpperCAmelCase  )
													lowercase__				 =							quantize(Path(_UpperCAmelCase  )  )

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


			@require_torch
			@slow
			def 	snake_case__	(     self  ):
								'''simple docstring'''



								for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													lowercase__				 =							self._test_export(_UpperCAmelCase,				"pt",				12,				**_UpperCAmelCase  )
													lowercase__				 =							quantize(_UpperCAmelCase  )

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


			def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase=None,				**_UpperCAmelCase  ):
								'''simple docstring'''



								try:
													# Compute path
													with TemporaryDirectory() as tempdir:
																		lowercase__				 =							Path(_UpperCAmelCase  ).joinpath("model.onnx"  )

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

													# Export
													convert(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  )

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


			@require_torch
			@require_tokenizers
			@slow
			def 	snake_case__	(     self  ):
								'''simple docstring'''



								from transformers import BertModel

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


			@require_tf
			@require_tokenizers
			@slow
			def 	snake_case__	(     self  ):
								'''simple docstring'''



								from transformers import TFBertModel

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


			def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
								'''simple docstring'''



								lowercase__				 =							FeatureExtractionPipeline(_UpperCAmelCase,				_UpperCAmelCase  )

								lowercase__				 =							["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
								lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__				 =							infer_shapes(_UpperCAmelCase,				_UpperCAmelCase  )

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

								# 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 	snake_case__	(     self  ):
								'''simple docstring'''



								lowercase__				 =							["input_ids", "attention_mask", "token_type_ids"]
								lowercase__				 =							{"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
								lowercase__ ,						lowercase__				 =							ensure_valid_input(FuncContiguousArgs(),				_UpperCAmelCase,				_UpperCAmelCase  )

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

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

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

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

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

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


			def 	snake_case__	(     self  ):
								'''simple docstring'''



								lowercase__				 =							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"""


from typing import Any

import numpy as np







def 					__a   (		A      ):






     '''simple docstring'''



     return np.array_equal(A    ,				matrix.conjugate().T      )







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							v.conjugate().T
     lowercase__				 =							v_star.dot(A      )
     assert isinstance(A    ,				np.ndarray      )
     return (v_star_dot.dot(A      )) / (v_star.dot(A      ))







def 					__a   (		):






     '''simple docstring'''



     lowercase__				 =							np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]      )
     lowercase__				 =							np.array([[1], [2], [3]]      )
     assert is_hermitian(A      ), f'''{a} is not hermitian.'''
     print(rayleigh_quotient(A    ,				A      )      )

     lowercase__				 =							np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]      )
     assert is_hermitian(A      ), f'''{a} is not hermitian.'''
     assert rayleigh_quotient(A    ,				A      ) == float(3      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 tests()




