"""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"""


lowerCAmelCase_:					Any     =					"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"







def 					__a   (		A      ):






     '''simple docstring'''



     if not isinstance(A    ,				A      ):
          lowercase__				 =							f'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
          raise TypeError(A      )

     lowercase__				 =							"".join(bin(A      )[2:].zfill(8      ) for byte in data      )

     lowercase__				 =							len(A      ) % 6 != 0

     if padding_needed:
          # The padding that will be added later
          lowercase__				 =							b"=" * ((6 - len(A      ) % 6) // 2)

          # Append binary_stream with arbitrary binary digits (0's by default) to make its
          # length a multiple of 6.
          binary_stream += "0" * (6 - len(A      ) % 6)
     else:
          lowercase__				 =							b""

     # Encode every 6 binary digits to their corresponding Base64 character
     return (
         "".join(
             B64_CHARSET[int(binary_stream[index : index + 6]    ,				2      )]
             for index in range(0    ,				len(A      )    ,				6      )      ).encode()
         + padding
     )







def 					__a   (		A      ):






     '''simple docstring'''



     if not isinstance(A    ,				A      ) and not isinstance(A    ,				A      ):
          lowercase__				 =							(
              "argument should be a bytes-like object or ASCII string, "
              f'''not \'{encoded_data.__class__.__name__}\''''
          )
          raise TypeError(A      )

     # In case encoded_data is a bytes-like object, make sure it contains only
     # ASCII characters so we convert it to a string object
     if isinstance(A    ,				A      ):
          try:
               lowercase__				 =							encoded_data.decode("utf-8"      )
          except UnicodeDecodeError:
               raise ValueError("base64 encoded data should only contain ASCII characters"      )

     lowercase__				 =							encoded_data.count("="      )

     # Check if the encoded string contains non base64 characters
     if padding:
          assert all(
              char in B64_CHARSET for char in encoded_data[:-padding]      ), "Invalid base64 character(s) found."
     else:
          assert all(
              char in B64_CHARSET for char in encoded_data      ), "Invalid base64 character(s) found."

     # Check the padding
     assert len(A      ) % 4 == 0 and padding < 3, "Incorrect padding"

     if padding:
          # Remove padding if there is one
          lowercase__				 =							encoded_data[:-padding]

          lowercase__				 =							"".join(
              bin(B64_CHARSET.index(A      )      )[2:].zfill(6      ) for char in encoded_data      )[: -padding * 2]
     else:
          lowercase__				 =							"".join(
              bin(B64_CHARSET.index(A      )      )[2:].zfill(6      ) for char in encoded_data      )

     lowercase__				 =							[
         int(binary_stream[index : index + 8]    ,				2      )
         for index in range(0    ,				len(A      )    ,				8      )
     ]

     return bytes(A      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()




