


'''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 	UpperCamelCase__					:



     """simple docstring"""






     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            return None
class 	UpperCamelCase__					:



     """simple docstring"""






     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            return None
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






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







     @require_tf
     @slow
     def a	( self   ):

            '''simple docstring'''

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







     @require_torch
     @slow
     def a	( self   ):

            '''simple docstring'''

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







     @require_torch
     @slow
     def a	( self   ):

            '''simple docstring'''

            from transformers import BertModel

            _lowerCAmelCase    :							Optional[int]   			=       ['[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()
                   _lowerCAmelCase    :							List[Any]   			=       BertTokenizerFast(vocab_file.name   )

            with TemporaryDirectory() as bert_save_dir:
                   _lowerCAmelCase    :							Optional[int]   			=       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 a	( self   ):

            '''simple docstring'''

            for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                   _lowerCAmelCase    :							int   			=       self._test_export(snake_case__      ,	'tf'      ,	12      ,	**snake_case__   )
                   _lowerCAmelCase    :							str   			=       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 a	( self   ):

            '''simple docstring'''

            for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                   _lowerCAmelCase    :							Dict   			=       self._test_export(snake_case__      ,	'pt'      ,	12      ,	**snake_case__   )
                   _lowerCAmelCase    :							Optional[Any]   			=       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 a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__=None      ,	**snake_case__   ):

            '''simple docstring'''

            try:
                   # Compute path
                   with TemporaryDirectory() as tempdir:
                          _lowerCAmelCase    :							List[str]   			=       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 a	( self   ):

            '''simple docstring'''

            from transformers import BertModel

            _lowerCAmelCase    :							str   			=       BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'   )   )
            _lowerCAmelCase    :							Dict   			=       BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'   )
            self._test_infer_dynamic_axis(snake_case__      ,	snake_case__      ,	'pt'   )







     @require_tf
     @require_tokenizers
     @slow
     def a	( self   ):

            '''simple docstring'''

            from transformers import TFBertModel

            _lowerCAmelCase    :							str   			=       TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'   )   )
            _lowerCAmelCase    :							Dict   			=       BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'   )
            self._test_infer_dynamic_axis(snake_case__      ,	snake_case__      ,	'tf'   )







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       FeatureExtractionPipeline(snake_case__      ,	snake_case__   )

            _lowerCAmelCase    :							Tuple   			=       ['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
            _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase    :							str   			=       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 a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       ['input_ids', 'attention_mask', 'token_type_ids']
            _lowerCAmelCase    :							List[Any]   			=       {'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
            _lowerCAmelCase			,     _lowerCAmelCase    :							str   			=       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)
            _lowerCAmelCase			,     _lowerCAmelCase    :							List[Any]   			=       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 a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       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






def 			lowercase				(_A					):







       """simple docstring"""







       if not input_list:
              return []
       _lowerCAmelCase    :							Optional[int]   			=       [input_list.count(_A					) for value in input_list]
       _lowerCAmelCase    :							int   			=       max(_A					)  # Gets the maximum count in the input list.
       # Gets values of modes
       return sorted({input_list[i] for i, value in enumerate(_A					) if value == y}					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

