



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     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					Union[str, Any]			,						__lowerCAmelCase					:					Tuple			,						__lowerCAmelCase					:					List[Any]      )   ->	Dict:
    return None


class 	lowerCamelCase__	:



 """simple docstring"""

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					Optional[int]			,						__lowerCAmelCase					:					Any			,						__lowerCAmelCase					:					Dict			,						__lowerCAmelCase					:					Optional[int]      )   ->	List[str]:
    return None


class 	lowerCamelCase__	(       unittest.TestCase):



 """simple docstring"""

 a__					: Optional[int] =  [
     # (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					:					Tuple      )   ->	Dict:
    for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
       self._test_export(__lowerCAmelCase			,						'''tf'''			,						12			,						**__lowerCAmelCase      )

 @require_torch
 @slow
 def     snake_case_      (    self					:					Dict      )   ->	str:
    for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
       self._test_export(__lowerCAmelCase			,						'''pt'''			,						12			,						**__lowerCAmelCase      )

 @require_torch
 @slow
 def     snake_case_      (    self					:					Union[str, Any]      )   ->	List[Any]:
    from transformers import BertModel

    _A      =       ['''[UNK]''', '''[SEP]''', '''[CLS]''', '''[PAD]''', '''[MASK]''', '''some''', '''other''', '''words''']
    with NamedTemporaryFile(mode='''w+t'''      ) as vocab_file:
       vocab_file.write('''\n'''.join(__lowerCAmelCase      )      )
       vocab_file.flush()
       _A      =       BertTokenizerFast(vocab_file.name      )

    with TemporaryDirectory() as bert_save_dir:
       _A      =       BertModel(BertConfig(vocab_size=len(__lowerCAmelCase      )      )      )
       model.save_pretrained(__lowerCAmelCase      )
       self._test_export(__lowerCAmelCase			,						'''pt'''			,						12			,						__lowerCAmelCase      )

 @require_tf
 @slow
 def     snake_case_      (    self					:					Optional[int]      )   ->	str:
    for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
       _A      =       self._test_export(__lowerCAmelCase			,						'''tf'''			,						12			,						**__lowerCAmelCase      )
       _A      =       quantize(Path(__lowerCAmelCase      )      )

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

 @require_torch
 @slow
 def     snake_case_      (    self					:					Tuple      )   ->	Optional[int]:
    for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
       _A      =       self._test_export(__lowerCAmelCase			,						'''pt'''			,						12			,						**__lowerCAmelCase      )
       _A      =       quantize(__lowerCAmelCase      )

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

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					Union[str, Any]			,						__lowerCAmelCase					:					Any			,						__lowerCAmelCase					:					Optional[int]			,						__lowerCAmelCase					:					Optional[Any]=None			,						**__lowerCAmelCase					:					Any      )   ->	Any:
    try:
       # Compute path
       with TemporaryDirectory() as tempdir:
          _A      =       Path(__lowerCAmelCase      ).joinpath('''model.onnx'''      )

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

       # Export
       convert(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						**__lowerCAmelCase      )

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

 @require_torch
 @require_tokenizers
 @slow
 def     snake_case_      (    self					:					Union[str, Any]      )   ->	Optional[Any]:
    from transformers import BertModel

    _A      =       BertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''      )      )
    _A      =       BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''      )
    self._test_infer_dynamic_axis(__lowerCAmelCase			,						__lowerCAmelCase			,						'''pt'''      )

 @require_tf
 @require_tokenizers
 @slow
 def     snake_case_      (    self					:					int      )   ->	str:
    from transformers import TFBertModel

    _A      =       TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''      )      )
    _A      =       BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''      )
    self._test_infer_dynamic_axis(__lowerCAmelCase			,						__lowerCAmelCase			,						'''tf'''      )

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					Optional[Any]			,						__lowerCAmelCase					:					Optional[int]			,						__lowerCAmelCase					:					Tuple      )   ->	Union[str, Any]:
    _A      =       FeatureExtractionPipeline(__lowerCAmelCase			,						__lowerCAmelCase      )

    _A      =       ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
    _A  ,    _A  ,    _A  ,    _A      =       infer_shapes(__lowerCAmelCase			,						__lowerCAmelCase      )

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

    # 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					:					int      )   ->	List[str]:
    _A      =       ['''input_ids''', '''attention_mask''', '''token_type_ids''']
    _A      =       {'''input_ids''': [1, 2, 3, 4], '''attention_mask''': [0, 0, 0, 0], '''token_type_ids''': [1, 1, 1, 1]}
    _A  ,    _A      =       ensure_valid_input(FuncContiguousArgs()			,						__lowerCAmelCase			,						__lowerCAmelCase      )

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

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

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

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

    # Should have exactly the one arg (all before the one not provided "some_other_args")
    self.assertEqual(len(__lowerCAmelCase      )			,						1      )
    self.assertEqual(len(__lowerCAmelCase      )			,						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					:					List[Any]      )   ->	int:
    _A      =       generate_identified_filename(Path('''/home/something/my_fake_model.onnx'''      )			,						'''-test'''      )
    self.assertEqual('''/home/something/my_fake_model-test.onnx'''			,						generated.as_posix()      )








def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     int:
   if not isinstance(_snake_case	,   _snake_case				):
      raise ValueError('''Input must be an integer'''				)
   if input_num <= 0:
      raise ValueError('''Input must be positive'''				)
   return sum(
       divisor for divisor in range(1	,   input_num // 2 + 1				) if input_num % divisor == 0				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



