






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_    :






 '''simple docstring'''




 def  snake_case__				(	self							,  _A							,  _A							,  _A) ->		Tuple:

     """simple docstring"""



     return None



class 							A_    :






 '''simple docstring'''




 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A) ->		Dict:

     """simple docstring"""



     return None



class 							A_    (						unittest.TestCase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Any =					[
     # (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) ->		str:

     """simple docstring"""



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


 @require_torch
 @slow
 def  snake_case__				(	self) ->		Optional[int]:

     """simple docstring"""



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


 @require_torch
 @slow
 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



     from transformers import BertModel

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

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


 @require_tf
 @slow
 def  snake_case__				(	self) ->		List[str]:

     """simple docstring"""



     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         _UpperCAmelCase				:      Tuple			  =				self._test_export(_A							,  '''tf'''							,  12							,  **_A)
         _UpperCAmelCase				:      int			  =				quantize(Path(_A))

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


 @require_torch
 @slow
 def  snake_case__				(	self) ->		str:

     """simple docstring"""



     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         _UpperCAmelCase				:      Any			  =				self._test_export(_A							,  '''pt'''							,  12							,  **_A)
         _UpperCAmelCase				:      List[str]			  =				quantize(_A)

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


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A=None							,  **_A) ->		Dict:

     """simple docstring"""



     try:
         # Compute path
         with TemporaryDirectory() as tempdir:
             _UpperCAmelCase				:      Union[str, Any]			  =				Path(_A).joinpath('''model.onnx''')

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

         # Export
         convert(_A							,  _A							,  _A							,  _A							,  _A							,  **_A)

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


 @require_torch
 @require_tokenizers
 @slow
 def  snake_case__				(	self) ->		str:

     """simple docstring"""



     from transformers import BertModel

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


 @require_tf
 @require_tokenizers
 @slow
 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     from transformers import TFBertModel

     _UpperCAmelCase				:      Optional[Any]			  =				TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''))
     _UpperCAmelCase				:      Optional[int]			  =				BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random''')
     self._test_infer_dynamic_axis(_A							,  _A							,  '''tf''')


 def  snake_case__				(	self							,  _A							,  _A							,  _A) ->		Dict:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				FeatureExtractionPipeline(_A							,  _A)

     _UpperCAmelCase				:      List[str]			  =				['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
     _UpperCAmelCase				:      Optional[int]			  =				infer_shapes(_A							,  _A)

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

     # 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:

     """simple docstring"""



     _UpperCAmelCase				:      Any			  =				['''input_ids''', '''attention_mask''', '''token_type_ids''']
     _UpperCAmelCase				:      List[str]			  =				{'''input_ids''': [1, 2, 3, 4], '''attention_mask''': [0, 0, 0, 0], '''token_type_ids''': [1, 1, 1, 1]}
     _UpperCAmelCase				:      Optional[int]			  =				ensure_valid_input(FuncContiguousArgs()							,  _A							,  _A)

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

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

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

     # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
     _UpperCAmelCase				:      Optional[int]			  =				ensure_valid_input(FuncNonContiguousArgs()							,  _A							,  _A)

     # Should have exactly the one arg (all before the one not provided "some_other_args")
     self.assertEqual(len(_A)							,  1)
     self.assertEqual(len(_A)							,  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[str]:

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				generate_identified_filename(Path('''/home/something/my_fake_model.onnx''')							,  '''-test''')
     self.assertEqual('''/home/something/my_fake_model-test.onnx'''							,  generated.as_posix())












from math import pow, sqrt

def      _lowerCamelCase	(						*__A    :							float )      ->					bool:
    _UpperCAmelCase				:      str			  =				len(__A ) > 0 and all(value > 0.0 for value in values )
    return result

def      _lowerCamelCase	(						__A    :							float		,		__A    :							float )      ->					float | ValueError:
    return (
        round(sqrt(molar_mass_a / molar_mass_a )		,		6 )
        if validate(__A		,		__A )
        else ValueError('''Input Error: Molar mass values must greater than 0.''' )
    )

def      _lowerCamelCase	(						__A    :							float		,		__A    :							float		,		__A    :							float )      ->					float | ValueError:
    return (
        round(effusion_rate * sqrt(molar_mass_a / molar_mass_a )		,		6 )
        if validate(__A		,		__A		,		__A )
        else ValueError(
            '''Input Error: Molar mass and effusion rate values must greater than 0.''' )
    )

def      _lowerCamelCase	(						__A    :							float		,		__A    :							float		,		__A    :							float )      ->					float | ValueError:
    return (
        round(effusion_rate / sqrt(molar_mass_a / molar_mass_a )		,		6 )
        if validate(__A		,		__A		,		__A )
        else ValueError(
            '''Input Error: Molar mass and effusion rate values must greater than 0.''' )
    )

def      _lowerCamelCase	(						__A    :							float		,		__A    :							float		,		__A    :							float )      ->					float | ValueError:
    return (
        round(molar_mass / pow(effusion_rate_a / effusion_rate_a		,		2 )		,		6 )
        if validate(__A		,		__A		,		__A )
        else ValueError(
            '''Input Error: Molar mass and effusion rate values must greater than 0.''' )
    )


def      _lowerCamelCase	(						__A    :							float		,		__A    :							float		,		__A    :							float )      ->					float | ValueError:
    return (
        round(pow(effusion_rate_a / effusion_rate_a		,		2 ) / molar_mass		,		6 )
        if validate(__A		,		__A		,		__A )
        else ValueError(
            '''Input Error: Molar mass and effusion rate values must greater than 0.''' )
    )




