



"""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  lowercase__  :







       '''simple docstring'''


       def 	lowercase__							(				self     :    Optional[Any]						,							_UpperCAmelCase     :    Any						,							_UpperCAmelCase     :    str						,							_UpperCAmelCase     :    str       )					->		Dict:


             '''simple docstring'''




             return None



class  lowercase__  :







       '''simple docstring'''


       def 	lowercase__							(				self     :    Tuple						,							_UpperCAmelCase     :    Dict						,							_UpperCAmelCase     :    List[Any]						,							_UpperCAmelCase     :    List[Any]						,							_UpperCAmelCase     :    Union[str, Any]       )					->		Dict:


             '''simple docstring'''




             return None



class  lowercase__  (       unittest.TestCase ):







       '''simple docstring'''


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



       @require_tf
       @slow
       def 	lowercase__							(				self     :    Union[str, Any]       )					->		List[Any]:


             '''simple docstring'''




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



       @require_torch
       @slow
       def 	lowercase__							(				self     :    List[str]       )					->		Union[str, Any]:


             '''simple docstring'''




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



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


             '''simple docstring'''




             from transformers import BertModel

             UpperCAmelCase_     					=				["[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()
                   UpperCAmelCase_     					=				BertTokenizerFast(vocab_file.name       )

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



       @require_tf
       @slow
       def 	lowercase__							(				self     :    str       )					->		Any:


             '''simple docstring'''




             for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                   UpperCAmelCase_     					=				self._test_export(_UpperCAmelCase						,							"tf"						,							12						,							**_UpperCAmelCase       )
                   UpperCAmelCase_     					=				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 	lowercase__							(				self     :    Union[str, Any]       )					->		List[str]:


             '''simple docstring'''




             for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                   UpperCAmelCase_     					=				self._test_export(_UpperCAmelCase						,							"pt"						,							12						,							**_UpperCAmelCase       )
                   UpperCAmelCase_     					=				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 	lowercase__							(				self     :    Optional[Any]						,							_UpperCAmelCase     :    List[Any]						,							_UpperCAmelCase     :    str						,							_UpperCAmelCase     :    List[str]						,							_UpperCAmelCase     :    List[Any]=None						,							**_UpperCAmelCase     :    Dict       )					->		Optional[Any]:


             '''simple docstring'''




             try:
                   # Compute path
                   with TemporaryDirectory() as tempdir:
                         UpperCAmelCase_     					=				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 	lowercase__							(				self     :    Optional[int]       )					->		List[Any]:


             '''simple docstring'''




             from transformers import BertModel

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



       @require_tf
       @require_tokenizers
       @slow
       def 	lowercase__							(				self     :    Optional[Any]       )					->		List[Any]:


             '''simple docstring'''




             from transformers import TFBertModel

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



       def 	lowercase__							(				self     :    Tuple						,							_UpperCAmelCase     :    Union[str, Any]						,							_UpperCAmelCase     :    Union[str, Any]						,							_UpperCAmelCase     :    List[Any]       )					->		int:


             '''simple docstring'''




             UpperCAmelCase_     					=				FeatureExtractionPipeline(_UpperCAmelCase						,							_UpperCAmelCase       )

             UpperCAmelCase_     					=				["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
             UpperCAmelCase_					,       UpperCAmelCase_					,       UpperCAmelCase_					,       UpperCAmelCase_     					=				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 	lowercase__							(				self     :    Any       )					->		List[Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				["input_ids", "attention_mask", "token_type_ids"]
             UpperCAmelCase_     					=				{"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
             UpperCAmelCase_					,       UpperCAmelCase_     					=				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)
             UpperCAmelCase_					,       UpperCAmelCase_     					=				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 	lowercase__							(				self     :    int       )					->		List[Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				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 math import pow, sqrt



def   _A					(	*_a	:						float				):



      """simple docstring"""






      A				  =		len(_a				) > 0 and all(value > 0.0 for value in values				)
      return result



def   _A					(	_a	:						float     ,   _a	:						float				):



      """simple docstring"""






      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   _A					(	_a	:						float     ,   _a	:						float     ,   _a	:						float				):



      """simple docstring"""






      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   _A					(	_a	:						float     ,   _a	:						float     ,   _a	:						float				):



      """simple docstring"""






      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   _A					(	_a	:						float     ,   _a	:						float     ,   _a	:						float				):



      """simple docstring"""






      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   _A					(	_a	:						float     ,   _a	:						float     ,   _a	:						float				):



      """simple docstring"""






      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."""				)
      )




