






'''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						:




    def 				lowerCAmelCase__				(			self  :       Optional[Any]  ,     a  :       Optional[int]  ,     a  :       List[Any]  ,     a  :       str			):






       '''simple docstring'''






       return None


class        _UpperCAmelCase						:




    def 				lowerCAmelCase__				(			self  :       int  ,     a  :       Union[str, Any]  ,     a  :       Any  ,     a  :       Optional[int]  ,     a  :       Optional[Any]			):






       '''simple docstring'''






       return None


class        _UpperCAmelCase						(		unittest.TestCase			):
    __lowerCamelCase:   Tuple		       =    [
        # (model_name, model_kwargs)
        ('bert-base-cased', {}),
        ('gpt2', {'use_cache': False}),  # We don't support exporting GPT2 past keys anymore
    ]




    @require_tf
    @slow
    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
          self._test_export(a  ,     "tf"  ,     1_2  ,     **a			)




    @require_torch
    @slow
    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
          self._test_export(a  ,     "pt"  ,     1_2  ,     **a			)




    @require_torch
    @slow
    def 				lowerCAmelCase__				(			self  :       List[Any]			):






       '''simple docstring'''






       from transformers import BertModel

       lowercase_	:    List[str]           =     ["[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()
          lowercase_	:    Optional[int]           =     BertTokenizerFast(vocab_file.name			)

       with TemporaryDirectory() as bert_save_dir:
          lowercase_	:    Optional[int]           =     BertModel(BertConfig(vocab_size=len(a			)			)			)
          model.save_pretrained(a			)
          self._test_export(a  ,     "pt"  ,     1_2  ,     a			)




    @require_tf
    @slow
    def 				lowerCAmelCase__				(			self  :       Dict			):






       '''simple docstring'''






       for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
          lowercase_	:    Dict           =     self._test_export(a  ,     "tf"  ,     1_2  ,     **a			)
          lowercase_	:    List[Any]           =     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 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






       '''simple docstring'''






       for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
          lowercase_	:    Any           =     self._test_export(a  ,     "pt"  ,     1_2  ,     **a			)
          lowercase_	:    int           =     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 				lowerCAmelCase__				(			self  :       Optional[Any]  ,     a  :       Dict  ,     a  :       Union[str, Any]  ,     a  :       str  ,     a  :       int=None  ,     **a  :       int			):






       '''simple docstring'''






       try:
          # Compute path
          with TemporaryDirectory() as tempdir:
             lowercase_	:    Optional[int]           =     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 				lowerCAmelCase__				(			self  :       Optional[int]			):






       '''simple docstring'''






       from transformers import BertModel

       lowercase_	:    str           =     BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"			)			)
       lowercase_	:    str           =     BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"			)
       self._test_infer_dynamic_axis(a  ,     a  ,     "pt"			)




    @require_tf
    @require_tokenizers
    @slow
    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       from transformers import TFBertModel

       lowercase_	:    Optional[Any]           =     TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"			)			)
       lowercase_	:    Optional[Any]           =     BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"			)
       self._test_infer_dynamic_axis(a  ,     a  ,     "tf"			)




    def 				lowerCAmelCase__				(			self  :       Any  ,     a  :       str  ,     a  :       Union[str, Any]  ,     a  :       Union[str, Any]			):






       '''simple docstring'''






       lowercase_	:    int           =     FeatureExtractionPipeline(a  ,     a			)

       lowercase_	:    List[str]           =     ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
       lowercase_    ,		lowercase_    ,		lowercase_    ,		lowercase_	:    Optional[Any]           =     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 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






       '''simple docstring'''






       lowercase_	:    str           =     ["input_ids", "attention_mask", "token_type_ids"]
       lowercase_	:    Optional[Any]           =     {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
       lowercase_    ,		lowercase_	:    Dict           =     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)
       lowercase_    ,		lowercase_	:    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 				lowerCAmelCase__				(			self  :       Dict			):






       '''simple docstring'''






       lowercase_	:    str           =     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'''
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   return [
       {
           0: [1, 2],
           1: [0, 2],
           2: [0, 1, 3, 5],
           3: [2, 4],
           4: [3],
           5: [2, 6, 8],
           6: [5, 7],
           7: [6, 8],
           8: [5, 7],
       },
       {
           0: [6],
           1: [9],
           2: [4, 5],
           3: [4],
           4: [2, 3],
           5: [2],
           6: [0, 7],
           7: [6],
           8: [],
           9: [1],
       },
       {
           0: [4],
           1: [6],
           2: [],
           3: [5, 6, 7],
           4: [0, 6],
           5: [3, 8, 9],
           6: [1, 3, 4, 7],
           7: [3, 6, 8, 9],
           8: [5, 7],
           9: [5, 7],
       },
       {
           0: [1, 3],
           1: [0, 2, 4],
           2: [1, 3, 4],
           3: [0, 2, 4],
           4: [1, 2, 3],
       },
   ][index]




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Dict           =     0
   lowercase_	:    Optional[Any]           =     len(_UpperCamelCase							)  # No of vertices in graph
   lowercase_	:    Union[str, Any]           =     [0] * n
   lowercase_	:    Optional[int]           =     [False] * n

   def dfs(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):
      lowercase_	:    Union[str, Any]           =     True
      lowercase_	:    Dict           =     id_
      id_ += 1
      for to in graph[at]:
         if to == parent:
            pass
         elif not visited[to]:
            dfs(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		id_							)
            lowercase_	:    str           =     min(low[at]						,		low[to]							)
            if id_ <= low[to]:
               bridges.append((at, to) if at < to else (to, at)							)
         else:
            # This edge is a back edge and cannot be a bridge
            lowercase_	:    Optional[int]           =     min(low[at]						,		low[to]							)

   lowercase_	:    list[tuple[int, int]]           =     []
   for i in range(_UpperCamelCase							):
      if not visited[i]:
         dfs(_UpperCamelCase						,		-1						,		_UpperCamelCase						,		id_							)
   return bridges


if __name__ == "__main__":
 import doctest

 doctest.testmod()




