




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        a				(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       )				->	Dict:
         return None






class 			__lowerCamelCase	:




    """simple docstring"""

    def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	int:
         return None






class 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    snake_case__ =				[
        # (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					:					List[str]       )				->	int:
         for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              self._test_export(SCREAMING_SNAKE_CASE__       ,     "tf"       ,     12       ,     **SCREAMING_SNAKE_CASE__       )


    @require_torch
    @slow
    def        a				(	self					:					Tuple       )				->	Any:
         for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              self._test_export(SCREAMING_SNAKE_CASE__       ,     "pt"       ,     12       ,     **SCREAMING_SNAKE_CASE__       )


    @require_torch
    @slow
    def        a				(	self					:					Dict       )				->	Optional[int]:
         from transformers import BertModel

         lowerCAmelCase__          =			["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
         with NamedTemporaryFile(mode="w+t"       ) as vocab_file:
              vocab_file.write("\n".join(SCREAMING_SNAKE_CASE__       )       )
              vocab_file.flush()
              lowerCAmelCase__          =			BertTokenizerFast(vocab_file.name       )

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


    @require_tf
    @slow
    def        a				(	self					:					str       )				->	List[str]:
         for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              lowerCAmelCase__          =			self._test_export(SCREAMING_SNAKE_CASE__       ,     "tf"       ,     12       ,     **SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			quantize(Path(SCREAMING_SNAKE_CASE__       )       )

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


    @require_torch
    @slow
    def        a				(	self					:					Optional[Any]       )				->	List[Any]:
         for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              lowerCAmelCase__          =			self._test_export(SCREAMING_SNAKE_CASE__       ,     "pt"       ,     12       ,     **SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			quantize(SCREAMING_SNAKE_CASE__       )

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


    def        a				(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					str=None       ,     **SCREAMING_SNAKE_CASE__					:					Union[str, Any]       )				->	List[str]:
         try:
              # Compute path
              with TemporaryDirectory() as tempdir:
                   lowerCAmelCase__          =			Path(SCREAMING_SNAKE_CASE__       ).joinpath("model.onnx"       )

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

              # Export
              convert(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )

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


    @require_torch
    @require_tokenizers
    @slow
    def        a				(	self					:					int       )				->	Any:
         from transformers import BertModel

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


    @require_tf
    @require_tokenizers
    @slow
    def        a				(	self					:					Optional[int]       )				->	Tuple:
         from transformers import TFBertModel

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


    def        a				(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       )				->	str:
         lowerCAmelCase__          =			FeatureExtractionPipeline(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
         lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			infer_shapes(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

         # Assert all variables are present
         self.assertEqual(len(SCREAMING_SNAKE_CASE__       )       ,     len(SCREAMING_SNAKE_CASE__       )       )
         self.assertTrue(all(var_name in shapes for var_name in variable_names       )       )
         self.assertSequenceEqual(variable_names[:3]       ,     SCREAMING_SNAKE_CASE__       )
         self.assertSequenceEqual(variable_names[3:]       ,     SCREAMING_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					:					str       )				->	List[str]:
         lowerCAmelCase__          =			["input_ids", "attention_mask", "token_type_ids"]
         lowerCAmelCase__          =			{"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
         lowerCAmelCase__     ,		lowerCAmelCase__          =			ensure_valid_input(FuncContiguousArgs()       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

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

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

         # Parameter should be reordered according to their respective place in the function:
         # (input_ids, token_type_ids, attention_mask)
         self.assertEqual(SCREAMING_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__          =			ensure_valid_input(FuncNonContiguousArgs()       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

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






import random



def 							_A       (						lowerCAmelCase_					: list       ,    lowerCAmelCase_					: List[str]      ):
     """simple docstring"""

     lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			[], [], []
     for element in data:
          if element < pivot:
               less.append(lowerCAmelCase_      )
          elif element > pivot:
               greater.append(lowerCAmelCase_      )
          else:
               equal.append(lowerCAmelCase_      )
     return less, equal, greater






def 							_A       (						lowerCAmelCase_					: list       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     if index >= len(lowerCAmelCase_      ) or index < 0:
          return None

     lowerCAmelCase__          =			items[random.randint(0       ,    len(lowerCAmelCase_      ) - 1      )]
     lowerCAmelCase__          =			0
     lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			_partition(lowerCAmelCase_       ,    lowerCAmelCase_      )
     lowerCAmelCase__          =			len(lowerCAmelCase_      )
     lowerCAmelCase__          =			len(lowerCAmelCase_      )

     # index is the pivot
     if m <= index < m + count:
          return pivot
     # must be in smaller
     elif m > index:
          return quick_select(lowerCAmelCase_       ,    lowerCAmelCase_      )
     # must be in larger
     else:
          return quick_select(lowerCAmelCase_       ,    index - (m + count)      )
