'''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    a :



  """simple docstring"""







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    return None




class    a :



  """simple docstring"""







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    return None




class    a (       unittest.TestCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					[
      # (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					):

    '''simple docstring'''







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







  @require_torch
  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







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







  @require_torch
  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    from transformers import BertModel

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

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







  @require_tf
  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
      __UpperCAmelCase:					Dict		= self._test_export(snake_case_   ,    """tf"""   ,    12   ,    **snake_case_					)
      __UpperCAmelCase:					str		= quantize(Path(snake_case_					)					)

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







  @require_torch
  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
      __UpperCAmelCase:					Tuple		= self._test_export(snake_case_   ,    """pt"""   ,    12   ,    **snake_case_					)
      __UpperCAmelCase:					Tuple		= quantize(snake_case_					)

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







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_=None   ,    **snake_case_					):

    '''simple docstring'''







    try:
      # Compute path
      with TemporaryDirectory() as tempdir:
        __UpperCAmelCase:					Union[str, Any]		= Path(snake_case_					).joinpath("""model.onnx"""					)

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

      # Export
      convert(snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    **snake_case_					)

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







  @require_torch
  @require_tokenizers
  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    from transformers import BertModel

    __UpperCAmelCase:					Any		= BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""					)					)
    __UpperCAmelCase:					int		= BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""					)
    self._test_infer_dynamic_axis(snake_case_   ,    snake_case_   ,    """pt"""					)







  @require_tf
  @require_tokenizers
  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    from transformers import TFBertModel

    __UpperCAmelCase:					str		= TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""					)					)
    __UpperCAmelCase:					int		= BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""					)
    self._test_infer_dynamic_axis(snake_case_   ,    snake_case_   ,    """tf"""					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= FeatureExtractionPipeline(snake_case_   ,    snake_case_					)

    __UpperCAmelCase:					List[Any]		= ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
    __UpperCAmelCase:					List[Any]		= infer_shapes(snake_case_   ,    snake_case_					)

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

    '''simple docstring'''







    __UpperCAmelCase:					int		= ["""input_ids""", """attention_mask""", """token_type_ids"""]
    __UpperCAmelCase:					str		= {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
    __UpperCAmelCase:					str		= ensure_valid_input(FuncContiguousArgs()   ,    snake_case_   ,    snake_case_					)

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

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

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

    # Should have exactly the one arg (all before the one not provided "some_other_args")
    self.assertEqual(len(snake_case_					)   ,    1					)
    self.assertEqual(len(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    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= 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  UpperCamelCase__					(					_lowercase    :				int     )							->	int:

  if not isinstance(_lowercase   ,    _lowercase     ):
    __UpperCAmelCase:					List[str]		= F'''Input value of [number={number}] must be an integer'''
    raise TypeError(_lowercase     )

  if number < 1:
    __UpperCAmelCase:					Dict		= F'''Input value of [number={number}] must be > 0'''
    raise ValueError(_lowercase     )

  __UpperCAmelCase:					int		= 1

  for i in range(1   ,    _lowercase     ):
    current_number *= 4 * i - 2
    current_number //= i + 1

  return current_number


if __name__ == "__main__":
    import doctest

    doctest.testmod()