'''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:					Dict		= ['''[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:					List[str]		= BertTokenizerFast(vocab_file.name					)

    with TemporaryDirectory() as bert_save_dir:
      __UpperCAmelCase:					Any		= 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:					Tuple		= self._test_export(snake_case_   ,    """tf"""   ,    12   ,    **snake_case_					)
      __UpperCAmelCase:					int		= 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:					Any		= self._test_export(snake_case_   ,    """pt"""   ,    12   ,    **snake_case_					)
      __UpperCAmelCase:					List[str]		= 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:					Dict		= BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""					)					)
    __UpperCAmelCase:					Dict		= 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:					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(snake_case_   ,    snake_case_   ,    """tf"""					)







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

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= FeatureExtractionPipeline(snake_case_   ,    snake_case_					)

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






import numpy

# List of input, output pairs
SCREAMING_SNAKE_CASE_	   =			(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
SCREAMING_SNAKE_CASE_	   =			(((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
SCREAMING_SNAKE_CASE_	   =			[2, 4, 1, 5]
SCREAMING_SNAKE_CASE_	   =			len(train_data)
SCREAMING_SNAKE_CASE_	   =			0.009






def  UpperCamelCase__					(					_lowercase    :				Union[str, Any]   ,    _lowercase    :				List[Any]="train"     )							->	int:
  return calculate_hypothesis_value(_lowercase   ,    _lowercase     ) - output(
      _lowercase   ,    _lowercase     )






def  UpperCamelCase__					(					_lowercase    :				Dict     )							->	Optional[Any]:
  __UpperCAmelCase:					List[Any]		= 0
  for i in range(len(_lowercase     ) - 1     ):
    hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
  hyp_val += parameter_vector[0]
  return hyp_val






def  UpperCamelCase__					(					_lowercase    :				List[str]   ,    _lowercase    :				List[str]     )							->	Optional[Any]:
  if data_set == "train":
    return train_data[example_no][1]
  elif data_set == "test":
    return test_data[example_no][1]
  return None






def  UpperCamelCase__					(					_lowercase    :				Dict   ,    _lowercase    :				str     )							->	Dict:
  if data_set == "train":
    return _hypothesis_value(train_data[example_no][0]     )
  elif data_set == "test":
    return _hypothesis_value(test_data[example_no][0]     )
  return None






def  UpperCamelCase__					(					_lowercase    :				Optional[int]   ,    _lowercase    :				Union[str, Any]=m     )							->	Optional[Any]:
  __UpperCAmelCase:					int		= 0
  for i in range(_lowercase     ):
    if index == -1:
      summation_value += _error(_lowercase     )
    else:
      summation_value += _error(_lowercase     ) * train_data[i][0][index]
  return summation_value






def  UpperCamelCase__					(					_lowercase    :				Tuple     )							->	Optional[int]:
  __UpperCAmelCase:					Any		= summation_of_cost_derivative(_lowercase   ,    _lowercase     ) / m
  return cost_derivative_value






def  UpperCamelCase__					(					)							->	Tuple:
  global parameter_vector
  # Tune these values to set a tolerance value for predicted output
  __UpperCAmelCase:					str		= 0.00_00_02
  __UpperCAmelCase:					List[Any]		= 0
  __UpperCAmelCase:					Optional[Any]		= 0
  while True:
    j += 1
    __UpperCAmelCase:					Any		= [0, 0, 0, 0]
    for i in range(0   ,    len(_lowercase     )     ):
      __UpperCAmelCase:					str		= get_cost_derivative(i - 1     )
      __UpperCAmelCase:					List[str]		= (
          parameter_vector[i] - LEARNING_RATE * cost_derivative
      )
    if numpy.allclose(
        _lowercase   ,    _lowercase   ,    atol=_lowercase   ,    rtol=_lowercase   ,    ):
      break
    __UpperCAmelCase:					List[Any]		= temp_parameter_vector
  print(("""Number of iterations:""", j)     )






def  UpperCamelCase__					(					)							->	Dict:
  for i in range(len(_lowercase     )     ):
    print(("""Actual output value:""", output(_lowercase   ,    """test"""     ))     )
    print(("""Hypothesis output:""", calculate_hypothesis_value(_lowercase   ,    """test"""     ))     )


if __name__ == "__main__":
    run_gradient_descent()
    print('\nTesting gradient descent for a linear hypothesis function.\n')
    test_gradient_descent()