





import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
UpperCAmelCase	:	Tuple		     =       {"""LayoutLMv2Config""", """LayoutLMv3Config"""}







@is_pipeline_test
class 				__lowerCAmelCase       (    unittest.TestCase):
      _lowercase				:					Dict						=			MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      _lowercase				:					int						=			TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

      if model_mapping is not None:
            _lowercase				:					Dict						=			{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
      if tf_model_mapping is not None:
            _lowercase				:					Optional[Any]						=			{
                config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
            }







      @require_torch
      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        a__	:					Union[str, Any]										=pipeline(
            task="text-classification"		,      model="hf-internal-testing/tiny-random-distilbert"		,      framework="pt"							)

        a__	:					str										=text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": "LABEL_0", "score": 0.5_04}]							)

        a__	:					Tuple										=text_classifier("This is great !"		,      top_k=2							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,      [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]							)

        a__	:					Optional[int]										=text_classifier(["This is great !", "This is bad"]		,      top_k=2							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,      [
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
            ]		,      )

        a__	:					Any										=text_classifier("This is great !"		,      top_k=1							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": "LABEL_0", "score": 0.5_04}]							)

        # Legacy behavior
        a__	:					Optional[int]										=text_classifier("This is great !"		,      return_all_scores=lowerCAmelCase__							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": "LABEL_0", "score": 0.5_04}]							)

        a__	:					Any										=text_classifier("This is great !"		,      return_all_scores=lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,      [[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]]							)

        a__	:					int										=text_classifier(["This is great !", "Something else"]		,      return_all_scores=lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,      [
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
            ]		,      )

        a__	:					List[Any]										=text_classifier(["This is great !", "Something else"]		,      return_all_scores=lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,      [
                {"label": "LABEL_0", "score": 0.5_04},
                {"label": "LABEL_0", "score": 0.5_04},
            ]		,      )







      @require_torch
      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        import torch

        a__	:					Any										=pipeline(
            task="text-classification"		,      model="hf-internal-testing/tiny-random-distilbert"		,      framework="pt"		,      device=torch.device("cpu"							)		,      )

        a__	:					Optional[int]										=text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": "LABEL_0", "score": 0.5_04}]							)







      @require_tf
      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					Optional[Any]										=pipeline(
            task="text-classification"		,      model="hf-internal-testing/tiny-random-distilbert"		,      framework="tf"							)

        a__	:					List[str]										=text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": "LABEL_0", "score": 0.5_04}]							)







      @slow
      @require_torch
      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        a__	:					Optional[Any]										=pipeline("text-classification"							)

        a__	:					Dict										=text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": "POSITIVE", "score": 1.0}]							)
        a__	:					Tuple										=text_classifier("This is bad !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": "NEGATIVE", "score": 1.0}]							)
        a__	:					Union[str, Any]										=text_classifier("Birds are a type of animal"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": "POSITIVE", "score": 0.9_88}]							)







      @slow
      @require_tf
      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        a__	:					Optional[int]										=pipeline("text-classification"		,      framework="tf"							)

        a__	:					str										=text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": "POSITIVE", "score": 1.0}]							)
        a__	:					List[str]										=text_classifier("This is bad !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": "NEGATIVE", "score": 1.0}]							)
        a__	:					List[str]										=text_classifier("Birds are a type of animal"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": "POSITIVE", "score": 0.9_88}]							)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						List[Any]:



        '''simple docstring'''




        a__	:					Optional[int]										=TextClassificationPipeline(model=lowerCAmelCase__		,      tokenizer=lowerCAmelCase__							)
        return text_classifier, ["HuggingFace is in", "This is another test"]







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						List[str]:



        '''simple docstring'''




        a__	:					int										=text_classifier.model
        # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
        a__	:					Optional[Any]										="HuggingFace is in"
        a__	:					List[str]										=text_classifier(lowerCAmelCase__							)

        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}]							)
        self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()							)

        a__	:					Optional[int]										=["HuggingFace is in ", "Paris is in France"]
        a__	:					Optional[Any]										=text_classifier(lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,      [{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}, {"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}]		,      )
        self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()							)
        self.assertTrue(outputs[1]["label"] in model.config.idalabel.values()							)

        # Forcing to get all results with `top_k=None`
        # This is NOT the legacy format
        a__	:					List[str]										=text_classifier(lowerCAmelCase__		,      top_k=lowerCAmelCase__							)
        a__	:					Any										=len(model.config.idalabel.values()							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,      [[{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}] * N, [{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}] * N]		,      )

        a__	:					Optional[int]										={"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
        a__	:					str										=text_classifier(lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,      {"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}		,      )
        self.assertTrue(outputs["label"] in model.config.idalabel.values()							)

        # This might be used a text pair, but tokenizer + pipe interaction
        # makes it hard to understand that it's not using the pair properly
        # https://github.com/huggingface/transformers/issues/17305
        # We disabled this usage instead as it was outputting wrong outputs.
        a__	:					List[str]										=[["HuggingFace is in ", "Paris is in France"]]
        with self.assertRaises(lowerCAmelCase__							):
          text_classifier(lowerCAmelCase__							)

        # This used to be valid for doing text pairs
        # We're keeping it working because of backward compatibility
        a__	:					Dict										=text_classifier([[["HuggingFace is in ", "Paris is in France"]]]							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,      [{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}]		,      )
        self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()							)













import numpy

# List of input, output pairs
UpperCAmelCase	:	str		     =       (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
UpperCAmelCase	:	Optional[int]		     =       (((515, 22, 13), 555), ((61, 35, 49), 150))
UpperCAmelCase	:	str		     =       [2, 4, 1, 5]
UpperCAmelCase	:	List[str]		     =       len(train_data)
UpperCAmelCase	:	Dict		     =       0.0_0_9
def      _A       (		SCREAMING_SNAKE_CASE     :   Tuple       ,       SCREAMING_SNAKE_CASE     :   Tuple="train"						):





  """simple docstring"""



  return calculate_hypothesis_value(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						) - output(
      SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
def      _A       (		SCREAMING_SNAKE_CASE     :   Tuple						):





  """simple docstring"""



  a__	:					Tuple										=0
  for i in range(len(SCREAMING_SNAKE_CASE						) - 1						):
    hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
  hyp_val += parameter_vector[0]
  return hyp_val
def      _A       (		SCREAMING_SNAKE_CASE     :   Dict       ,       SCREAMING_SNAKE_CASE     :   Dict						):





  """simple docstring"""



  if data_set == "train":
    return train_data[example_no][1]
  elif data_set == "test":
    return test_data[example_no][1]
  return None
def      _A       (		SCREAMING_SNAKE_CASE     :   Optional[Any]       ,       SCREAMING_SNAKE_CASE     :   Tuple						):





  """simple docstring"""



  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      _A       (		SCREAMING_SNAKE_CASE     :   Union[str, Any]       ,       SCREAMING_SNAKE_CASE     :   int=m						):





  """simple docstring"""



  a__	:					Any										=0
  for i in range(SCREAMING_SNAKE_CASE						):
    if index == -1:
      summation_value += _error(SCREAMING_SNAKE_CASE						)
    else:
      summation_value += _error(SCREAMING_SNAKE_CASE						) * train_data[i][0][index]
  return summation_value
def      _A       (		SCREAMING_SNAKE_CASE     :   int						):





  """simple docstring"""



  a__	:					Any										=summation_of_cost_derivative(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						) / m
  return cost_derivative_value
def      _A       (		):





  """simple docstring"""



  global parameter_vector
  # Tune these values to set a tolerance value for predicted output
  a__	:					Dict										=0.0_0_0_0_0_2
  a__	:					Union[str, Any]										=0
  a__	:					Any										=0
  while True:
    j += 1
    a__	:					Any										=[0, 0, 0, 0]
    for i in range(0       ,       len(SCREAMING_SNAKE_CASE						)						):
      a__	:					Tuple										=get_cost_derivative(i - 1						)
      a__	:					List[Any]										=(
          parameter_vector[i] - LEARNING_RATE * cost_derivative
      )
    if numpy.allclose(
        SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       atol=SCREAMING_SNAKE_CASE       ,       rtol=SCREAMING_SNAKE_CASE       ,       ):
      break
    a__	:					Optional[Any]										=temp_parameter_vector
  print(("Number of iterations:", j)						)
def      _A       (		):





  """simple docstring"""



  for i in range(len(SCREAMING_SNAKE_CASE						)						):
    print(("Actual output value:", output(SCREAMING_SNAKE_CASE       ,       "test"						))						)
    print(("Hypothesis output:", calculate_hypothesis_value(SCREAMING_SNAKE_CASE       ,       "test"						))						)


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






