

"""simple docstring"""





def   _A  (  UpperCamelCase_					:	int,      UpperCamelCase_					:	list)     ->						Any:
   '''simple docstring'''


   _enforce_args(UpperCamelCase_,      UpperCamelCase_)
   if n == 0:
      return 0
   __lowercase								=						float("-inf")
   for i in range(1,      n + 1):
      __lowercase								=						max(
          UpperCamelCase_,      prices[i - 1] + naive_cut_rod_recursive(n - i,      UpperCamelCase_))

   return max_revue




def   _A  (  UpperCamelCase_					:	int,      UpperCamelCase_					:	list)     ->						Dict:
   '''simple docstring'''


   _enforce_args(UpperCamelCase_,      UpperCamelCase_)
   __lowercase								=						[float("-inf") for _ in range(n + 1)]
   return _top_down_cut_rod_recursive(UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_)




def   _A  (  UpperCamelCase_					:	int,      UpperCamelCase_					:	list,      UpperCamelCase_					:	list)     ->						Union[str, Any]:
   '''simple docstring'''


   if max_rev[n] >= 0:
      return max_rev[n]
   elif n == 0:
      return 0
   else:
      __lowercase								=						float("-inf")
      for i in range(1,      n + 1):
         __lowercase								=						max(
             UpperCamelCase_,      prices[i - 1] + _top_down_cut_rod_recursive(n - i,      UpperCamelCase_,      UpperCamelCase_),      )

      __lowercase								=						max_revenue

   return max_rev[n]




def   _A  (  UpperCamelCase_					:	int,      UpperCamelCase_					:	list)     ->						Optional[Any]:
   '''simple docstring'''


   _enforce_args(UpperCamelCase_,      UpperCamelCase_)

   # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
   # length 0.
   __lowercase								=						[float("-inf") for _ in range(n + 1)]
   __lowercase								=						0

   for i in range(1,      n + 1):
      __lowercase								=						max_rev[i]
      for j in range(1,      i + 1):
         __lowercase								=						max(UpperCamelCase_,      prices[j - 1] + max_rev[i - j])

      __lowercase								=						max_revenue_i

   return max_rev[n]




def   _A  (  UpperCamelCase_					:	int,      UpperCamelCase_					:	list)     ->						Dict:
   '''simple docstring'''


   if n < 0:
      __lowercase								=						F"""n must be greater than or equal to 0. Got n = {n}"""
      raise ValueError(UpperCamelCase_)

   if n > len(UpperCamelCase_):
      __lowercase								=						(
          "Each integral piece of rod must have a corresponding price. "
          F"""Got n = {n} but length of prices = {len(UpperCamelCase_)}"""
      )
      raise ValueError(UpperCamelCase_)




def   _A  (  )     ->						Dict:
   '''simple docstring'''


   __lowercase								=						[6, 10, 12, 15, 20, 23]
   __lowercase								=						len(UpperCamelCase_)

   # the best revenue comes from cutting the rod into 6 pieces, each
   # of length 1 resulting in a revenue of 6 * 6 = 36.
   __lowercase								=						36

   __lowercase								=						top_down_cut_rod(UpperCamelCase_,      UpperCamelCase_)
   __lowercase								=						bottom_up_cut_rod(UpperCamelCase_,      UpperCamelCase_)
   __lowercase								=						naive_cut_rod_recursive(UpperCamelCase_,      UpperCamelCase_)

   assert expected_max_revenue == max_rev_top_down
   assert max_rev_top_down == max_rev_bottom_up
   assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
   main()







"""simple docstring"""





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.
_a							=							{'LayoutLMv2Config', 'LayoutLMv3Config'}




@is_pipeline_test
class  _lowerCAmelCase  (     unittest.TestCase       ):







 """simple docstring"""







 __UpperCAmelCase     : str       		=     MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
 __UpperCAmelCase     : str       		=     TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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


 @require_torch
 def  _lowercase			(      self							:      Optional[int]     ):
    __lowercase								=						pipeline(
        task="text-classification", model="hf-internal-testing/tiny-random-distilbert", framework="pt"     )

    __lowercase								=						text_classifier("This is great !"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "LABEL_0", "score": 0.504}]     )

    __lowercase								=						text_classifier("This is great !", top_k=2     )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__     ), [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]     )

    __lowercase								=						text_classifier(["This is great !", "This is bad"], top_k=2     )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__     ), [
            [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
            [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
        ], )

    __lowercase								=						text_classifier("This is great !", top_k=1     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "LABEL_0", "score": 0.504}]     )

    # Legacy behavior
    __lowercase								=						text_classifier("This is great !", return_all_scores=UpperCAmelCase__     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "LABEL_0", "score": 0.504}]     )

    __lowercase								=						text_classifier("This is great !", return_all_scores=UpperCAmelCase__     )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__     ), [[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]]     )

    __lowercase								=						text_classifier(["This is great !", "Something else"], return_all_scores=UpperCAmelCase__     )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__     ), [
            [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
            [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
        ], )

    __lowercase								=						text_classifier(["This is great !", "Something else"], return_all_scores=UpperCAmelCase__     )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__     ), [
            {"label": "LABEL_0", "score": 0.504},
            {"label": "LABEL_0", "score": 0.504},
        ], )


 @require_torch
 def  _lowercase			(      self							:      Dict     ):
    import torch

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

    __lowercase								=						text_classifier("This is great !"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "LABEL_0", "score": 0.504}]     )


 @require_tf
 def  _lowercase			(      self							:      Union[str, Any]     ):
    __lowercase								=						pipeline(
        task="text-classification", model="hf-internal-testing/tiny-random-distilbert", framework="tf"     )

    __lowercase								=						text_classifier("This is great !"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "LABEL_0", "score": 0.504}]     )


 @slow
 @require_torch
 def  _lowercase			(      self							:      Dict     ):
    __lowercase								=						pipeline("text-classification"     )

    __lowercase								=						text_classifier("This is great !"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "POSITIVE", "score": 1.0}]     )
    __lowercase								=						text_classifier("This is bad !"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "NEGATIVE", "score": 1.0}]     )
    __lowercase								=						text_classifier("Birds are a type of animal"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "POSITIVE", "score": 0.988}]     )


 @slow
 @require_tf
 def  _lowercase			(      self							:      Tuple     ):
    __lowercase								=						pipeline("text-classification", framework="tf"     )

    __lowercase								=						text_classifier("This is great !"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "POSITIVE", "score": 1.0}]     )
    __lowercase								=						text_classifier("This is bad !"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "NEGATIVE", "score": 1.0}]     )
    __lowercase								=						text_classifier("Birds are a type of animal"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "POSITIVE", "score": 0.988}]     )


 def  _lowercase			(      self							:      Union[str, Any], UpperCAmelCase__							:      str, UpperCAmelCase__							:      int, UpperCAmelCase__							:      Tuple     ):
    __lowercase								=						TextClassificationPipeline(model=UpperCAmelCase__, tokenizer=UpperCAmelCase__     )
    return text_classifier, ["HuggingFace is in", "This is another test"]



 def  _lowercase			(      self							:      Any, UpperCAmelCase__							:      List[Any], UpperCAmelCase__							:      str     ):
    __lowercase								=						text_classifier.model
    # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
    __lowercase								=						"HuggingFace is in"
    __lowercase								=						text_classifier(UpperCAmelCase__     )

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

    __lowercase								=						["HuggingFace is in ", "Paris is in France"]
    __lowercase								=						text_classifier(UpperCAmelCase__     )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__     ), [{"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}, {"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}], )
    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
    __lowercase								=						text_classifier(UpperCAmelCase__, top_k=UpperCAmelCase__     )
    __lowercase								=						len(model.config.idalabel.values()     )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__     ), [[{"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}] * N, [{"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}] * N], )

    __lowercase								=						{"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
    __lowercase								=						text_classifier(UpperCAmelCase__     )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__     ), {"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}, )
    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.
    __lowercase								=						[["HuggingFace is in ", "Paris is in France"]]
    with self.assertRaises(UpperCAmelCase__     ):
       text_classifier(UpperCAmelCase__     )

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




