



"""simple docstring"""







from manim import *






class 						_A  (  _a  ):







    """simple docstring"""




    def      __snake_case							( self    :   Optional[Any]):
      a						:		str											=      Rectangle(height=0.5    ,			width=0.5)
      a						:		Optional[Any]											=      Rectangle(height=0.46    ,			width=0.46).set_stroke(width=0)

      a						:		List[Any]											=      Rectangle(height=0.25    ,			width=0.25)

      a						:		Tuple											=      [mem.copy() for i in range(6)]
      a						:		str											=      [mem.copy() for i in range(6)]
      a						:		List[Any]											=      VGroup(*__UpperCAmelCase).arrange(__UpperCAmelCase    ,			buff=0)
      a						:		Union[str, Any]											=      VGroup(*__UpperCAmelCase).arrange(__UpperCAmelCase    ,			buff=0)
      a						:		List[str]											=      VGroup(__UpperCAmelCase    ,			__UpperCAmelCase).arrange(__UpperCAmelCase    ,			buff=0)
      a						:		str											=      Text("CPU"    ,			font_size=24)
      a						:		str											=      Group(__UpperCAmelCase    ,			__UpperCAmelCase).arrange(__UpperCAmelCase    ,			buff=0.5    ,			aligned_edge=__UpperCAmelCase)
      cpu.move_to([-2.5, -0.5, 0])
      self.add(__UpperCAmelCase)

      a						:		List[Any]											=      [mem.copy() for i in range(4)]
      a						:		Union[str, Any]											=      VGroup(*__UpperCAmelCase).arrange(__UpperCAmelCase    ,			buff=0)
      a						:		int											=      Text("GPU"    ,			font_size=24)
      a						:		Any											=      Group(__UpperCAmelCase    ,			__UpperCAmelCase).arrange(__UpperCAmelCase    ,			buff=0.5    ,			aligned_edge=__UpperCAmelCase)
      gpu.move_to([-1, -1, 0])
      self.add(__UpperCAmelCase)

      a						:		Any											=      [mem.copy() for i in range(6)]
      a						:		Dict											=      VGroup(*__UpperCAmelCase).arrange(__UpperCAmelCase    ,			buff=0)

      a						:		Dict											=      Text("Model"    ,			font_size=24)
      a						:		Optional[int]											=      Group(__UpperCAmelCase    ,			__UpperCAmelCase).arrange(__UpperCAmelCase    ,			buff=0.5    ,			aligned_edge=__UpperCAmelCase)
      model.move_to([3, -1.0, 0])
      self.add(__UpperCAmelCase)

      a						:		Dict											=      []
      a						:		Optional[int]											=      []

      for i, rect in enumerate(__UpperCAmelCase):
        a						:		Tuple											=      fill.copy().set_fill(__UpperCAmelCase    ,			opacity=0.8)
        target.move_to(__UpperCAmelCase)
        model_arr.append(__UpperCAmelCase)

        a						:		Any											=      Rectangle(height=0.46    ,			width=0.46).set_stroke(width=0.0).set_fill(__UpperCAmelCase    ,			opacity=0.8)
        cpu_target.move_to(cpu_left_col_base[i])
        model_cpu_arr.append(__UpperCAmelCase)

      self.add(*__UpperCAmelCase    ,			*__UpperCAmelCase)

      a						:		Optional[int]											=      [meta_mem.copy() for i in range(6)]
      a						:		List[Any]											=      [meta_mem.copy() for i in range(6)]
      a						:		int											=      VGroup(*__UpperCAmelCase).arrange(__UpperCAmelCase    ,			buff=0)
      a						:		Optional[Any]											=      VGroup(*__UpperCAmelCase).arrange(__UpperCAmelCase    ,			buff=0)
      a						:		List[Any]											=      VGroup(__UpperCAmelCase    ,			__UpperCAmelCase).arrange(__UpperCAmelCase    ,			buff=0)
      a						:		int											=      Text("Disk"    ,			font_size=24)
      a						:		Union[str, Any]											=      Group(__UpperCAmelCase    ,			__UpperCAmelCase).arrange(__UpperCAmelCase    ,			buff=0.5    ,			aligned_edge=__UpperCAmelCase)
      disk.move_to([-4, -1.25, 0])
      self.add(__UpperCAmelCase    ,			__UpperCAmelCase)

      a						:		Dict											=      Square(side_length=2.2)
      key.move_to([-5, 2, 0])

      a						:		str											=      MarkupText(
          f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''    ,			font_size=18    ,			)

      key_text.move_to([-5, 2.4, 0])

      self.add(__UpperCAmelCase    ,			__UpperCAmelCase)

      a						:		int											=      MarkupText(
          f'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'''    ,			font_size=18    ,			)

      blue_text.next_to(__UpperCAmelCase    ,			DOWN * 2.4    ,			aligned_edge=key_text.get_left())
      self.add(__UpperCAmelCase)

      a						:		Optional[int]											=      MarkupText(
          f'''Now watch as an input is passed through the model\nand how the memory is utilized and handled.'''    ,			font_size=24    ,			)
      step_a.move_to([2, 2, 0])

      self.play(Write(__UpperCAmelCase))

      a						:		Any											=      Square(0.3)
      input.set_fill(__UpperCAmelCase    ,			opacity=1.0)
      input.set_stroke(width=0.0)
      input.next_to(model_base[0]    ,			__UpperCAmelCase    ,			buff=0.5)

      self.play(Write(__UpperCAmelCase))

      input.generate_target()
      input.target.next_to(model_arr[0]    ,			direction=__UpperCAmelCase    ,			buff=0.02)
      self.play(MoveToTarget(__UpperCAmelCase))

      self.play(FadeOut(__UpperCAmelCase))

      a						:		Optional[int]											=      Arrow(start=__UpperCAmelCase    ,			end=__UpperCAmelCase    ,			color=__UpperCAmelCase    ,			buff=0.5)
      a.next_to(model_arr[0].get_left()    ,			__UpperCAmelCase    ,			buff=0.2)

      model_cpu_arr[0].generate_target()
      model_cpu_arr[0].target.move_to(gpu_rect[0])

      a						:		Any											=      MarkupText(
          f'''As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.'''    ,			font_size=24    ,			)
      step_a.move_to([2, 2, 0])

      self.play(Write(__UpperCAmelCase    ,			run_time=3))

      a						:		Tuple											=      {"run_time": 1, "fade_in": True, "fade_out": True, "buff": 0.02}

      self.play(
          Write(__UpperCAmelCase)    ,			Circumscribe(model_arr[0]    ,			color=__UpperCAmelCase    ,			**__UpperCAmelCase)    ,			Circumscribe(model_cpu_arr[0]    ,			color=__UpperCAmelCase    ,			**__UpperCAmelCase)    ,			Circumscribe(gpu_rect[0]    ,			color=__UpperCAmelCase    ,			**__UpperCAmelCase)    ,			)
      self.play(MoveToTarget(model_cpu_arr[0]))

      a						:		List[Any]											=      a.copy()
      for i in range(6):
        a_c.next_to(model_arr[i].get_right() + 0.02    ,			__UpperCAmelCase    ,			buff=0.2)

        input.generate_target()
        input.target.move_to(model_arr[i].get_right() + 0.02)

        a						:		List[str]											=      AnimationGroup(
            FadeOut(__UpperCAmelCase    ,			run_time=0.5)    ,			MoveToTarget(__UpperCAmelCase    ,			run_time=0.5)    ,			FadeIn(__UpperCAmelCase    ,			run_time=0.5)    ,			lag_ratio=0.2)

        self.play(__UpperCAmelCase)

        model_cpu_arr[i].generate_target()
        model_cpu_arr[i].target.move_to(cpu_left_col_base[i])

        if i < 5:
          model_cpu_arr[i + 1].generate_target()
          model_cpu_arr[i + 1].target.move_to(gpu_rect[0])
          if i >= 1:
            a						:		List[Any]											=      0.7

          self.play(
              Circumscribe(model_arr[i]    ,			**__UpperCAmelCase)    ,			Circumscribe(cpu_left_col_base[i]    ,			**__UpperCAmelCase)    ,			Circumscribe(cpu_left_col_base[i + 1]    ,			color=__UpperCAmelCase    ,			**__UpperCAmelCase)    ,			Circumscribe(gpu_rect[0]    ,			color=__UpperCAmelCase    ,			**__UpperCAmelCase)    ,			Circumscribe(model_arr[i + 1]    ,			color=__UpperCAmelCase    ,			**__UpperCAmelCase)    ,			)
          if i < 1:
            self.play(
                MoveToTarget(model_cpu_arr[i])    ,			MoveToTarget(model_cpu_arr[i + 1])    ,			)
          else:
            self.play(
                MoveToTarget(model_cpu_arr[i]    ,			run_time=0.7)    ,			MoveToTarget(model_cpu_arr[i + 1]    ,			run_time=0.7)    ,			)
        else:
          model_cpu_arr[i].generate_target()
          model_cpu_arr[i].target.move_to(cpu_left_col_base[-1])
          input.generate_target()
          input.target.next_to(model_arr[-1].get_right()    ,			RIGHT + 0.02    ,			buff=0.2)

          self.play(
              Circumscribe(model_arr[-1]    ,			color=__UpperCAmelCase    ,			**__UpperCAmelCase)    ,			Circumscribe(cpu_left_col_base[-1]    ,			color=__UpperCAmelCase    ,			**__UpperCAmelCase)    ,			Circumscribe(gpu_rect[0]    ,			color=__UpperCAmelCase    ,			**__UpperCAmelCase)    ,			)

          self.play(MoveToTarget(model_cpu_arr[i]))

        a						:		str											=      a_c
        a						:		Dict											=      a_c.copy()

      input.generate_target()
      input.target.next_to(model_base[-1]    ,			RIGHT + 0.02    ,			buff=0.5)
      self.play(
          FadeOut(__UpperCAmelCase)    ,			FadeOut(__UpperCAmelCase    ,			run_time=0.5)    ,			)

      a						:		List[Any]											=      MarkupText(f'''Inference on a model too large for GPU memory\nis successfully completed.'''    ,			font_size=24)
      step_a.move_to([2, 2, 0])

      self.play(Write(__UpperCAmelCase    ,			run_time=3)    ,			MoveToTarget(__UpperCAmelCase))

      self.wait()










"""simple docstring"""







import datasets


__lowercase    =				"""\
@InProceedings{conneau2018xnli,
  author = \"Conneau, Alexis
                 and Rinott, Ruty
                 and Lample, Guillaume
                 and Williams, Adina
                 and Bowman, Samuel R.
                 and Schwenk, Holger
                 and Stoyanov, Veselin\",
  title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",
  booktitle = \"Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing\",
  year = \"2018\",
  publisher = \"Association for Computational Linguistics\",
  location = \"Brussels, Belgium\",
}
"""

__lowercase    =				"""\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
"""

__lowercase    =				"""
Computes XNLI score which is just simple accuracy.
Args:
    predictions: Predicted labels.
    references: Ground truth labels.
Returns:
    'accuracy': accuracy
Examples:

    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> xnli_metric = datasets.load_metric(\"xnli\")
    >>> results = xnli_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}
"""


def 					lowercase      (    A_  ,       A_	)->				List[str]:
  '''simple docstring'''







  return (preds == labels).mean()







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,_KWARGS_DESCRIPTION  )
class 						_A  (  datasets.Metric  ):







    """simple docstring"""




    def      __snake_case							( self    :   List[str]):
      return datasets.MetricInfo(
          description=_DESCRIPTION    ,			citation=_CITATION    ,			inputs_description=_KWARGS_DESCRIPTION    ,			features=datasets.Features(
              {
                  "predictions": datasets.Value("int64" if self.config_name != "sts-b" else "float32"),
                  "references": datasets.Value("int64" if self.config_name != "sts-b" else "float32"),
              })    ,			codebase_urls=[]    ,			reference_urls=[]    ,			format="numpy"    ,			)






    def      __snake_case							( self    :   str    ,			__UpperCAmelCase    :   List[Any]    ,			__UpperCAmelCase    :   Tuple):
      return {"accuracy": simple_accuracy(__UpperCAmelCase    ,			__UpperCAmelCase)}






