"""simple docstring"""
import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
lowercase__   			=      '\\n@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n'

# TODO: Add description of the dataset here
# You can copy an official description
lowercase__   			=      '\\nThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.\n'

# TODO: Add a link to an official homepage for the dataset here
lowercase__   			=      ''

# TODO: Add the licence for the dataset here if you can find it
lowercase__   			=      ''

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
lowercase__   			=      {
    'first_domain': 'https://huggingface.co/great-new-dataset-first_domain.zip',
    'second_domain': 'https://huggingface.co/great-new-dataset-second_domain.zip',
}




class   __snake_case			(	datasets.GeneratorBasedBuilder							):
      a__								=						datasets.Version("""1.1.0"""							)

      # This is an example of a dataset with multiple configurations.
      # If you don't want/need to define several sub-sets in your dataset,
      # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

      # If you need to make complex sub-parts in the datasets with configurable options
      # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
      # BUILDER_CONFIG_CLASS = MyBuilderConfig

      # You will be able to load one or the other configurations in the following list with
      # data = datasets.load_dataset('my_dataset', 'first_domain')
      # data = datasets.load_dataset('my_dataset', 'second_domain')
      a__								=						[
          datasets.BuilderConfig(name="""first_domain"""			, version=__lowerCAmelCase			, description="""This part of my dataset covers a first domain"""							),
          datasets.BuilderConfig(name="""second_domain"""			, version=__lowerCAmelCase			, description="""This part of my dataset covers a second domain"""							),
      ]

      a__								=						"""first_domain"""  # It's not mandatory to have a default configuration. Just use one if it make sense.



      def     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





        if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
          a__:	Optional[int]     				=      datasets.Features(
              {
                  'sentence': datasets.Value('string'),
                  'option1': datasets.Value('string'),
                  'answer': datasets.Value('string')
                  # These are the features of your dataset like images, labels ...
              })
        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
          a__:	Any     				=      datasets.Features(
              {
                  'sentence': datasets.Value('string'),
                  'option2': datasets.Value('string'),
                  'second_domain_answer': datasets.Value('string')
                  # These are the features of your dataset like images, labels ...
              })
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION       ,     features=lowercase       ,     homepage=_HOMEPAGE       ,     license=_LICENSE       ,     citation=_CITATION       ,     )



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Optional[int]:





        '''simple docstring'''





        a__:	Tuple     				=      _URLS[self.config.name]
        a__:	List[str]     				=      dl_manager.download_and_extract(lowercase)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN       ,     gen_kwargs={
                    'filepath': os.path.join(lowercase       ,     'train.jsonl'),
                    'split': 'train',
                }       ,     ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION       ,     gen_kwargs={
                    'filepath': os.path.join(lowercase       ,     'dev.jsonl'),
                    'split': 'dev',
                }       ,     ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST       ,     gen_kwargs={
                    'filepath': os.path.join(lowercase       ,     'test.jsonl'),
                    'split': 'test'
                }       ,     ),
        ]



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase)      ->     List[str]:





        '''simple docstring'''





        with open(lowercase       ,     encoding='utf-8') as f:
          for key, row in enumerate(lowercase):
            a__:	List[Any]     				=      json.loads(lowercase)
            if self.config.name == "first_domain":
              # Yields examples as (key, example) tuples
              yield key, {
                  "sentence": data["sentence"],
                  "option1": data["option1"],
                  "answer": "" if split == "test" else data["answer"],
              }
            else:
              yield key, {
                  "sentence": data["sentence"],
                  "option2": data["option2"],
                  "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
              }





"""simple docstring"""






from manim import *
class 	_lowerCAmelCase    (      __snake_case       ):

							def 					_lowerCAmelCase  ( self       :    Dict    )   ->    Tuple:

									"""simple docstring"""
									lowercase										=							Rectangle(height=0.5 ,		width=0.5    )
									lowercase										=							Rectangle(height=0.46 ,		width=0.46    ).set_stroke(width=0    )
									lowercase										=							Rectangle(height=0.25 ,		width=0.25    )

									lowercase										=							[mem.copy() for i in range(6    )]
									lowercase										=							[mem.copy() for i in range(6    )]
									lowercase										=							VGroup(*a    ).arrange(a ,		buff=0    )
									lowercase										=							VGroup(*a    ).arrange(a ,		buff=0    )
									lowercase										=							VGroup(a ,		a    ).arrange(a ,		buff=0    )
									lowercase										=							Text('''CPU''' ,		font_size=24    )
									lowercase										=							Group(a ,		a    ).arrange(a ,		buff=0.5 ,		aligned_edge=a    )
									cpu.move_to([-2.5, -0.5, 0]    )
									self.add(a    )

									lowercase										=							[mem.copy() for i in range(4    )]
									lowercase										=							VGroup(*a    ).arrange(a ,		buff=0    )
									lowercase										=							Text('''GPU''' ,		font_size=24    )
									lowercase										=							Group(a ,		a    ).arrange(a ,		buff=0.5 ,		aligned_edge=a    )
									gpu.move_to([-1, -1, 0]    )
									self.add(a    )

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

									lowercase										=							Text('''Model''' ,		font_size=24    )
									lowercase										=							Group(a ,		a    ).arrange(a ,		buff=0.5 ,		aligned_edge=a    )
									model.move_to([3, -1.0, 0]    )
									self.add(a    )

									lowercase										=							[]
									lowercase										=							[]

									for i, rect in enumerate(a    ):
											rect.set_stroke(a    )

											lowercase										=							Rectangle(height=0.46 / 4 ,		width=0.46 / 3    ).set_stroke(width=0.0    ).set_fill(a ,		opacity=0.7    )

											if i == 0:
													cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT    ) ,		buff=0.02 ,		direction=a    )
													cpu_target.set_x(cpu_target.get_x() + 0.1    )
											elif i == 3:
													cpu_target.next_to(model_cpu_arr[0] ,		direction=a ,		buff=0.0    )
											else:
													cpu_target.next_to(model_cpu_arr[i - 1] ,		direction=a ,		buff=0.0    )
											self.add(a    )
											model_cpu_arr.append(a    )

									self.add(*a ,		*a    )

									lowercase										=							[meta_mem.copy() for i in range(6    )]
									lowercase										=							[meta_mem.copy() for i in range(6    )]
									lowercase										=							VGroup(*a    ).arrange(a ,		buff=0    )
									lowercase										=							VGroup(*a    ).arrange(a ,		buff=0    )
									lowercase										=							VGroup(a ,		a    ).arrange(a ,		buff=0    )
									lowercase										=							Text('''Disk''' ,		font_size=24    )
									lowercase										=							Group(a ,		a    ).arrange(a ,		buff=0.5 ,		aligned_edge=a    )
									disk.move_to([-4.0, -1.25, 0]    )
									self.add(a ,		a    )

									lowercase										=							[]

									for i in range(6    ):
											lowercase										=							fill.copy().set_fill(a ,		opacity=0.8    )
											target.move_to(disk_left_col_base[i]    ).scale(0.5    )
											cpu_disk_arr.append(a    )

									self.add(*a    )

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

									lowercase										=							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(a ,		a    )

									lowercase										=							MarkupText(
									    f"""<span fgcolor='{BLUE}'>●</span> Checkpoint""" ,		font_size=18 ,		)

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

									lowercase										=							MarkupText(f"""The offloaded weights are all sent to the CPU.""" ,		font_size=24    )
									step_a.move_to([2, 2, 0]    )

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

									for i in range(6    ):
											lowercase										=							cpu_disk_arr[i]
											lowercase										=							rect.copy().set_fill(a ,		opacity=0.8    ).scale(2.0    )
											cpa.generate_target()
											cpa.target.move_to(model_base[i]    )

											if i == 0:
													rect.set_fill(a ,		opacity=0.8    )
													rect.generate_target()
													rect.target.move_to(cpu_left_col_base[0]    ).scale(2.0    )

													self.remove(
													    *a ,		*a ,		)

											else:
													rect.generate_target()
													rect.target.move_to(cpu_left_col_base[i]    ).scale(2.0    )
											self.play(MoveToTarget(a    ) ,		MoveToTarget(a    ) ,		model_base[i].animate.set_stroke(a    )    )
									self.play(FadeOut(a    )    )

									lowercase										=							MarkupText(
									    f"""Finally, hooks are added to each weight in the model\nto transfer the weights from CPU to GPU\n\t\tand back when needed.""" ,		font_size=24 ,		)
									step_a.move_to([2, 2, 0]    )

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

									lowercase										=							[]
									lowercase										=							[]
									for i in range(6    ):
											lowercase										=							Arrow(start=a ,		end=a ,		color=a ,		buff=0.5    )
											a.next_to(model_base[i].get_left() ,		a ,		buff=0.2    )
											arrows.append(a    )
											animations.append(Write(a    )    )
									self.play(*a    )
									self.wait()