




'''simple docstring'''


from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml

class     A					(	yaml.SafeLoader					):







							def      __lowerCAmelCase    (						self :						Optional[Any]							,    lowerCAmelCase_ :						str      )    ->							str:


														"""simple docstring"""


														_a			  =       [self.constructed_objects[key_node] for key_node, _ in node.value]
														_a			  =       [tuple(lowerCAmelCase_      ) if isinstance(lowerCAmelCase_							,    lowerCAmelCase_      ) else key for key in keys]
														_a			  =       Counter(lowerCAmelCase_      )
														_a			  =       [key for key in counter if counter[key] > 1]
														if duplicate_keys:
																					raise TypeError(F'Got duplicate yaml keys: {duplicate_keys}'      )







							def      __lowerCAmelCase    (						self :						str							,    lowerCAmelCase_ :						List[str]							,    lowerCAmelCase_ :						Union[str, Any]=False      )    ->							Dict:


														"""simple docstring"""


														_a			  =       super().construct_mapping(lowerCAmelCase_							,    deep=lowerCAmelCase_      )
														self._check_no_duplicates_on_constructed_node(lowerCAmelCase_      )
														return mapping



def 	snake_case_					(UpperCamelCase :						str   ):





							'''simple docstring'''


							_a			  =       list(readme_content.splitlines()   )
							if full_content and full_content[0] == "---" and "---" in full_content[1:]:
														_a			  =       full_content[1:].index('''---'''   ) + 1
														_a			  =       '''\n'''.join(full_content[1:sep_idx]   )
														return yamlblock, "\n".join(full_content[sep_idx + 1 :]   )

							return None, "\n".join(UpperCamelCase   )

class     A					(	_a					):
							# class attributes
							lowercase_           =       {'train_eval_index'}  # train-eval-index in the YAML metadata







							@classmethod
							def      __lowerCAmelCase    (						cls :						int							,    lowerCAmelCase_ :						Path      )    ->							"DatasetMetadata":


														"""simple docstring"""


														with open(lowerCAmelCase_							,    encoding='''utf-8'''      ) as readme_file:
																					_a  ,							_a			  =       _split_yaml_from_readme(readme_file.read()      )
														if yaml_string is not None:
																					return cls.from_yaml_string(lowerCAmelCase_      )
														else:
																					return cls()







							def      __lowerCAmelCase    (						self :						Any							,    lowerCAmelCase_ :						Path      )    ->							List[Any]:


														"""simple docstring"""


														if path.exists():
																					with open(lowerCAmelCase_							,    encoding='''utf-8'''      ) as readme_file:
																												_a			  =       readme_file.read()
														else:
																					_a			  =       None
														_a			  =       self._to_readme(lowerCAmelCase_      )
														with open(lowerCAmelCase_							,    '''w'''							,    encoding='''utf-8'''      ) as readme_file:
																					readme_file.write(lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Any							,    lowerCAmelCase_ :						Optional[str] = None      )    ->							str:


														"""simple docstring"""


														if readme_content is not None:
																					_a  ,							_a			  =       _split_yaml_from_readme(lowerCAmelCase_      )
																					_a			  =       '''---\n''' + self.to_yaml_string() + '''---\n''' + content
														else:
																					_a			  =       '''---\n''' + self.to_yaml_string() + '''---\n'''
														return full_content







							@classmethod
							def      __lowerCAmelCase    (						cls :						Union[str, Any]							,    lowerCAmelCase_ :						str      )    ->							"DatasetMetadata":


														"""simple docstring"""


														_a			  =       yaml.load(lowerCAmelCase_							,    Loader=_NoDuplicateSafeLoader      ) or {}

														# Convert the YAML keys to DatasetMetadata fields
														_a			  =       {
														    (key.replace('''-'''							,    '''_'''      ) if key.replace('''-'''							,    '''_'''      ) in cls._FIELDS_WITH_DASHES else key): value
														    for key, value in metadata_dict.items()
														}
														return cls(**lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Union[str, Any]      )    ->							str:


														"""simple docstring"""


														return yaml.safe_dump(
														    {
														        (key.replace('''_'''							,    '''-'''      ) if key in self._FIELDS_WITH_DASHES else key): value
														        for key, value in self.items()
														    }							,    sort_keys=lowerCAmelCase_							,    allow_unicode=lowerCAmelCase_							,    encoding='''utf-8'''							,    ).decode('''utf-8'''      )



_snake_case   :   Dict     							=	{
    'image-classification': [],
    'translation': [],
    'image-segmentation': [],
    'fill-mask': [],
    'automatic-speech-recognition': [],
    'token-classification': [],
    'sentence-similarity': [],
    'audio-classification': [],
    'question-answering': [],
    'summarization': [],
    'zero-shot-classification': [],
    'table-to-text': [],
    'feature-extraction': [],
    'other': [],
    'multiple-choice': [],
    'text-classification': [],
    'text-to-image': [],
    'text2text-generation': [],
    'zero-shot-image-classification': [],
    'tabular-classification': [],
    'tabular-regression': [],
    'image-to-image': [],
    'tabular-to-text': [],
    'unconditional-image-generation': [],
    'text-retrieval': [],
    'text-to-speech': [],
    'object-detection': [],
    'audio-to-audio': [],
    'text-generation': [],
    'conversational': [],
    'table-question-answering': [],
    'visual-question-answering': [],
    'image-to-text': [],
    'reinforcement-learning': [],
    'voice-activity-detection': [],
    'time-series-forecasting': [],
    'document-question-answering': [],
}


if __name__ == "__main__":
				from argparse import ArgumentParser

				_snake_case   :   str     							=	ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
				ap.add_argument('readme_filepath')
				_snake_case   :   List[Any]     							=	ap.parse_args()

				_snake_case   :   int     							=	Path(args.readme_filepath)
				_snake_case   :   int     							=	DatasetMetadata.from_readme(readme_filepath)
				print(dataset_metadata)
				dataset_metadata.to_readme(readme_filepath)







'''simple docstring'''


from manim import *

class     A					(	_a					):







							def      __lowerCAmelCase    (						self :						Tuple      )    ->							Dict:


														"""simple docstring"""


														_a			  =       Rectangle(height=0.5							,    width=0.5      )
														_a			  =       Rectangle(height=0.4_6							,    width=0.4_6      ).set_stroke(width=0      )

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

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

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

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

														_a			  =       []
														for i, rect in enumerate(lowerCAmelCase_      ):
																					rect.set_stroke(lowerCAmelCase_      )
																					# target = fill.copy().set_fill(YELLOW, opacity=0.7)
																					# target.move_to(rect)
																					# self.add(target)

																					_a			  =       Rectangle(height=0.4_6 / 4							,    width=0.4_6 / 3      ).set_stroke(width=0.0      ).set_fill(lowerCAmelCase_							,    opacity=0.7      )

																					if i == 0:
																												cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT      )							,    buff=0.0_2							,    direction=lowerCAmelCase_      )
																												cpu_target.set_x(cpu_target.get_x() + 0.1      )
																					elif i == 3:
																												cpu_target.next_to(cpu_targs[0]							,    direction=lowerCAmelCase_							,    buff=0.0      )
																					else:
																												cpu_target.next_to(cpu_targs[i - 1]							,    direction=lowerCAmelCase_							,    buff=0.0      )
																					self.add(lowerCAmelCase_      )
																					cpu_targs.append(lowerCAmelCase_      )

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

														_a			  =       Text('''Loaded Checkpoint'''							,    font_size=24      )
														_a			  =       Group(lowerCAmelCase_							,    lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    aligned_edge=lowerCAmelCase_							,    buff=0.4      )
														checkpoint.move_to([3, 0.5, 0]      )

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

														_a			  =       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(lowerCAmelCase_							,    lowerCAmelCase_      )

														_a			  =       MarkupText(
														    F'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'							,    font_size=18							,    )

														blue_text.next_to(lowerCAmelCase_							,    DOWN * 2.4							,    aligned_edge=key_text.get_left()      )

														_a			  =       MarkupText(
														    F'Next, a <i><span fgcolor="{BLUE}">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor="{BLUE}">single shard</span>.'							,    font_size=24							,    )
														step_a.move_to([2, 2, 0]      )
														self.play(Write(lowerCAmelCase_      )							,    Write(lowerCAmelCase_      )      )

														self.play(Write(lowerCAmelCase_							,    run_time=1      )							,    Create(lowerCAmelCase_							,    run_time=1      )      )

														_a			  =       []
														_a			  =       []
														for i, rect in enumerate(lowerCAmelCase_      ):
																					_a			  =       fill.copy().set_fill(lowerCAmelCase_							,    opacity=0.7      )
																					target.move_to(lowerCAmelCase_      )
																					first_animations.append(GrowFromCenter(lowerCAmelCase_							,    run_time=1      )      )

																					_a			  =       target.copy()
																					cpu_target.generate_target()
																					if i < 5:
																												cpu_target.target.move_to(cpu_left_col_base[i + 1]      )
																					else:
																												cpu_target.target.move_to(cpu_right_col_base[i - 5]      )
																					second_animations.append(MoveToTarget(lowerCAmelCase_							,    run_time=1.5      )      )

														self.play(*lowerCAmelCase_      )
														self.play(*lowerCAmelCase_      )
														self.wait()

