



'''simple docstring'''


import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


_lowerCamelCase      :			Union[str, Any]       =      '\\n@inproceedings{lin-2004-rouge,\n    title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",\n    author = "Lin, Chin-Yew",\n    booktitle = "Text Summarization Branches Out",\n    month = jul,\n    year = "2004",\n    address = "Barcelona, Spain",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W04-1013",\n    pages = "74--81",\n}\n'

_lowerCamelCase      :			Dict       =      '\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n'

_lowerCamelCase      :			List[Any]       =      '\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,\n        `"rougeL"`: Longest common subsequence based scoring.\n        `"rougeLSum"`: rougeLsum splits text using `"\n"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric(\'rouge\')\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    [\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']\n    >>> print(results["rouge1"])\n    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n    >>> print(results["rouge1"].mid.fmeasure)\n    1.0\n'

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






    """simple docstring"""





    def 					A			(    self :   str					):


       """simple docstring"""







       return datasets.MetricInfo(
           description=_DESCRIPTION    ,		citation=_CITATION    ,		inputs_description=_KWARGS_DESCRIPTION    ,		features=datasets.Features(
               {
                   'predictions': datasets.Value('string'    ,		id='sequence'					),
                   'references': datasets.Value('string'    ,		id='sequence'					),
               }					)    ,		codebase_urls=['https://github.com/google-research/google-research/tree/master/rouge']    ,		reference_urls=[
               'https://en.wikipedia.org/wiki/ROUGE_(metric)',
               'https://github.com/google-research/google-research/tree/master/rouge',
           ]    ,		)






    def 					A			(    self :   List[str]    ,		UpperCamelCase__ :   Any    ,		UpperCamelCase__ :   List[Any]    ,		UpperCamelCase__ :   str=None    ,		UpperCamelCase__ :   List[str]=True    ,		UpperCamelCase__ :   Any=False					):


       """simple docstring"""







       if rouge_types is None:
          UpperCamelCase      							=    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']

       UpperCamelCase      							=    rouge_scorer.RougeScorer(rouge_types=lowercase_    ,		use_stemmer=lowercase_					)
       if use_aggregator:
          UpperCamelCase      							=    scoring.BootstrapAggregator()
       else:
          UpperCamelCase      							=    []

       for ref, pred in zip(lowercase_    ,		lowercase_					):
          UpperCamelCase      							=    scorer.score(lowercase_    ,		lowercase_					)
          if use_aggregator:
             aggregator.add_scores(lowercase_					)
          else:
             scores.append(lowercase_					)

       if use_aggregator:
          UpperCamelCase      							=    aggregator.aggregate()
       else:
          UpperCamelCase      							=    {}
          for key in scores[0]:
             UpperCamelCase      							=    [score[key] for score in scores]

       return result





from manim import *




class       lowercase_					(lowercase__   ):





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =Rectangle(height=0.5       ,  width=0.5)
											a__      =Rectangle(height=0.46       ,  width=0.46).set_stroke(width=0)

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

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

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

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

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

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

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

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

											a__      =Text('Loaded Checkpoint'       ,  font_size=24)
											a__      =Group(lowercase_       ,  lowercase_).arrange(lowercase_       ,  aligned_edge=lowercase_       ,  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(lowercase_       ,  lowercase_)

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

											blue_text.next_to(lowercase_       ,  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(lowercase_)       ,  Write(lowercase_))

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

											a__      =[]
											a__      =[]
											for i, rect in enumerate(lowercase_):
																		a__      =fill.copy().set_fill(lowercase_       ,  opacity=0.7)
																		target.move_to(lowercase_)
																		first_animations.append(GrowFromCenter(lowercase_       ,  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(lowercase_       ,  run_time=1.5))

											self.play(*lowercase_)
											self.play(*lowercase_)
											self.wait()

