

from manim import *




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):

    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			Rectangle(height=0.5							,    width=0.5    )
     SCREAMING_SNAKE_CASE								=			Rectangle(height=0.46							,    width=0.46    ).set_stroke(width=0    )

     SCREAMING_SNAKE_CASE								=			[mem.copy() for i in range(6    )]
     SCREAMING_SNAKE_CASE								=			[mem.copy() for i in range(6    )]
     SCREAMING_SNAKE_CASE								=			VGroup(*snake_case__    ).arrange(snake_case__							,    buff=0    )
     SCREAMING_SNAKE_CASE								=			VGroup(*snake_case__    ).arrange(snake_case__							,    buff=0    )
     SCREAMING_SNAKE_CASE								=			VGroup(snake_case__							,    snake_case__    ).arrange(snake_case__							,    buff=0    )
     SCREAMING_SNAKE_CASE								=			Text('CPU'							,    font_size=2_4    )
     SCREAMING_SNAKE_CASE								=			Group(snake_case__							,    snake_case__    ).arrange(snake_case__							,    buff=0.5							,    aligned_edge=snake_case__    )
     cpu.move_to([-2.5, -0.5, 0]    )
     self.add(snake_case__    )

     SCREAMING_SNAKE_CASE								=			[mem.copy() for i in range(1    )]
     SCREAMING_SNAKE_CASE								=			VGroup(*snake_case__    ).arrange(snake_case__							,    buff=0    )
     SCREAMING_SNAKE_CASE								=			Text('GPU'							,    font_size=2_4    )
     SCREAMING_SNAKE_CASE								=			Group(snake_case__							,    snake_case__    ).arrange(snake_case__							,    buff=0.5							,    aligned_edge=snake_case__    )
     gpu.align_to(snake_case__							,    snake_case__    )
     gpu.set_x(gpu.get_x() - 1    )

     self.add(snake_case__    )

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

     SCREAMING_SNAKE_CASE								=			Text('Model'							,    font_size=2_4    )
     SCREAMING_SNAKE_CASE								=			Group(snake_case__							,    snake_case__    ).arrange(snake_case__							,    buff=0.5							,    aligned_edge=snake_case__    )
     model.move_to([3, -1.0, 0]    )

     self.play(
         Create(snake_case__							,    run_time=1    )							,    Create(snake_case__							,    run_time=1    )							,    Create(snake_case__							,    run_time=1    )							,    )

     SCREAMING_SNAKE_CASE								=			MarkupText(
         F"""First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM."""							,    font_size=2_4							,    )

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

     SCREAMING_SNAKE_CASE								=			MarkupText(
         F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""							,    font_size=1_8							,    )

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

     step_a.move_to([2, 2, 0]    )
     self.play(Write(snake_case__							,    run_time=2.5    )							,    Write(snake_case__    )							,    Write(snake_case__    )    )

     self.add(snake_case__    )

     SCREAMING_SNAKE_CASE								=			[]
     SCREAMING_SNAKE_CASE								=			[]
     SCREAMING_SNAKE_CASE								=			[]
     for i, rect in enumerate(snake_case__    ):
      SCREAMING_SNAKE_CASE								=			Rectangle(height=0.46							,    width=0.46    ).set_stroke(width=0.0    ).set_fill(snake_case__							,    opacity=0.7    )
      cpu_target.move_to(snake_case__    )
      cpu_target.generate_target()
      SCREAMING_SNAKE_CASE								=			0.46 / 4
      SCREAMING_SNAKE_CASE								=			0.46 / 3

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

      first_animations.append(rect.animate(run_time=0.5    ).set_stroke(snake_case__    )    )
      second_animations.append(MoveToTarget(snake_case__							,    run_time=1.5    )    )

     self.play(*snake_case__    )
     self.play(*snake_case__    )

     self.wait()




import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


a_    :						List[str]    		=      version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
      from nltk import word_tokenize


a_    :						Dict    		=      "\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n"

a_    :						str    		=      "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"

a_    :						int    		=      "\nComputes METEOR score of translated segments against one or more 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    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    'meteor': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric('meteor')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n"




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

    def UpperCamelCase			(			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/nltk/nltk/blob/develop/nltk/translate/meteor_score.py']							,    reference_urls=[
             'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
             'https://en.wikipedia.org/wiki/METEOR',
         ]							,    )

    def UpperCamelCase			(			self :      Dict							,    snake_case__ :      int    ):

     """simple docstring"""






     import nltk

     nltk.download('wordnet'    )
     if NLTK_VERSION >= version.Version('3.6.5'    ):
      nltk.download('punkt'    )
     if NLTK_VERSION >= version.Version('3.6.6'    ):
      nltk.download('omw-1.4'    )




    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      str							,    snake_case__ :      List[Any]							,    snake_case__ :      List[Any]=0.9							,    snake_case__ :      Optional[Any]=3							,    snake_case__ :      Any=0.5    ):

     """simple docstring"""






     if NLTK_VERSION >= version.Version('3.6.5'    ):
      SCREAMING_SNAKE_CASE								=			[
          meteor_score.single_meteor_score(
              word_tokenize(snake_case__    )							,    word_tokenize(snake_case__    )							,    alpha=snake_case__							,    beta=snake_case__							,    gamma=snake_case__    )
          for ref, pred in zip(snake_case__							,    snake_case__    )
      ]
     else:
      SCREAMING_SNAKE_CASE								=			[
          meteor_score.single_meteor_score(snake_case__							,    snake_case__							,    alpha=snake_case__							,    beta=snake_case__							,    gamma=snake_case__    )
          for ref, pred in zip(snake_case__							,    snake_case__    )
      ]

     return {"meteor": np.mean(snake_case__    )}

