


import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


lowercase__				     =		datasets.logging.get_logger(__name__)


lowercase__				     =		"\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n"

lowercase__				     =		"\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project's README at https://github.com/google-research/bleurt#readme for more information.\n"

lowercase__				     =		"\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    'scores': List of scores.\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bleurt = datasets.load_metric(\"bleurt\")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [1.03, 1.04]\n"

lowercase__				     =		{
    "bleurt-tiny-128": "https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip",
    "bleurt-tiny-512": "https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip",
    "bleurt-base-128": "https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip",
    "bleurt-base-512": "https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip",
    "bleurt-large-128": "https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip",
    "bleurt-large-512": "https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip",
    "BLEURT-20-D3": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip",
    "BLEURT-20-D6": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip",
    "BLEURT-20-D12": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip",
    "BLEURT-20": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip",
}





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






     def  a_					(			self		)       ->    List[str]:
          return datasets.MetricInfo(
              description=_DESCRIPTION     ,							citation=_CITATION     ,							homepage="https://github.com/google-research/bleurt"     ,							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/bleurt"]     ,							reference_urls=["https://github.com/google-research/bleurt", "https://arxiv.org/abs/2004.04696"]     ,							)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    Any:
          # check that config name specifies a valid BLEURT model
          if self.config_name == "default":
               logger.warning(
                   "Using default BLEURT-Base checkpoint for sequence maximum length 128. "
                   "You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512')."		)
               _a						=		"bleurt-base-128"

          if self.config_name.lower() in CHECKPOINT_URLS:
               _a						=		self.config_name.lower()

          elif self.config_name.upper() in CHECKPOINT_URLS:
               _a						=		self.config_name.upper()

          else:
               raise KeyError(
                   f"{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}"		)

          # download the model checkpoint specified by self.config_name and set up the scorer
          _a						=		dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]		)
          _a						=		score.BleurtScorer(os.path.join(__UpperCamelCase     ,							__UpperCamelCase		)		)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    str:
          _a						=		self.scorer.score(references=__UpperCamelCase     ,							candidates=__UpperCamelCase		)
          return {"scores": scores}





'''simple docstring'''

from manim import *





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):






     def  a_					(			self		)       ->    str:
          _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(*__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0		)
          _a						=		VGroup(*__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0		)
          _a						=		VGroup(__UpperCamelCase     ,							__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0		)
          _a						=		Text("CPU"     ,							font_size=24		)
          _a						=		Group(__UpperCamelCase     ,							__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0.5     ,							aligned_edge=__UpperCamelCase		)
          cpu.move_to([-2.5, -0.5, 0]		)
          self.add(__UpperCamelCase		)

          _a						=		[mem.copy() for i in range(1		)]
          _a						=		VGroup(*__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0		)
          _a						=		Text("GPU"     ,							font_size=24		)
          _a						=		Group(__UpperCamelCase     ,							__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0.5     ,							aligned_edge=__UpperCamelCase		)
          gpu.align_to(__UpperCamelCase     ,							__UpperCamelCase		)
          gpu.set_x(gpu.get_x() - 1		)

          self.add(__UpperCamelCase		)

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

          _a						=		Text("Model"     ,							font_size=24		)
          _a						=		Group(__UpperCamelCase     ,							__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0.5     ,							aligned_edge=__UpperCamelCase		)
          model.move_to([3, -1.0, 0]		)

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

          _a						=		MarkupText(
              f"First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM."     ,							font_size=24     ,							)

          _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]		)

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

          self.add(__UpperCamelCase		)

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

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

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

          self.play(*__UpperCamelCase		)
          self.play(*__UpperCamelCase		)

          self.wait()

