

"""simple docstring"""


import os
from collections import deque

import torch
from torch.utils.data import Dataset





class    snake_case     (    __snake_case  ):

				def __init__(     self							:    Any					,  UpperCamelCase__							:    Optional[int]=""					,  UpperCamelCase__							:    Dict="train")->       Optional[int]:



						'''simple docstring'''


						assert os.path.isdir(UpperCamelCase__)

						__lowerCAmelCase:	str   				=      []
						__lowerCAmelCase:	Union[str, Any]   				=      os.listdir(UpperCamelCase__)
						for story_filename in story_filenames_list:
								if "summary" in story_filename:
										continue
								__lowerCAmelCase:	int   				=      os.path.join(UpperCamelCase__					,  UpperCamelCase__)
								if not os.path.isfile(UpperCamelCase__):
										continue
								self.documents.append(UpperCamelCase__)

				def __len__(     self							:    str)->       Optional[Any]:



						'''simple docstring'''


						return len(self.documents)




				def __getitem__(     self							:    Optional[int]					,  UpperCamelCase__							:    Union[str, Any])->       Any:



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      self.documents[idx]
						__lowerCAmelCase:	Any   				=      document_path.split("/")[-1]
						with open(UpperCamelCase__					,  encoding="utf-8") as source:
								__lowerCAmelCase:	int   				=      source.read()
								__lowerCAmelCase				,			__lowerCAmelCase:	Optional[Any]   				=      process_story(UpperCamelCase__)
						return document_name, story_lines, summary_lines






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Optional[int]:
		__lowerCAmelCase:	Any   				=      list(filter(lambda __SCREAMING_SNAKE_CASE			: len(__SCREAMING_SNAKE_CASE			) != 0 ,			[line.strip() for line in raw_story.split("\n"			)]			)			)

		# for some unknown reason some lines miss a period, add it
		__lowerCAmelCase:	Optional[int]   				=      [_add_missing_period(__SCREAMING_SNAKE_CASE			) for line in nonempty_lines]

		# gather article lines
		__lowerCAmelCase:	Optional[Any]   				=      []
		__lowerCAmelCase:	Tuple   				=      deque(__SCREAMING_SNAKE_CASE			)
		while True:
				try:
						__lowerCAmelCase:	str   				=      lines.popleft()
						if element.startswith("@highlight"			):
								break
						story_lines.append(__SCREAMING_SNAKE_CASE			)
				except IndexError:
						# if "@highlight" is absent from the file we pop
						# all elements until there is None, raising an exception.
						return story_lines, []

    # gather summary lines
		__lowerCAmelCase:	Any   				=      list(filter(lambda __SCREAMING_SNAKE_CASE			: not t.startswith("@highlight"			) ,			__SCREAMING_SNAKE_CASE			)			)

		return story_lines, summary_lines






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Optional[int]:
		__lowerCAmelCase:	str   				=      [".", "!", "?", "...", "'", "`", "\"", "\u2019", "\u2019", ")"]
		if line.startswith("@highlight"			):
				return line
		if line[-1] in END_TOKENS:
				return line
		return line + "."






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Union[str, Any]:
		if len(__SCREAMING_SNAKE_CASE			) > block_size:
				return sequence[:block_size]
		else:
				sequence.extend([pad_token_id] * (block_size - len(__SCREAMING_SNAKE_CASE			))			)
				return sequence






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     int:
		__lowerCAmelCase:	Optional[int]   				=      torch.ones_like(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Optional[int]   				=      sequence == pad_token_id
		__lowerCAmelCase:	Optional[Any]   				=      0
		return mask






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Optional[Any]:
		__lowerCAmelCase:	Union[str, Any]   				=      [tokenizer.encode(__SCREAMING_SNAKE_CASE			) for line in story_lines]
		__lowerCAmelCase:	Union[str, Any]   				=      [token for sentence in story_lines_token_ids for token in sentence]
		__lowerCAmelCase:	Union[str, Any]   				=      [tokenizer.encode(__SCREAMING_SNAKE_CASE			) for line in summary_lines]
		__lowerCAmelCase:	Optional[Any]   				=      [token for sentence in summary_lines_token_ids for token in sentence]

		return story_token_ids, summary_token_ids






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Tuple:
		__lowerCAmelCase:	int   				=      []
		for sequence in batch:
				__lowerCAmelCase:	Optional[Any]   				=      -1
				__lowerCAmelCase:	int   				=      []
				for s in sequence:
						if s == separator_token_id:
								sentence_num += 1
						embeddings.append(sentence_num % 2			)
				batch_embeddings.append(__SCREAMING_SNAKE_CASE			)
		return torch.tensor(__SCREAMING_SNAKE_CASE			)





"""simple docstring"""


import comet  # From: unbabel-comet
import torch

import datasets


__A						       =      datasets.logging.get_logger(__name__)

__A						       =      "\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel's Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = \"{COMET}: A Neural Framework for {MT} Evaluation\",\n   author = \"Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon\",\n   booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n   month = nov,\n   year = \"2020\",\n   address = \"Online\",\n   publisher = \"Association for Computational Linguistics\",\n   url = \"https://www.aclweb.org/anthology/2020.emnlp-main.213\",\n   pages = \"2685--2702\",\n}\n"

__A						       =      "\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA's or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n"

__A						       =      "\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric('comet')\n    >>> # comet_metric = load_metric('comet', 'wmt20-comet-da')  # you can also choose which model to use\n    >>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]\n    >>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]\n    >>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [0.19, 0.92]\n"





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

				def 							lowercase_					(     self							:    List[Any])->       Dict:



						'''simple docstring'''


						return datasets.MetricInfo(
						    description=_DESCRIPTION					,  citation=_CITATION					,  homepage="https://unbabel.github.io/COMET/html/index.html"					,  inputs_description=_KWARGS_DESCRIPTION					,  features=datasets.Features(
						        {
						            "sources": datasets.Value("string"					,  id="sequence"),
						            "predictions": datasets.Value("string"					,  id="sequence"),
						            "references": datasets.Value("string"					,  id="sequence"),
						        })					,  codebase_urls=["https://github.com/Unbabel/COMET"]					,  reference_urls=[
						        "https://github.com/Unbabel/COMET",
						        "https://www.aclweb.org/anthology/2020.emnlp-main.213/",
						        "http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6",
						    ]					,  )

				def 							lowercase_					(     self							:    Tuple					,  UpperCamelCase__							:    Any)->       Dict:



						'''simple docstring'''


						if self.config_name == "default":
								__lowerCAmelCase:	Union[str, Any]   				=      comet.load_from_checkpoint(comet.download_model("wmt20-comet-da"))
						else:
								__lowerCAmelCase:	Tuple   				=      comet.load_from_checkpoint(comet.download_model(self.config_name))




				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    Optional[Any]					,  UpperCamelCase__							:    Optional[Any]					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    Optional[int]=None					,  UpperCamelCase__							:    List[str]=False)->       str:



						'''simple docstring'''


						if gpus is None:
								__lowerCAmelCase:	Union[str, Any]   				=      1 if torch.cuda.is_available() else 0
						__lowerCAmelCase:	Dict   				=      {"src": sources, "mt": predictions, "ref": references}
						__lowerCAmelCase:	Union[str, Any]   				=      [dict(zip(UpperCamelCase__					,  UpperCamelCase__)) for t in zip(*data.values())]
						__lowerCAmelCase				,			__lowerCAmelCase:	str   				=      self.scorer.predict(UpperCamelCase__					,  gpus=UpperCamelCase__					,  progress_bar=UpperCamelCase__)
						return {"mean_score": mean_score, "scores": scores}


