import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer




def 							_UpperCAmelCase  (UpperCamelCase_ : List[Any]      ,    UpperCamelCase_ : str      ,    UpperCamelCase_ : Optional[int]      ,    UpperCamelCase_ : List[Any]      ,    UpperCamelCase_ : List[str]=True      ,    UpperCamelCase_ : Any="pt"       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Dict										=      {"""add_prefix_space""": True} if isinstance(UpperCamelCase_      ,    UpperCamelCase_       ) and not line.startswith(""" """       ) else {}
				_lowerCAmelCase    :    Any										=      padding_side
				return tokenizer(
				    [line]      ,    max_length=UpperCamelCase_      ,    padding="""max_length""" if pad_to_max_length else None      ,    truncation=UpperCamelCase_      ,    return_tensors=UpperCamelCase_      ,    add_special_tokens=UpperCamelCase_      ,    **UpperCamelCase_      ,    )




def 							_UpperCAmelCase  (UpperCamelCase_ : str      ,    UpperCamelCase_ : List[str]      ,    UpperCamelCase_ : Optional[int]=None      ,    ):
				'''simple docstring'''
				_lowerCAmelCase    :    List[Any]										=      input_ids.ne(UpperCamelCase_       ).any(dim=0       )
				if attention_mask is None:
								return input_ids[:, keep_column_mask]
				else:
								return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class        __snake_case  (_a		):



				def __init__(      self      :		Tuple		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		Optional[int]		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		Any		,	_UpperCAmelCase      :		List[Any]="train"		,	_UpperCAmelCase      :		Optional[Any]=None		,	_UpperCAmelCase      :		Optional[Any]=None		,	_UpperCAmelCase      :		Union[str, Any]=None		,	_UpperCAmelCase      :		Dict=""		,	)  ->    str:





								'''simple docstring'''




								super().__init__()
								_lowerCAmelCase    :    Tuple										=      Path(_UpperCAmelCase      ).joinpath(type_path + """.source"""      )
								_lowerCAmelCase    :    Tuple										=      Path(_UpperCAmelCase      ).joinpath(type_path + """.target"""      )
								_lowerCAmelCase    :    Any										=      self.get_char_lens(self.src_file      )
								_lowerCAmelCase    :    Tuple										=      max_source_length
								_lowerCAmelCase    :    List[Any]										=      max_target_length
								assert min(self.src_lens      ) > 0, f"found empty line in {self.src_file}"
								_lowerCAmelCase    :    Dict										=      tokenizer
								_lowerCAmelCase    :    Tuple										=      prefix
								if n_obs is not None:
												_lowerCAmelCase    :    int										=      self.src_lens[:n_obs]
								_lowerCAmelCase    :    List[str]										=      src_lang
								_lowerCAmelCase    :    Union[str, Any]										=      tgt_lang



				def __len__(      self      :		int      )  ->    Dict:





								'''simple docstring'''




								return len(self.src_lens      )



				def __getitem__(      self      :		Dict		,	_UpperCAmelCase      :		List[Any]      )  ->    Dict[str, torch.Tensor]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      index + 1  # linecache starts at 1
								_lowerCAmelCase    :    str										=      self.prefix + linecache.getline(str(self.src_file      )		,	_UpperCAmelCase      ).rstrip("""\n"""      )
								_lowerCAmelCase    :    str										=      linecache.getline(str(self.tgt_file      )		,	_UpperCAmelCase      ).rstrip("""\n"""      )
								assert source_line, f"empty source line for index {index}"
								assert tgt_line, f"empty tgt line for index {index}"

								# Need to add eos token manually for T5
								if isinstance(self.tokenizer		,	_UpperCAmelCase      ):
												source_line += self.tokenizer.eos_token
												tgt_line += self.tokenizer.eos_token

								# Pad source and target to the right
								_lowerCAmelCase    :    Optional[int]										=      (
								    self.tokenizer.question_encoder if isinstance(self.tokenizer		,	_UpperCAmelCase      ) else self.tokenizer
								)
								_lowerCAmelCase    :    Optional[int]										=      self.tokenizer.generator if isinstance(self.tokenizer		,	_UpperCAmelCase      ) else self.tokenizer

								_lowerCAmelCase    :    int										=      encode_line(_UpperCAmelCase		,	_UpperCAmelCase		,	self.max_source_length		,	"""right"""      )
								_lowerCAmelCase    :    Any										=      encode_line(_UpperCAmelCase		,	_UpperCAmelCase		,	self.max_target_length		,	"""right"""      )

								_lowerCAmelCase    :    Tuple										=      source_inputs["""input_ids"""].squeeze()
								_lowerCAmelCase    :    Optional[int]										=      target_inputs["""input_ids"""].squeeze()
								_lowerCAmelCase    :    List[Any]										=      source_inputs["""attention_mask"""].squeeze()
								return {
								    "input_ids": source_ids,
								    "attention_mask": src_mask,
								    "decoder_input_ids": target_ids,
								}



				@staticmethod
				def       SCREAMING_SNAKE_CASE       (      _UpperCAmelCase      :		Union[str, Any]      )  ->    Any:





								'''simple docstring'''




								return [len(_UpperCAmelCase      ) for x in Path(_UpperCAmelCase      ).open().readlines()]



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	_UpperCAmelCase      :		List[str]      )  ->    Dict[str, torch.Tensor]:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      torch.stack([x["""input_ids"""] for x in batch]      )
								_lowerCAmelCase    :    List[str]										=      torch.stack([x["""attention_mask"""] for x in batch]      )
								_lowerCAmelCase    :    Optional[int]										=      torch.stack([x["""decoder_input_ids"""] for x in batch]      )
								_lowerCAmelCase    :    Optional[int]										=      (
								    self.tokenizer.generator.pad_token_id
								    if isinstance(self.tokenizer		,	_UpperCAmelCase      )
								    else self.tokenizer.pad_token_id
								)
								_lowerCAmelCase    :    str										=      (
								    self.tokenizer.question_encoder.pad_token_id
								    if isinstance(self.tokenizer		,	_UpperCAmelCase      )
								    else self.tokenizer.pad_token_id
								)
								_lowerCAmelCase    :    Union[str, Any]										=      trim_batch(_UpperCAmelCase		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    List[str]										=      trim_batch(_UpperCAmelCase		,	_UpperCAmelCase		,	attention_mask=_UpperCAmelCase      )
								_lowerCAmelCase    :    Dict										=      {
								    """input_ids""": source_ids,
								    """attention_mask""": source_mask,
								    """decoder_input_ids""": y,
								}
								return batch






_lowerCamelCase      :  Optional[Any]      						=						getLogger(__name__)




def 							_UpperCAmelCase  (UpperCamelCase_ : List[List]       ):
				'''simple docstring'''
				return list(itertools.chain.from_iterable(UpperCamelCase_       )       )




def 							_UpperCAmelCase  (UpperCamelCase_ : str       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Union[str, Any]										=      get_git_info()
				save_json(UpperCamelCase_      ,    os.path.join(UpperCamelCase_      ,    """git_log.json"""       )       )




def 							_UpperCAmelCase  (UpperCamelCase_ : int      ,    UpperCamelCase_ : int      ,    UpperCamelCase_ : Dict=4      ,    **UpperCamelCase_ : Union[str, Any]       ):
				'''simple docstring'''
				with open(UpperCamelCase_      ,    """w"""       ) as f:
								json.dump(UpperCamelCase_      ,    UpperCamelCase_      ,    indent=UpperCamelCase_      ,    **UpperCamelCase_       )




def 							_UpperCAmelCase  (UpperCamelCase_ : List[Any]       ):
				'''simple docstring'''
				with open(UpperCamelCase_       ) as f:
								return json.load(UpperCamelCase_       )




def 							_UpperCAmelCase  ():
				'''simple docstring'''
				_lowerCAmelCase    :    Union[str, Any]										=      git.Repo(search_parent_directories=UpperCamelCase_       )
				_lowerCAmelCase    :    int										=      {
				    """repo_id""": str(UpperCamelCase_       ),
				    """repo_sha""": str(repo.head.object.hexsha       ),
				    """repo_branch""": str(repo.active_branch       ),
				    """hostname""": str(socket.gethostname()       ),
				}
				return repo_infos




def 							_UpperCAmelCase  (UpperCamelCase_ : Callable      ,    UpperCamelCase_ : Iterable       ):
				'''simple docstring'''
				return list(map(UpperCamelCase_      ,    UpperCamelCase_       )       )




def 							_UpperCAmelCase  (UpperCamelCase_ : Optional[int]      ,    UpperCamelCase_ : List[str]       ):
				'''simple docstring'''
				with open(UpperCamelCase_      ,    """wb"""       ) as f:
								return pickle.dump(UpperCamelCase_      ,    UpperCamelCase_       )




def 							_UpperCAmelCase  (UpperCamelCase_ : int       ):
				'''simple docstring'''

				def remove_articles(UpperCamelCase_ : Any       ):
								return re.sub(R"""\b(a|an|the)\b"""      ,    """ """      ,    UpperCamelCase_       )

				def white_space_fix(UpperCamelCase_ : str       ):
								return " ".join(text.split()       )

				def remove_punc(UpperCamelCase_ : Any       ):
								_lowerCAmelCase    :    Dict										=      set(string.punctuation       )
								return "".join(ch for ch in text if ch not in exclude       )

				def lower(UpperCamelCase_ : List[Any]       ):
								return text.lower()

				return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase_       )       )       )       )




def 							_UpperCAmelCase  (UpperCamelCase_ : Union[str, Any]      ,    UpperCamelCase_ : Optional[int]       ):
				'''simple docstring'''
				_lowerCAmelCase    :    List[str]										=      normalize_answer(UpperCamelCase_       ).split()
				_lowerCAmelCase    :    str										=      normalize_answer(UpperCamelCase_       ).split()
				_lowerCAmelCase    :    Optional[int]										=      Counter(UpperCamelCase_       ) & Counter(UpperCamelCase_       )
				_lowerCAmelCase    :    List[str]										=      sum(common.values()       )
				if num_same == 0:
								return 0
				_lowerCAmelCase    :    Tuple										=      1.0 * num_same / len(UpperCamelCase_       )
				_lowerCAmelCase    :    Dict										=      1.0 * num_same / len(UpperCamelCase_       )
				_lowerCAmelCase    :    int										=      (2 * precision * recall) / (precision + recall)
				return fa




def 							_UpperCAmelCase  (UpperCamelCase_ : Optional[int]      ,    UpperCamelCase_ : Optional[int]       ):
				'''simple docstring'''
				return normalize_answer(UpperCamelCase_       ) == normalize_answer(UpperCamelCase_       )




def 							_UpperCAmelCase  (UpperCamelCase_ : List[str]      ,    UpperCamelCase_ : List[str]       ):
				'''simple docstring'''
				assert len(UpperCamelCase_       ) == len(UpperCamelCase_       )
				_lowerCAmelCase    :    Dict										=      0
				for hypo, pred in zip(UpperCamelCase_      ,    UpperCamelCase_       ):
								em += exact_match_score(UpperCamelCase_      ,    UpperCamelCase_       )
				if len(UpperCamelCase_       ) > 0:
								em /= len(UpperCamelCase_       )
				return {"em": em}




def 							_UpperCAmelCase  (UpperCamelCase_ : Optional[Any]       ):
				'''simple docstring'''
				return model_prefix.startswith("""rag"""       )





def 							_UpperCAmelCase  (UpperCamelCase_ : List[str]      ,    UpperCamelCase_ : List[Any]      ,    UpperCamelCase_ : Optional[Any]       ):
				'''simple docstring'''
				_lowerCAmelCase    :    List[Any]										=      {p: p for p in extra_params}
				# T5 models don't have `dropout` param, they have `dropout_rate` instead
				_lowerCAmelCase    :    List[Any]										=      """dropout_rate"""
				for p in extra_params:
								if getattr(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_       ):
												if not hasattr(UpperCamelCase_      ,    UpperCamelCase_       ) and not hasattr(UpperCamelCase_      ,    equivalent_param[p]       ):
																logger.info("""config doesn't have a `{}` attribute""".format(UpperCamelCase_       )       )
																delattr(UpperCamelCase_      ,    UpperCamelCase_       )
																continue
												_lowerCAmelCase    :    str										=      p if hasattr(UpperCamelCase_      ,    UpperCamelCase_       ) else equivalent_param[p]
												setattr(UpperCamelCase_      ,    UpperCamelCase_      ,    getattr(UpperCamelCase_      ,    UpperCamelCase_       )       )
												delattr(UpperCamelCase_      ,    UpperCamelCase_       )
				return hparams, config







import comet  # From: unbabel-comet
import torch

import datasets


_lowerCamelCase      :  List[Any]      						=						datasets.logging.get_logger(__name__)

_lowerCamelCase      :  Optional[Any]      						=						"\\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"

_lowerCamelCase      :  Union[str, Any]      						=						"\\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"

_lowerCamelCase      :  Optional[int]      						=						"\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       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    Tuple:





								'''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       SCREAMING_SNAKE_CASE       (      self      :		Dict		,	_UpperCAmelCase      :		List[str]      )  ->    Dict:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		Dict		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		Union[str, Any]		,	_UpperCAmelCase      :		Dict=None		,	_UpperCAmelCase      :		Tuple=False      )  ->    int:





								'''simple docstring'''




								if gpus is None:
												_lowerCAmelCase    :    Tuple										=      1 if torch.cuda.is_available() else 0
								_lowerCAmelCase    :    Optional[int]										=      {"""src""": sources, """mt""": predictions, """ref""": references}
								_lowerCAmelCase    :    List[Any]										=      [dict(zip(_UpperCAmelCase		,	_UpperCAmelCase      )      ) for t in zip(*data.values()      )]
								_lowerCAmelCase					,    _lowerCAmelCase    :    Optional[Any]										=      self.scorer.predict(_UpperCAmelCase		,	gpus=_UpperCAmelCase		,	progress_bar=_UpperCAmelCase      )
								return {"mean_score": mean_score, "scores": scores}

