'''simple docstring'''
import comet  # From: unbabel-comet
import torch

import datasets


lowercase__  :      str        =					datasets.logging.get_logger(__name__)

lowercase__  :      Dict        =					"\\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"

lowercase__  :      str        =					"\\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"

lowercase__  :      str        =					"\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 lowerCamelCase							(	datasets.Metric					):




  '''simple docstring'''



  def  lowerCAmelCase__ (      self			:       List[Any]  )				->Any:
        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  lowerCAmelCase__ (      self			:       int  ,							UpperCAmelCase__			:       Union[str, Any]  )				->Any:
        if self.config_name == "default":
              UpperCAmelCase_            =  comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da'''  )  )
        else:
              UpperCAmelCase_            =  comet.load_from_checkpoint(comet.download_model(self.config_name  )  )






  def  lowerCAmelCase__ (      self			:       List[Any]  ,							UpperCAmelCase__			:       Union[str, Any]  ,							UpperCAmelCase__			:       Tuple  ,							UpperCAmelCase__			:       Union[str, Any]  ,							UpperCAmelCase__			:       Union[str, Any]=None  ,							UpperCAmelCase__			:       int=False  )				->Optional[Any]:
        if gpus is None:
              UpperCAmelCase_            =  1 if torch.cuda.is_available() else 0
        UpperCAmelCase_            =  {'''src''': sources, '''mt''': predictions, '''ref''': references}
        UpperCAmelCase_            =  [dict(zip(UpperCAmelCase__  ,							UpperCAmelCase__  )  ) for t in zip(*data.values()  )]
        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.scorer.predict(UpperCAmelCase__  ,							gpus=UpperCAmelCase__  ,							progress_bar=UpperCAmelCase__  )
        return {"mean_score": mean_score, "scores": scores}






'''simple docstring'''
from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__  :      int        =					logging.get_logger(__name__)

lowercase__  :      List[str]        =					{
    "huggingface/time-series-transformer-tourism-monthly": (
        "https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json"
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	'''time_series_transformer'''
  lowerCAmelCase__										=	{
      '''hidden_size''': '''d_model''',
      '''num_attention_heads''': '''encoder_attention_heads''',
      '''num_hidden_layers''': '''encoder_layers''',
  }






  def __init__(      self			:       Optional[int]  ,							UpperCAmelCase__			:       Optional[int] = None  ,							UpperCAmelCase__			:       Optional[int] = None  ,							UpperCAmelCase__			:       str = "student_t"  ,							UpperCAmelCase__			:       str = "nll"  ,							UpperCAmelCase__			:       int = 1  ,							UpperCAmelCase__			:       List[int] = [1, 2, 3, 4, 5, 6, 7]  ,							UpperCAmelCase__			:       Optional[Union[str, bool]] = "mean"  ,							UpperCAmelCase__			:       int = 0  ,							UpperCAmelCase__			:       int = 0  ,							UpperCAmelCase__			:       int = 0  ,							UpperCAmelCase__			:       int = 0  ,							UpperCAmelCase__			:       Optional[List[int]] = None  ,							UpperCAmelCase__			:       Optional[List[int]] = None  ,							UpperCAmelCase__			:       int = 32  ,							UpperCAmelCase__			:       int = 32  ,							UpperCAmelCase__			:       int = 2  ,							UpperCAmelCase__			:       int = 2  ,							UpperCAmelCase__			:       int = 2  ,							UpperCAmelCase__			:       int = 2  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       str = "gelu"  ,							UpperCAmelCase__			:       int = 64  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       int = 100  ,							UpperCAmelCase__			:       float = 0.02  ,							UpperCAmelCase__			:       Union[str, Any]=True  ,							**UpperCAmelCase__			:       Tuple  ,							)				->Optional[int]:
        # time series specific configuration
        UpperCAmelCase_            =  prediction_length
        UpperCAmelCase_            =  context_length or prediction_length
        UpperCAmelCase_            =  distribution_output
        UpperCAmelCase_            =  loss
        UpperCAmelCase_            =  input_size
        UpperCAmelCase_            =  num_time_features
        UpperCAmelCase_            =  lags_sequence
        UpperCAmelCase_            =  scaling
        UpperCAmelCase_            =  num_dynamic_real_features
        UpperCAmelCase_            =  num_static_real_features
        UpperCAmelCase_            =  num_static_categorical_features
        if cardinality and num_static_categorical_features > 0:
              if len(UpperCAmelCase__  ) != num_static_categorical_features:
                    raise ValueError(
                        '''The cardinality should be a list of the same length as `num_static_categorical_features`'''  )
              UpperCAmelCase_            =  cardinality
        else:
              UpperCAmelCase_            =  [0]
        if embedding_dimension and num_static_categorical_features > 0:
              if len(UpperCAmelCase__  ) != num_static_categorical_features:
                    raise ValueError(
                        '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''  )
              UpperCAmelCase_            =  embedding_dimension
        else:
              UpperCAmelCase_            =  [min(50  ,							(cat + 1) // 2  ) for cat in self.cardinality]
        UpperCAmelCase_            =  num_parallel_samples

        # Transformer architecture configuration
        UpperCAmelCase_            =  input_size * len(UpperCAmelCase__  ) + self._number_of_features
        UpperCAmelCase_            =  d_model
        UpperCAmelCase_            =  encoder_attention_heads
        UpperCAmelCase_            =  decoder_attention_heads
        UpperCAmelCase_            =  encoder_ffn_dim
        UpperCAmelCase_            =  decoder_ffn_dim
        UpperCAmelCase_            =  encoder_layers
        UpperCAmelCase_            =  decoder_layers

        UpperCAmelCase_            =  dropout
        UpperCAmelCase_            =  attention_dropout
        UpperCAmelCase_            =  activation_dropout
        UpperCAmelCase_            =  encoder_layerdrop
        UpperCAmelCase_            =  decoder_layerdrop

        UpperCAmelCase_            =  activation_function
        UpperCAmelCase_            =  init_std

        UpperCAmelCase_            =  use_cache

        super().__init__(is_encoder_decoder=UpperCAmelCase__  ,							**UpperCAmelCase__  )






  @property
  def  lowerCAmelCase__ (      self			:       List[str]  )				->int:
        return (
            sum(self.embedding_dimension  )
            + self.num_dynamic_real_features
            + self.num_time_features
            + self.num_static_real_features
            + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
        )





