'''simple docstring'''







from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer




@dataclass
class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							torch.FloatTensor




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						_SCREAMING_SNAKE_CASE					):




						@register_to_config
						def __init__(    self :       Tuple							,							UpperCamelCase_ :       int = 3							,							UpperCamelCase_ :       int = 3							,							UpperCamelCase_ :       Tuple[str] = ("DownEncoderBlock2D",)							,							UpperCamelCase_ :       Tuple[str] = ("UpDecoderBlock2D",)							,							UpperCamelCase_ :       Tuple[int] = (64,)							,							UpperCamelCase_ :       int = 1							,							UpperCamelCase_ :       str = "silu"							,							UpperCamelCase_ :       int = 3							,							UpperCamelCase_ :       int = 32							,							UpperCamelCase_ :       int = 2_56							,							UpperCamelCase_ :       int = 32							,							UpperCamelCase_ :       Optional[int] = None							,							UpperCamelCase_ :       float = 0.1_8215							,							UpperCamelCase_ :       str = "group"							,							)     ->				Tuple:
													super().__init__()

													# pass init params to Encoder
													SCREAMING_SNAKE_CASE__     :Dict						    = Encoder(
													    in_channels=UpperCamelCase_							,							out_channels=UpperCamelCase_							,							down_block_types=UpperCamelCase_							,							block_out_channels=UpperCamelCase_							,							layers_per_block=UpperCamelCase_							,							act_fn=UpperCamelCase_							,							norm_num_groups=UpperCamelCase_							,							double_z=UpperCamelCase_							,							)

													SCREAMING_SNAKE_CASE__     :List[Any]						    = vq_embed_dim if vq_embed_dim is not None else latent_channels

													SCREAMING_SNAKE_CASE__     :List[Any]						    = nn.Convad(UpperCamelCase_							,							UpperCamelCase_							,							1      )
													SCREAMING_SNAKE_CASE__     :Dict						    = VectorQuantizer(UpperCamelCase_							,							UpperCamelCase_							,							beta=0.25							,							remap=UpperCamelCase_							,							sane_index_shape=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Any						    = nn.Convad(UpperCamelCase_							,							UpperCamelCase_							,							1      )

													# pass init params to Decoder
													SCREAMING_SNAKE_CASE__     :str						    = Decoder(
													    in_channels=UpperCamelCase_							,							out_channels=UpperCamelCase_							,							up_block_types=UpperCamelCase_							,							block_out_channels=UpperCamelCase_							,							layers_per_block=UpperCamelCase_							,							act_fn=UpperCamelCase_							,							norm_num_groups=UpperCamelCase_							,							norm_type=UpperCamelCase_							,							)




						@apply_forward_hook
						def      __lowerCamelCase       (    self :       int							,							UpperCamelCase_ :       torch.FloatTensor							,							UpperCamelCase_ :       bool = True      )     ->				VQEncoderOutput:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.encoder(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :str						    = self.quant_conv(UpperCamelCase_      )

													if not return_dict:
																				return (h,)

													return VQEncoderOutput(latents=UpperCamelCase_      )




						@apply_forward_hook
						def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       torch.FloatTensor							,							UpperCamelCase_ :       bool = False							,							UpperCamelCase_ :       bool = True      )     ->				Union[DecoderOutput, torch.FloatTensor]:
													# also go through quantization layer
													if not force_not_quantize:
																				SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Dict						    = self.quantize(UpperCamelCase_      )
													else:
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = h
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.post_quant_conv(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :str						    = self.decoder(UpperCamelCase_							,							quant if self.config.norm_type == 'spatial' else None      )

													if not return_dict:
																				return (dec,)

													return DecoderOutput(sample=UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       torch.FloatTensor							,							UpperCamelCase_ :       bool = True      )     ->				Union[DecoderOutput, torch.FloatTensor]:
													SCREAMING_SNAKE_CASE__     :str						    = sample
													SCREAMING_SNAKE_CASE__     :List[Any]						    = self.encode(UpperCamelCase_      ).latents
													SCREAMING_SNAKE_CASE__     :List[str]						    = self.decode(UpperCamelCase_      ).sample

													if not return_dict:
																				return (dec,)

													return DecoderOutput(sample=UpperCamelCase_      )







'''simple docstring'''







import comet  # From: unbabel-comet
import torch

import datasets


UpperCamelCase_      	=	datasets.logging.get_logger(__name__)

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

UpperCamelCase_      	=	'''\
Crosslingual 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).
With 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.

See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
'''

UpperCamelCase_      	=	'''
COMET score.

Args:

`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.

Returns:
    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
    `scores`: List of scores.

Examples:

    >>> comet_metric = datasets.load_metric(\'comet\')
    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use
    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]
    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]
    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]
    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
    >>> print([round(v, 2) for v in results["scores"]])
    [0.19, 0.92]
'''




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




						def      __lowerCamelCase       (    self :       Tuple      )     ->				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 :       List[str]							,							UpperCamelCase_ :       List[str]      )     ->				Dict:
													if self.config_name == "default":
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = comet.load_from_checkpoint(comet.download_model('wmt20-comet-da'      )      )
													else:
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = comet.load_from_checkpoint(comet.download_model(self.config_name      )      )




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






