


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       lowerCamelCase__   (       datasets.Metric):





     def      __A					(self       )     ->   Optional[int]:
      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      __A					(self		,     UpperCAmelCase       )     ->   Dict:
      if self.config_name == "default":
       _lowercase							      =comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da'''       )       )
      else:
       _lowercase							      =comet.load_from_checkpoint(comet.download_model(self.config_name       )       )





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



from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
				import tensorflow as tf

				from ..tf_utils import stable_softmax


if is_torch_available():
				import torch


__snake_case								=  logging.get_logger(__name__)




@add_end_docstrings(
    _UpperCAmelCase     ,       R"""
        top_k (`int`, defaults to 5):
            The number of predictions to return.
        targets (`str` or `List[str]`, *optional*):
            When passed, the model will limit the scores to the passed targets instead of looking up in the whole
            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
            token will be used (with a warning, and that might be slower).

    """     ,       )
class 						lowercase__  (						_UpperCAmelCase  ):
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   GenericTensor							):
						if self.framework == "tf":
											SCREAMING_SNAKE_CASE__													= tf.where(input_ids == self.tokenizer.mask_token_id							).numpy()
						elif self.framework == "pt":
											SCREAMING_SNAKE_CASE__													= torch.nonzero(input_ids == self.tokenizer.mask_token_id       ,   as_tuple=UpperCAmelCase_							)
						else:
											raise ValueError('Unsupported framework'							)
						return masked_index
	def      A_				(			self							:   Optional[Any]       ,   UpperCAmelCase_							:   GenericTensor							):
						SCREAMING_SNAKE_CASE__													= self.get_masked_index(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= np.prod(masked_index.shape							)
						if numel < 1:
											raise PipelineException(
											    'fill-mask'       ,   self.model.base_model_prefix       ,   F'No mask_token ({self.tokenizer.mask_token}) found on the input'       ,   )
	def      A_				(			self							:   Union[str, Any]       ,   UpperCAmelCase_							:   GenericTensor							):
						if isinstance(UpperCAmelCase_       ,   UpperCAmelCase_							):
											for model_input in model_inputs:
																self._ensure_exactly_one_mask_token(model_input['input_ids'][0]							)
						else:
											for input_ids in model_inputs["input_ids"]:
																self._ensure_exactly_one_mask_token(UpperCAmelCase_							)
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   Optional[Any]       ,   UpperCAmelCase_							:   str=None       ,   **UpperCAmelCase_							:   Union[str, Any]							):
						if return_tensors is None:
											SCREAMING_SNAKE_CASE__													= self.framework
						SCREAMING_SNAKE_CASE__													= self.tokenizer(UpperCAmelCase_       ,   return_tensors=UpperCAmelCase_							)
						self.ensure_exactly_one_mask_token(UpperCAmelCase_							)
						return model_inputs
	def      A_				(			self							:   Tuple       ,   UpperCAmelCase_							:   Dict							):
						SCREAMING_SNAKE_CASE__													= self.model(**UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= model_inputs['input_ids']
						return model_outputs
	def      A_				(			self							:   Optional[int]       ,   UpperCAmelCase_							:   List[Any]       ,   UpperCAmelCase_							:   Union[str, Any]=5       ,   UpperCAmelCase_							:   Dict=None							):
						# Cap top_k if there are targets
						if target_ids is not None and target_ids.shape[0] < top_k:
											SCREAMING_SNAKE_CASE__													= target_ids.shape[0]
						SCREAMING_SNAKE_CASE__													= model_outputs['input_ids'][0]
						SCREAMING_SNAKE_CASE__													= model_outputs['logits']

						if self.framework == "tf":
											SCREAMING_SNAKE_CASE__													= tf.where(input_ids == self.tokenizer.mask_token_id							).numpy()[:, 0]

											SCREAMING_SNAKE_CASE__													= outputs.numpy()

											SCREAMING_SNAKE_CASE__													= outputs[0, masked_index, :]
											SCREAMING_SNAKE_CASE__													= stable_softmax(UpperCAmelCase_       ,   axis=-1							)
											if target_ids is not None:
																SCREAMING_SNAKE_CASE__													= tf.gather_nd(tf.squeeze(UpperCAmelCase_       ,   0							)       ,   target_ids.reshape(-1       ,   1							)							)
																SCREAMING_SNAKE_CASE__													= tf.expand_dims(UpperCAmelCase_       ,   0							)

											SCREAMING_SNAKE_CASE__													= tf.math.top_k(UpperCAmelCase_       ,   k=UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= topk.values.numpy(), topk.indices.numpy()
						else:
											SCREAMING_SNAKE_CASE__													= torch.nonzero(input_ids == self.tokenizer.mask_token_id       ,   as_tuple=UpperCAmelCase_							).squeeze(-1							)
											# Fill mask pipeline supports only one ${mask_token} per sample

											SCREAMING_SNAKE_CASE__													= outputs[0, masked_index, :]
											SCREAMING_SNAKE_CASE__													= logits.softmax(dim=-1							)
											if target_ids is not None:
																SCREAMING_SNAKE_CASE__													= probs[..., target_ids]

											SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= probs.topk(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= []
						SCREAMING_SNAKE_CASE__													= values.shape[0] == 1
						for i, (_values, _predictions) in enumerate(zip(values.tolist()       ,   predictions.tolist()							)							):
											SCREAMING_SNAKE_CASE__													= []
											for v, p in zip(_values       ,   _predictions							):
																# Copy is important since we're going to modify this array in place
																SCREAMING_SNAKE_CASE__													= input_ids.numpy().copy()
																if target_ids is not None:
																					SCREAMING_SNAKE_CASE__													= target_ids[p].tolist()

																SCREAMING_SNAKE_CASE__													= p
																# Filter padding out:
																SCREAMING_SNAKE_CASE__													= tokens[np.where(tokens != self.tokenizer.pad_token_id							)]
																# Originally we skip special tokens to give readable output.
																# For multi masks though, the other [MASK] would be removed otherwise
																# making the output look odd, so we add them back
																SCREAMING_SNAKE_CASE__													= self.tokenizer.decode(UpperCAmelCase_       ,   skip_special_tokens=UpperCAmelCase_							)
																SCREAMING_SNAKE_CASE__													= {'score': v, 'token': p, 'token_str': self.tokenizer.decode([p]							), 'sequence': sequence}
																row.append(UpperCAmelCase_							)
											result.append(UpperCAmelCase_							)
						if single_mask:
											return result[0]
						return result
	def      A_				(			self							:   Dict       ,   UpperCAmelCase_							:   Tuple       ,   UpperCAmelCase_							:   List[Any]=None							):
						if isinstance(UpperCAmelCase_       ,   UpperCAmelCase_							):
											SCREAMING_SNAKE_CASE__													= [targets]
						try:
											SCREAMING_SNAKE_CASE__													= self.tokenizer.get_vocab()
						except Exception:
											SCREAMING_SNAKE_CASE__													= {}
						SCREAMING_SNAKE_CASE__													= []
						for target in targets:
											SCREAMING_SNAKE_CASE__													= vocab.get(UpperCAmelCase_       ,   UpperCAmelCase_							)
											if id_ is None:
																SCREAMING_SNAKE_CASE__													= self.tokenizer(
																    UpperCAmelCase_       ,   add_special_tokens=UpperCAmelCase_       ,   return_attention_mask=UpperCAmelCase_       ,   return_token_type_ids=UpperCAmelCase_       ,   max_length=1       ,   truncation=UpperCAmelCase_       ,   )['input_ids']
																if len(UpperCAmelCase_							) == 0:
																					logger.warning(
																					    F'The specified target token `{target}` does not exist in the model vocabulary. '
																					    'We cannot replace it with anything meaningful, ignoring it'							)
																					continue
																SCREAMING_SNAKE_CASE__													= input_ids[0]
																# XXX: If users encounter this pass
																# it becomes pretty slow, so let's make sure
																# The warning enables them to fix the input to
																# get faster performance.
																logger.warning(
																    F'The specified target token `{target}` does not exist in the model vocabulary. '
																    F'Replacing with `{self.tokenizer.convert_ids_to_tokens(id_							)}`.'							)
											target_ids.append(id_							)
						SCREAMING_SNAKE_CASE__													= list(set(UpperCAmelCase_							)							)
						if len(UpperCAmelCase_							) == 0:
											raise ValueError('At least one target must be provided when passed.'							)
						SCREAMING_SNAKE_CASE__													= np.array(UpperCAmelCase_							)
						return target_ids
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   Optional[Any]=None       ,   UpperCAmelCase_							:   List[Any]=None							):
						SCREAMING_SNAKE_CASE__													= {}

						if targets is not None:
											SCREAMING_SNAKE_CASE__													= self.get_target_ids(UpperCAmelCase_       ,   UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__													= target_ids

						if top_k is not None:
											SCREAMING_SNAKE_CASE__													= top_k

						if self.tokenizer.mask_token_id is None:
											raise PipelineException(
											    'fill-mask'       ,   self.model.base_model_prefix       ,   'The tokenizer does not define a `mask_token`.'							)
						return {}, {}, postprocess_params





	def __call__(			self							:   Tuple       ,   UpperCAmelCase_							:   Union[str, Any]       ,   *UpperCAmelCase_							:   int       ,   **UpperCAmelCase_							:   Tuple							):
						SCREAMING_SNAKE_CASE__													= super().__call__(UpperCAmelCase_       ,   **UpperCAmelCase_							)
						if isinstance(UpperCAmelCase_       ,   UpperCAmelCase_							) and len(UpperCAmelCase_							) == 1:
											return outputs[0]
						return outputs





