




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__  		=		{"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__  		=		["""XLNetTokenizer"""]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__  		=		["""XLNetTokenizerFast"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__  		=		[
			    """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """XLNetForMultipleChoice""",
			    """XLNetForQuestionAnswering""",
			    """XLNetForQuestionAnsweringSimple""",
			    """XLNetForSequenceClassification""",
			    """XLNetForTokenClassification""",
			    """XLNetLMHeadModel""",
			    """XLNetModel""",
			    """XLNetPreTrainedModel""",
			    """load_tf_weights_in_xlnet""",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__  		=		[
			    """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """TFXLNetForMultipleChoice""",
			    """TFXLNetForQuestionAnsweringSimple""",
			    """TFXLNetForSequenceClassification""",
			    """TFXLNetForTokenClassification""",
			    """TFXLNetLMHeadModel""",
			    """TFXLNetMainLayer""",
			    """TFXLNetModel""",
			    """TFXLNetPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlnet import XLNetTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlnet_fast import XLNetTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlnet import (
						    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLNetForMultipleChoice,
						    XLNetForQuestionAnswering,
						    XLNetForQuestionAnsweringSimple,
						    XLNetForSequenceClassification,
						    XLNetForTokenClassification,
						    XLNetLMHeadModel,
						    XLNetModel,
						    XLNetPreTrainedModel,
						    load_tf_weights_in_xlnet,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xlnet import (
						    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLNetForMultipleChoice,
						    TFXLNetForQuestionAnsweringSimple,
						    TFXLNetForSequenceClassification,
						    TFXLNetForTokenClassification,
						    TFXLNetLMHeadModel,
						    TFXLNetMainLayer,
						    TFXLNetModel,
						    TFXLNetPreTrainedModel,
						)

else:
			import sys

			lowercase__  		=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





"""simple docstring"""






import comet  # From: unbabel-comet
import torch

import datasets


lowercase__  		=		datasets.logging.get_logger(__name__)

lowercase__  		=		"""\
@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\",
}
"""

lowercase__  		=		"""\
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.
"""

lowercase__  		=		"""
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		):






			'''simple docstring'''



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