




"""simple docstring"""






import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			def __init__(						self						,   lowercase						,   lowercase=None						,   lowercase=True						,   lowercase=None						,   **lowercase						):
							_lowerCamelCase  :    int					=					parent
							_lowerCamelCase  :    List[Any]					=					config_class
							_lowerCamelCase  :    List[Any]					=					has_text_modality
							_lowerCamelCase  :    str					=					kwargs
							_lowerCamelCase  :    Optional[Any]					=					common_properties
			def       A_					(						self						):
							_lowerCamelCase  :    str					=					self.config_class(**self.inputs_dict						)
							_lowerCamelCase  :    List[str]					=					(
							    ['hidden_size', 'num_attention_heads', 'num_hidden_layers']
							    if self.common_properties is None
							    else self.common_properties
							)

							# Add common fields for text models
							if self.has_text_modality:
											common_properties.extend(['vocab_size']						)

							# Test that config has the common properties as getters
							for prop in common_properties:
											self.parent.assertTrue(hasattr(lowercase						,   lowercase						)						,   msg=F'''`{prop}` does not exist'''						)

							# Test that config has the common properties as setter
							for idx, name in enumerate(lowercase						):
											try:
															setattr(lowercase						,   lowercase						,   lowercase						)
															self.parent.assertEqual(
															    getattr(lowercase						,   lowercase						)						,   lowercase						,   msg=F'''`{name} value {idx} expected, but was {getattr(lowercase						,   lowercase						)}'''						)
											except NotImplementedError:
															# Some models might not be able to implement setters for common_properties
															# In that case, a NotImplementedError is raised
															pass

        # Test if config class can be called with Config(prop_name=..)
							for idx, name in enumerate(lowercase						):
											try:
															_lowerCamelCase  :    str					=					self.config_class(**{name: idx}						)
															self.parent.assertEqual(
															    getattr(lowercase						,   lowercase						)						,   lowercase						,   msg=F'''`{name} value {idx} expected, but was {getattr(lowercase						,   lowercase						)}'''						)
											except NotImplementedError:
															# Some models might not be able to implement setters for common_properties
															# In that case, a NotImplementedError is raised
															pass
			def       A_					(						self						):
							_lowerCamelCase  :    Union[str, Any]					=					self.config_class(**self.inputs_dict						)
							_lowerCamelCase  :    Optional[int]					=					json.loads(config.to_json_string()						)
							for key, value in self.inputs_dict.items():
											self.parent.assertEqual(obj[key]						,   lowercase						)
			def       A_					(						self						):
							_lowerCamelCase  :    List[Any]					=					self.config_class(**self.inputs_dict						)

							with tempfile.TemporaryDirectory() as tmpdirname:
											_lowerCamelCase  :    Tuple					=					os.path.join(lowercase						,   'config.json'						)
											config_first.to_json_file(lowercase						)
											_lowerCamelCase  :    str					=					self.config_class.from_json_file(lowercase						)

							self.parent.assertEqual(config_second.to_dict()						,   config_first.to_dict()						)
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[int]					=					self.config_class(**self.inputs_dict						)

							with tempfile.TemporaryDirectory() as tmpdirname:
											config_first.save_pretrained(lowercase						)
											_lowerCamelCase  :    List[Any]					=					self.config_class.from_pretrained(lowercase						)

							self.parent.assertEqual(config_second.to_dict()						,   config_first.to_dict()						)
			def       A_					(						self						):
							_lowerCamelCase  :    Dict					=					self.config_class(**self.inputs_dict						)

							_lowerCamelCase  :    Any					=					'test'
							with tempfile.TemporaryDirectory() as tmpdirname:
											_lowerCamelCase  :    Dict					=					os.path.join(lowercase						,   lowercase						)
											config_first.save_pretrained(lowercase						)
											_lowerCamelCase  :    Dict					=					self.config_class.from_pretrained(lowercase						,   subfolder=lowercase						)

							self.parent.assertEqual(config_second.to_dict()						,   config_first.to_dict()						)
			def       A_					(						self						):
							_lowerCamelCase  :    str					=					self.config_class(**self.inputs_dict						,   num_labels=5						)
							self.parent.assertEqual(len(config.idalabel						)						,   5						)
							self.parent.assertEqual(len(config.labelaid						)						,   5						)

							_lowerCamelCase  :    Dict					=					3
							self.parent.assertEqual(len(config.idalabel						)						,   3						)
							self.parent.assertEqual(len(config.labelaid						)						,   3						)
			def       A_					(						self						):
							if self.config_class.is_composition:
											return
							_lowerCamelCase  :    Any					=					self.config_class()
							self.parent.assertIsNotNone(lowercase						)
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[Any]					=					copy.deepcopy(lowercase						)
							_lowerCamelCase  :    str					=					self.config_class(**lowercase						)
							_lowerCamelCase  :    Optional[int]					=					[]
							for key, value in config_common_kwargs.items():
											if key == "torch_dtype":
															if not is_torch_available():
																			continue
															else:
																			import torch

																			if config.torch_dtype != torch.floataa:
																							wrong_values.append(('torch_dtype', config.torch_dtype, torch.floataa)						)
											elif getattr(lowercase						,   lowercase						) != value:
															wrong_values.append((key, getattr(lowercase						,   lowercase						), value)						)

							if len(lowercase						) > 0:
											_lowerCamelCase  :    List[Any]					=					'\n'.join([F'''- {v[0]}: got {v[1]} instead of {v[2]}''' for v in wrong_values]						)
											raise ValueError(F'''The following keys were not properly set in the config:\n{errors}'''						)






			def       A_					(						self						):
							self.create_and_test_config_common_properties()
							self.create_and_test_config_to_json_string()
							self.create_and_test_config_to_json_file()
							self.create_and_test_config_from_and_save_pretrained()
							self.create_and_test_config_from_and_save_pretrained_subfolder()
							self.create_and_test_config_with_num_labels()
							self.check_config_can_be_init_without_params()
							self.check_config_arguments_init()





"""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}