

'''simple docstring'''





import comet  # From: unbabel-comet
import torch

import datasets


A__						=							datasets.logging.get_logger(__name__)

A__						=							"\\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"

A__						=							"\\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"

A__						=							"\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 				__UpperCamelCase				(					datasets.Metric			):







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						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 	_SCREAMING_SNAKE_CASE							(   self: int  , __UpperCamelCase: Union[str, Any]      ):

						'''simple docstring'''





						if self.config_name == "default":
											__magic_name__							=    comet.load_from_checkpoint(comet.download_model('wmt20-comet-da'      )      )
						else:
											__magic_name__							=    comet.load_from_checkpoint(comet.download_model(self.config_name      )      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]  , __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: Tuple  , __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: Union[str, Any]=None  , __UpperCamelCase: int=False      ):

						'''simple docstring'''





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







import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler

@dataclass
class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   int       				=  0
	_lowercase  :   bool       				=  False
	_lowercase  :   float       				=  3.0




class 				__UpperCamelCase				(					unittest.TestCase			):







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						self.assertDictEqual(MockClass().to_kwargs()  , {}      )
						self.assertDictEqual(MockClass(a=2      ).to_kwargs()  , {'a': 2}      )
						self.assertDictEqual(MockClass(a=2  , b=__UpperCamelCase      ).to_kwargs()  , {'a': 2, 'b': True}      )
						self.assertDictEqual(MockClass(a=2  , c=2.25      ).to_kwargs()  , {'a': 2, 'c': 2.25}      )







	@require_cuda
	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						__magic_name__							=    GradScalerKwargs(init_scale=10_24  , growth_factor=2      )
						AcceleratorState._reset_state()
						__magic_name__							=    Accelerator(mixed_precision='fp16'  , kwargs_handlers=[scaler_handler]      )
						print(accelerator.use_fpaa      )
						__magic_name__							=    accelerator.scaler

						# Check the kwargs have been applied
						self.assertEqual(scaler._init_scale  , 1024.0      )
						self.assertEqual(scaler._growth_factor  , 2.0      )

						# Check the other values are at the default
						self.assertEqual(scaler._backoff_factor  , 0.5      )
						self.assertEqual(scaler._growth_interval  , 20_00      )
						self.assertEqual(scaler._enabled  , __UpperCamelCase      )







	@require_multi_gpu
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    ['torchrun', F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__      )]
						execute_subprocess_async(__UpperCamelCase  , env=os.environ.copy()      )


if __name__ == "__main__":
						A__						=							DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
						A__						=							Accelerator(kwargs_handlers=[ddp_scaler])
						A__						=							torch.nn.Linear(100, 200)
						A__						=							accelerator.prepare(model)

						# Check the values changed in kwargs
						A__						=							""
						A__						=							model.bucket_bytes_cap // (1024 * 1024)
						if observed_bucket_cap_map != 15:
												error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
						if model.find_unused_parameters is not True:
												error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

						# Check the values of the defaults
						if model.dim != 0:
												error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
						if model.broadcast_buffers is not True:
												error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
						if model.gradient_as_bucket_view is not False:
												error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

						# Raise error at the end to make sure we don't stop at the first failure.
						if len(error_msg) > 0:
												raise ValueError(error_msg)




