






from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)

def _UpperCAmelCase   (	a						:	Tuple  ,					a						:	Optional[Any] ):
							snake_case__	  =       []
							for part_id in partition_order:
														snake_case__	  =       df.where(F'''SPARK_PARTITION_ID() = {part_id}''' ).collect()
														for row_idx, row in enumerate(snake_case__ ):
																					expected_row_ids_and_row_dicts.append((F'''{part_id}_{row_idx}''', row.asDict()) )
							return expected_row_ids_and_row_dicts

@require_not_windows
@require_dill_gt_0_3_2
def _UpperCAmelCase   (	):
							snake_case__	  =       pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
							snake_case__	  =       spark.range(100 ).repartition(1 )
							snake_case__	  =       Spark(snake_case__ )
							# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
							# that each partition can hold 2 rows.
							spark_builder._repartition_df_if_needed(max_shard_size=16 )
							# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
							assert spark_builder.df.rdd.getNumPartitions() == 50

@require_not_windows
@require_dill_gt_0_3_2
def _UpperCAmelCase   (	):
							snake_case__	  =       pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
							snake_case__	  =       spark.range(10 ).repartition(2 )
							snake_case__	  =       [1, 0]
							snake_case__	  =       _generate_iterable_examples(snake_case__  ,					snake_case__ )  # Reverse the partitions.
							snake_case__	  =       _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__  ,					snake_case__ )

							for i, (row_id, row_dict) in enumerate(generate_fn() ):
														snake_case__  , snake_case__	  =       expected_row_ids_and_row_dicts[i]
														assert row_id == expected_row_id
														assert row_dict == expected_row_dict

@require_not_windows
@require_dill_gt_0_3_2
def _UpperCAmelCase   (	):
							snake_case__	  =       pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
							snake_case__	  =       spark.range(10 ).repartition(1 )
							snake_case__	  =       SparkExamplesIterable(snake_case__ )
							assert it.n_shards == 1
							for i, (row_id, row_dict) in enumerate(snake_case__ ):
														assert row_id == F'''0_{i}'''
														assert row_dict == {"id": i}

@require_not_windows
@require_dill_gt_0_3_2
def _UpperCAmelCase   (	):
							snake_case__	  =       pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
							snake_case__	  =       spark.range(30 ).repartition(3 )
							# Mock the generator so that shuffle reverses the partition indices.
							with patch("""numpy.random.Generator""" ) as generator_mock:
														snake_case__	  =       lambda a : x.reverse()
														snake_case__	  =       _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__  ,					[2, 1, 0] )

														snake_case__	  =       SparkExamplesIterable(snake_case__ ).shuffle_data_sources(snake_case__ )
														assert shuffled_it.n_shards == 3
														for i, (row_id, row_dict) in enumerate(snake_case__ ):
																					snake_case__  , snake_case__	  =       expected_row_ids_and_row_dicts[i]
																					assert row_id == expected_row_id
																					assert row_dict == expected_row_dict

@require_not_windows
@require_dill_gt_0_3_2
def _UpperCAmelCase   (	):
							snake_case__	  =       pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
							snake_case__	  =       spark.range(20 ).repartition(4 )

							# Partitions 0 and 2
							snake_case__	  =       SparkExamplesIterable(snake_case__ ).shard_data_sources(worker_id=0  ,					num_workers=2 )
							assert shard_it_a.n_shards == 2
							snake_case__	  =       _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__  ,					[0, 2] )
							for i, (row_id, row_dict) in enumerate(snake_case__ ):
														snake_case__  , snake_case__	  =       expected_row_ids_and_row_dicts_a[i]
														assert row_id == expected_row_id
														assert row_dict == expected_row_dict

							# Partitions 1 and 3
							snake_case__	  =       SparkExamplesIterable(snake_case__ ).shard_data_sources(worker_id=1  ,					num_workers=2 )
							assert shard_it_a.n_shards == 2
							snake_case__	  =       _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__  ,					[1, 3] )
							for i, (row_id, row_dict) in enumerate(snake_case__ ):
														snake_case__  , snake_case__	  =       expected_row_ids_and_row_dicts_a[i]
														assert row_id == expected_row_id
														assert row_dict == expected_row_dict



@require_not_windows
@require_dill_gt_0_3_2
def _UpperCAmelCase   (	):
							snake_case__	  =       pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
							snake_case__	  =       spark.range(100 ).repartition(1 )
							snake_case__	  =       Spark(snake_case__ )
							# Choose a small max_shard_size for maximum partitioning.
							spark_builder._repartition_df_if_needed(max_shard_size=1 )
							# The new number of partitions should not be greater than the number of rows.
							assert spark_builder.df.rdd.getNumPartitions() == 100








import comet  # From: unbabel-comet
import torch

import datasets


a__       =     datasets.logging.get_logger(__name__)

a__       =     """\
@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\",
}
"""

a__       =     """\
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.
"""

a__       =     """
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 					__magic_name__							(      self    :    Optional[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 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    Dict):






											'''simple docstring'''







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



				def 					__magic_name__							(      self    :    List[str]			,		UpperCamelCase__    :    str			,		UpperCamelCase__    :    Tuple			,		UpperCamelCase__    :    List[Any]			,		UpperCamelCase__    :    int=None			,		UpperCamelCase__    :    List[Any]=False):






											'''simple docstring'''







											if gpus is None:
																		snake_case__	  =       1 if torch.cuda.is_available() else 0
											snake_case__	  =       {"""src""": sources, """mt""": predictions, """ref""": references}
											snake_case__	  =       [dict(zip(UpperCamelCase__			,		UpperCamelCase__)) for t in zip(*data.values())]
											snake_case__  , snake_case__	  =       self.scorer.predict(UpperCamelCase__			,		gpus=UpperCamelCase__			,		progress_bar=UpperCamelCase__)
											return {"mean_score": mean_score, "scores": scores}
