
import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets




@contextmanager
def 				__magic_name__				(     )    ->     Any:
				def filter_log(__lowerCAmelCase			:		Tuple							):
								return False if "This IS expected if you are initializing" in record.msg else True

				__lowerCamelCase					      =			datasets.utils.logging.get_logger('''transformers.modeling_utils'''							)
				logger.addFilter(__lowerCAmelCase							)
				try:
								yield
				finally:
								logger.removeFilter(__lowerCAmelCase							)


SCREAMING_SNAKE_CASE__     :    Optional[Any]      							=					"\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n"

SCREAMING_SNAKE_CASE__     :    Dict      							=					"\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project's README at https://github.com/Tiiiger/bert_score#readme for more information.\n"

SCREAMING_SNAKE_CASE__     :    Tuple      							=					"\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. 'en').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bertscore = datasets.load_metric(\"bertscore\")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang=\"en\")\n    >>> print([round(v, 2) for v in results[\"f1\"]])\n    [1.0, 1.0]\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,			_KWARGS_DESCRIPTION    )
class      lowerCAmelCase__  (			datasets.Metric    ):






		def 				__A (	self	:							str						)  ->							List[str]:
						return datasets.MetricInfo(
						    description=_DESCRIPTION							,    citation=_CITATION							,    homepage='''https://github.com/Tiiiger/bert_score'''							,    inputs_description=_KWARGS_DESCRIPTION							,    features=datasets.Features(
						        {
						            '''predictions''': datasets.Value('''string'''							,    id='''sequence'''						),
						            '''references''': datasets.Sequence(datasets.Value('''string'''							,    id='''sequence'''						)							,    id='''references'''						),
						        }						)							,    codebase_urls=['''https://github.com/Tiiiger/bert_score''']							,    reference_urls=[
						        '''https://github.com/Tiiiger/bert_score''',
						        '''https://arxiv.org/abs/1904.09675''',
						    ]							,    )






		def 				__A (	self	:							Union[str, Any]							,    SCREAMING_SNAKE_CASE__	:							Dict							,    SCREAMING_SNAKE_CASE__	:							List[str]							,    SCREAMING_SNAKE_CASE__	:							Dict=None							,    SCREAMING_SNAKE_CASE__	:							str=None							,    SCREAMING_SNAKE_CASE__	:							Tuple=None							,    SCREAMING_SNAKE_CASE__	:							Optional[int]=False							,    SCREAMING_SNAKE_CASE__	:							Any=False							,    SCREAMING_SNAKE_CASE__	:							Dict=None							,    SCREAMING_SNAKE_CASE__	:							Tuple=64							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=4							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=False							,    SCREAMING_SNAKE_CASE__	:							List[str]=False							,    SCREAMING_SNAKE_CASE__	:							int=None							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=False							,    )  ->							int:
						__lowerCamelCase					      =			bert_score.utils.get_hash
						__lowerCamelCase					      =			bert_score.BERTScorer

						if version.parse(bert_score.__version__						) >= version.parse('''0.3.10'''						):
										__lowerCamelCase					      =			functools.partial(SCREAMING_SNAKE_CASE__							,    use_fast_tokenizer=SCREAMING_SNAKE_CASE__						)
										__lowerCamelCase					      =			functools.partial(SCREAMING_SNAKE_CASE__							,    use_fast_tokenizer=SCREAMING_SNAKE_CASE__						)
						elif use_fast_tokenizer:
										raise ImportWarning(
										    '''To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn\'t match this condition.\n'''
										    '''You can install it with `pip install "bert-score>=0.3.10"`.'''						)

						if model_type is None:
										assert lang is not None, "either lang or model_type should be specified"
										__lowerCamelCase					      =			bert_score.utils.langamodel[lang.lower()]

						if num_layers is None:
										__lowerCamelCase					      =			bert_score.utils.modelalayers[model_type]

						__lowerCamelCase					      =			get_hash(
						    model=SCREAMING_SNAKE_CASE__							,    num_layers=SCREAMING_SNAKE_CASE__							,    idf=SCREAMING_SNAKE_CASE__							,    rescale_with_baseline=SCREAMING_SNAKE_CASE__							,    use_custom_baseline=baseline_path is not None							,    )

						with filter_logging_context():
										if not hasattr(self							,    '''cached_bertscorer'''						) or self.cached_bertscorer.hash != hashcode:
														__lowerCamelCase					      =			scorer(
														    model_type=SCREAMING_SNAKE_CASE__							,    num_layers=SCREAMING_SNAKE_CASE__							,    batch_size=SCREAMING_SNAKE_CASE__							,    nthreads=SCREAMING_SNAKE_CASE__							,    all_layers=SCREAMING_SNAKE_CASE__							,    idf=SCREAMING_SNAKE_CASE__							,    device=SCREAMING_SNAKE_CASE__							,    lang=SCREAMING_SNAKE_CASE__							,    rescale_with_baseline=SCREAMING_SNAKE_CASE__							,    baseline_path=SCREAMING_SNAKE_CASE__							,    )

						((__lowerCamelCase)      ,					(__lowerCamelCase)      ,					(__lowerCamelCase))					      =			self.cached_bertscorer.score(
						    cands=SCREAMING_SNAKE_CASE__							,    refs=SCREAMING_SNAKE_CASE__							,    verbose=SCREAMING_SNAKE_CASE__							,    batch_size=SCREAMING_SNAKE_CASE__							,    )
						__lowerCamelCase					      =			{
						    '''precision''': P.tolist(),
						    '''recall''': R.tolist(),
						    '''f1''': F.tolist(),
						    '''hashcode''': hashcode,
						}
						return output_dict






		def 				__A (	self	:							Any							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=None							,    SCREAMING_SNAKE_CASE__	:							Any=None							,    **SCREAMING_SNAKE_CASE__	:							Dict						)  ->							Optional[Any]:
						# References can be strings or lists of strings
						# Let's change strings to lists of strings with one element
						if references is not None:
										__lowerCamelCase					      =			[[ref] if isinstance(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						) else ref for ref in references]
						super().add_batch(predictions=SCREAMING_SNAKE_CASE__							,    references=SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)







		def 				__A (	self	:							Dict							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=None							,    SCREAMING_SNAKE_CASE__	:							int=None							,    **SCREAMING_SNAKE_CASE__	:							int						)  ->							Optional[Any]:
						# References can be strings or lists of strings
						# Let's change strings to lists of strings with one element
						if isinstance(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						):
										__lowerCamelCase					      =			[reference]
						super().add(prediction=SCREAMING_SNAKE_CASE__							,    reference=SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch




def 				__magic_name__				(     )    ->     Union[str, Any]:
				__lowerCamelCase					      =			Dataset.from_dict({'''i''': range(17							)}							)
				__lowerCamelCase					      =			len(__lowerCAmelCase							)
				__lowerCamelCase					      =			3
				__lowerCamelCase					      =			[
				    split_dataset_by_node(__lowerCAmelCase	,						rank=__lowerCAmelCase	,						world_size=__lowerCAmelCase							) for rank in range(__lowerCAmelCase							)
				]
				assert sum(len(__lowerCAmelCase							) for ds in datasets_per_rank							) == full_size
				assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




def 				__magic_name__				(     )    ->     Optional[Any]:
				def gen():
								return ({"i": i} for i in range(17							))

				__lowerCamelCase					      =			3
				__lowerCamelCase					      =			IterableDataset.from_generator(__lowerCAmelCase							)
				__lowerCamelCase					      =			len(list(__lowerCAmelCase							)							)
				__lowerCamelCase					      =			[
				    split_dataset_by_node(__lowerCAmelCase	,						rank=__lowerCAmelCase	,						world_size=__lowerCAmelCase							) for rank in range(__lowerCAmelCase							)
				]
				assert sum(len(list(__lowerCAmelCase							)							) for ds in datasets_per_rank							) == full_size
				assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




@pytest.mark.parametrize('''shards_per_node'''	,						[1, 2, 3]							)
def 				__magic_name__				(     __lowerCAmelCase			:		Any							)    ->     Union[str, Any]:
				def gen(__lowerCAmelCase			:		Union[str, Any]							):
								for shard in shards:
												yield from ({"i": i, "shard": shard} for i in range(17							))

				__lowerCamelCase					      =			3
				__lowerCamelCase					      =			shards_per_node * world_size
				__lowerCamelCase					      =			{'''shards''': [f'''shard_{shard_idx}.txt''' for shard_idx in range(__lowerCAmelCase							)]}
				__lowerCamelCase					      =			IterableDataset.from_generator(__lowerCAmelCase	,						gen_kwargs=__lowerCAmelCase							)
				__lowerCamelCase					      =			len(list(__lowerCAmelCase							)							)
				assert full_ds.n_shards == world_size * shards_per_node
				__lowerCamelCase					      =			[
				    split_dataset_by_node(__lowerCAmelCase	,						rank=__lowerCAmelCase	,						world_size=__lowerCAmelCase							) for rank in range(__lowerCAmelCase							)
				]
				assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
				assert sum(len(list(__lowerCAmelCase							)							) for ds in datasets_per_rank							) == full_size
				assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




def 				__magic_name__				(     )    ->     Optional[int]:
				def gen():
								return ({"i": i} for i in range(17							))

				__lowerCamelCase					      =			2
				__lowerCamelCase					      =			IterableDataset.from_generator(__lowerCAmelCase							)
				__lowerCamelCase					      =			len(list(__lowerCAmelCase							)							)

				__lowerCamelCase					      =			split_dataset_by_node(__lowerCAmelCase	,						rank=0	,						world_size=__lowerCAmelCase							).shuffle(seed=42							)
				assert len(list(__lowerCAmelCase							)							) == 1 + full_size // world_size
				with pytest.raises(__lowerCAmelCase							):
								split_dataset_by_node(__lowerCAmelCase	,						rank=0	,						world_size=__lowerCAmelCase							).shuffle()

				__lowerCamelCase					      =			split_dataset_by_node(full_ds.shuffle(seed=42							)	,						rank=0	,						world_size=__lowerCAmelCase							)
				assert len(list(__lowerCAmelCase							)							) == 1 + full_size // world_size
				with pytest.raises(__lowerCAmelCase							):
								split_dataset_by_node(full_ds.shuffle()	,						rank=0	,						world_size=__lowerCAmelCase							)




@pytest.mark.parametrize('''streaming'''	,						[False, True]							)
@require_torch
@pytest.mark.skipif(os.name == '''nt'''	,						reason='''execute_subprocess_async doesn\'t support windows'''							)
@pytest.mark.integration
def 				__magic_name__				(     __lowerCAmelCase			:		List[Any]							)    ->     Optional[int]:
				__lowerCamelCase					      =			2
				__lowerCamelCase					      =			get_torch_dist_unique_port()
				__lowerCamelCase					      =			Path(__lowerCAmelCase							).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
				__lowerCamelCase					      =			f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
				__lowerCamelCase					      =			f'''
        --streaming={streaming}
    '''.split()
				__lowerCamelCase					      =			[sys.executable] + distributed_args + args
				execute_subprocess_async(__lowerCAmelCase	,						env=os.environ.copy()							)







@pytest.mark.parametrize(
    '''nproc_per_node, num_workers'''	,						[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]	,						)
@require_torch
@pytest.mark.skipif(os.name == '''nt'''	,						reason='''execute_subprocess_async doesn\'t support windows'''							)
@pytest.mark.integration
def 				__magic_name__				(     __lowerCAmelCase			:		Any	,						__lowerCAmelCase			:		Any							)    ->     int:
				__lowerCamelCase					      =			True
				__lowerCamelCase					      =			get_torch_dist_unique_port()
				__lowerCamelCase					      =			Path(__lowerCAmelCase							).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
				__lowerCamelCase					      =			f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
				__lowerCamelCase					      =			f'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
				__lowerCamelCase					      =			[sys.executable] + distributed_args + args
				execute_subprocess_async(__lowerCAmelCase	,						env=os.environ.copy()							)



