




'''simple docstring'''



import inspect
import os

import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed

import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.testing import (
    AccelerateTestCase,
    TempDirTestCase,
    execute_subprocess_async,
    require_cuda,
    require_fsdp,
    require_multi_gpu,
    slow,
)
from accelerate.utils.constants import (
    FSDP_AUTO_WRAP_POLICY,
    FSDP_BACKWARD_PREFETCH,
    FSDP_SHARDING_STRATEGY,
    FSDP_STATE_DICT_TYPE,
)
from accelerate.utils.dataclasses import FullyShardedDataParallelPlugin
from accelerate.utils.other import patch_environment


set_seed(42)

_lowercase    =		"""bert-base-cased"""
_lowercase    =		"""fp16"""
_lowercase    =		"""bf16"""
_lowercase    =		[FPaa, BFaa]




@require_fsdp
@require_cuda
class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						def  _lowercase     ( self				):

												"""simple docstring"""



												super().setUp()

												_lowerCAmelCase  						=			dict(
												    ACCELERATE_USE_FSDP="""true"""   ,       MASTER_ADDR="""localhost"""   ,       MASTER_PORT="""10999"""   ,       RANK="""0"""   ,       LOCAL_RANK="""0"""   ,       WORLD_SIZE="""1"""   ,       )




						def  _lowercase     ( self				):

												"""simple docstring"""



												from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

												for i, strategy in enumerate(_lowercase				):
																		_lowerCAmelCase  						=			self.dist_env.copy()
																		_lowerCAmelCase  						=			F'{i + 1}'
																		_lowerCAmelCase  						=			strategy
																		with mockenv_context(**_lowercase				):
																								_lowerCAmelCase  						=			FullyShardedDataParallelPlugin()
																								self.assertEqual(fsdp_plugin.sharding_strategy   ,       ShardingStrategy(i + 1				)				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

												for i, prefetch_policy in enumerate(_lowercase				):
																		_lowerCAmelCase  						=			self.dist_env.copy()
																		_lowerCAmelCase  						=			prefetch_policy
																		with mockenv_context(**_lowercase				):
																								_lowerCAmelCase  						=			FullyShardedDataParallelPlugin()
																								if prefetch_policy == "NO_PREFETCH":
																														self.assertIsNone(fsdp_plugin.backward_prefetch				)
																								else:
																														self.assertEqual(fsdp_plugin.backward_prefetch   ,       BackwardPrefetch(i + 1				)				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

												for i, state_dict_type in enumerate(_lowercase				):
																		_lowerCAmelCase  						=			self.dist_env.copy()
																		_lowerCAmelCase  						=			state_dict_type
																		with mockenv_context(**_lowercase				):
																								_lowerCAmelCase  						=			FullyShardedDataParallelPlugin()
																								self.assertEqual(fsdp_plugin.state_dict_type   ,       StateDictType(i + 1				)				)
																								if state_dict_type == "FULL_STATE_DICT":
																														self.assertTrue(fsdp_plugin.state_dict_config.offload_to_cpu				)
																														self.assertTrue(fsdp_plugin.state_dict_config.ranka_only				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			AutoModel.from_pretrained(_lowercase				)
												for policy in FSDP_AUTO_WRAP_POLICY:
																		_lowerCAmelCase  						=			self.dist_env.copy()
																		_lowerCAmelCase  						=			policy
																		if policy == "TRANSFORMER_BASED_WRAP":
																								_lowerCAmelCase  						=			"""BertLayer"""
																		elif policy == "SIZE_BASED_WRAP":
																								_lowerCAmelCase  						=			"""2000"""
																		with mockenv_context(**_lowercase				):
																								_lowerCAmelCase  						=			FullyShardedDataParallelPlugin()
																								fsdp_plugin.set_auto_wrap_policy(_lowercase				)
																								if policy == "NO_WRAP":
																														self.assertIsNone(fsdp_plugin.auto_wrap_policy				)
																								else:
																														self.assertIsNotNone(fsdp_plugin.auto_wrap_policy				)

												_lowerCAmelCase  						=			self.dist_env.copy()
												_lowerCAmelCase  						=			"""TRANSFORMER_BASED_WRAP"""
												_lowerCAmelCase  						=			"""T5Layer"""
												with mockenv_context(**_lowercase				):
																		_lowerCAmelCase  						=			FullyShardedDataParallelPlugin()
																		with self.assertRaises(_lowercase				) as cm:
																								fsdp_plugin.set_auto_wrap_policy(_lowercase				)
																		self.assertTrue("""Could not find the transformer layer class to wrap in the model.""" in str(cm.exception				)				)

												_lowerCAmelCase  						=			self.dist_env.copy()
												_lowerCAmelCase  						=			"""SIZE_BASED_WRAP"""
												_lowerCAmelCase  						=			"""0"""
												with mockenv_context(**_lowercase				):
																		_lowerCAmelCase  						=			FullyShardedDataParallelPlugin()
																		fsdp_plugin.set_auto_wrap_policy(_lowercase				)
																		self.assertIsNone(fsdp_plugin.auto_wrap_policy				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
												from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler

												for mp_dtype in dtypes:
																		_lowerCAmelCase  						=			self.dist_env.copy()
																		_lowerCAmelCase  						=			mp_dtype
																		with mockenv_context(**_lowercase				):
																								_lowerCAmelCase  						=			Accelerator()
																								if mp_dtype == "fp16":
																														_lowerCAmelCase  						=			torch.floataa
																								elif mp_dtype == "bf16":
																														_lowerCAmelCase  						=			torch.bfloataa
																								_lowerCAmelCase  						=			MixedPrecision(param_dtype=_lowercase   ,       reduce_dtype=_lowercase   ,       buffer_dtype=_lowercase				)
																								self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy   ,       _lowercase				)
																								if mp_dtype == FPaa:
																														self.assertTrue(isinstance(accelerator.scaler   ,       _lowercase				)				)
																								elif mp_dtype == BFaa:
																														self.assertIsNone(accelerator.scaler				)
																								AcceleratorState._reset_state(_lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

												for flag in [True, False]:
																		_lowerCAmelCase  						=			self.dist_env.copy()
																		_lowerCAmelCase  						=			str(_lowercase				).lower()
																		with mockenv_context(**_lowercase				):
																								_lowerCAmelCase  						=			FullyShardedDataParallelPlugin()
																								self.assertEqual(fsdp_plugin.cpu_offload   ,       CPUOffload(offload_params=_lowercase				)				)






@require_fsdp
@require_multi_gpu
@slow
class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						def  _lowercase     ( self				):

												"""simple docstring"""



												super().setUp()
												_lowerCAmelCase  						=			0.82
												_lowerCAmelCase  						=			[
												    """fsdp_shard_grad_op_transformer_based_wrap""",
												    """fsdp_full_shard_transformer_based_wrap""",
												]
												_lowerCAmelCase  						=			{
												    """multi_gpu_fp16""": 3_200,
												    """fsdp_shard_grad_op_transformer_based_wrap_fp16""": 2_000,
												    """fsdp_full_shard_transformer_based_wrap_fp16""": 1_900,
												    # Disabling below test as it overwhelms the RAM memory usage
												    # on CI self-hosted runner leading to tests getting killed.
												    # "fsdp_full_shard_cpu_offload_transformer_based_wrap_fp32": 1500,  # fp16 was leading to indefinite hang
												}
												_lowerCAmelCase  						=			160
												_lowerCAmelCase  						=			160

												_lowerCAmelCase  						=			inspect.getfile(accelerate.test_utils				)
												_lowerCAmelCase  						=			os.path.sep.join(mod_file.split(os.path.sep				)[:-1] + ["""scripts""", """external_deps"""]				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			os.path.join(self.test_scripts_folder   ,       """test_performance.py"""				)
												_lowerCAmelCase  						=			["""accelerate""", """launch""", """--num_processes=2""", """--num_machines=1""", """--machine_rank=0""", """--use_fsdp"""]
												for config in self.performance_configs:
																		_lowerCAmelCase  						=			cmd.copy()
																		for i, strategy in enumerate(_lowercase				):
																								if strategy.lower() in config:
																														cmd_config.append(F'--fsdp_sharding_strategy={i+1}'				)
																														break

																		if "fp32" in config:
																								cmd_config.append("""--mixed_precision=no"""				)
																		else:
																								cmd_config.append("""--mixed_precision=fp16"""				)

																		if "cpu_offload" in config:
																								cmd_config.append("""--fsdp_offload_params=True"""				)

																		for policy in FSDP_AUTO_WRAP_POLICY:
																								if policy.lower() in config:
																														cmd_config.append(F'--fsdp_auto_wrap_policy={policy}'				)
																														break

																		if policy == "TRANSFORMER_BASED_WRAP":
																								cmd_config.append("""--fsdp_transformer_layer_cls_to_wrap=BertLayer"""				)
																		elif policy == "SIZE_BASED_WRAP":
																								cmd_config.append("""--fsdp_min_num_params=2000"""				)

																		cmd_config.extend(
																		    [
																		        self.test_file_path,
																		        F'--output_dir={self.tmpdir}',
																		        F'--performance_lower_bound={self.performance_lower_bound}',
																		    ]				)
																		with patch_environment(omp_num_threads=1				):
																								execute_subprocess_async(_lowercase   ,       env=os.environ.copy()				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			os.path.join(self.test_scripts_folder   ,       """test_checkpointing.py"""				)
												_lowerCAmelCase  						=			[
												    """accelerate""",
												    """launch""",
												    """--num_processes=2""",
												    """--num_machines=1""",
												    """--machine_rank=0""",
												    """--use_fsdp""",
												    """--mixed_precision=fp16""",
												    """--fsdp_transformer_layer_cls_to_wrap=BertLayer""",
												]

												for i, strategy in enumerate(_lowercase				):
																		_lowerCAmelCase  						=			cmd.copy()
																		cmd_config.append(F'--fsdp_sharding_strategy={i+1}'				)
																		if strategy != "FULL_SHARD":
																								continue
																		_lowerCAmelCase  						=			len(_lowercase				)
																		for state_dict_type in FSDP_STATE_DICT_TYPE:
																								_lowerCAmelCase  						=			cmd_config[:state_dict_config_index]
																								cmd_config.append(F'--fsdp_state_dict_type={state_dict_type}'				)
																								cmd_config.extend(
																								    [
																								        self.test_file_path,
																								        F'--output_dir={self.tmpdir}',
																								        """--partial_train_epoch=1""",
																								    ]				)
																								with patch_environment(omp_num_threads=1				):
																														execute_subprocess_async(_lowercase   ,       env=os.environ.copy()				)

																								_lowerCAmelCase  						=			cmd_config[:-1]
																								_lowerCAmelCase  						=			os.path.join(self.tmpdir   ,       """epoch_0"""				)
																								cmd_config.extend(
																								    [
																								        F'--resume_from_checkpoint={resume_from_checkpoint}',
																								    ]				)
																								with patch_environment(omp_num_threads=1				):
																														execute_subprocess_async(_lowercase   ,       env=os.environ.copy()				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			os.path.join(self.test_scripts_folder   ,       """test_peak_memory_usage.py"""				)
												_lowerCAmelCase  						=			[
												    """accelerate""",
												    """launch""",
												    """--num_processes=2""",
												    """--num_machines=1""",
												    """--machine_rank=0""",
												]
												for spec, peak_mem_upper_bound in self.peak_memory_usage_upper_bound.items():
																		_lowerCAmelCase  						=			cmd.copy()
																		if "fp16" in spec:
																								cmd_config.extend(["""--mixed_precision=fp16"""]				)
																		else:
																								cmd_config.extend(["""--mixed_precision=no"""]				)

																		if "multi_gpu" in spec:
																								continue
																		else:
																								cmd_config.extend(["""--use_fsdp"""]				)
																								for i, strategy in enumerate(_lowercase				):
																														if strategy.lower() in spec:
																																				cmd_config.append(F'--fsdp_sharding_strategy={i+1}'				)
																																				break

																								if "cpu_offload" in spec:
																														cmd_config.append("""--fsdp_offload_params=True"""				)

																								for policy in FSDP_AUTO_WRAP_POLICY:
																														if policy.lower() in spec:
																																				cmd_config.append(F'--fsdp_auto_wrap_policy={policy}'				)
																																				break

																								if policy == "TRANSFORMER_BASED_WRAP":
																														cmd_config.append("""--fsdp_transformer_layer_cls_to_wrap=BertLayer"""				)
																								elif policy == "SIZE_BASED_WRAP":
																														cmd_config.append("""--fsdp_min_num_params=2000"""				)

																		cmd_config.extend(
																		    [
																		        self.test_file_path,
																		        F'--output_dir={self.tmpdir}',
																		        F'--peak_memory_upper_bound={peak_mem_upper_bound}',
																		        F'--n_train={self.n_train}',
																		        F'--n_val={self.n_val}',
																		    ]				)
																		with patch_environment(omp_num_threads=1				):
																								execute_subprocess_async(_lowercase   ,       env=os.environ.copy()				)


'''simple docstring'''



import coval  # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator

import datasets


_lowercase    =		datasets.logging.get_logger(__name__)


_lowercase    =		"""\
@InProceedings{moosavi2019minimum,
    author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},
    title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},
    year = {2019},
    booktitle = {Proceedings of the 57th Annual Meeting of
        the Association for Computational Linguistics (Volume 1: Long Papers)},
    publisher = {Association for Computational Linguistics},
    address = {Florence, Italy},
}

@inproceedings{10.3115/1072399.1072405,
author = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},
title = {A Model-Theoretic Coreference Scoring Scheme},
year = {1995},
isbn = {1558604022},
publisher = {Association for Computational Linguistics},
address = {USA},
url = {https://doi.org/10.3115/1072399.1072405},
doi = {10.3115/1072399.1072405},
booktitle = {Proceedings of the 6th Conference on Message Understanding},
pages = {45–52},
numpages = {8},
location = {Columbia, Maryland},
series = {MUC6 ’95}
}

@INPROCEEDINGS{Bagga98algorithmsfor,
    author = {Amit Bagga and Breck Baldwin},
    title = {Algorithms for Scoring Coreference Chains},
    booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},
    year = {1998},
    pages = {563--566}
}

@INPROCEEDINGS{Luo05oncoreference,
    author = {Xiaoqiang Luo},
    title = {On coreference resolution performance metrics},
    booktitle = {In Proc. of HLT/EMNLP},
    year = {2005},
    pages = {25--32},
    publisher = {URL}
}

@inproceedings{moosavi-strube-2016-coreference,
    title = \"Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric\",
    author = \"Moosavi, Nafise Sadat  and
      Strube, Michael\",
    booktitle = \"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",
    month = aug,
    year = \"2016\",
    address = \"Berlin, Germany\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/P16-1060\",
    doi = \"10.18653/v1/P16-1060\",
    pages = \"632--642\",
}

"""

_lowercase    =		"""\
CoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which
implements of the common evaluation metrics including MUC [Vilain et al, 1995],
B-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],
LEA [Moosavi and Strube, 2016] and the averaged CoNLL score
(the average of the F1 values of MUC, B-cubed and CEAFe)
[Denis and Baldridge, 2009a; Pradhan et al., 2011].

This wrapper of CoVal currently only work with CoNLL line format:
The CoNLL format has one word per line with all the annotation for this word in column separated by spaces:
Column	Type	Description
1	Document ID	This is a variation on the document filename
2	Part number	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
3	Word number
4	Word itself	This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.
5	Part-of-Speech
6	Parse bit	This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the \"([pos] [word])\" string (or leaf) and concatenating the items in the rows of that column.
7	Predicate lemma	The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a \"-\"
8	Predicate Frameset ID	This is the PropBank frameset ID of the predicate in Column 7.
9	Word sense	This is the word sense of the word in Column 3.
10	Speaker/Author	This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.
11	Named Entities	These columns identifies the spans representing various named entities.
12:N	Predicate Arguments	There is one column each of predicate argument structure information for the predicate mentioned in Column 7.
N	Coreference	Coreference chain information encoded in a parenthesis structure.
More informations on the format can be found here (section \"*_conll File Format\"): http://www.conll.cemantix.org/2012/data.html

Details on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md

CoVal code was written by @ns-moosavi.
Some parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py
The test suite is taken from https://github.com/conll/reference-coreference-scorers/
Mention evaluation and the test suite are added by @andreasvc.
Parsing CoNLL files is developed by Leo Born.
"""

_lowercase    =		"""
Calculates coreference evaluation metrics.
Args:
    predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.
        Each prediction is a word with its annotations as a string made of columns joined with spaces.
        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
        See the details on the format in the description of the metric.
    references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.
        Each reference is a word with its annotations as a string made of columns joined with spaces.
        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
        See the details on the format in the description of the metric.
    keep_singletons: After extracting all mentions of key or system files,
        mentions whose corresponding coreference chain is of size one,
        are considered as singletons. The default evaluation mode will include
        singletons in evaluations if they are included in the key or the system files.
        By setting 'keep_singletons=False', all singletons in the key and system files
        will be excluded from the evaluation.
    NP_only: Most of the recent coreference resolvers only resolve NP mentions and
        leave out the resolution of VPs. By setting the 'NP_only' option, the scorer will only evaluate the resolution of NPs.
    min_span: By setting 'min_span', the scorer reports the results based on automatically detected minimum spans.
        Minimum spans are determined using the MINA algorithm.

Returns:
    'mentions': mentions
    'muc': MUC metric [Vilain et al, 1995]
    'bcub': B-cubed [Bagga and Baldwin, 1998]
    'ceafe': CEAFe [Luo et al., 2005]
    'lea': LEA [Moosavi and Strube, 2016]
    'conll_score': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)

Examples:

    >>> coval = datasets.load_metric('coval')
    >>> words = ['bc/cctv/00/cctv_0005   0   0       Thank   VBP  (TOP(S(VP*    thank  01   1    Xu_li  *           (V*)        *       -',
    ... 'bc/cctv/00/cctv_0005   0   1         you   PRP        (NP*)      -    -   -    Xu_li  *        (ARG1*)   (ARG0*)   (116)',
    ... 'bc/cctv/00/cctv_0005   0   2    everyone    NN        (NP*)      -    -   -    Xu_li  *    (ARGM-DIS*)        *    (116)',
    ... 'bc/cctv/00/cctv_0005   0   3         for    IN        (PP*       -    -   -    Xu_li  *        (ARG2*         *       -',
    ... 'bc/cctv/00/cctv_0005   0   4    watching   VBG   (S(VP*))))   watch  01   1    Xu_li  *             *)      (V*)      -',
    ... 'bc/cctv/00/cctv_0005   0   5           .     .          *))      -    -   -    Xu_li  *             *         *       -']
    >>> references = [words]
    >>> predictions = [words]
    >>> results = coval.compute(predictions=predictions, references=references)
    >>> print(results) # doctest:+ELLIPSIS
    {'mentions/recall': 1.0,[...] 'conll_score': 100.0}
"""


def A				(__lowerCamelCase				:str			,     __lowerCamelCase				:Optional[Any]			,     __lowerCamelCase				:Union[str, Any]=False			,     __lowerCamelCase				:List[Any]=False			,     __lowerCamelCase				:str=True			,     __lowerCamelCase				:str=False			,     __lowerCamelCase				:str="dummy_doc"		):
						_lowerCAmelCase  						=			{doc: key_lines}
						_lowerCAmelCase  						=			{doc: sys_lines}

						_lowerCAmelCase  						=			{}

						_lowerCAmelCase  						=			0
						_lowerCAmelCase  						=			0
						_lowerCAmelCase  						=			0
						_lowerCAmelCase  						=			0
						_lowerCAmelCase  						=			0
						_lowerCAmelCase  						=			0

						_lowerCAmelCase						,   _lowerCAmelCase  						=			reader.get_doc_mentions(__lowerCamelCase			,     key_doc_lines[doc]			,     __lowerCamelCase		)
						key_singletons_num += singletons_num

						if NP_only or min_span:
												_lowerCAmelCase  						=			reader.set_annotated_parse_trees(__lowerCamelCase			,     key_doc_lines[doc]			,     __lowerCamelCase			,     __lowerCamelCase		)

						_lowerCAmelCase						,   _lowerCAmelCase  						=			reader.get_doc_mentions(__lowerCamelCase			,     sys_doc_lines[doc]			,     __lowerCamelCase		)
						sys_singletons_num += singletons_num

						if NP_only or min_span:
												_lowerCAmelCase  						=			reader.set_annotated_parse_trees(__lowerCamelCase			,     key_doc_lines[doc]			,     __lowerCamelCase			,     __lowerCamelCase		)

						if remove_nested:
												_lowerCAmelCase						,   _lowerCAmelCase  						=			reader.remove_nested_coref_mentions(__lowerCamelCase			,     __lowerCamelCase		)
												key_nested_coref_num += nested_mentions
												key_removed_nested_clusters += removed_clusters

												_lowerCAmelCase						,   _lowerCAmelCase  						=			reader.remove_nested_coref_mentions(__lowerCamelCase			,     __lowerCamelCase		)
												sys_nested_coref_num += nested_mentions
												sys_removed_nested_clusters += removed_clusters

						_lowerCAmelCase  						=			reader.get_mention_assignments(__lowerCamelCase			,     __lowerCamelCase		)
						_lowerCAmelCase  						=			reader.get_mention_assignments(__lowerCamelCase			,     __lowerCamelCase		)

						_lowerCAmelCase  						=			(key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)

						if remove_nested:
												logger.info(
												    """Number of removed nested coreferring mentions in the key """
												    f'annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}'		)
												logger.info(
												    """Number of resulting singleton clusters in the key """
												    f'annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}'		)

						if not keep_singletons:
												logger.info(
												    f'{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system '
												    """files, respectively"""		)

						return doc_coref_infos


def A				(__lowerCamelCase				:List[str]			,     __lowerCamelCase				:str			,     __lowerCamelCase				:str			,     __lowerCamelCase				:int			,     __lowerCamelCase				:int			,     __lowerCamelCase				:Optional[Any]			,     __lowerCamelCase				:Optional[Any]		):
						_lowerCAmelCase  						=			get_coref_infos(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase		)

						_lowerCAmelCase  						=			{}
						_lowerCAmelCase  						=			0
						_lowerCAmelCase  						=			0

						for name, metric in metrics:
												_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			evaluator.evaluate_documents(__lowerCamelCase			,     __lowerCamelCase			,     beta=1		)
												if name in ["muc", "bcub", "ceafe"]:
																		conll += fa
																		conll_subparts_num += 1
												output_scores.update({f'{name}/recall': recall, f'{name}/precision': precision, f'{name}/f1': fa}		)

												logger.info(
												    name.ljust(10		)			,     f'Recall: {recall * 100:.2f}'			,     f' Precision: {precision * 100:.2f}'			,     f' F1: {fa * 100:.2f}'			,     )

						if conll_subparts_num == 3:
												_lowerCAmelCase  						=			(conll / 3) * 100
												logger.info(f'CoNLL score: {conll:.2f}'		)
												output_scores.update({"""conll_score""": conll}		)

						return output_scores


def A				(__lowerCamelCase				:List[str]		):
						_lowerCAmelCase  						=			False
						for line in key_lines:
												if not line.startswith("""#"""		):
																		if len(line.split()		) > 6:
																								_lowerCAmelCase  						=			line.split()[5]
																								if not parse_col == "-":
																														_lowerCAmelCase  						=			True
																														break
																								else:
																														break
						return has_gold_parse





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






						'''simple docstring'''






						def  _lowercase     ( self				):

												"""simple docstring"""



												return datasets.MetricInfo(
												    description=_DESCRIPTION   ,       citation=_CITATION   ,       inputs_description=_KWARGS_DESCRIPTION   ,       features=datasets.Features(
												        {
												            """predictions""": datasets.Sequence(datasets.Value("""string"""				)				),
												            """references""": datasets.Sequence(datasets.Value("""string"""				)				),
												        }				)   ,       codebase_urls=["""https://github.com/ns-moosavi/coval"""]   ,       reference_urls=[
												        """https://github.com/ns-moosavi/coval""",
												        """https://www.aclweb.org/anthology/P16-1060""",
												        """http://www.conll.cemantix.org/2012/data.html""",
												    ]   ,       )




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase=True   ,       _lowercase=False   ,       _lowercase=False   ,       _lowercase=False				):

												"""simple docstring"""



												_lowerCAmelCase  						=			[
												    ("""mentions""", evaluator.mentions),
												    ("""muc""", evaluator.muc),
												    ("""bcub""", evaluator.b_cubed),
												    ("""ceafe""", evaluator.ceafe),
												    ("""lea""", evaluator.lea),
												]

												if min_span:
																		_lowerCAmelCase  						=			util.check_gold_parse_annotation(_lowercase				)
																		if not has_gold_parse:
																								raise NotImplementedError("""References should have gold parse annotation to use 'min_span'."""				)
																								# util.parse_key_file(key_file)
																								# key_file = key_file + ".parsed"

												_lowerCAmelCase  						=			evaluate(
												    key_lines=_lowercase   ,       sys_lines=_lowercase   ,       metrics=_lowercase   ,       NP_only=_lowercase   ,       remove_nested=_lowercase   ,       keep_singletons=_lowercase   ,       min_span=_lowercase   ,       )

												return score
