


'''simple docstring'''



import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
						import torch

						from transformers import (
						    MraForMaskedLM,
						    MraForMultipleChoice,
						    MraForQuestionAnswering,
						    MraForSequenceClassification,
						    MraForTokenClassification,
						    MraModel,
						)
						from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST



class 			a       :


					"""simple docstring"""






					def __init__(       self  :	int							,				snake_case  :	int							,				snake_case  :	List[Any]=2							,				snake_case  :	List[str]=8							,				snake_case  :	str=True							,				snake_case  :	Tuple=True							,				snake_case  :	Any=True							,				snake_case  :	Any=True							,				snake_case  :	Optional[Any]=99							,				snake_case  :	Dict=16							,				snake_case  :	Union[str, Any]=5							,				snake_case  :	str=2							,				snake_case  :	int=36							,				snake_case  :	str="gelu"							,				snake_case  :	Optional[int]=0.0							,				snake_case  :	int=0.0							,				snake_case  :	Optional[int]=512							,				snake_case  :	Dict=16							,				snake_case  :	List[Any]=2							,				snake_case  :	Dict=0.02							,				snake_case  :	Any=3							,				snake_case  :	Dict=4							,				snake_case  :	Optional[int]=None							,				)		->							List[Any]:
										__UpperCAmelCase			:				List[str]													=    parent
										__UpperCAmelCase			:				int													=    batch_size
										__UpperCAmelCase			:				Union[str, Any]													=    seq_length
										__UpperCAmelCase			:				Optional[Any]													=    is_training
										__UpperCAmelCase			:				Union[str, Any]													=    use_input_mask
										__UpperCAmelCase			:				int													=    use_token_type_ids
										__UpperCAmelCase			:				Dict													=    use_labels
										__UpperCAmelCase			:				Optional[Any]													=    vocab_size
										__UpperCAmelCase			:				int													=    hidden_size
										__UpperCAmelCase			:				Dict													=    num_hidden_layers
										__UpperCAmelCase			:				Optional[int]													=    num_attention_heads
										__UpperCAmelCase			:				Tuple													=    intermediate_size
										__UpperCAmelCase			:				Dict													=    hidden_act
										__UpperCAmelCase			:				Any													=    hidden_dropout_prob
										__UpperCAmelCase			:				Optional[int]													=    attention_probs_dropout_prob
										__UpperCAmelCase			:				Tuple													=    max_position_embeddings
										__UpperCAmelCase			:				Tuple													=    type_vocab_size
										__UpperCAmelCase			:				List[Any]													=    type_sequence_label_size
										__UpperCAmelCase			:				Tuple													=    initializer_range
										__UpperCAmelCase			:				Any													=    num_labels
										__UpperCAmelCase			:				Union[str, Any]													=    num_choices
										__UpperCAmelCase			:				List[str]													=    scope

					def 				lowerCamelCase__				(       self  :	int    )		->							Optional[int]:
										__UpperCAmelCase			:				Any													=    ids_tensor([self.batch_size, self.seq_length]							,				self.vocab_size    )

										__UpperCAmelCase			:				List[Any]													=    None
										if self.use_input_mask:
															__UpperCAmelCase			:				Union[str, Any]													=    random_attention_mask([self.batch_size, self.seq_length]    )

										__UpperCAmelCase			:				str													=    None
										if self.use_token_type_ids:
															__UpperCAmelCase			:				List[str]													=    ids_tensor([self.batch_size, self.seq_length]							,				self.type_vocab_size    )

										__UpperCAmelCase			:				Optional[Any]													=    None
										__UpperCAmelCase			:				int													=    None
										__UpperCAmelCase			:				Dict													=    None
										if self.use_labels:
															__UpperCAmelCase			:				Union[str, Any]													=    ids_tensor([self.batch_size]							,				self.type_sequence_label_size    )
															__UpperCAmelCase			:				List[str]													=    ids_tensor([self.batch_size, self.seq_length]							,				self.num_labels    )
															__UpperCAmelCase			:				Dict													=    ids_tensor([self.batch_size]							,				self.num_choices    )

										__UpperCAmelCase			:				str													=    self.get_config()

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

					def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							List[Any]:
										return MraConfig(
										    vocab_size=self.vocab_size							,				hidden_size=self.hidden_size							,				num_hidden_layers=self.num_hidden_layers							,				num_attention_heads=self.num_attention_heads							,				intermediate_size=self.intermediate_size							,				hidden_act=self.hidden_act							,				hidden_dropout_prob=self.hidden_dropout_prob							,				attention_probs_dropout_prob=self.attention_probs_dropout_prob							,				max_position_embeddings=self.max_position_embeddings							,				type_vocab_size=self.type_vocab_size							,				is_decoder=_a							,				initializer_range=self.initializer_range							,				)

					def 				lowerCamelCase__				(       self  :	List[Any]    )		->							Union[str, Any]:
										__UpperCAmelCase			:				Union[str, Any]													=    self.get_config()
										__UpperCAmelCase			:				Union[str, Any]													=    300
										return config

					def 				lowerCamelCase__				(       self  :	str    )		->							List[str]:
										(
										    (
										    __UpperCAmelCase  
										)							,     (
										    __UpperCAmelCase  
										)							,     (
										    __UpperCAmelCase  
										)							,     (
										    __UpperCAmelCase  
										)							,     (
										    __UpperCAmelCase  
										)							,     (
										    __UpperCAmelCase  
										)							,     (
										    __UpperCAmelCase  
										)							,       
										)			:				Union[str, Any]													=    self.prepare_config_and_inputs()

										__UpperCAmelCase			:				Optional[Any]													=    True
										__UpperCAmelCase			:				int													=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
										__UpperCAmelCase			:				Any													=    ids_tensor([self.batch_size, self.seq_length]							,				vocab_size=2    )

										return (
										    config,
										    input_ids,
										    token_type_ids,
										    input_mask,
										    sequence_labels,
										    token_labels,
										    choice_labels,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)

					def 				lowerCamelCase__				(       self  :	List[str]							,				snake_case  :	Any							,				snake_case  :	Tuple							,				snake_case  :	Optional[Any]							,				snake_case  :	List[Any]							,				snake_case  :	str							,				snake_case  :	Optional[int]							,				snake_case  :	Optional[Any]    )		->							List[Any]:
										__UpperCAmelCase			:				Optional[int]													=    MraModel(config=_a    )
										model.to(_a    )
										model.eval()
										__UpperCAmelCase			:				Tuple													=    model(_a							,				attention_mask=_a							,				token_type_ids=_a    )
										__UpperCAmelCase			:				Tuple													=    model(_a							,				token_type_ids=_a    )
										__UpperCAmelCase			:				Optional[int]													=    model(_a    )
										self.parent.assertEqual(result.last_hidden_state.shape							,				(self.batch_size, self.seq_length, self.hidden_size)    )

					def 				lowerCamelCase__				(       self  :	Tuple							,				snake_case  :	str							,				snake_case  :	Optional[int]							,				snake_case  :	Any							,				snake_case  :	Any							,				snake_case  :	Optional[Any]							,				snake_case  :	Optional[Any]							,				snake_case  :	Optional[Any]							,				snake_case  :	List[Any]							,				snake_case  :	int							,				)		->							Tuple:
										__UpperCAmelCase			:				List[Any]													=    True
										__UpperCAmelCase			:				List[str]													=    MraModel(_a    )
										model.to(_a    )
										model.eval()
										__UpperCAmelCase			:				Dict													=    model(
										    _a							,				attention_mask=_a							,				token_type_ids=_a							,				encoder_hidden_states=_a							,				encoder_attention_mask=_a							,				)
										__UpperCAmelCase			:				List[Any]													=    model(
										    _a							,				attention_mask=_a							,				token_type_ids=_a							,				encoder_hidden_states=_a							,				)
										__UpperCAmelCase			:				Optional[Any]													=    model(_a							,				attention_mask=_a							,				token_type_ids=_a    )
										self.parent.assertEqual(result.last_hidden_state.shape							,				(self.batch_size, self.seq_length, self.hidden_size)    )

					def 				lowerCamelCase__				(       self  :	Dict							,				snake_case  :	int							,				snake_case  :	List[Any]							,				snake_case  :	Tuple							,				snake_case  :	Tuple							,				snake_case  :	Tuple							,				snake_case  :	str							,				snake_case  :	Dict    )		->							Optional[int]:
										__UpperCAmelCase			:				Dict													=    MraForMaskedLM(config=_a    )
										model.to(_a    )
										model.eval()
										__UpperCAmelCase			:				Union[str, Any]													=    model(_a							,				attention_mask=_a							,				token_type_ids=_a							,				labels=_a    )
										self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.seq_length, self.vocab_size)    )

					def 				lowerCamelCase__				(       self  :	str							,				snake_case  :	Tuple							,				snake_case  :	Tuple							,				snake_case  :	Optional[Any]							,				snake_case  :	Union[str, Any]							,				snake_case  :	int							,				snake_case  :	List[Any]							,				snake_case  :	str    )		->							Dict:
										__UpperCAmelCase			:				List[Any]													=    MraForQuestionAnswering(config=_a    )
										model.to(_a    )
										model.eval()
										__UpperCAmelCase			:				Optional[Any]													=    model(
										    _a							,				attention_mask=_a							,				token_type_ids=_a							,				start_positions=_a							,				end_positions=_a							,				)
										self.parent.assertEqual(result.start_logits.shape							,				(self.batch_size, self.seq_length)    )
										self.parent.assertEqual(result.end_logits.shape							,				(self.batch_size, self.seq_length)    )

					def 				lowerCamelCase__				(       self  :	Dict							,				snake_case  :	Dict							,				snake_case  :	List[str]							,				snake_case  :	str							,				snake_case  :	str							,				snake_case  :	Dict							,				snake_case  :	Optional[Any]							,				snake_case  :	Dict    )		->							Optional[int]:
										__UpperCAmelCase			:				Dict													=    self.num_labels
										__UpperCAmelCase			:				Tuple													=    MraForSequenceClassification(_a    )
										model.to(_a    )
										model.eval()
										__UpperCAmelCase			:				Optional[Any]													=    model(_a							,				attention_mask=_a							,				token_type_ids=_a							,				labels=_a    )
										self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.num_labels)    )

					def 				lowerCamelCase__				(       self  :	Tuple							,				snake_case  :	Any							,				snake_case  :	str							,				snake_case  :	str							,				snake_case  :	Dict							,				snake_case  :	List[Any]							,				snake_case  :	Optional[int]							,				snake_case  :	Optional[Any]    )		->							Dict:
										__UpperCAmelCase			:				Optional[int]													=    self.num_labels
										__UpperCAmelCase			:				List[Any]													=    MraForTokenClassification(config=_a    )
										model.to(_a    )
										model.eval()
										__UpperCAmelCase			:				int													=    model(_a							,				attention_mask=_a							,				token_type_ids=_a							,				labels=_a    )
										self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.seq_length, self.num_labels)    )

					def 				lowerCamelCase__				(       self  :	Any							,				snake_case  :	Tuple							,				snake_case  :	List[str]							,				snake_case  :	List[str]							,				snake_case  :	Optional[Any]							,				snake_case  :	int							,				snake_case  :	str							,				snake_case  :	Tuple    )		->							Union[str, Any]:
										__UpperCAmelCase			:				Tuple													=    self.num_choices
										__UpperCAmelCase			:				str													=    MraForMultipleChoice(config=_a    )
										model.to(_a    )
										model.eval()
										__UpperCAmelCase			:				List[str]													=    input_ids.unsqueeze(1    ).expand(-1							,				self.num_choices							,				-1    ).contiguous()
										__UpperCAmelCase			:				Any													=    token_type_ids.unsqueeze(1    ).expand(-1							,				self.num_choices							,				-1    ).contiguous()
										__UpperCAmelCase			:				List[Any]													=    input_mask.unsqueeze(1    ).expand(-1							,				self.num_choices							,				-1    ).contiguous()
										__UpperCAmelCase			:				Tuple													=    model(
										    _a							,				attention_mask=_a							,				token_type_ids=_a							,				labels=_a							,				)
										self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.num_choices)    )






					def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							str:
										__UpperCAmelCase			:				List[str]													=    self.prepare_config_and_inputs()
										(
										    (
										    __UpperCAmelCase  
										)							,     (
										    __UpperCAmelCase  
										)							,     (
										    __UpperCAmelCase  
										)							,     (
										    __UpperCAmelCase  
										)							,     (
										    __UpperCAmelCase  
										)							,     (
										    __UpperCAmelCase  
										)							,     (
										    __UpperCAmelCase  
										)							,       
										)			:				Union[str, Any]													=    config_and_inputs
										__UpperCAmelCase			:				Union[str, Any]													=    {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
										return config, inputs_dict



@require_torch
class 			a       (	__SCREAMING_SNAKE_CASE			,						unittest.TestCase ):


					"""simple docstring"""






					SCREAMING_SNAKE_CASE :				Optional[Any]							     =     (
					    (
					        MraModel,
					        MraForMaskedLM,
					        MraForMultipleChoice,
					        MraForQuestionAnswering,
					        MraForSequenceClassification,
					        MraForTokenClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					SCREAMING_SNAKE_CASE :				str							     =     False
					SCREAMING_SNAKE_CASE :				Any							     =     False
					SCREAMING_SNAKE_CASE :				List[Any]							     =     False
					SCREAMING_SNAKE_CASE :				Optional[Any]							     =     False

					SCREAMING_SNAKE_CASE :				Dict							     =     ()

					def 				lowerCamelCase__				(       self  :	Tuple    )		->							List[str]:
										__UpperCAmelCase			:				Union[str, Any]													=    MraModelTester(self    )
										__UpperCAmelCase			:				Tuple													=    ConfigTester(self							,				config_class=_a							,				hidden_size=37    )

					def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							str:
										self.config_tester.run_common_tests()

					def 				lowerCamelCase__				(       self  :	Dict    )		->							Optional[int]:
										__UpperCAmelCase			:				Any													=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_a    )

					def 				lowerCamelCase__				(       self  :	List[str]    )		->							List[str]:
										__UpperCAmelCase			:				Union[str, Any]													=    self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
															__UpperCAmelCase			:				List[str]													=    type
															self.model_tester.create_and_check_model(*_a    )

					def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							Dict:
										__UpperCAmelCase			:				List[str]													=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*_a    )

					def 				lowerCamelCase__				(       self  :	Dict    )		->							int:
										__UpperCAmelCase			:				List[str]													=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*_a    )

					def 				lowerCamelCase__				(       self  :	List[str]    )		->							int:
										__UpperCAmelCase			:				Dict													=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*_a    )

					def 				lowerCamelCase__				(       self  :	int    )		->							Optional[int]:
										__UpperCAmelCase			:				str													=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*_a    )

					def 				lowerCamelCase__				(       self  :	Tuple    )		->							List[str]:
										__UpperCAmelCase			:				List[Any]													=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*_a    )

					@slow
					def 				lowerCamelCase__				(       self  :	List[Any]    )		->							List[Any]:
										for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															__UpperCAmelCase			:				List[str]													=    MraModel.from_pretrained(_a    )
															self.assertIsNotNone(_a    )






					@unittest.skip(reason='''MRA does not output attentions'''    )
					def 				lowerCamelCase__				(       self  :	List[Any]    )		->							int:
										return



@require_torch
class 			a       (	unittest.TestCase ):


					"""simple docstring"""






					@slow
					def 				lowerCamelCase__				(       self  :	List[Any]    )		->							List[Any]:
										__UpperCAmelCase			:				Dict													=    MraModel.from_pretrained('''uw-madison/mra-base-512-4'''    )
										__UpperCAmelCase			:				Optional[int]													=    torch.arange(256    ).unsqueeze(0    )

										with torch.no_grad():
															__UpperCAmelCase			:				Union[str, Any]													=    model(_a    )[0]

										__UpperCAmelCase			:				Dict													=    torch.Size((1, 256, 768)    )
										self.assertEqual(output.shape							,				_a    )

										__UpperCAmelCase			:				Any													=    torch.tensor(
										    [[[-0.0_140, 0.0_830, -0.0_381], [0.1_546, 0.1_402, 0.0_220], [0.1_162, 0.0_851, 0.0_165]]]    )

										self.assertTrue(torch.allclose(output[:, :3, :3]							,				_a							,				atol=1E-4    )    )

					@slow
					def 				lowerCamelCase__				(       self  :	List[Any]    )		->							Any:
										__UpperCAmelCase			:				Union[str, Any]													=    MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4'''    )
										__UpperCAmelCase			:				Tuple													=    torch.arange(256    ).unsqueeze(0    )

										with torch.no_grad():
															__UpperCAmelCase			:				Optional[int]													=    model(_a    )[0]

										__UpperCAmelCase			:				Optional[Any]													=    5_0265

										__UpperCAmelCase			:				Optional[int]													=    torch.Size((1, 256, vocab_size)    )
										self.assertEqual(output.shape							,				_a    )

										__UpperCAmelCase			:				Union[str, Any]													=    torch.tensor(
										    [[[9.2_595, -3.6_038, 11.8_819], [9.3_869, -3.2_693, 11.0_956], [11.8_524, -3.4_938, 13.1_210]]]    )

										self.assertTrue(torch.allclose(output[:, :3, :3]							,				_a							,				atol=1E-4    )    )






					@slow
					def 				lowerCamelCase__				(       self  :	Any    )		->							Any:
										__UpperCAmelCase			:				Union[str, Any]													=    MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3'''    )
										__UpperCAmelCase			:				List[str]													=    torch.arange(4096    ).unsqueeze(0    )

										with torch.no_grad():
															__UpperCAmelCase			:				List[str]													=    model(_a    )[0]

										__UpperCAmelCase			:				Optional[int]													=    5_0265

										__UpperCAmelCase			:				int													=    torch.Size((1, 4096, vocab_size)    )
										self.assertEqual(output.shape							,				_a    )

										__UpperCAmelCase			:				List[str]													=    torch.tensor(
										    [[[5.4_789, -2.3_564, 7.5_064], [7.9_067, -1.3_369, 9.9_668], [9.0_712, -1.8_106, 7.0_380]]]    )

										self.assertTrue(torch.allclose(output[:, :3, :3]							,				_a							,				atol=1E-4    )    )



'''simple docstring'''



import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


__UpperCAmelCase    :str				       =     "\\n@inproceedings{popovic-2015-chrf,\n    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",\n    month = sep,\n    year = \"2015\",\n    address = \"Lisbon, Portugal\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W15-3049\",\n    doi = \"10.18653/v1/W15-3049\",\n    pages = \"392--395\",\n}\n@inproceedings{popovic-2017-chrf,\n    title = \"chr{F}++: words helping character n-grams\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Second Conference on Machine Translation\",\n    month = sep,\n    year = \"2017\",\n    address = \"Copenhagen, Denmark\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W17-4770\",\n    doi = \"10.18653/v1/W17-4770\",\n    pages = \"612--618\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

__UpperCAmelCase    :Tuple				       =     "\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n"

__UpperCAmelCase    :List[Any]				       =     "\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    'score' (float): The chrF (chrF++) score,\n    'char_order' (int): The character n-gram order,\n    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    'beta' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}\n"



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


     """simple docstring"""






     def 				lowerCamelCase__				(       self  :	Dict    )		->							str:
          if version.parse(scb.__version__    ) < version.parse('''1.4.12'''    ):
               raise ImportWarning(
                   '''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
                   '''You can install it with `pip install "sacrebleu>=1.4.12"`.'''    )
          return datasets.MetricInfo(
              description=_DESCRIPTION							,				citation=_CITATION							,				homepage='''https://github.com/mjpost/sacreBLEU#chrf--chrf'''							,				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/mjpost/sacreBLEU#chrf--chrf''']							,				reference_urls=[
                  '''https://github.com/m-popovic/chrF''',
              ]							,				)






     def 				lowerCamelCase__				(       self  :	Any							,				snake_case  :	str							,				snake_case  :	int							,				snake_case  :	int = CHRF.CHAR_ORDER							,				snake_case  :	int = CHRF.WORD_ORDER							,				snake_case  :	int = CHRF.BETA							,				snake_case  :	bool = False							,				snake_case  :	bool = False							,				snake_case  :	bool = False							,				)		->							Optional[int]:
          __UpperCAmelCase			:				Any													=    len(references[0]    )
          if any(len(snake_case    ) != references_per_prediction for refs in references    ):
               raise ValueError('''Sacrebleu requires the same number of references for each prediction'''    )
          __UpperCAmelCase			:				int													=    [[refs[i] for refs in references] for i in range(snake_case    )]

          __UpperCAmelCase			:				int													=    CHRF(snake_case							,				snake_case							,				snake_case							,				snake_case							,				snake_case							,				snake_case    )
          __UpperCAmelCase			:				Dict													=    sb_chrf.corpus_score(snake_case							,				snake_case    )

          return {
              "score": output.score,
              "char_order": output.char_order,
              "word_order": output.word_order,
              "beta": output.beta,
          }