


import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
		    MODEL_MAPPING,
		    LevitForImageClassification,
		    LevitForImageClassificationWithTeacher,
		    LevitModel,
		)
		from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import LevitImageProcessor


class        lowerCAmelCase_			(							__a):




						def 			_snake_case       (						self    : int    )   ->Any:






									"""simple docstring"""






									a__			:Dict    	=     self.config_class(**self.inputs_dict    )
									self.parent.assertTrue(hasattr(__UpperCamelCase ,  "hidden_sizes"    )    )
									self.parent.assertTrue(hasattr(__UpperCamelCase ,  "num_attention_heads"    )    )






class        lowerCAmelCase_			:




						def __init__(						self    : str ,  __A    : Dict ,  __A    : List[Any]=13 ,  __A    : int=64 ,  __A    : List[Any]=3 ,  __A    : Tuple=3 ,  __A    : List[str]=2 ,  __A    : str=1 ,  __A    : List[Any]=16 ,  __A    : Any=[128, 256, 384] ,  __A    : str=[4, 6, 8] ,  __A    : Any=[2, 3, 4] ,  __A    : Dict=[16, 16, 16] ,  __A    : int=0 ,  __A    : Union[str, Any]=[2, 2, 2] ,  __A    : List[Any]=[2, 2, 2] ,  __A    : Tuple=0.02 ,  __A    : str=True ,  __A    : Dict=True ,  __A    : Tuple=2 ,  )   ->Optional[int]:






									"""simple docstring"""






									a__			:Tuple    	=     parent
									a__			:Optional[Any]    	=     batch_size
									a__			:Dict    	=     image_size
									a__			:Dict    	=     num_channels
									a__			:Optional[Any]    	=     kernel_size
									a__			:int    	=     stride
									a__			:str    	=     padding
									a__			:Tuple    	=     hidden_sizes
									a__			:int    	=     num_attention_heads
									a__			:List[str]    	=     depths
									a__			:Dict    	=     key_dim
									a__			:Any    	=     drop_path_rate
									a__			:List[Any]    	=     patch_size
									a__			:Any    	=     attention_ratio
									a__			:Optional[Any]    	=     mlp_ratio
									a__			:Optional[int]    	=     initializer_range
									a__			:Optional[Any]    	=     [
									    ["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
									    ["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
									]
									a__			:Tuple    	=     is_training
									a__			:Any    	=     use_labels
									a__			:Dict    	=     num_labels
									a__			:List[str]    	=     initializer_range




						def 			_snake_case       (						self    : List[Any]    )   ->Dict:






									"""simple docstring"""






									a__			:Optional[Any]    	=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

									a__			:List[str]    	=     None
									if self.use_labels:
												a__			:List[str]    	=     ids_tensor([self.batch_size] ,  self.num_labels    )

									a__			:Any    	=     self.get_config()
									return config, pixel_values, labels




						def 			_snake_case       (						self    : int    )   ->Optional[int]:






									"""simple docstring"""






									return LevitConfig(
									    image_size=self.image_size ,  num_channels=self.num_channels ,  kernel_size=self.kernel_size ,  stride=self.stride ,  padding=self.padding ,  patch_size=self.patch_size ,  hidden_sizes=self.hidden_sizes ,  num_attention_heads=self.num_attention_heads ,  depths=self.depths ,  key_dim=self.key_dim ,  drop_path_rate=self.drop_path_rate ,  mlp_ratio=self.mlp_ratio ,  attention_ratio=self.attention_ratio ,  initializer_range=self.initializer_range ,  down_ops=self.down_ops ,  )




						def 			_snake_case       (						self    : Any ,  __A    : str ,  __A    : str ,  __A    : List[Any]    )   ->List[Any]:






									"""simple docstring"""






									a__			:int    	=     LevitModel(config=__UpperCamelCase    )
									model.to(__UpperCamelCase    )
									model.eval()
									a__			:List[Any]    	=     model(__UpperCamelCase    )
									a__			:int    	=     (self.image_size, self.image_size)
									a__			:Optional[int]    	=     image_size[0], image_size[1]
									for _ in range(4    ):
												a__			:Union[str, Any]    	=     floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1    )
												a__			:List[Any]    	=     floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1    )
									self.parent.assertEqual(
									    result.last_hidden_state.shape ,  (self.batch_size, ceil(height / 4    ) * ceil(width / 4    ), self.hidden_sizes[-1]) ,  )




						def 			_snake_case       (						self    : Optional[int] ,  __A    : Optional[int] ,  __A    : Union[str, Any] ,  __A    : Optional[int]    )   ->Any:






									"""simple docstring"""






									a__			:List[str]    	=     self.num_labels
									a__			:Any    	=     LevitForImageClassification(__UpperCamelCase    )
									model.to(__UpperCamelCase    )
									model.eval()
									a__			:int    	=     model(__UpperCamelCase ,  labels=__UpperCamelCase    )
									self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_labels)    )






						def 			_snake_case       (						self    : int    )   ->str:






									"""simple docstring"""






									a__			:Tuple    	=     self.prepare_config_and_inputs()
									a__			:Any    	=     config_and_inputs
									a__			:Tuple    	=     {"""pixel_values""": pixel_values}
									return config, inputs_dict






@require_torch
class        lowerCAmelCase_			(							__a      ,__a      ,unittest.TestCase):
						lowerCamelCase_    =  (
						    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
						    if is_torch_available()
						    else ()
						)
						lowerCamelCase_    =  (
						    {
						        '''feature-extraction''': LevitModel,
						        '''image-classification''': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
						    }
						    if is_torch_available()
						    else {}
						)

						lowerCamelCase_    =  False
						lowerCamelCase_    =  False
						lowerCamelCase_    =  False
						lowerCamelCase_    =  False
						lowerCamelCase_    =  False




						def 			_snake_case       (						self    : Any    )   ->List[Any]:






									"""simple docstring"""






									a__			:int    	=     LevitModelTester(self    )
									a__			:Union[str, Any]    	=     ConfigTester(self ,  config_class=__UpperCamelCase ,  has_text_modality=__UpperCamelCase ,  hidden_size=37    )




						def 			_snake_case       (						self    : List[Any]    )   ->Dict:






									"""simple docstring"""






									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()




						def 			_snake_case       (						self    : str    )   ->Optional[Any]:






									"""simple docstring"""






									return




						@unittest.skip(reason="Levit does not use inputs_embeds"    )
						def 			_snake_case       (						self    : List[Any]    )   ->int:






									"""simple docstring"""






									pass




						@unittest.skip(reason="Levit does not support input and output embeddings"    )
						def 			_snake_case       (						self    : List[Any]    )   ->Any:






									"""simple docstring"""






									pass




						@unittest.skip(reason="Levit does not output attentions"    )
						def 			_snake_case       (						self    : Any    )   ->List[str]:






									"""simple docstring"""






									pass




						def 			_snake_case       (						self    : Any    )   ->Union[str, Any]:






									"""simple docstring"""






									a__			:Any    	=     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												a__			:str    	=     model_class(__UpperCamelCase    )
												a__			:Any    	=     inspect.signature(model.forward    )
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												a__			:Dict    	=     [*signature.parameters.keys()]

												a__			:Tuple    	=     ["""pixel_values"""]
												self.assertListEqual(arg_names[:1] ,  __UpperCamelCase    )




						def 			_snake_case       (						self    : Any    )   ->Optional[Any]:






									"""simple docstring"""






									def check_hidden_states_output(__A    : Any ,  __A    : Optional[Any] ,  __A    : Any    ):
												a__			:List[str]    	=     model_class(__UpperCamelCase    )
												model.to(__UpperCamelCase    )
												model.eval()

												with torch.no_grad():
															a__			:Union[str, Any]    	=     model(**self._prepare_for_class(__UpperCamelCase ,  __UpperCamelCase    )    )

												a__			:Optional[Any]    	=     outputs.hidden_states

												a__			:Optional[int]    	=     len(self.model_tester.depths    ) + 1
												self.assertEqual(len(__UpperCamelCase    ) ,  __UpperCamelCase    )

												a__			:Tuple    	=     (self.model_tester.image_size, self.model_tester.image_size)
												a__			:Optional[int]    	=     image_size[0], image_size[1]
												for _ in range(4    ):
															a__			:Dict    	=     floor(
															    (
															        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
															        / self.model_tester.stride
															    )
															    + 1    )
															a__			:List[str]    	=     floor(
															    (
															        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
															        / self.model_tester.stride
															    )
															    + 1    )
												# verify the first hidden states (first block)
												self.assertListEqual(
												    list(hidden_states[0].shape[-2:]    ) ,  [
												        height * width,
												        self.model_tester.hidden_sizes[0],
												    ] ,  )

									a__			:Optional[int]    	=     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												a__			:Union[str, Any]    	=     True
												check_hidden_states_output(__UpperCamelCase ,  __UpperCamelCase ,  __UpperCamelCase    )

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												a__			:List[str]    	=     True

												check_hidden_states_output(__UpperCamelCase ,  __UpperCamelCase ,  __UpperCamelCase    )




						@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."    )
						def 			_snake_case       (						self    : int    )   ->Dict:






									"""simple docstring"""






									pass




						def 			_snake_case       (						self    : Union[str, Any] ,  __A    : List[str] ,  __A    : Tuple ,  __A    : Any=False    )   ->Tuple:






									"""simple docstring"""






									a__			:List[str]    	=     super()._prepare_for_class(__UpperCamelCase ,  __UpperCamelCase ,  return_labels=__UpperCamelCase    )

									if return_labels:
												if model_class.__name__ == "LevitForImageClassificationWithTeacher":
															del inputs_dict["labels"]

									return inputs_dict




						def 			_snake_case       (						self    : Optional[Any]    )   ->Tuple:






									"""simple docstring"""






									a__			:Tuple    	=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__UpperCamelCase    )




						def 			_snake_case       (						self    : List[str]    )   ->List[Any]:






									"""simple docstring"""






									a__			:int    	=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase    )




						def 			_snake_case       (						self    : Dict    )   ->Optional[Any]:






									"""simple docstring"""






									if not self.model_tester.is_training:
												return

									a__			:List[str]    	=     self.model_tester.prepare_config_and_inputs_for_common()
									a__			:List[Any]    	=     True

									for model_class in self.all_model_classes:
												# LevitForImageClassificationWithTeacher supports inference-only
												if (
												    model_class in get_values(__UpperCamelCase    )
												    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
												):
															continue
												a__			:int    	=     model_class(__UpperCamelCase    )
												model.to(__UpperCamelCase    )
												model.train()
												a__			:List[str]    	=     self._prepare_for_class(__UpperCamelCase ,  __UpperCamelCase ,  return_labels=__UpperCamelCase    )
												a__			:int    	=     model(**__UpperCamelCase    ).loss
												loss.backward()




						def 			_snake_case       (						self    : Optional[int]    )   ->Union[str, Any]:






									"""simple docstring"""






									a__			:Optional[int]    	=     self.model_tester.prepare_config_and_inputs_for_common()
									if not self.model_tester.is_training:
												return

									a__			:Tuple    	=     False
									a__			:str    	=     True

									for model_class in self.all_model_classes:
												if model_class in get_values(__UpperCamelCase    ) or not model_class.supports_gradient_checkpointing:
															continue
												# LevitForImageClassificationWithTeacher supports inference-only
												if model_class.__name__ == "LevitForImageClassificationWithTeacher":
															continue
												a__			:str    	=     model_class(__UpperCamelCase    )
												model.gradient_checkpointing_enable()
												model.to(__UpperCamelCase    )
												model.train()
												a__			:Optional[Any]    	=     self._prepare_for_class(__UpperCamelCase ,  __UpperCamelCase ,  return_labels=__UpperCamelCase    )
												a__			:Optional[Any]    	=     model(**__UpperCamelCase    ).loss
												loss.backward()




						def 			_snake_case       (						self    : Union[str, Any]    )   ->Union[str, Any]:






									"""simple docstring"""






									a__			:Optional[Any]    	=     self.model_tester.prepare_config_and_inputs_for_common()

									a__			:Any    	=     [
									    {"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
									    {"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
									    {"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
									]

									for model_class in self.all_model_classes:
												if (
												    model_class
												    not in [
												        *get_values(__UpperCamelCase    ),
												    ]
												    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
												):
															continue

												for problem_type in problem_types:
															with self.subTest(msg=F'''Testing {model_class} with {problem_type['title']}'''    ):
																		a__			:Any    	=     problem_type["""title"""]
																		a__			:Dict    	=     problem_type["""num_labels"""]

																		a__			:Dict    	=     model_class(__UpperCamelCase    )
																		model.to(__UpperCamelCase    )
																		model.train()

																		a__			:Optional[int]    	=     self._prepare_for_class(__UpperCamelCase ,  __UpperCamelCase ,  return_labels=__UpperCamelCase    )

																		if problem_type["num_labels"] > 1:
																					a__			:Union[str, Any]    	=     inputs["""labels"""].unsqueeze(1    ).repeat(1 ,  problem_type["num_labels"]    )

																		a__			:Tuple    	=     inputs["""labels"""].to(problem_type["dtype"]    )

																		# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																		# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																		# they have the same size." which is a symptom something in wrong for the regression problem.
																		# See https://github.com/huggingface/transformers/issues/11780
																		with warnings.catch_warnings(record=__UpperCamelCase    ) as warning_list:
																					a__			:str    	=     model(**__UpperCamelCase    ).loss
																		for w in warning_list:
																					if "Using a target size that is different to the input size" in str(w.message    ):
																								raise ValueError(
																								    F'''Something is going wrong in the regression problem: intercepted {w.message}'''    )

																		loss.backward()






						@slow
						def 			_snake_case       (						self    : Any    )   ->Dict:






									"""simple docstring"""






									for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												a__			:Any    	=     LevitModel.from_pretrained(__UpperCamelCase    )
												self.assertIsNotNone(__UpperCamelCase    )






def 					lowerCamelCase__						(   )     ->						str:





			"""simple docstring"""
			a__			:str    	=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
			return image







@require_torch
@require_vision
class        lowerCAmelCase_			(							unittest.TestCase):




						@cached_property
						def 			_snake_case       (						self    : List[str]    )   ->Any:






									"""simple docstring"""






									return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )






						@slow
						def 			_snake_case       (						self    : int    )   ->str:






									"""simple docstring"""






									a__			:Tuple    	=     LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(
									    __UpperCamelCase    )

									a__			:Optional[Any]    	=     self.default_image_processor
									a__			:List[str]    	=     prepare_img()
									a__			:Optional[int]    	=     image_processor(images=__UpperCamelCase ,  return_tensors="pt"    ).to(__UpperCamelCase    )

									# forward pass
									with torch.no_grad():
												a__			:Union[str, Any]    	=     model(**__UpperCamelCase    )

									# verify the logits
									a__			:List[str]    	=     torch.Size((1, 1000)    )
									self.assertEqual(outputs.logits.shape ,  __UpperCamelCase    )

									a__			:Any    	=     torch.tensor([1.0_448, -0.3_745, -1.8_317]    ).to(__UpperCamelCase    )

									self.assertTrue(torch.allclose(outputs.logits[0, :3] ,  __UpperCamelCase ,  atol=1E-4    )    )



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

import datasets


SCREAMING_SNAKE_CASE		:     Tuple         =	"\\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"

SCREAMING_SNAKE_CASE		:     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"

SCREAMING_SNAKE_CASE		:     Optional[int]         =	"\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        UpperCamelCase						(				datasets.Metric				):





						def        A_							(self						)				->				Dict:
												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        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase = CHRF.CHAR_ORDER			,     __UpperCamelCase = CHRF.WORD_ORDER			,     __UpperCamelCase = CHRF.BETA			,     __UpperCamelCase = False			,     __UpperCamelCase = False			,     __UpperCamelCase = False			,     )				->				List[Any]:
												UpperCamelCase_		:							List[str]  =		len(references[0]						)
												if any(len(__UpperCamelCase						) != references_per_prediction for refs in references						):
																		raise ValueError("""Sacrebleu requires the same number of references for each prediction"""						)
												UpperCamelCase_		:							Optional[Any]  =		[[refs[i] for refs in references] for i in range(__UpperCamelCase						)]

												UpperCamelCase_		:							List[Any]  =		CHRF(__UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase						)
												UpperCamelCase_		:							int  =		sb_chrf.corpus_score(__UpperCamelCase			,     __UpperCamelCase						)

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






