"""simple docstring"""




from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
				from ...onnx.config import PatchingSpec
				from ...tokenization_utils_base import PreTrainedTokenizerBase


__lowerCamelCase							 =     logging.get_logger(__name__)

__lowerCamelCase							 =     {
    "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
    "allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
    "allenai/longformer-large-4096-finetuned-triviaqa": (
        "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
    ),
    "allenai/longformer-base-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
    "allenai/longformer-large-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
}
class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  Any  							=						'longformer'





						def __init__(				self						,__UpperCAmelCase = 5_12						,__UpperCAmelCase = 2						,__UpperCAmelCase = 1						,__UpperCAmelCase = 0						,__UpperCAmelCase = 2						,__UpperCAmelCase = 3_05_22						,__UpperCAmelCase = 7_68						,__UpperCAmelCase = 12						,__UpperCAmelCase = 12						,__UpperCAmelCase = 30_72						,__UpperCAmelCase = "gelu"						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 5_12						,__UpperCAmelCase = 2						,__UpperCAmelCase = 0.0_2						,__UpperCAmelCase = 1e-12						,__UpperCAmelCase = False						,**__UpperCAmelCase						,)    -> Optional[int]:
													super().__init__(pad_token_id=__UpperCAmelCase						,**__UpperCAmelCase   )

													A__		   =							attention_window
													A__		   =							sep_token_id
													A__		   =							bos_token_id
													A__		   =							eos_token_id
													A__		   =							vocab_size
													A__		   =							hidden_size
													A__		   =							num_hidden_layers
													A__		   =							num_attention_heads
													A__		   =							hidden_act
													A__		   =							intermediate_size
													A__		   =							hidden_dropout_prob
													A__		   =							attention_probs_dropout_prob
													A__		   =							max_position_embeddings
													A__		   =							type_vocab_size
													A__		   =							initializer_range
													A__		   =							layer_norm_eps
													A__		   =							onnx_export






class 			UpperCamelCase__(	__A       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase = "default"						,__UpperCAmelCase = None   )    -> int:
													super().__init__(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )
													A__		   =							True





						@property
						def  snake_case__		(				self   )    -> Mapping[str, Mapping[int, str]]:
													if self.task == "multiple-choice":
																				A__		   =							{0: 'batch', 1: 'choice', 2: 'sequence'}
													else:
																				A__		   =							{0: 'batch', 1: 'sequence'}
													return OrderedDict(
													    [
													        ('input_ids', dynamic_axis),
													        ('attention_mask', dynamic_axis),
													        ('global_attention_mask', dynamic_axis),
													    ]   )





						@property
						def  snake_case__		(				self   )    -> Mapping[str, Mapping[int, str]]:
													A__		   =							super().outputs
													if self.task == "default":
																				A__		   =							{0: 'batch'}
													return outputs





						@property
						def  snake_case__		(				self   )    -> float:
													return 1e-4





						@property
						def  snake_case__		(				self   )    -> int:
													# needs to be >= 14 to support tril operator
													return max(super().default_onnx_opset						,14   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = -1						,__UpperCAmelCase = -1						,__UpperCAmelCase = False						,__UpperCAmelCase = None						,)    -> Mapping[str, Any]:
													A__		   =							super().generate_dummy_inputs(
													    preprocessor=__UpperCAmelCase						,batch_size=__UpperCAmelCase						,seq_length=__UpperCAmelCase						,is_pair=__UpperCAmelCase						,framework=__UpperCAmelCase   )
													import torch

													# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
													# makes the export fail randomly
													A__		   =							torch.zeros_like(inputs['input_ids']   )
													# make every second token global
													A__		   =							1

													return inputs






"""simple docstring"""




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

import datasets


__lowerCamelCase							 =     "\\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"

__lowerCamelCase							 =     "\\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"

__lowerCamelCase							 =     "\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  snake_case__		(				self   )    -> Tuple:
													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  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = CHRF.CHAR_ORDER						,__UpperCAmelCase = CHRF.WORD_ORDER						,__UpperCAmelCase = CHRF.BETA						,__UpperCAmelCase = False						,__UpperCAmelCase = False						,__UpperCAmelCase = False						,)    -> Union[str, Any]:
													A__		   =							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'   )
													A__		   =							[[refs[i] for refs in references] for i in range(__UpperCAmelCase   )]

													A__		   =							CHRF(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )
													A__		   =							sb_chrf.corpus_score(__UpperCAmelCase						,__UpperCAmelCase   )

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





