





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

import datasets


__UpperCAmelCase       :   Any				       =							"\\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       :   List[Any]				       =							"\\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       :   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     __snake_case       (							datasets.Metric			):





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    int		):
         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 							UpperCAmelCase__ (  self :    Optional[int]							,				A :    int							,				A :    Any							,				A :    int = CHRF.CHAR_ORDER							,				A :    int = CHRF.WORD_ORDER							,				A :    int = CHRF.BETA							,				A :    bool = False							,				A :    bool = False							,				A :    bool = False							,				):
         __snake_case:						Any   =						len(references[0]		)
         if any(len(A		) != references_per_prediction for refs in references		):
           raise ValueError("""Sacrebleu requires the same number of references for each prediction"""		)
         __snake_case:						Any   =						[[refs[i] for refs in references] for i in range(A		)]

         __snake_case:						int   =						CHRF(A							,				A							,				A							,				A							,				A							,				A		)
         __snake_case:						List[Any]   =						sb_chrf.corpus_score(A							,				A		)

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













from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
      from ..image_utils import load_image


if is_torch_available():
      import torch

      from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

__UpperCAmelCase       :   Any				       =							logging.get_logger(__name__)


__UpperCAmelCase       :   str				       =							Dict[str, Any]
__UpperCAmelCase       :   int				       =							List[Prediction]







@add_end_docstrings(__lowerCamelCase			)
class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    int							,				*A :    Optional[int]							,				**A :    Optional[int]		):
         super().__init__(*A							,				**A		)

         if self.framework == "tf":
           raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''		)

         requires_backends(self							,				"""vision"""		)
         self.check_model_type(
             dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()		)		)




       def 							UpperCAmelCase__ (  self :    List[str]							,				**A :    Tuple		):
         __snake_case:						List[str]   =						{}
         if "threshold" in kwargs:
           __snake_case:						Optional[Any]   =						kwargs["""threshold"""]
         return {}, {}, postprocess_kwargs




       def __call__(  self :    int							,				*A :    Optional[Any]							,				**A :    Tuple		):
         return super().__call__(*A							,				**A		)




       def 							UpperCAmelCase__ (  self :    Optional[int]							,				A :    str		):
         __snake_case:						Optional[Any]   =						load_image(A		)
         __snake_case:						Dict   =						torch.IntTensor([[image.height, image.width]]		)
         __snake_case:						str   =						self.image_processor(images=[image]							,				return_tensors="""pt"""		)
         if self.tokenizer is not None:
           __snake_case:						Optional[Any]   =						self.tokenizer(text=inputs["""words"""]							,				boxes=inputs["""boxes"""]							,				return_tensors="""pt"""		)
         __snake_case:						Any   =						target_size
         return inputs




       def 							UpperCAmelCase__ (  self :    Optional[int]							,				A :    Dict		):
         __snake_case:						int   =						model_inputs.pop("""target_size"""		)
         __snake_case:						int   =						self.model(**A		)
         __snake_case:						Any   =						outputs.__class__({"""target_size""": target_size, **outputs}		)
         if self.tokenizer is not None:
           __snake_case:						Optional[int]   =						model_inputs["""bbox"""]
         return model_outputs




       def 							UpperCAmelCase__ (  self :    List[Any]							,				A :    Optional[int]							,				A :    Union[str, Any]=0.9		):
         __snake_case:						Optional[Any]   =						model_outputs["""target_size"""]
         if self.tokenizer is not None:
           # This is a LayoutLMForTokenClassification variant.
           # The OCR got the boxes and the model classified the words.
           __snake_case  ,   __snake_case:						Union[str, Any]   =						target_size[0].tolist()

           def unnormalize(A :    Tuple		):
             return self._get_bounding_box(
                 torch.Tensor(
                     [
                         (width * bbox[0] / 1_000),
                         (height * bbox[1] / 1_000),
                         (width * bbox[2] / 1_000),
                         (height * bbox[3] / 1_000),
                     ]		)		)

           __snake_case  ,   __snake_case:						Optional[int]   =						model_outputs["""logits"""].squeeze(0		).softmax(dim=-1		).max(dim=-1		)
           __snake_case:						List[Any]   =						[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
           __snake_case:						int   =						[unnormalize(A		) for bbox in model_outputs["""bbox"""].squeeze(0		)]
           __snake_case:						int   =						["""score""", """label""", """box"""]
           __snake_case:						List[Any]   =						[dict(zip(A							,				A		)		) for vals in zip(scores.tolist()							,				A							,				A		) if vals[0] > threshold]
         else:
           # This is a regular ForObjectDetectionModel
           __snake_case:						Tuple   =						self.image_processor.post_process_object_detection(A							,				A							,				A		)
           __snake_case:						Optional[Any]   =						raw_annotations[0]
           __snake_case:						int   =						raw_annotation["""scores"""]
           __snake_case:						int   =						raw_annotation["""labels"""]
           __snake_case:						Optional[Any]   =						raw_annotation["""boxes"""]

           __snake_case:						Union[str, Any]   =						scores.tolist()
           __snake_case:						List[str]   =						[self.model.config.idalabel[label.item()] for label in labels]
           __snake_case:						List[str]   =						[self._get_bounding_box(A		) for box in boxes]

           # {"scores": [...], ...} --> [{"score":x, ...}, ...]
           __snake_case:						List[Any]   =						["""score""", """label""", """box"""]
           __snake_case:						Dict   =						[
               dict(zip(A							,				A		)		)
               for vals in zip(raw_annotation["""scores"""]							,				raw_annotation["""labels"""]							,				raw_annotation["""boxes"""]		)
           ]

         return annotation






       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    "torch.Tensor"		):
         if self.framework != "pt":
           raise ValueError("""The ObjectDetectionPipeline is only available in PyTorch."""		)
         __snake_case  ,   __snake_case  ,   __snake_case  ,   __snake_case:						Union[str, Any]   =						box.int().tolist()
         __snake_case:						Optional[Any]   =						{
             """xmin""": xmin,
             """ymin""": ymin,
             """xmax""": xmax,
             """ymax""": ymax,
         }
         return bbox






