

from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase_			=  logging.get_logger(__name__)


class 			A						(							_lowerCAmelCase			):
    lowerCamelCase		     =							["pixel_values"]


    def __init__(				self			:	str,lowercase_			:	bool = True,lowercase_			:	Dict[str, int] = None,lowercase_			:	PILImageResampling = PILImageResampling.BICUBIC,lowercase_			:	bool = True,lowercase_			:	Dict[str, int] = None,lowercase_			:	bool = True,lowercase_			:	Union[int, float] = 1 / 2_5_5,lowercase_			:	bool = True,lowercase_			:	Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN,lowercase_			:	Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD,**lowercase_			:	Dict,)->   List[Any]:





        '''simple docstring'''







        super().__init__(**_lowercase		)
        A__   				=       size if size is not None else {'shortest_edge': 2_2_4}
        A__   				=       get_size_dict(_lowercase,default_to_square=_lowercase		)
        A__   				=       crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
        A__   				=       get_size_dict(_lowercase,param_name='crop_size'		)

        A__   				=       do_resize
        A__   				=       size
        A__   				=       resample
        A__   				=       do_center_crop
        A__   				=       crop_size
        A__   				=       do_rescale
        A__   				=       rescale_factor
        A__   				=       do_normalize
        A__   				=       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        A__   				=       image_std if image_std is not None else IMAGENET_DEFAULT_STD


    def 						snake_case__					(				self			:	Any,lowercase_			:	np.ndarray,lowercase_			:	Dict[str, int],lowercase_			:	PILImageResampling = PILImageResampling.BICUBIC,lowercase_			:	Optional[Union[str, ChannelDimension]] = None,**lowercase_			:	Optional[Any],)->   Any:





        '''simple docstring'''







        A__   				=       get_size_dict(_lowercase,default_to_square=_lowercase		)
        # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
        if "shortest_edge" in size:
            A__   				=       int((2_5_6 / 2_2_4) * size['shortest_edge']		)
            A__   				=       get_resize_output_image_size(_lowercase,size=_lowercase,default_to_square=_lowercase		)
            A__   				=       {'height': output_size[0], 'width': output_size[1]}
        if "height" not in size_dict or "width" not in size_dict:
            raise ValueError(
                F'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'		)
        return resize(
            _lowercase,size=(size_dict['height'], size_dict['width']),resample=_lowercase,data_format=_lowercase,**_lowercase		)


    def 						snake_case__					(				self			:	str,lowercase_			:	np.ndarray,lowercase_			:	Dict[str, int],lowercase_			:	Optional[Union[str, ChannelDimension]] = None,**lowercase_			:	Any,)->   Tuple:





        '''simple docstring'''







        A__   				=       get_size_dict(_lowercase		)
        if "height" not in size or "width" not in size:
            raise ValueError(F'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'		)
        return center_crop(_lowercase,size=(size['height'], size['width']),data_format=_lowercase,**_lowercase		)


    def 						snake_case__					(				self			:	int,lowercase_			:	np.ndarray,lowercase_			:	Union[int, float],lowercase_			:	Optional[Union[str, ChannelDimension]] = None,**lowercase_			:	Union[str, Any],)->   str:





        '''simple docstring'''







        return rescale(_lowercase,scale=_lowercase,data_format=_lowercase,**_lowercase		)


    def 						snake_case__					(				self			:	str,lowercase_			:	np.ndarray,lowercase_			:	Union[float, List[float]],lowercase_			:	Union[float, List[float]],lowercase_			:	Optional[Union[str, ChannelDimension]] = None,**lowercase_			:	Any,)->   Union[str, Any]:





        '''simple docstring'''







        return normalize(_lowercase,mean=_lowercase,std=_lowercase,data_format=_lowercase,**_lowercase		)


    def 						snake_case__					(				self			:	Dict,lowercase_			:	ImageInput,lowercase_			:	Optional[bool] = None,lowercase_			:	Optional[Dict[str, int]] = None,lowercase_			:	PILImageResampling = None,lowercase_			:	Optional[bool] = None,lowercase_			:	Optional[Dict[str, int]] = None,lowercase_			:	Optional[bool] = None,lowercase_			:	Optional[float] = None,lowercase_			:	Optional[bool] = None,lowercase_			:	Optional[Union[float, Iterable[float]]] = None,lowercase_			:	Optional[Union[float, Iterable[float]]] = None,lowercase_			:	Optional[TensorType] = None,lowercase_			:	ChannelDimension = ChannelDimension.FIRST,**lowercase_			:	Tuple,)->   int:





        '''simple docstring'''







        A__   				=       do_resize if do_resize is not None else self.do_resize
        A__   				=       resample if resample is not None else self.resample
        A__   				=       do_center_crop if do_center_crop is not None else self.do_center_crop
        A__   				=       do_rescale if do_rescale is not None else self.do_rescale
        A__   				=       rescale_factor if rescale_factor is not None else self.rescale_factor
        A__   				=       do_normalize if do_normalize is not None else self.do_normalize
        A__   				=       image_mean if image_mean is not None else self.image_mean
        A__   				=       image_std if image_std is not None else self.image_std

        A__   				=       size if size is not None else self.size
        A__   				=       get_size_dict(_lowercase,default_to_square=_lowercase		)
        A__   				=       crop_size if crop_size is not None else self.crop_size
        A__   				=       get_size_dict(_lowercase,param_name='crop_size'		)

        A__   				=       make_list_of_images(_lowercase		)

        if not valid_images(_lowercase		):
            raise ValueError(
                'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                'torch.Tensor, tf.Tensor or jax.ndarray.'		)

        if do_resize and size is None:
            raise ValueError('Size must be specified if do_resize is True.'		)

        if do_center_crop and crop_size is None:
            raise ValueError('Crop size must be specified if do_center_crop is True.'		)

        if do_rescale and rescale_factor is None:
            raise ValueError('Rescale factor must be specified if do_rescale is True.'		)

        if do_normalize and (image_mean is None or image_std is None):
            raise ValueError('Image mean and std must be specified if do_normalize is True.'		)

        # All transformations expect numpy arrays.
        A__   				=       [to_numpy_array(_lowercase		) for image in images]

        if do_resize:
            A__   				=       [self.resize(_lowercase,_lowercase,_lowercase		) for image in images]

        if do_center_crop:
            A__   				=       [self.center_crop(_lowercase,_lowercase		) for image in images]

        if do_rescale:
            A__   				=       [self.rescale(_lowercase,_lowercase		) for image in images]

        if do_normalize:
            A__   				=       [self.normalize(_lowercase,_lowercase,_lowercase		) for image in images]

        A__   				=       [to_channel_dimension_format(_lowercase,_lowercase		) for image in images]

        A__   				=       {'pixel_values': images}
        return BatchFeature(data=_lowercase,tensor_type=_lowercase		)




import comet  # From: unbabel-comet
import torch

import datasets


lowercase_			=  datasets.logging.get_logger(__name__)

lowercase_			=  "\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel's Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = \"{COMET}: A Neural Framework for {MT} Evaluation\",\n   author = \"Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon\",\n   booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n   month = nov,\n   year = \"2020\",\n   address = \"Online\",\n   publisher = \"Association for Computational Linguistics\",\n   url = \"https://www.aclweb.org/anthology/2020.emnlp-main.213\",\n   pages = \"2685--2702\",\n}\n"

lowercase_			=  "\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA's or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n"

lowercase_			=  "\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric('comet')\n    >>> # comet_metric = load_metric('comet', 'wmt20-comet-da')  # you can also choose which model to use\n    >>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]\n    >>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]\n    >>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [0.19, 0.92]\n"


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







    """simple docstring"""





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





        '''simple docstring'''







        return datasets.MetricInfo(
            description=_DESCRIPTION,citation=_CITATION,homepage='https://unbabel.github.io/COMET/html/index.html',inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
                {
                    'sources': datasets.Value('string',id='sequence'		),
                    'predictions': datasets.Value('string',id='sequence'		),
                    'references': datasets.Value('string',id='sequence'		),
                }		),codebase_urls=['https://github.com/Unbabel/COMET'],reference_urls=[
                'https://github.com/Unbabel/COMET',
                'https://www.aclweb.org/anthology/2020.emnlp-main.213/',
                'http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6',
            ],)


    def 						snake_case__					(				self			:	Optional[Any],lowercase_			:	Dict		)->   str:





        '''simple docstring'''







        if self.config_name == "default":
            A__   				=       comet.load_from_checkpoint(comet.download_model('wmt20-comet-da'		)		)
        else:
            A__   				=       comet.load_from_checkpoint(comet.download_model(self.config_name		)		)


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	Dict,lowercase_			:	int,lowercase_			:	List[Any],lowercase_			:	Tuple=None,lowercase_			:	List[str]=False		)->   int:





        '''simple docstring'''







        if gpus is None:
            A__   				=       1 if torch.cuda.is_available() else 0
        A__   				=       {'src': sources, 'mt': predictions, 'ref': references}
        A__   				=       [dict(zip(lowercase_,lowercase_		)		) for t in zip(*data.values()		)]
        A__				,						A__   				=       self.scorer.predict(lowercase_,gpus=lowercase_,progress_bar=lowercase_		)
        return {"mean_score": mean_score, "scores": scores}

