
import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__   :			Dict												=    logging.get_logger(__name__)




def  __magic_name__ (   __lowerCAmelCase	:						Tuple		,  __lowerCAmelCase	:						Optional[Any]=False							)			->   Any:
				__lowerCamelCase    					=	OrderedDict()
				for key, value in state_dict.items():
								if encoder_only and not key.startswith('''head'''							):
												__lowerCamelCase    					=	'''segformer.encoder.''' + key
								if key.startswith('''backbone'''							):
												__lowerCamelCase    					=	key.replace('''backbone'''		,  '''segformer.encoder'''							)
								if "patch_embed" in key:
												# replace for example patch_embed1 by patch_embeddings.0
												__lowerCamelCase    					=	key[key.find('''patch_embed'''							) + len('''patch_embed'''							)]
												__lowerCamelCase    					=	key.replace(f'''patch_embed{idx}'''		,  f'''patch_embeddings.{int(__lowerCAmelCase							)-1}'''							)
								if "norm" in key:
												__lowerCamelCase    					=	key.replace('''norm'''		,  '''layer_norm'''							)
								if "segformer.encoder.layer_norm" in key:
												# replace for example layer_norm1 by layer_norm.0
												__lowerCamelCase    					=	key[key.find('''segformer.encoder.layer_norm'''							) + len('''segformer.encoder.layer_norm'''							)]
												__lowerCamelCase    					=	key.replace(f'''layer_norm{idx}'''		,  f'''layer_norm.{int(__lowerCAmelCase							)-1}'''							)
								if "layer_norm1" in key:
												__lowerCamelCase    					=	key.replace('''layer_norm1'''		,  '''layer_norm_1'''							)
								if "layer_norm2" in key:
												__lowerCamelCase    					=	key.replace('''layer_norm2'''		,  '''layer_norm_2'''							)
								if "block" in key:
												# replace for example block1 by block.0
												__lowerCamelCase    					=	key[key.find('''block'''							) + len('''block'''							)]
												__lowerCamelCase    					=	key.replace(f'''block{idx}'''		,  f'''block.{int(__lowerCAmelCase							)-1}'''							)
								if "attn.q" in key:
												__lowerCamelCase    					=	key.replace('''attn.q'''		,  '''attention.self.query'''							)
								if "attn.proj" in key:
												__lowerCamelCase    					=	key.replace('''attn.proj'''		,  '''attention.output.dense'''							)
								if "attn" in key:
												__lowerCamelCase    					=	key.replace('''attn'''		,  '''attention.self'''							)
								if "fc1" in key:
												__lowerCamelCase    					=	key.replace('''fc1'''		,  '''dense1'''							)
								if "fc2" in key:
												__lowerCamelCase    					=	key.replace('''fc2'''		,  '''dense2'''							)
								if "linear_pred" in key:
												__lowerCamelCase    					=	key.replace('''linear_pred'''		,  '''classifier'''							)
								if "linear_fuse" in key:
												__lowerCamelCase    					=	key.replace('''linear_fuse.conv'''		,  '''linear_fuse'''							)
												__lowerCamelCase    					=	key.replace('''linear_fuse.bn'''		,  '''batch_norm'''							)
								if "linear_c" in key:
												# replace for example linear_c4 by linear_c.3
												__lowerCamelCase    					=	key[key.find('''linear_c'''							) + len('''linear_c'''							)]
												__lowerCamelCase    					=	key.replace(f'''linear_c{idx}'''		,  f'''linear_c.{int(__lowerCAmelCase							)-1}'''							)
								if key.startswith('''head'''							):
												__lowerCamelCase    					=	key.replace('''head'''		,  '''classifier'''							)
								__lowerCamelCase    					=	value

				return new_state_dict




def  __magic_name__ (   __lowerCAmelCase	:						str		,  __lowerCAmelCase	:						Optional[Any]							)			->   int:
				# for each of the encoder blocks:
				for i in range(config.num_encoder_blocks							):
								for j in range(config.depths[i]							):
												# read in weights + bias of keys and values (which is a single matrix in the original implementation)
												__lowerCamelCase    					=	state_dict.pop(f'''segformer.encoder.block.{i}.{j}.attention.self.kv.weight'''							)
												__lowerCamelCase    					=	state_dict.pop(f'''segformer.encoder.block.{i}.{j}.attention.self.kv.bias'''							)
												# next, add keys and values (in that order) to the state dict
												__lowerCamelCase    					=	kv_weight[
												    : config.hidden_sizes[i], :
												]
												__lowerCamelCase    					=	kv_bias[: config.hidden_sizes[i]]
												__lowerCamelCase    					=	kv_weight[
												    config.hidden_sizes[i] :, :
												]
												__lowerCamelCase    					=	kv_bias[
												    config.hidden_sizes[i] :
												]




def  __magic_name__ (   )			->   Dict:
				__lowerCamelCase    					=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__lowerCamelCase    					=	Image.open(requests.get(__lowerCAmelCase		,  stream=__lowerCAmelCase							).raw							)

				return image




@torch.no_grad()
def  __magic_name__ (   __lowerCAmelCase	:						int		,  __lowerCAmelCase	:						Dict		,  __lowerCAmelCase	:						Union[str, Any]							)			->   Dict:
				__lowerCamelCase    					=	SegformerConfig()
				__lowerCamelCase    					=	False

				# set attributes based on model_name
				__lowerCamelCase    					=	'''huggingface/label-files'''
				if "segformer" in model_name:
								__lowerCamelCase    					=	model_name[len('''segformer.'''							) : len('''segformer.'''							) + 2]
								if "ade" in model_name:
												__lowerCamelCase    					=	150
												__lowerCamelCase    					=	'''ade20k-id2label.json'''
												__lowerCamelCase    					=	(1, 150, 128, 128)
								elif "city" in model_name:
												__lowerCamelCase    					=	19
												__lowerCamelCase    					=	'''cityscapes-id2label.json'''
												__lowerCamelCase    					=	(1, 19, 128, 128)
								else:
												raise ValueError(f'''Model {model_name} not supported'''							)
				elif "mit" in model_name:
								__lowerCamelCase    					=	True
								__lowerCamelCase    					=	model_name[4:6]
								__lowerCamelCase    					=	1000
								__lowerCamelCase    					=	'''imagenet-1k-id2label.json'''
								__lowerCamelCase    					=	(1, 1000)
				else:
								raise ValueError(f'''Model {model_name} not supported'''							)

				# set config attributes
				__lowerCamelCase    					=	json.load(open(hf_hub_download(__lowerCAmelCase		,  __lowerCAmelCase		,  repo_type='''dataset'''							)		,  '''r'''							)							)
				__lowerCamelCase    					=	{int(__lowerCAmelCase							): v for k, v in idalabel.items()}
				__lowerCamelCase    					=	idalabel
				__lowerCamelCase    					=	{v: k for k, v in idalabel.items()}
				if size == "b0":
								pass
				elif size == "b1":
								__lowerCamelCase    					=	[64, 128, 320, 512]
								__lowerCamelCase    					=	256
				elif size == "b2":
								__lowerCamelCase    					=	[64, 128, 320, 512]
								__lowerCamelCase    					=	768
								__lowerCamelCase    					=	[3, 4, 6, 3]
				elif size == "b3":
								__lowerCamelCase    					=	[64, 128, 320, 512]
								__lowerCamelCase    					=	768
								__lowerCamelCase    					=	[3, 4, 18, 3]
				elif size == "b4":
								__lowerCamelCase    					=	[64, 128, 320, 512]
								__lowerCamelCase    					=	768
								__lowerCamelCase    					=	[3, 8, 27, 3]
				elif size == "b5":
								__lowerCamelCase    					=	[64, 128, 320, 512]
								__lowerCamelCase    					=	768
								__lowerCamelCase    					=	[3, 6, 40, 3]
				else:
								raise ValueError(f'''Size {size} not supported'''							)

				# load image processor (only resize + normalize)
				__lowerCamelCase    					=	SegformerImageProcessor(
				    image_scale=(512, 512)		,  keep_ratio=__lowerCAmelCase		,  align=__lowerCAmelCase		,  do_random_crop=__lowerCAmelCase							)

				# prepare image
				__lowerCamelCase    					=	prepare_img()
				__lowerCamelCase    					=	image_processor(images=__lowerCAmelCase		,  return_tensors='''pt'''							).pixel_values

				logger.info(f'''Converting model {model_name}...'''							)

				# load original state dict
				if encoder_only:
								__lowerCamelCase    					=	torch.load(__lowerCAmelCase		,  map_location=torch.device('''cpu'''							)							)
				else:
								__lowerCamelCase    					=	torch.load(__lowerCAmelCase		,  map_location=torch.device('''cpu'''							)							)['''state_dict''']

				# rename keys
				__lowerCamelCase    					=	rename_keys(__lowerCAmelCase		,  encoder_only=__lowerCAmelCase							)
				if not encoder_only:
								del state_dict["decode_head.conv_seg.weight"]
								del state_dict["decode_head.conv_seg.bias"]

				# key and value matrices need special treatment
				read_in_k_v(__lowerCAmelCase		,  __lowerCAmelCase							)

				# create HuggingFace model and load state dict
				if encoder_only:
								__lowerCamelCase    					=	False
								__lowerCamelCase    					=	SegformerForImageClassification(__lowerCAmelCase							)
				else:
								__lowerCamelCase    					=	SegformerForSemanticSegmentation(__lowerCAmelCase							)
				model.load_state_dict(__lowerCAmelCase							)
				model.eval()

				# forward pass
				__lowerCamelCase    					=	model(__lowerCAmelCase							)
				__lowerCamelCase    					=	outputs.logits

				# set expected_slice based on model name
				# ADE20k checkpoints
				if model_name == "segformer.b0.512x512.ade.160k":
								__lowerCamelCase    					=	torch.tensor(
								    [
								        [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
								        [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
								        [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
								    ]							)
				elif model_name == "segformer.b1.512x512.ade.160k":
								__lowerCamelCase    					=	torch.tensor(
								    [
								        [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
								        [[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
								        [[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
								    ]							)
				elif model_name == "segformer.b2.512x512.ade.160k":
								__lowerCamelCase    					=	torch.tensor(
								    [
								        [[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]],
								        [[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]],
								        [[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]],
								    ]							)
				elif model_name == "segformer.b3.512x512.ade.160k":
								__lowerCamelCase    					=	torch.tensor(
								    [
								        [[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
								        [[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
								        [[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
								    ]							)
				elif model_name == "segformer.b4.512x512.ade.160k":
								__lowerCamelCase    					=	torch.tensor(
								    [
								        [[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]],
								        [[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]],
								        [[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]],
								    ]							)
				elif model_name == "segformer.b5.640x640.ade.160k":
								__lowerCamelCase    					=	torch.tensor(
								    [
								        [[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]],
								        [[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]],
								        [[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]],
								    ]							)
				# Cityscapes checkpoints
				elif model_name == "segformer.b0.1024x1024.city.160k":
								__lowerCamelCase    					=	torch.tensor(
								    [
								        [[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]],
								        [[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]],
								        [[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
								    ]							)
				elif model_name == "segformer.b0.512x1024.city.160k":
								__lowerCamelCase    					=	torch.tensor(
								    [
								        [[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]],
								        [[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
								        [[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
								    ]							)
				elif model_name == "segformer.b0.640x1280.city.160k":
								__lowerCamelCase    					=	torch.tensor(
								    [
								        [
								            [-1.1372E01, -1.2787E01, -1.3477E01],
								            [-1.2536E01, -1.4194E01, -1.4409E01],
								            [-1.3217E01, -1.4888E01, -1.5327E01],
								        ],
								        [
								            [-1.4791E01, -1.7122E01, -1.8277E01],
								            [-1.7163E01, -1.9192E01, -1.9533E01],
								            [-1.7897E01, -1.9991E01, -2.0315E01],
								        ],
								        [
								            [7.6723E-01, 4.1921E-01, -7.7878E-02],
								            [4.7772E-01, 9.5557E-03, -2.8082E-01],
								            [3.6032E-01, -2.4826E-01, -5.1168E-01],
								        ],
								    ]							)
				elif model_name == "segformer.b0.768x768.city.160k":
								__lowerCamelCase    					=	torch.tensor(
								    [
								        [[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
								        [[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
								        [[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
								    ]							)
				elif model_name == "segformer.b1.1024x1024.city.160k":
								__lowerCamelCase    					=	torch.tensor(
								    [
								        [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
								        [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
								        [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
								    ]							)
				elif model_name == "segformer.b2.1024x1024.city.160k":
								__lowerCamelCase    					=	torch.tensor(
								    [
								        [[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]],
								        [[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]],
								        [[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
								    ]							)
				elif model_name == "segformer.b3.1024x1024.city.160k":
								__lowerCamelCase    					=	torch.tensor(
								    [
								        [[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]],
								        [[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]],
								        [[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
								    ]							)
				elif model_name == "segformer.b4.1024x1024.city.160k":
								__lowerCamelCase    					=	torch.tensor(
								    [
								        [[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
								        [[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
								        [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
								    ]							)
				elif model_name == "segformer.b5.1024x1024.city.160k":
								__lowerCamelCase    					=	torch.tensor(
								    [
								        [[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]],
								        [[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]],
								        [[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
								    ]							)
				else:
								__lowerCamelCase    					=	logits.argmax(-1							).item()
								print('''Predicted class:'''		,  model.config.idalabel[predicted_class_idx]							)

				# verify logits
				if not encoder_only:
								assert logits.shape == expected_shape
								assert torch.allclose(logits[0, :3, :3, :3]		,  __lowerCAmelCase		,  atol=1E-2							)

				# finally, save model and image processor
				logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''							)
				Path(__lowerCAmelCase							).mkdir(exist_ok=__lowerCAmelCase							)
				model.save_pretrained(__lowerCAmelCase							)
				image_processor.save_pretrained(__lowerCAmelCase							)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    argparse.ArgumentParser()

					parser.add_argument(
					    "--model_name",
					    default="segformer.b0.512x512.ade.160k",
					    type=str,
					    help="Name of the model you'd like to convert.",
					)
					parser.add_argument(
					    "--checkpoint_path", default=None, type=str, help="Path to the original PyTorch checkpoint (.pth file)."
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
					)
					SCREAMING_SNAKE_CASE__   :			str												=    parser.parse_args()
					convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





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

import datasets


SCREAMING_SNAKE_CASE__   :			Union[str, 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"

SCREAMING_SNAKE_CASE__   :			int												=    "\\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[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    lowerCAmelCase__     (	datasets.Metric			):






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

						__lowerCamelCase    					=	CHRF(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	sb_chrf.corpus_score(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )

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



