


'''simple docstring'''
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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE				:							Optional[int]		       =		logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__SCREAMING_SNAKE_CASE				:							Union[str, Any]		       =		[]
for i in range(6):
							# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
							rename_keys.append(
							    (F"transformer.encoder.layers.{i}.self_attn.out_proj.weight", F"encoder.layers.{i}.self_attn.out_proj.weight")
							)
							rename_keys.append(
							    (F"transformer.encoder.layers.{i}.self_attn.out_proj.bias", F"encoder.layers.{i}.self_attn.out_proj.bias")
							)
							rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"encoder.layers.{i}.fc1.weight"))
							rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"encoder.layers.{i}.fc1.bias"))
							rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"encoder.layers.{i}.fc2.weight"))
							rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"encoder.layers.{i}.fc2.bias"))
							rename_keys.append(
							    (F"transformer.encoder.layers.{i}.norm1.weight", F"encoder.layers.{i}.self_attn_layer_norm.weight")
							)
							rename_keys.append((F"transformer.encoder.layers.{i}.norm1.bias", F"encoder.layers.{i}.self_attn_layer_norm.bias"))
							rename_keys.append((F"transformer.encoder.layers.{i}.norm2.weight", F"encoder.layers.{i}.final_layer_norm.weight"))
							rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"encoder.layers.{i}.final_layer_norm.bias"))
							# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
							rename_keys.append(
							    (F"transformer.decoder.layers.{i}.self_attn.out_proj.weight", F"decoder.layers.{i}.self_attn.out_proj.weight")
							)
							rename_keys.append(
							    (F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"decoder.layers.{i}.self_attn.out_proj.bias")
							)
							rename_keys.append(
							    (
							        F"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
							        F"decoder.layers.{i}.encoder_attn.out_proj.weight",
							    )
							)
							rename_keys.append(
							    (
							        F"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
							        F"decoder.layers.{i}.encoder_attn.out_proj.bias",
							    )
							)
							rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"decoder.layers.{i}.fc1.weight"))
							rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"decoder.layers.{i}.fc1.bias"))
							rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"decoder.layers.{i}.fc2.weight"))
							rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"decoder.layers.{i}.fc2.bias"))
							rename_keys.append(
							    (F"transformer.decoder.layers.{i}.norm1.weight", F"decoder.layers.{i}.self_attn_layer_norm.weight")
							)
							rename_keys.append((F"transformer.decoder.layers.{i}.norm1.bias", F"decoder.layers.{i}.self_attn_layer_norm.bias"))
							rename_keys.append(
							    (F"transformer.decoder.layers.{i}.norm2.weight", F"decoder.layers.{i}.encoder_attn_layer_norm.weight")
							)
							rename_keys.append(
							    (F"transformer.decoder.layers.{i}.norm2.bias", F"decoder.layers.{i}.encoder_attn_layer_norm.bias")
							)
							rename_keys.append((F"transformer.decoder.layers.{i}.norm3.weight", F"decoder.layers.{i}.final_layer_norm.weight"))
							rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"decoder.layers.{i}.final_layer_norm.bias"))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
    ]
)







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							A_ 						=		state_dict.pop(UpperCamelCase_   )
							A_ 						=		val







def 			a_							(  UpperCamelCase_   ):
							A_ 						=		OrderedDict()
							for key, value in state_dict.items():
														if "backbone.0.body" in key:
																					A_ 						=		key.replace("backbone.0.body"	,     "backbone.conv_encoder.model"   )
																					A_ 						=		value
														else:
																					A_ 						=		value

							return new_state_dict







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_=False   ):
							A_ 						=		""
							if is_panoptic:
														A_ 						=		"detr."

							# first: transformer encoder
							for i in range(6   ):
														# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
														A_ 						=		state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"   )
														A_ 						=		state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"   )
														# next, add query, keys and values (in that order) to the state dict
														A_ 						=		in_proj_weight[:2_5_6, :]
														A_ 						=		in_proj_bias[:2_5_6]
														A_ 						=		in_proj_weight[2_5_6:5_1_2, :]
														A_ 						=		in_proj_bias[2_5_6:5_1_2]
														A_ 						=		in_proj_weight[-2_5_6:, :]
														A_ 						=		in_proj_bias[-2_5_6:]
							# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
							for i in range(6   ):
														# read in weights + bias of input projection layer of self-attention
														A_ 						=		state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"   )
														A_ 						=		state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"   )
														# next, add query, keys and values (in that order) to the state dict
														A_ 						=		in_proj_weight[:2_5_6, :]
														A_ 						=		in_proj_bias[:2_5_6]
														A_ 						=		in_proj_weight[2_5_6:5_1_2, :]
														A_ 						=		in_proj_bias[2_5_6:5_1_2]
														A_ 						=		in_proj_weight[-2_5_6:, :]
														A_ 						=		in_proj_bias[-2_5_6:]
														# read in weights + bias of input projection layer of cross-attention
														A_ 						=		state_dict.pop(
														    f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"   )
														A_ 						=		state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"   )
														# next, add query, keys and values (in that order) of cross-attention to the state dict
														A_ 						=		in_proj_weight_cross_attn[:2_5_6, :]
														A_ 						=		in_proj_bias_cross_attn[:2_5_6]
														A_ 						=		in_proj_weight_cross_attn[2_5_6:5_1_2, :]
														A_ 						=		in_proj_bias_cross_attn[2_5_6:5_1_2]
														A_ 						=		in_proj_weight_cross_attn[-2_5_6:, :]
														A_ 						=		in_proj_bias_cross_attn[-2_5_6:]







def 			a_							(  ):
							A_ 						=		"http://images.cocodataset.org/val2017/000000039769.jpg"
							A_ 						=		Image.open(requests.get(UpperCamelCase_	,     stream=UpperCamelCase_   ).raw   )

							return im







@torch.no_grad()
def 			a_							(  UpperCamelCase_	,     UpperCamelCase_   ):
							A_ 						=		DetrConfig()
							# set backbone and dilation attributes
							if "resnet101" in model_name:
														A_ 						=		"resnet101"
							if "dc5" in model_name:
														A_ 						=		True
							A_ 						=		"panoptic" in model_name
							if is_panoptic:
														A_ 						=		2_5_0
							else:
														A_ 						=		9_1
														A_ 						=		"huggingface/label-files"
														A_ 						=		"coco-detection-id2label.json"
														A_ 						=		json.load(open(hf_hub_download(UpperCamelCase_	,     UpperCamelCase_	,     repo_type="dataset"   )	,     "r"   )   )
														A_ 						=		{int(UpperCamelCase_   ): v for k, v in idalabel.items()}
														A_ 						=		idalabel
														A_ 						=		{v: k for k, v in idalabel.items()}

							# load image processor
							A_ 						=		"coco_panoptic" if is_panoptic else "coco_detection"
							A_ 						=		DetrImageProcessor(format=UpperCamelCase_   )

							# prepare image
							A_ 						=		prepare_img()
							A_ 						=		image_processor(images=UpperCamelCase_	,     return_tensors="pt"   )
							A_ 						=		encoding["pixel_values"]

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

							# load original model from torch hub
							A_ 						=		torch.hub.load("facebookresearch/detr"	,     UpperCamelCase_	,     pretrained=UpperCamelCase_   ).eval()
							A_ 						=		detr.state_dict()
							# rename keys
							for src, dest in rename_keys:
														if is_panoptic:
																					A_ 						=		"detr." + src
														rename_key(UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   )
							A_ 						=		rename_backbone_keys(UpperCamelCase_   )
							# query, key and value matrices need special treatment
							read_in_q_k_v(UpperCamelCase_	,     is_panoptic=UpperCamelCase_   )
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							A_ 						=		"detr.model." if is_panoptic else "model."
							for key in state_dict.copy().keys():
														if is_panoptic:
																					if (
																					    key.startswith("detr"   )
																					    and not key.startswith("class_labels_classifier"   )
																					    and not key.startswith("bbox_predictor"   )
																					):
																												A_ 						=		state_dict.pop(UpperCamelCase_   )
																												A_ 						=		val
																					elif "class_labels_classifier" in key or "bbox_predictor" in key:
																												A_ 						=		state_dict.pop(UpperCamelCase_   )
																												A_ 						=		val
																					elif key.startswith("bbox_attention"   ) or key.startswith("mask_head"   ):
																												continue
																					else:
																												A_ 						=		state_dict.pop(UpperCamelCase_   )
																												A_ 						=		val
														else:
																					if not key.startswith("class_labels_classifier"   ) and not key.startswith("bbox_predictor"   ):
																												A_ 						=		state_dict.pop(UpperCamelCase_   )
																												A_ 						=		val
    # finally, create HuggingFace model and load state dict
							A_ 						=		DetrForSegmentation(UpperCamelCase_   ) if is_panoptic else DetrForObjectDetection(UpperCamelCase_   )
							model.load_state_dict(UpperCamelCase_   )
							model.eval()
							# verify our conversion
							A_ 						=		detr(UpperCamelCase_   )
							A_ 						=		model(UpperCamelCase_   )
							assert torch.allclose(outputs.logits	,     original_outputs["pred_logits"]	,     atol=1e-4   )
							assert torch.allclose(outputs.pred_boxes	,     original_outputs["pred_boxes"]	,     atol=1e-4   )
							if is_panoptic:
														assert torch.allclose(outputs.pred_masks	,     original_outputs["pred_masks"]	,     atol=1e-4   )

							# Save model and image processor
							logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."   )
							Path(UpperCamelCase_   ).mkdir(exist_ok=UpperCamelCase_   )
							model.save_pretrained(UpperCamelCase_   )
							image_processor.save_pretrained(UpperCamelCase_   )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE				:							Optional[Any]		       =		argparse.ArgumentParser()

							parser.add_argument(
							    '''--model_name''', default='''detr_resnet50''', type=str, help='''Name of the DETR model you\'d like to convert.'''
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
							)
							__SCREAMING_SNAKE_CASE				:							List[Any]		       =		parser.parse_args()
							convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)









'''simple docstring'''
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__SCREAMING_SNAKE_CASE				:							Optional[int]		       =		'''\
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415},
}
'''

__SCREAMING_SNAKE_CASE				:							List[Any]		       =		'''\
SARI is a metric used for evaluating automatic text simplification systems.
The metric compares the predicted simplified sentences against the reference
and the source sentences. It explicitly measures the goodness of words that are
added, deleted and kept by the system.
Sari = (F1_add + F1_keep + P_del) / 3
where
F1_add: n-gram F1 score for add operation
F1_keep: n-gram F1 score for keep operation
P_del: n-gram precision score for delete operation
n = 4, as in the original paper.

This implementation is adapted from Tensorflow\'s tensor2tensor implementation [3].
It has two differences with the original GitHub [1] implementation:
  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match
      a target exactly.
  (2) Fixes an alleged bug [2] in the keep score computation.
[1] https://github.com/cocoxu/simplification/blob/master/SARI.py
    (commit 0210f15)
[2] https://github.com/cocoxu/simplification/issues/6
[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py
'''


__SCREAMING_SNAKE_CASE				:							Optional[int]		       =		'''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
Examples:
    >>> sources=["About 95 species are currently accepted ."]
    >>> predictions=["About 95 you now get in ."]
    >>> references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]
    >>> sari = datasets.load_metric("sari")
    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {\'sari\': 26.953601953601954}
'''







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							A_ 						=		[rgram for rgrams in rgramslist for rgram in rgrams]
							A_ 						=		Counter(UpperCamelCase_   )

							A_ 						=		Counter(UpperCamelCase_   )
							A_ 						=		Counter()
							for sgram, scount in sgramcounter.items():
														A_ 						=		scount * numref

							A_ 						=		Counter(UpperCamelCase_   )
							A_ 						=		Counter()
							for cgram, ccount in cgramcounter.items():
														A_ 						=		ccount * numref

							# KEEP
							A_ 						=		sgramcounter_rep & cgramcounter_rep
							A_ 						=		keepgramcounter_rep & rgramcounter
							A_ 						=		sgramcounter_rep & rgramcounter

							A_ 						=		0
							A_ 						=		0
							for keepgram in keepgramcountergood_rep:
														keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
														# Fix an alleged bug [2] in the keep score computation.
														# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
														keeptmpscorea += keepgramcountergood_rep[keepgram]
							# Define 0/0=1 instead of 0 to give higher scores for predictions that match
							#      a target exactly.
							A_ 						=		1
							A_ 						=		1
							if len(UpperCamelCase_   ) > 0:
														A_ 						=		keeptmpscorea / len(UpperCamelCase_   )
							if len(UpperCamelCase_   ) > 0:
														# Fix an alleged bug [2] in the keep score computation.
														# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
														A_ 						=		keeptmpscorea / sum(keepgramcounterall_rep.values()   )
							A_ 						=		0
							if keepscore_precision > 0 or keepscore_recall > 0:
														A_ 						=		2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

							# DELETION
							A_ 						=		sgramcounter_rep - cgramcounter_rep
							A_ 						=		delgramcounter_rep - rgramcounter
							A_ 						=		sgramcounter_rep - rgramcounter
							A_ 						=		0
							A_ 						=		0
							for delgram in delgramcountergood_rep:
														deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
														deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
							# Define 0/0=1 instead of 0 to give higher scores for predictions that match
							# a target exactly.
							A_ 						=		1
							if len(UpperCamelCase_   ) > 0:
														A_ 						=		deltmpscorea / len(UpperCamelCase_   )

							# ADDITION
							A_ 						=		set(UpperCamelCase_   ) - set(UpperCamelCase_   )
							A_ 						=		set(UpperCamelCase_   ) & set(UpperCamelCase_   )
							A_ 						=		set(UpperCamelCase_   ) - set(UpperCamelCase_   )

							A_ 						=		0
							for addgram in addgramcountergood:
														addtmpscore += 1

							# Define 0/0=1 instead of 0 to give higher scores for predictions that match
							# a target exactly.
							A_ 						=		1
							A_ 						=		1
							if len(UpperCamelCase_   ) > 0:
														A_ 						=		addtmpscore / len(UpperCamelCase_   )
							if len(UpperCamelCase_   ) > 0:
														A_ 						=		addtmpscore / len(UpperCamelCase_   )
							A_ 						=		0
							if addscore_precision > 0 or addscore_recall > 0:
														A_ 						=		2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

							return (keepscore, delscore_precision, addscore)







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							A_ 						=		len(UpperCamelCase_   )

							A_ 						=		ssent.split(" "   )
							A_ 						=		csent.split(" "   )
							A_ 						=		[]
							A_ 						=		[]
							A_ 						=		[]
							A_ 						=		[]
							A_ 						=		[]
							A_ 						=		[]

							A_ 						=		[]
							A_ 						=		[]
							A_ 						=		[]
							A_ 						=		[]
							for rsent in rsents:
														A_ 						=		rsent.split(" "   )
														A_ 						=		[]
														A_ 						=		[]
														A_ 						=		[]
														ragramslist.append(UpperCamelCase_   )
														for i in range(0	,     len(UpperCamelCase_   ) - 1   ):
																					if i < len(UpperCamelCase_   ) - 1:
																												A_ 						=		ragrams[i] + " " + ragrams[i + 1]
																												ragrams.append(UpperCamelCase_   )
																					if i < len(UpperCamelCase_   ) - 2:
																												A_ 						=		ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
																												ragrams.append(UpperCamelCase_   )
																					if i < len(UpperCamelCase_   ) - 3:
																												A_ 						=		ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2] + " " + ragrams[i + 3]
																												ragrams.append(UpperCamelCase_   )
														ragramslist.append(UpperCamelCase_   )
														ragramslist.append(UpperCamelCase_   )
														ragramslist.append(UpperCamelCase_   )

							for i in range(0	,     len(UpperCamelCase_   ) - 1   ):
														if i < len(UpperCamelCase_   ) - 1:
																					A_ 						=		sagrams[i] + " " + sagrams[i + 1]
																					sagrams.append(UpperCamelCase_   )
														if i < len(UpperCamelCase_   ) - 2:
																					A_ 						=		sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
																					sagrams.append(UpperCamelCase_   )
														if i < len(UpperCamelCase_   ) - 3:
																					A_ 						=		sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
																					sagrams.append(UpperCamelCase_   )

							for i in range(0	,     len(UpperCamelCase_   ) - 1   ):
														if i < len(UpperCamelCase_   ) - 1:
																					A_ 						=		cagrams[i] + " " + cagrams[i + 1]
																					cagrams.append(UpperCamelCase_   )
														if i < len(UpperCamelCase_   ) - 2:
																					A_ 						=		cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
																					cagrams.append(UpperCamelCase_   )
														if i < len(UpperCamelCase_   ) - 3:
																					A_ 						=		cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
																					cagrams.append(UpperCamelCase_   )

							((A_)						,				(A_)						,				(A_)) 						=		SARIngram(UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   )
							((A_)						,				(A_)						,				(A_)) 						=		SARIngram(UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   )
							((A_)						,				(A_)						,				(A_)) 						=		SARIngram(UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   )
							((A_)						,				(A_)						,				(A_)) 						=		SARIngram(UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   )
							A_ 						=		sum([keepascore, keepascore, keepascore, keepascore]   ) / 4
							A_ 						=		sum([delascore, delascore, delascore, delascore]   ) / 4
							A_ 						=		sum([addascore, addascore, addascore, addascore]   ) / 4
							A_ 						=		(avgkeepscore + avgdelscore + avgaddscore) / 3
							return finalscore







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_ = True	,     UpperCamelCase_ = "13a"	,     UpperCamelCase_ = True   ):
							# Normalization is requried for the ASSET dataset (one of the primary
							# datasets in sentence simplification) to allow using space
							# to split the sentence. Even though Wiki-Auto and TURK datasets,
							# do not require normalization, we do it for consistency.
							# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
							# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

							if lowercase:
														A_ 						=		sentence.lower()

							if tokenizer in ["13a", "intl"]:
														if version.parse(sacrebleu.__version__   ).major >= 2:
																					A_ 						=		sacrebleu.metrics.bleu._get_tokenizer(UpperCamelCase_   )()(UpperCamelCase_   )
														else:
																					A_ 						=		sacrebleu.TOKENIZERS[tokenizer]()(UpperCamelCase_   )
							elif tokenizer == "moses":
														A_ 						=		sacremoses.MosesTokenizer().tokenize(UpperCamelCase_	,     return_str=UpperCamelCase_	,     escape=UpperCamelCase_   )
							elif tokenizer == "penn":
														A_ 						=		sacremoses.MosesTokenizer().penn_tokenize(UpperCamelCase_	,     return_str=UpperCamelCase_   )
							else:
														A_ 						=		sentence

							if not return_str:
														A_ 						=		normalized_sent.split()

							return normalized_sent







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

						"""simple docstring"""






						def 							_UpperCAmelCase					(     self       :			Optional[int]  ):
													return datasets.MetricInfo(
													    description=_DESCRIPTION   ,     citation=_CITATION   ,     inputs_description=_KWARGS_DESCRIPTION   ,     features=datasets.Features(
													        {
													            "sources": datasets.Value("string"   ,     id="sequence"  ),
													            "predictions": datasets.Value("string"   ,     id="sequence"  ),
													            "references": datasets.Sequence(datasets.Value("string"   ,     id="sequence"  )   ,     id="references"  ),
													        }  )   ,     codebase_urls=[
													        "https://github.com/cocoxu/simplification/blob/master/SARI.py",
													        "https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py",
													    ]   ,     reference_urls=["https://www.aclweb.org/anthology/Q16-1029.pdf"]   ,     )






						def 							_UpperCAmelCase					(     self       :			Tuple   ,     lowerCAmelCase       :			Optional[Any]   ,     lowerCAmelCase       :			Dict   ,     lowerCAmelCase       :			List[Any]  ):
													if not (len(lowerCAmelCase  ) == len(lowerCAmelCase  ) == len(lowerCAmelCase  )):
																				raise ValueError("Sources length must match predictions and references lengths."  )
													A_ 						=		0
													for src, pred, refs in zip(lowerCAmelCase   ,     lowerCAmelCase   ,     lowerCAmelCase  ):
																				sari_score += SARIsent(normalize(lowerCAmelCase  )   ,     normalize(lowerCAmelCase  )   ,     [normalize(lowerCAmelCase  ) for sent in refs]  )
													A_ 						=		sari_score / len(lowerCAmelCase  )
													return {"sari": 1_00 * sari_score}





