

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()
_a	 =     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_a	 =     []
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				( SCREAMING_SNAKE_CASE	:    Dict					,      SCREAMING_SNAKE_CASE	:    Tuple					,      SCREAMING_SNAKE_CASE	:    Optional[Any]	)					->					Optional[Any]:

 """simple docstring"""






 __lowerCAmelCase:     Tuple     			=   state_dict.pop(SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     Optional[int]     			=   val







def _a				( SCREAMING_SNAKE_CASE	:    str	)					->					Tuple:

 """simple docstring"""






 __lowerCAmelCase:     List[str]     			=   OrderedDict()
 for key, value in state_dict.items():
  if "backbone.0.body" in key:
   __lowerCAmelCase:     int     			=   key.replace('backbone.0.body'					,      'backbone.conv_encoder.model'	)
   __lowerCAmelCase:     Any     			=   value
  else:
   __lowerCAmelCase:     Tuple     			=   value

 return new_state_dict







def _a				( SCREAMING_SNAKE_CASE	:    List[Any]					,      SCREAMING_SNAKE_CASE	:    int=False	)					->					str:

 """simple docstring"""






 __lowerCAmelCase:     Optional[Any]     			=   ''
 if is_panoptic:
  __lowerCAmelCase:     Optional[Any]     			=   '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)
  __lowerCAmelCase:     Optional[Any]     			=   state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''	)
  __lowerCAmelCase:     List[str]     			=   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
  __lowerCAmelCase:     Dict     			=   in_proj_weight[:2_56, :]
  __lowerCAmelCase:     Any     			=   in_proj_bias[:2_56]
  __lowerCAmelCase:     List[Any]     			=   in_proj_weight[2_56:5_12, :]
  __lowerCAmelCase:     Tuple     			=   in_proj_bias[2_56:5_12]
  __lowerCAmelCase:     int     			=   in_proj_weight[-2_56:, :]
  __lowerCAmelCase:     Optional[int]     			=   in_proj_bias[-2_56:]
 # 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
  __lowerCAmelCase:     Optional[Any]     			=   state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''	)
  __lowerCAmelCase:     int     			=   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
  __lowerCAmelCase:     List[Any]     			=   in_proj_weight[:2_56, :]
  __lowerCAmelCase:     Optional[int]     			=   in_proj_bias[:2_56]
  __lowerCAmelCase:     Tuple     			=   in_proj_weight[2_56:5_12, :]
  __lowerCAmelCase:     Optional[Any]     			=   in_proj_bias[2_56:5_12]
  __lowerCAmelCase:     Optional[int]     			=   in_proj_weight[-2_56:, :]
  __lowerCAmelCase:     str     			=   in_proj_bias[-2_56:]
  # read in weights + bias of input projection layer of cross-attention
  __lowerCAmelCase:     int     			=   state_dict.pop(
      f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''	)
  __lowerCAmelCase:     Dict     			=   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
  __lowerCAmelCase:     Any     			=   in_proj_weight_cross_attn[:2_56, :]
  __lowerCAmelCase:     str     			=   in_proj_bias_cross_attn[:2_56]
  __lowerCAmelCase:     Optional[Any]     			=   in_proj_weight_cross_attn[2_56:5_12, :]
  __lowerCAmelCase:     Optional[Any]     			=   in_proj_bias_cross_attn[2_56:5_12]
  __lowerCAmelCase:     List[str]     			=   in_proj_weight_cross_attn[-2_56:, :]
  __lowerCAmelCase:     Tuple     			=   in_proj_bias_cross_attn[-2_56:]







def _a				( )					->					str:

 """simple docstring"""






 __lowerCAmelCase:     Any     			=   'http://images.cocodataset.org/val2017/000000039769.jpg'
 __lowerCAmelCase:     List[str]     			=   Image.open(requests.get(SCREAMING_SNAKE_CASE					,      stream=SCREAMING_SNAKE_CASE	).raw	)

 return im







@torch.no_grad()
def _a				( SCREAMING_SNAKE_CASE	:    Optional[int]					,      SCREAMING_SNAKE_CASE	:    int	)					->					Tuple:

 """simple docstring"""






 __lowerCAmelCase:     List[Any]     			=   DetrConfig()
 # set backbone and dilation attributes
 if "resnet101" in model_name:
  __lowerCAmelCase:     str     			=   'resnet101'
 if "dc5" in model_name:
  __lowerCAmelCase:     Tuple     			=   True
 __lowerCAmelCase:     List[Any]     			=   'panoptic' in model_name
 if is_panoptic:
  __lowerCAmelCase:     List[Any]     			=   2_50
 else:
  __lowerCAmelCase:     Dict     			=   91
  __lowerCAmelCase:     List[str]     			=   'huggingface/label-files'
  __lowerCAmelCase:     Optional[Any]     			=   'coco-detection-id2label.json'
  __lowerCAmelCase:     Optional[Any]     			=   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      repo_type='dataset'	)					,      'r'	)	)
  __lowerCAmelCase:     int     			=   {int(SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}
  __lowerCAmelCase:     Optional[int]     			=   idalabel
  __lowerCAmelCase:     List[str]     			=   {v: k for k, v in idalabel.items()}

 # load image processor
 __lowerCAmelCase:     Optional[Any]     			=   'coco_panoptic' if is_panoptic else 'coco_detection'
 __lowerCAmelCase:     List[Any]     			=   DetrImageProcessor(format=SCREAMING_SNAKE_CASE	)

 # prepare image
 __lowerCAmelCase:     Tuple     			=   prepare_img()
 __lowerCAmelCase:     Optional[Any]     			=   image_processor(images=SCREAMING_SNAKE_CASE					,      return_tensors='pt'	)
 __lowerCAmelCase:     List[str]     			=   encoding['pixel_values']

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

 # load original model from torch hub
 __lowerCAmelCase:     List[Any]     			=   torch.hub.load('facebookresearch/detr'					,      SCREAMING_SNAKE_CASE					,      pretrained=SCREAMING_SNAKE_CASE	).eval()
 __lowerCAmelCase:     Optional[int]     			=   detr.state_dict()
 # rename keys
 for src, dest in rename_keys:
  if is_panoptic:
   __lowerCAmelCase:     Dict     			=   'detr.' + src
  rename_key(SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     List[str]     			=   rename_backbone_keys(SCREAMING_SNAKE_CASE	)
 # query, key and value matrices need special treatment
 read_in_q_k_v(SCREAMING_SNAKE_CASE					,      is_panoptic=SCREAMING_SNAKE_CASE	)
 # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
 __lowerCAmelCase:     int     			=   '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'	)
   ):
    __lowerCAmelCase:     int     			=   state_dict.pop(SCREAMING_SNAKE_CASE	)
    __lowerCAmelCase:     Union[str, Any]     			=   val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    __lowerCAmelCase:     Optional[int]     			=   state_dict.pop(SCREAMING_SNAKE_CASE	)
    __lowerCAmelCase:     int     			=   val
   elif key.startswith('bbox_attention'	) or key.startswith('mask_head'	):
    continue
   else:
    __lowerCAmelCase:     Optional[Any]     			=   state_dict.pop(SCREAMING_SNAKE_CASE	)
    __lowerCAmelCase:     Optional[Any]     			=   val
  else:
   if not key.startswith('class_labels_classifier'	) and not key.startswith('bbox_predictor'	):
    __lowerCAmelCase:     Optional[Any]     			=   state_dict.pop(SCREAMING_SNAKE_CASE	)
    __lowerCAmelCase:     List[str]     			=   val
    # finally, create HuggingFace model and load state dict
 __lowerCAmelCase:     Any     			=   DetrForSegmentation(SCREAMING_SNAKE_CASE	) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE	)
 model.load_state_dict(SCREAMING_SNAKE_CASE	)
 model.eval()
 # verify our conversion
 __lowerCAmelCase:     Dict     			=   detr(SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     Union[str, Any]     			=   model(SCREAMING_SNAKE_CASE	)
 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(SCREAMING_SNAKE_CASE	).mkdir(exist_ok=SCREAMING_SNAKE_CASE	)
 model.save_pretrained(SCREAMING_SNAKE_CASE	)
 image_processor.save_pretrained(SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
  _a	 =     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.'''
  )
  _a	 =     parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_a	 =     '''\
@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},
}
'''

_a	 =     '''\
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
'''


_a	 =     '''
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				( SCREAMING_SNAKE_CASE	:    Dict					,      SCREAMING_SNAKE_CASE	:    Optional[Any]					,      SCREAMING_SNAKE_CASE	:    Tuple					,      SCREAMING_SNAKE_CASE	:    Optional[Any]	)					->					Dict:

 """simple docstring"""






 __lowerCAmelCase:     str     			=   [rgram for rgrams in rgramslist for rgram in rgrams]
 __lowerCAmelCase:     List[Any]     			=   Counter(SCREAMING_SNAKE_CASE	)

 __lowerCAmelCase:     List[str]     			=   Counter(SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     int     			=   Counter()
 for sgram, scount in sgramcounter.items():
  __lowerCAmelCase:     Optional[int]     			=   scount * numref

 __lowerCAmelCase:     Optional[Any]     			=   Counter(SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     Optional[Any]     			=   Counter()
 for cgram, ccount in cgramcounter.items():
  __lowerCAmelCase:     Tuple     			=   ccount * numref

 # KEEP
 __lowerCAmelCase:     int     			=   sgramcounter_rep & cgramcounter_rep
 __lowerCAmelCase:     Union[str, Any]     			=   keepgramcounter_rep & rgramcounter
 __lowerCAmelCase:     Optional[int]     			=   sgramcounter_rep & rgramcounter

 __lowerCAmelCase:     Union[str, Any]     			=   0
 __lowerCAmelCase:     Any     			=   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.
 __lowerCAmelCase:     Tuple     			=   1
 __lowerCAmelCase:     List[Any]     			=   1
 if len(SCREAMING_SNAKE_CASE	) > 0:
  __lowerCAmelCase:     Tuple     			=   keeptmpscorea / len(SCREAMING_SNAKE_CASE	)
 if len(SCREAMING_SNAKE_CASE	) > 0:
  # Fix an alleged bug [2] in the keep score computation.
  # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
  __lowerCAmelCase:     str     			=   keeptmpscorea / sum(keepgramcounterall_rep.values()	)
 __lowerCAmelCase:     List[str]     			=   0
 if keepscore_precision > 0 or keepscore_recall > 0:
  __lowerCAmelCase:     List[Any]     			=   2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

 # DELETION
 __lowerCAmelCase:     List[Any]     			=   sgramcounter_rep - cgramcounter_rep
 __lowerCAmelCase:     Optional[Any]     			=   delgramcounter_rep - rgramcounter
 __lowerCAmelCase:     str     			=   sgramcounter_rep - rgramcounter
 __lowerCAmelCase:     int     			=   0
 __lowerCAmelCase:     Optional[int]     			=   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.
 __lowerCAmelCase:     List[str]     			=   1
 if len(SCREAMING_SNAKE_CASE	) > 0:
  __lowerCAmelCase:     List[Any]     			=   deltmpscorea / len(SCREAMING_SNAKE_CASE	)

 # ADDITION
 __lowerCAmelCase:     Optional[Any]     			=   set(SCREAMING_SNAKE_CASE	) - set(SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     int     			=   set(SCREAMING_SNAKE_CASE	) & set(SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     Tuple     			=   set(SCREAMING_SNAKE_CASE	) - set(SCREAMING_SNAKE_CASE	)

 __lowerCAmelCase:     Union[str, Any]     			=   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.
 __lowerCAmelCase:     Optional[Any]     			=   1
 __lowerCAmelCase:     Union[str, Any]     			=   1
 if len(SCREAMING_SNAKE_CASE	) > 0:
  __lowerCAmelCase:     int     			=   addtmpscore / len(SCREAMING_SNAKE_CASE	)
 if len(SCREAMING_SNAKE_CASE	) > 0:
  __lowerCAmelCase:     str     			=   addtmpscore / len(SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     Dict     			=   0
 if addscore_precision > 0 or addscore_recall > 0:
  __lowerCAmelCase:     str     			=   2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

 return (keepscore, delscore_precision, addscore)







def _a				( SCREAMING_SNAKE_CASE	:    Optional[int]					,      SCREAMING_SNAKE_CASE	:    List[Any]					,      SCREAMING_SNAKE_CASE	:    Optional[Any]	)					->					List[str]:

 """simple docstring"""






 __lowerCAmelCase:     List[Any]     			=   len(SCREAMING_SNAKE_CASE	)

 __lowerCAmelCase:     int     			=   ssent.split(' '	)
 __lowerCAmelCase:     List[str]     			=   csent.split(' '	)
 __lowerCAmelCase:     Dict     			=   []
 __lowerCAmelCase:     Any     			=   []
 __lowerCAmelCase:     Optional[int]     			=   []
 __lowerCAmelCase:     str     			=   []
 __lowerCAmelCase:     Dict     			=   []
 __lowerCAmelCase:     str     			=   []

 __lowerCAmelCase:     str     			=   []
 __lowerCAmelCase:     Dict     			=   []
 __lowerCAmelCase:     Dict     			=   []
 __lowerCAmelCase:     Optional[Any]     			=   []
 for rsent in rsents:
  __lowerCAmelCase:     List[Any]     			=   rsent.split(' '	)
  __lowerCAmelCase:     Optional[int]     			=   []
  __lowerCAmelCase:     List[str]     			=   []
  __lowerCAmelCase:     Optional[int]     			=   []
  ragramslist.append(SCREAMING_SNAKE_CASE	)
  for i in range(0					,      len(SCREAMING_SNAKE_CASE	) - 1	):
   if i < len(SCREAMING_SNAKE_CASE	) - 1:
    __lowerCAmelCase:     List[str]     			=   ragrams[i] + ' ' + ragrams[i + 1]
    ragrams.append(SCREAMING_SNAKE_CASE	)
   if i < len(SCREAMING_SNAKE_CASE	) - 2:
    __lowerCAmelCase:     Union[str, Any]     			=   ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
    ragrams.append(SCREAMING_SNAKE_CASE	)
   if i < len(SCREAMING_SNAKE_CASE	) - 3:
    __lowerCAmelCase:     Optional[int]     			=   ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2] + ' ' + ragrams[i + 3]
    ragrams.append(SCREAMING_SNAKE_CASE	)
  ragramslist.append(SCREAMING_SNAKE_CASE	)
  ragramslist.append(SCREAMING_SNAKE_CASE	)
  ragramslist.append(SCREAMING_SNAKE_CASE	)

 for i in range(0					,      len(SCREAMING_SNAKE_CASE	) - 1	):
  if i < len(SCREAMING_SNAKE_CASE	) - 1:
   __lowerCAmelCase:     Optional[int]     			=   sagrams[i] + ' ' + sagrams[i + 1]
   sagrams.append(SCREAMING_SNAKE_CASE	)
  if i < len(SCREAMING_SNAKE_CASE	) - 2:
   __lowerCAmelCase:     Any     			=   sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
   sagrams.append(SCREAMING_SNAKE_CASE	)
  if i < len(SCREAMING_SNAKE_CASE	) - 3:
   __lowerCAmelCase:     Optional[int]     			=   sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
   sagrams.append(SCREAMING_SNAKE_CASE	)

 for i in range(0					,      len(SCREAMING_SNAKE_CASE	) - 1	):
  if i < len(SCREAMING_SNAKE_CASE	) - 1:
   __lowerCAmelCase:     Optional[int]     			=   cagrams[i] + ' ' + cagrams[i + 1]
   cagrams.append(SCREAMING_SNAKE_CASE	)
  if i < len(SCREAMING_SNAKE_CASE	) - 2:
   __lowerCAmelCase:     str     			=   cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
   cagrams.append(SCREAMING_SNAKE_CASE	)
  if i < len(SCREAMING_SNAKE_CASE	) - 3:
   __lowerCAmelCase:     str     			=   cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
   cagrams.append(SCREAMING_SNAKE_CASE	)

 ((__lowerCAmelCase)			,							(__lowerCAmelCase)			,							(__lowerCAmelCase)):     Union[str, Any]     			=   SARIngram(SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE	)
 ((__lowerCAmelCase)			,							(__lowerCAmelCase)			,							(__lowerCAmelCase)):     str     			=   SARIngram(SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE	)
 ((__lowerCAmelCase)			,							(__lowerCAmelCase)			,							(__lowerCAmelCase)):     Union[str, Any]     			=   SARIngram(SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE	)
 ((__lowerCAmelCase)			,							(__lowerCAmelCase)			,							(__lowerCAmelCase)):     Tuple     			=   SARIngram(SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     List[Any]     			=   sum([keepascore, keepascore, keepascore, keepascore]	) / 4
 __lowerCAmelCase:     Any     			=   sum([delascore, delascore, delascore, delascore]	) / 4
 __lowerCAmelCase:     Any     			=   sum([addascore, addascore, addascore, addascore]	) / 4
 __lowerCAmelCase:     List[Any]     			=   (avgkeepscore + avgdelscore + avgaddscore) / 3
 return finalscore







def _a				( SCREAMING_SNAKE_CASE	:    int					,      SCREAMING_SNAKE_CASE	:    bool = True					,      SCREAMING_SNAKE_CASE	:    str = "13a"					,      SCREAMING_SNAKE_CASE	:    bool = True	)					->					Optional[Any]:

 """simple docstring"""






 if lowercase:
  __lowerCAmelCase:     str     			=   sentence.lower()

 if tokenizer in ["13a", "intl"]:
  if version.parse(sacrebleu.__version__	).major >= 2:
   __lowerCAmelCase:     Dict     			=   sacrebleu.metrics.bleu._get_tokenizer(SCREAMING_SNAKE_CASE	)()(SCREAMING_SNAKE_CASE	)
  else:
   __lowerCAmelCase:     str     			=   sacrebleu.TOKENIZERS[tokenizer]()(SCREAMING_SNAKE_CASE	)
 elif tokenizer == "moses":
  __lowerCAmelCase:     Optional[int]     			=   sacremoses.MosesTokenizer().tokenize(SCREAMING_SNAKE_CASE					,      return_str=SCREAMING_SNAKE_CASE					,      escape=SCREAMING_SNAKE_CASE	)
 elif tokenizer == "penn":
  __lowerCAmelCase:     List[str]     			=   sacremoses.MosesTokenizer().penn_tokenize(SCREAMING_SNAKE_CASE					,      return_str=SCREAMING_SNAKE_CASE	)
 else:
  __lowerCAmelCase:     Any     			=   sentence

 if not return_str:
  __lowerCAmelCase:     List[Any]     			=   normalized_sent.split()

 return normalized_sent






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


     def 						UpperCAmelCase						(							self    :	int      )							->			Tuple:
      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    :	Any    ,						UpperCAmelCase    :	List[Any]    ,						UpperCAmelCase    :	List[str]    ,						UpperCAmelCase    :	int      )							->			Optional[Any]:
      if not (len(UpperCAmelCase      ) == len(UpperCAmelCase      ) == len(UpperCAmelCase      )):
       raise ValueError('Sources length must match predictions and references lengths.'      )
      __lowerCAmelCase:     Optional[int]     			=   0
      for src, pred, refs in zip(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      ):
       sari_score += SARIsent(normalize(UpperCAmelCase      )    ,						normalize(UpperCAmelCase      )    ,						[normalize(UpperCAmelCase      ) for sent in refs]      )
      __lowerCAmelCase:     int     			=   sari_score / len(UpperCAmelCase      )
      return {"sari": 1_0_0 * sari_score}





