





"""simple docstring"""





import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCamelCase							=      logging.get_logger(__name__)


_UpperCamelCase							=      'https://openaipublic.azureedge.net/jukebox/models/'
_UpperCamelCase							=      {
    'jukebox-1b-lyrics': [
        '5b/vqvae.pth.tar',
        '5b/prior_level_0.pth.tar',
        '5b/prior_level_1.pth.tar',
        '1b_lyrics/prior_level_2.pth.tar',
    ],
    'jukebox-5b-lyrics': [
        '5b/vqvae.pth.tar',
        '5b/prior_level_0.pth.tar',
        '5b/prior_level_1.pth.tar',
        '5b_lyrics/prior_level_2.pth.tar',
    ],
}






def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Tuple   ):


							'''simple docstring'''







							if key.endswith('''.model.1.bias'''   ) and len(key.split('''.'''   )   ) > 10:
														__lowerCamelCase      :      int         =key.replace('''.model.1.bias'''    ,						'''.conv1d_1.bias'''   )
							elif key.endswith('''.model.1.weight'''   ) and len(key.split('''.'''   )   ) > 10:
														__lowerCamelCase      :      Optional[Any]         =key.replace('''.model.1.weight'''    ,						'''.conv1d_1.weight'''   )
							elif key.endswith('''.model.3.bias'''   ) and len(key.split('''.'''   )   ) > 10:
														__lowerCamelCase      :      Dict         =key.replace('''.model.3.bias'''    ,						'''.conv1d_2.bias'''   )
							elif key.endswith('''.model.3.weight'''   ) and len(key.split('''.'''   )   ) > 10:
														__lowerCamelCase      :      int         =key.replace('''.model.3.weight'''    ,						'''.conv1d_2.weight'''   )

							if "conditioner_blocks.0." in key:
														__lowerCamelCase      :      List[Any]         =key.replace('''conditioner_blocks.0'''    ,						'''conditioner_blocks'''   )

							if "prime_prior" in key:
														__lowerCamelCase      :      Tuple         =key.replace('''prime_prior'''    ,						'''encoder'''   )

							if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
														__lowerCamelCase      :      List[str]         =key.replace('''.emb.'''    ,						'''.'''   )

							if key.endswith('''k'''   ):  # replace vqvae.X.k with vqvae.X.codebook
														return key.replace('''.k'''    ,						'''.codebook'''   )
							if "y_emb." in key:
														return key.replace('''y_emb.'''    ,						'''metadata_embedding.'''   )

							if "x_emb.emb." in key:
														__lowerCamelCase      :      List[str]         =key.replace('''0.x_emb.emb'''    ,						'''embed_tokens'''   )

							if "prime_state_ln" in key:
														return key.replace('''prime_state_ln'''    ,						'''encoder.final_layer_norm'''   )
							if ".ln" in key:
														return key.replace('''.ln'''    ,						'''.layer_norm'''   )
							if "_ln" in key:
														return key.replace('''_ln'''    ,						'''_layer_norm'''   )

							if "prime_state_proj" in key:
														return key.replace('''prime_state_proj'''    ,						'''encoder.proj_in'''   )
							if "prime_x_out" in key:
														return key.replace('''prime_x_out'''    ,						'''encoder.lm_head'''   )
							if "prior.x_out" in key:
														return key.replace('''x_out'''    ,						'''fc_proj_out'''   )
							if "x_emb" in key:
														return key.replace('''x_emb'''    ,						'''embed_tokens'''   )

							return key






def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[Any]    ,						SCREAMING_SNAKE_CASE       :							Any    ,						SCREAMING_SNAKE_CASE       :							Tuple    ,						SCREAMING_SNAKE_CASE       :							List[str]   ):


							'''simple docstring'''







							__lowerCamelCase      :      int         ={}
							import re

							__lowerCamelCase      :      List[str]         =re.compile(r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''   )
							__lowerCamelCase      :      Union[str, Any]         =re.compile(
							    r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''   )
							__lowerCamelCase      :      int         =re.compile(r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''   )

							__lowerCamelCase      :      List[str]         =re.compile(r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''   )
							__lowerCamelCase      :      int         =re.compile(
							    r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''   )
							__lowerCamelCase      :      str         =re.compile(r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''   )

							__lowerCamelCase      :      int         =re.compile(r'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'''   )
							__lowerCamelCase      :      List[str]         =re.compile(
							    r'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''   )
							__lowerCamelCase      :      Tuple         =re.compile(r'''conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)'''   )

							for original_key, value in state_dict.items():
														# rename vqvae.encoder keys
														if re_encoder_block_conv_in.fullmatch(SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      Optional[int]         =re_encoder_block_conv_in.match(SCREAMING_SNAKE_CASE   )
																					__lowerCamelCase      :      int         =regex_match.groups()
																					__lowerCamelCase      :      int         =int(groups[2]   ) * 2 + int(groups[3]   )
																					__lowerCamelCase      :      Tuple         =F'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'
																					__lowerCamelCase      :      int         =re_encoder_block_conv_in.sub(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

														elif re_encoder_block_resnet.fullmatch(SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      Union[str, Any]         =re_encoder_block_resnet.match(SCREAMING_SNAKE_CASE   )
																					__lowerCamelCase      :      List[str]         =regex_match.groups()
																					__lowerCamelCase      :      Any         =int(groups[2]   ) * 2 + int(groups[3]   )
																					__lowerCamelCase      :      Optional[int]         ={'''1''': 1, '''3''': 2}[groups[-2]]
																					__lowerCamelCase      :      Union[str, Any]         =F'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'
																					__lowerCamelCase      :      str         =F'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
																					__lowerCamelCase      :      List[Any]         =prefix + resnet_block
																					__lowerCamelCase      :      Tuple         =re_encoder_block_resnet.sub(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

														elif re_encoder_block_proj_out.fullmatch(SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      Optional[int]         =re_encoder_block_proj_out.match(SCREAMING_SNAKE_CASE   )
																					__lowerCamelCase      :      List[Any]         =regex_match.groups()
																					__lowerCamelCase      :      Optional[int]         =F'encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'
																					__lowerCamelCase      :      Tuple         =re_encoder_block_proj_out.sub(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

														# rename vqvae.decoder keys
														elif re_decoder_block_conv_out.fullmatch(SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      Optional[int]         =re_decoder_block_conv_out.match(SCREAMING_SNAKE_CASE   )
																					__lowerCamelCase      :      List[str]         =regex_match.groups()
																					__lowerCamelCase      :      Any         =int(groups[2]   ) * 2 + int(groups[3]   ) - 2
																					__lowerCamelCase      :      Any         =F'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'
																					__lowerCamelCase      :      str         =re_decoder_block_conv_out.sub(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

														elif re_decoder_block_resnet.fullmatch(SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      Union[str, Any]         =re_decoder_block_resnet.match(SCREAMING_SNAKE_CASE   )
																					__lowerCamelCase      :      List[str]         =regex_match.groups()
																					__lowerCamelCase      :      Union[str, Any]         =int(groups[2]   ) * 2 + int(groups[3]   ) - 2
																					__lowerCamelCase      :      List[str]         ={'''1''': 1, '''3''': 2}[groups[-2]]
																					__lowerCamelCase      :      List[Any]         =F'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'
																					__lowerCamelCase      :      str         =F'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
																					__lowerCamelCase      :      Tuple         =prefix + resnet_block
																					__lowerCamelCase      :      Union[str, Any]         =re_decoder_block_resnet.sub(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

														elif re_decoder_block_proj_in.fullmatch(SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      int         =re_decoder_block_proj_in.match(SCREAMING_SNAKE_CASE   )
																					__lowerCamelCase      :      List[Any]         =regex_match.groups()
																					__lowerCamelCase      :      Tuple         =F'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
																					__lowerCamelCase      :      Union[str, Any]         =re_decoder_block_proj_in.sub(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

														# rename prior cond.model to upsampler.upsample_block and resnet
														elif re_prior_cond_conv_out.fullmatch(SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      Any         =re_prior_cond_conv_out.match(SCREAMING_SNAKE_CASE   )
																					__lowerCamelCase      :      int         =regex_match.groups()
																					__lowerCamelCase      :      Optional[Any]         =int(groups[1]   ) * 2 + int(groups[2]   ) - 2
																					__lowerCamelCase      :      str         =F'conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'
																					__lowerCamelCase      :      List[Any]         =re_prior_cond_conv_out.sub(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

														elif re_prior_cond_resnet.fullmatch(SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      Optional[int]         =re_prior_cond_resnet.match(SCREAMING_SNAKE_CASE   )
																					__lowerCamelCase      :      Tuple         =regex_match.groups()
																					__lowerCamelCase      :      Optional[Any]         =int(groups[1]   ) * 2 + int(groups[2]   ) - 2
																					__lowerCamelCase      :      int         ={'''1''': 1, '''3''': 2}[groups[-2]]
																					__lowerCamelCase      :      List[Any]         =F'conditioner_blocks.upsampler.upsample_block.{block_index}.'
																					__lowerCamelCase      :      Union[str, Any]         =F'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
																					__lowerCamelCase      :      Tuple         =prefix + resnet_block
																					__lowerCamelCase      :      List[Any]         =re_prior_cond_resnet.sub(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

														elif re_prior_cond_proj_in.fullmatch(SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      List[Any]         =re_prior_cond_proj_in.match(SCREAMING_SNAKE_CASE   )
																					__lowerCamelCase      :      Union[str, Any]         =regex_match.groups()
																					__lowerCamelCase      :      str         =F'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
																					__lowerCamelCase      :      int         =re_prior_cond_proj_in.sub(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

														# keep original key
														else:
																					__lowerCamelCase      :      str         =original_key

														__lowerCamelCase      :      Optional[int]         =replace_key(SCREAMING_SNAKE_CASE   )

														if F'{key_prefix}.{key}' not in model_state_dict or key is None:
																					print(F'failed converting {original_key} to {key}, does not match'   )

														# handle missmatched shape
														elif value.shape != model_state_dict[F'{key_prefix}.{key}'].shape:
																					__lowerCamelCase      :      Union[str, Any]         =model_state_dict[F'{key_prefix}.{key}']
																					print(F'{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'   )
																					__lowerCamelCase      :      int         =original_key

														__lowerCamelCase      :      Union[str, Any]         =original_key
														__lowerCamelCase      :      Union[str, Any]         =value

							return new_dict






@torch.no_grad()
def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[str]=None    ,						SCREAMING_SNAKE_CASE       :							Tuple=None   ):


							'''simple docstring'''







							for file in MODEL_MAPPING[model_name]:
														if not os.path.isfile(F'{pytorch_dump_folder_path}/{file.split("/"   )[-1]}'   ):
																					__lowerCamelCase      :      int         =requests.get(F'{PREFIX}{file}'    ,						allow_redirects=SCREAMING_SNAKE_CASE   )
																					os.makedirs(F'{pytorch_dump_folder_path}/'    ,						exist_ok=SCREAMING_SNAKE_CASE   )
																					open(F'{pytorch_dump_folder_path}/{file.split("/"   )[-1]}'    ,						'''wb'''   ).write(r.content   )

							__lowerCamelCase      :      Any         =MODEL_MAPPING[model_name.split('''/'''   )[-1]]

							__lowerCamelCase      :      List[str]         =JukeboxConfig.from_pretrained(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Union[str, Any]         =JukeboxModel(SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      Union[str, Any]         =[]
							__lowerCamelCase      :      Dict         ={}
							for i, dict_name in enumerate(SCREAMING_SNAKE_CASE   ):
														__lowerCamelCase      :      List[Any]         =torch.load(F'{pytorch_dump_folder_path}/{dict_name.split("/"   )[-1]}'   )['''model''']

														__lowerCamelCase      :      List[Any]         ={}
														for k in old_dic.keys():
																					if k.endswith('''.b'''   ):
																												__lowerCamelCase      :      Tuple         =old_dic[k]
																					elif k.endswith('''.w'''   ):
																												__lowerCamelCase      :      str         =old_dic[k]
																					elif "level_2" not in dict_name and "cond.model." in k:
																												__lowerCamelCase      :      Dict         =old_dic[k]
																					else:
																												__lowerCamelCase      :      Dict         =old_dic[k]

														__lowerCamelCase      :      List[Any]         ='''vqvae''' if i == 0 else F'priors.{3 - i}'
														__lowerCamelCase      :      Any         =fix_jukebox_keys(SCREAMING_SNAKE_CASE    ,						model.state_dict()    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
														weight_dict.append(SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      Optional[Any]         =weight_dict.pop(0   )
							model.vqvae.load_state_dict(SCREAMING_SNAKE_CASE   )
							for i in range(len(SCREAMING_SNAKE_CASE   )   ):
														model.priors[i].load_state_dict(weight_dict[2 - i]   )

							Path(SCREAMING_SNAKE_CASE   ).mkdir(exist_ok=SCREAMING_SNAKE_CASE   )
							with open(F'{pytorch_dump_folder_path}/mapping.json'    ,						'''w'''   ) as txtfile:
														json.dump(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

							print(F'Saving model {model_name} to {pytorch_dump_folder_path}'   )
							model.save_pretrained(SCREAMING_SNAKE_CASE   )

							return weight_dict


if __name__ == "__main__":
							_UpperCamelCase							=      argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--model_name',
							    default='jukebox-5b-lyrics',
							    type=str,
							    help='Name of the model you\'d like to convert.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path',
							    default='jukebox-5b-lyrics-converted',
							    type=str,
							    help='Path to the output PyTorch model directory.',
							)
							_UpperCamelCase							=      parser.parse_args()
							convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)











"""simple docstring"""





from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


_UpperCamelCase							=      '\\n@misc{wu2016googles,\n      title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n              and Jeffrey Dean},\n      year={2016},\n      eprint={1609.08144},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n'

_UpperCamelCase							=      '\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe \'GLEU score\'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore\'s range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n'

_UpperCamelCase							=      '\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n    predictions (list of str): list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references (list of list of str): list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n    \'google_bleu\': google_bleu score\n\nExamples:\n    Example 1:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results["google_bleu"], 2))\n        0.44\n\n    Example 2:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results["google_bleu"], 2))\n        0.61\n\n    Example 3:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n        >>> print(round(results["google_bleu"], 2))\n        0.53\n\n    Example 4:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n        >>> print(round(results["google_bleu"], 2))\n        0.4\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,  _KWARGS_DESCRIPTION   )
class 		SCREAMING_SNAKE_CASE_							(    datasets.Metric   ):
				"""simple docstring"""




				def 					__lowercase  (     self  :Optional[Any]			):
											return datasets.MetricInfo(
											    description=_DESCRIPTION						,    citation=_CITATION						,    inputs_description=_KWARGS_DESCRIPTION						,    features=datasets.Features(
											        {
											            '''predictions''': datasets.Sequence(datasets.Value('''string'''						,    id='''token'''			)						,    id='''sequence'''			),
											            '''references''': datasets.Sequence(
											                datasets.Sequence(datasets.Value('''string'''						,    id='''token'''			)						,    id='''sequence'''			)						,    id='''references'''			),
											        }			)						,    )




				def 					__lowercase  (     self  :Optional[Any]						,    __lowercase  :List[List[List[str]]]						,    __lowercase  :List[List[str]]						,    __lowercase  :int = 1						,    __lowercase  :int = 4						,    ):
											return {
											    "google_bleu": gleu_score.corpus_gleu(
											        list_of_references=__lowercase						,    hypotheses=__lowercase						,    min_len=__lowercase						,    max_len=__lowercase			)
											}




