





"""simple docstring"""





import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Dict   ):


							'''simple docstring'''







							__lowerCamelCase      :      Optional[int]         =fname.split(os.path.sep   )[-1]
							return re.search(r'''^(.*)_\d+\.jpg$'''    ,						SCREAMING_SNAKE_CASE   ).groups()[0]
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""




				def __init__(     self  :Union[str, Any]						,    __lowercase  :int						,    __lowercase  :List[str]=None						,    __lowercase  :Any=None			):
											__lowerCamelCase      :      Optional[int]         =file_names
											__lowerCamelCase      :      Optional[Any]         =image_transform
											__lowerCamelCase      :      Tuple         =label_to_id




				def __len__(     self  :Optional[Any]			):
											return len(self.file_names			)




				def __getitem__(     self  :str						,    __lowercase  :List[str]			):
											__lowerCamelCase      :      Optional[Any]         =self.file_names[idx]
											__lowerCamelCase      :      Dict         =PIL.Image.open(__lowercase			)
											__lowerCamelCase      :      List[Any]         =raw_image.convert('''RGB'''			)
											if self.image_transform is not None:
																		__lowerCamelCase      :      int         =self.image_transform(__lowercase			)
											__lowerCamelCase      :      Optional[Any]         =extract_label(__lowercase			)
											if self.label_to_id is not None:
																		__lowerCamelCase      :      Union[str, Any]         =self.label_to_id[label]
											return {"image": image, "label": label}



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[Any]    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							if args.with_tracking:
														__lowerCamelCase      :      List[Any]         =Accelerator(
														    cpu=args.cpu    ,						mixed_precision=args.mixed_precision    ,						log_with='''all'''    ,						project_dir=args.project_dir   )
							else:
														__lowerCamelCase      :      Tuple         =Accelerator(cpu=args.cpu    ,						mixed_precision=args.mixed_precision   )

							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							__lowerCamelCase      :      List[str]         =config['''lr''']
							__lowerCamelCase      :      Optional[Any]         =int(config['''num_epochs''']   )
							__lowerCamelCase      :      Optional[int]         =int(config['''seed''']   )
							__lowerCamelCase      :      int         =int(config['''batch_size''']   )
							__lowerCamelCase      :      List[str]         =config['''image_size''']
							if not isinstance(SCREAMING_SNAKE_CASE    ,						(list, tuple)   ):
														__lowerCamelCase      :      str         =(image_size, image_size)

							# Parse out whether we are saving every epoch or after a certain number of batches
							if hasattr(args.checkpointing_steps    ,						'''isdigit'''   ):
														if args.checkpointing_steps == "epoch":
																					__lowerCamelCase      :      Any         =args.checkpointing_steps
														elif args.checkpointing_steps.isdigit():
																					__lowerCamelCase      :      Tuple         =int(args.checkpointing_steps   )
														else:
																					raise ValueError(
																					    F'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'   )
							else:
														__lowerCamelCase      :      Dict         =None

							# We need to initialize the trackers we use, and also store our configuration
							if args.with_tracking:
														__lowerCamelCase      :      Optional[int]         =os.path.split(SCREAMING_SNAKE_CASE   )[-1].split('''.'''   )[0]
														accelerator.init_trackers(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

							# Grab all the image filenames
							__lowerCamelCase      :      Tuple         =[os.path.join(args.data_dir    ,						SCREAMING_SNAKE_CASE   ) for fname in os.listdir(args.data_dir   ) if fname.endswith('''.jpg'''   )]

							# Build the label correspondences
							__lowerCamelCase      :      Any         =[extract_label(SCREAMING_SNAKE_CASE   ) for fname in file_names]
							__lowerCamelCase      :      Tuple         =list(set(SCREAMING_SNAKE_CASE   )   )
							id_to_label.sort()
							__lowerCamelCase      :      int         ={lbl: i for i, lbl in enumerate(SCREAMING_SNAKE_CASE   )}

							# Set the seed before splitting the data.
							np.random.seed(SCREAMING_SNAKE_CASE   )
							torch.manual_seed(SCREAMING_SNAKE_CASE   )
							torch.cuda.manual_seed_all(SCREAMING_SNAKE_CASE   )

							# Split our filenames between train and validation
							__lowerCamelCase      :      str         =np.random.permutation(len(SCREAMING_SNAKE_CASE   )   )
							__lowerCamelCase      :      List[str]         =int(0.8 * len(SCREAMING_SNAKE_CASE   )   )
							__lowerCamelCase      :      Optional[Any]         =random_perm[:cut]
							__lowerCamelCase      :      int         =random_perm[cut:]

							# For training we use a simple RandomResizedCrop
							__lowerCamelCase      :      Optional[Any]         =Compose([RandomResizedCrop(SCREAMING_SNAKE_CASE    ,						scale=(0.5, 1.0)   ), ToTensor()]   )
							__lowerCamelCase      :      Union[str, Any]         =PetsDataset(
							    [file_names[i] for i in train_split]    ,						image_transform=SCREAMING_SNAKE_CASE    ,						label_to_id=SCREAMING_SNAKE_CASE   )

							# For evaluation, we use a deterministic Resize
							__lowerCamelCase      :      Any         =Compose([Resize(SCREAMING_SNAKE_CASE   ), ToTensor()]   )
							__lowerCamelCase      :      Any         =PetsDataset([file_names[i] for i in eval_split]    ,						image_transform=SCREAMING_SNAKE_CASE    ,						label_to_id=SCREAMING_SNAKE_CASE   )

							# Instantiate dataloaders.
							__lowerCamelCase      :      int         =DataLoader(SCREAMING_SNAKE_CASE    ,						shuffle=SCREAMING_SNAKE_CASE    ,						batch_size=SCREAMING_SNAKE_CASE    ,						num_workers=4   )
							__lowerCamelCase      :      Any         =DataLoader(SCREAMING_SNAKE_CASE    ,						shuffle=SCREAMING_SNAKE_CASE    ,						batch_size=SCREAMING_SNAKE_CASE    ,						num_workers=4   )

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							__lowerCamelCase      :      Dict         =create_model('''resnet50d'''    ,						pretrained=SCREAMING_SNAKE_CASE    ,						num_classes=len(SCREAMING_SNAKE_CASE   )   )

							# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
							# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
							# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
							__lowerCamelCase      :      List[Any]         =model.to(accelerator.device   )

							# Freezing the base model
							for param in model.parameters():
														__lowerCamelCase      :      Union[str, Any]         =False
							for param in model.get_classifier().parameters():
														__lowerCamelCase      :      List[str]         =True

							# We normalize the batches of images to be a bit faster.
							__lowerCamelCase      :      List[Any]         =torch.tensor(model.default_cfg['''mean''']   )[None, :, None, None].to(accelerator.device   )
							__lowerCamelCase      :      Any         =torch.tensor(model.default_cfg['''std''']   )[None, :, None, None].to(accelerator.device   )

							# Instantiate optimizer
							__lowerCamelCase      :      Dict         =torch.optim.Adam(params=model.parameters()    ,						lr=lr / 25   )

							# Instantiate learning rate scheduler
							__lowerCamelCase      :      Dict         =OneCycleLR(optimizer=SCREAMING_SNAKE_CASE    ,						max_lr=SCREAMING_SNAKE_CASE    ,						epochs=SCREAMING_SNAKE_CASE    ,						steps_per_epoch=len(SCREAMING_SNAKE_CASE   )   )

							# Prepare everything
							# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
							# prepare method.
							__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase      :      Tuple         =accelerator.prepare(
							    SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							# We need to keep track of how many total steps we have iterated over
							__lowerCamelCase      :      Tuple         =0
							# We also need to keep track of the starting epoch so files are named properly
							__lowerCamelCase      :      List[str]         =0

							# Potentially load in the weights and states from a previous save
							if args.resume_from_checkpoint:
														if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																					accelerator.print(F'Resumed from checkpoint: {args.resume_from_checkpoint}'   )
																					accelerator.load_state(args.resume_from_checkpoint   )
																					__lowerCamelCase      :      Dict         =os.path.basename(args.resume_from_checkpoint   )
														else:
																					# Get the most recent checkpoint
																					__lowerCamelCase      :      str         =[f.name for f in os.scandir(os.getcwd()   ) if f.is_dir()]
																					dirs.sort(key=os.path.getctime   )
																					__lowerCamelCase      :      List[str]         =dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
														# Extract `epoch_{i}` or `step_{i}`
														__lowerCamelCase      :      str         =os.path.splitext(SCREAMING_SNAKE_CASE   )[0]

														if "epoch" in training_difference:
																					__lowerCamelCase      :      List[Any]         =int(training_difference.replace('''epoch_'''    ,						''''''   )   ) + 1
																					__lowerCamelCase      :      Dict         =None
														else:
																					__lowerCamelCase      :      Dict         =int(training_difference.replace('''step_'''    ,						''''''   )   )
																					__lowerCamelCase      :      Dict         =resume_step // len(SCREAMING_SNAKE_CASE   )
																					resume_step -= starting_epoch * len(SCREAMING_SNAKE_CASE   )

    # Now we train the model
							for epoch in range(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ):
														model.train()
														if args.with_tracking:
																					__lowerCamelCase      :      Optional[Any]         =0
														if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																					# We need to skip steps until we reach the resumed step
																					__lowerCamelCase      :      Any         =accelerator.skip_first_batches(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
																					overall_step += resume_step
														else:
																					# After the first iteration though, we need to go back to the original dataloader
																					__lowerCamelCase      :      Dict         =train_dataloader
														for batch in active_dataloader:
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					__lowerCamelCase      :      Union[str, Any]         ={k: v.to(accelerator.device   ) for k, v in batch.items()}
																					__lowerCamelCase      :      int         =(batch['''image'''] - mean) / std
																					__lowerCamelCase      :      Union[str, Any]         =model(SCREAMING_SNAKE_CASE   )
																					__lowerCamelCase      :      List[Any]         =torch.nn.functional.cross_entropy(SCREAMING_SNAKE_CASE    ,						batch['''label''']   )
																					# We keep track of the loss at each epoch
																					if args.with_tracking:
																												total_loss += loss.detach().float()
																					accelerator.backward(SCREAMING_SNAKE_CASE   )
																					optimizer.step()
																					lr_scheduler.step()
																					optimizer.zero_grad()
																					overall_step += 1
																					if isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ):
																												__lowerCamelCase      :      Dict         =F'step_{overall_step}'
																												if overall_step % checkpointing_steps == 0:
																																			if args.output_dir is not None:
																																										__lowerCamelCase      :      Tuple         =os.path.join(args.output_dir    ,						SCREAMING_SNAKE_CASE   )
																																			accelerator.save_state(SCREAMING_SNAKE_CASE   )
														model.eval()
														__lowerCamelCase      :      Any         =0
														__lowerCamelCase      :      Optional[int]         =0
														for step, batch in enumerate(SCREAMING_SNAKE_CASE   ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					__lowerCamelCase      :      int         ={k: v.to(accelerator.device   ) for k, v in batch.items()}
																					__lowerCamelCase      :      Dict         =(batch['''image'''] - mean) / std
																					with torch.no_grad():
																												__lowerCamelCase      :      Optional[Any]         =model(SCREAMING_SNAKE_CASE   )
																					__lowerCamelCase      :      Dict         =outputs.argmax(dim=-1   )
																					__lowerCamelCase    ,						__lowerCamelCase      :      Any         =accelerator.gather_for_metrics((predictions, batch['''label'''])   )
																					__lowerCamelCase      :      Optional[Any]         =predictions == references
																					num_elems += accurate_preds.shape[0]
																					accurate += accurate_preds.long().sum()

														__lowerCamelCase      :      Optional[Any]         =accurate.item() / num_elems
														# Use accelerator.print to print only on the main process.
														accelerator.print(F'epoch {epoch}: {100 * eval_metric:.2f}'   )
														if args.with_tracking:
																					accelerator.log(
																					    {
																					        '''accuracy''': 100 * eval_metric,
																					        '''train_loss''': total_loss.item() / len(SCREAMING_SNAKE_CASE   ),
																					        '''epoch''': epoch,
																					    }    ,						step=SCREAMING_SNAKE_CASE    ,						)
														if checkpointing_steps == "epoch":
																					__lowerCamelCase      :      Optional[Any]         =F'epoch_{epoch}'
																					if args.output_dir is not None:
																												__lowerCamelCase      :      List[Any]         =os.path.join(args.output_dir    ,						SCREAMING_SNAKE_CASE   )
																					accelerator.save_state(SCREAMING_SNAKE_CASE   )

							if args.with_tracking:
														accelerator.end_training()



def 				lowerCAmelCase_			(      ):


							'''simple docstring'''







							__lowerCamelCase      :      Dict         =argparse.ArgumentParser(description='''Simple example of training script.'''   )
							parser.add_argument('''--data_dir'''    ,						required=SCREAMING_SNAKE_CASE    ,						help='''The data folder on disk.'''   )
							parser.add_argument('''--fp16'''    ,						action='''store_true'''    ,						help='''If passed, will use FP16 training.'''   )
							parser.add_argument(
							    '''--mixed_precision'''    ,						type=SCREAMING_SNAKE_CASE    ,						default=SCREAMING_SNAKE_CASE    ,						choices=['''no''', '''fp16''', '''bf16''', '''fp8''']    ,						help='''Whether to use mixed precision. Choose'''
							    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
							    '''and an Nvidia Ampere GPU.'''    ,						)
							parser.add_argument('''--cpu'''    ,						action='''store_true'''    ,						help='''If passed, will train on the CPU.'''   )
							parser.add_argument(
							    '''--checkpointing_steps'''    ,						type=SCREAMING_SNAKE_CASE    ,						default=SCREAMING_SNAKE_CASE    ,						help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''    ,						)
							parser.add_argument(
							    '''--output_dir'''    ,						type=SCREAMING_SNAKE_CASE    ,						default='''.'''    ,						help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''    ,						)
							parser.add_argument(
							    '''--resume_from_checkpoint'''    ,						type=SCREAMING_SNAKE_CASE    ,						default=SCREAMING_SNAKE_CASE    ,						help='''If the training should continue from a checkpoint folder.'''    ,						)
							parser.add_argument(
							    '''--with_tracking'''    ,						action='''store_true'''    ,						help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''    ,						)
							parser.add_argument(
							    '''--project_dir'''    ,						type=SCREAMING_SNAKE_CASE    ,						default='''logs'''    ,						help='''Location on where to store experiment tracking logs` and relevent project information'''    ,						)
							__lowerCamelCase      :      int         =parser.parse_args()
							__lowerCamelCase      :      List[str]         ={'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
							training_function(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
							main()











"""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			)
											}




