






'''simple docstring'''

import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase							=logging.get_logger()




@dataclass
class     A    :






 """simple docstring"""





 __a			:						nn.Module
 __a			:						List[nn.Module]      =						field(default_factory=SCREAMING_SNAKE_CASE__						)
 __a			:						list      =						field(default_factory=SCREAMING_SNAKE_CASE__						)
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   int					    =							len(list(m.modules() ) ) == 1 or isinstance(__lowerCAmelCase			,     nn.Convad ) or isinstance(__lowerCAmelCase			,     nn.BatchNormad )
       if has_not_submodules:
             self.traced.append(__lowerCAmelCase )
 def __call__(							self			,     __lowerCAmelCase ):
       for m in self.module.modules():
             self.handles.append(m.register_forward_hook(self._forward_hook ) )
       self.module(__lowerCAmelCase )
       [x.remove() for x in self.handles]
       return self





 @property
 def   _UpperCAmelCase       (							self ):
       # check the len of the state_dict keys to see if we have learnable params
       return list(filter(lambda __lowerCAmelCase : len(list(x.state_dict().keys() ) ) > 0			,     self.traced ) )






@dataclass
class     A    :






 """simple docstring"""





 __a			:						nn.Module
 __a			:						nn.Module
 __a			:						int      =						1
 __a			:						List      =						field(default_factory=SCREAMING_SNAKE_CASE__						)
 __a			:						List      =						field(default_factory=SCREAMING_SNAKE_CASE__						)
 __a			:						bool      =						True
 def __call__(							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   int					    =							Tracker(self.dest )(__lowerCAmelCase ).parametrized
       UpperCamelCase_				:   Optional[Any]					    =							Tracker(self.src )(__lowerCAmelCase ).parametrized

       UpperCamelCase_				:   str					    =							list(filter(lambda __lowerCAmelCase : type(__lowerCAmelCase ) not in self.src_skip			,     __lowerCAmelCase ) )
       UpperCamelCase_				:   Union[str, Any]					    =							list(filter(lambda __lowerCAmelCase : type(__lowerCAmelCase ) not in self.dest_skip			,     __lowerCAmelCase ) )

       if len(__lowerCAmelCase ) != len(__lowerCAmelCase ) and self.raise_if_mismatch:
             raise Exception(
                 F"Numbers of operations are different. Source module has {len(__lowerCAmelCase )} operations while"
                 F" destination module has {len(__lowerCAmelCase )}." )

       for dest_m, src_m in zip(__lowerCAmelCase			,     __lowerCAmelCase ):
             dest_m.load_state_dict(src_m.state_dict() )
             if self.verbose == 1:
                   print(F"Transfered from={src_m} to={dest_m}" )






class     A    (      nn.Module						):






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase ):
       super().__init__()

       UpperCamelCase_				:   List[Tuple[str, nn.Module]]					    =							[]
       # - get the stem
       feature_blocks.append(("""conv1""", model.stem) )
       # - get all the feature blocks
       for k, v in model.trunk_output.named_children():
             assert k.startswith("""block""" ), F"Unexpected layer name {k}"
             UpperCamelCase_				:   Dict					    =							len(__lowerCAmelCase ) + 1
             feature_blocks.append((F"res{block_index}", v) )

       UpperCamelCase_				:   str					    =							nn.ModuleDict(__lowerCAmelCase )





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       return get_trunk_forward_outputs(
           __lowerCAmelCase			,     out_feat_keys=__lowerCAmelCase			,     feature_blocks=self._feature_blocks			,     )






class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Optional[int]					    =							x.split("""-""" )
       return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )





 def __getitem__(							self			,     __lowerCAmelCase ):
       # default to timm!
       if x not in self:
             UpperCamelCase_				:   Dict					    =							self.convert_name_to_timm(__lowerCAmelCase )
             UpperCamelCase_				:   List[str]					    =							partial(lambda: (timm.create_model(__lowerCAmelCase			,     pretrained=__lowerCAmelCase ).eval(), None) )

       else:
             UpperCamelCase_				:   Tuple					    =							super().__getitem__(__lowerCAmelCase )

       return val






class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 def __getitem__(							self			,     __lowerCAmelCase ):
       if "seer" in x and "in1k" not in x:
             UpperCamelCase_				:   Optional[Any]					    =							RegNetModel
       else:
             UpperCamelCase_				:   Dict					    =							RegNetForImageClassification
       return val






def 							snake_case					(     a_							: Tuple    ,   a_							: Optional[Any]    ,   a_							: List[Tuple[str, str]]	)       ->				Any:





      """simple docstring"""

      for from_key, to_key in keys:
            UpperCamelCase_				:   List[str]					    =							from_state_dict[from_key].clone()
            print(f"Copied key={from_key} to={to_key}"	)
      return to_state_dict



def 							snake_case					(     a_							: str    ,   a_							: Callable[[], nn.Module]    ,   a_							: Callable[[], nn.Module]    ,   a_							: RegNetConfig    ,   a_							: Path    ,   a_							: bool = True    ,   )       ->				List[Any]:





      """simple docstring"""

      print(f"Converting {name}..."	)
      with torch.no_grad():
            UpperCamelCase_				:   List[Any]					    =							from_model_func()
            UpperCamelCase_				:   Tuple					    =							our_model_func(a_	).eval()
            UpperCamelCase_				:   Dict					    =							ModuleTransfer(src=a_    ,   dest=a_    ,   raise_if_mismatch=a_	)
            UpperCamelCase_				:   str					    =							torch.randn((1, 3, 224, 224)	)
            module_transfer(a_	)

      if from_state_dict is not None:
            UpperCamelCase_				:   List[str]					    =							[]
            # for seer - in1k finetuned we have to manually copy the head
            if "seer" in name and "in1k" in name:
                  UpperCamelCase_				:   List[Any]					    =							[("""0.clf.0.weight""", """classifier.1.weight"""), ("""0.clf.0.bias""", """classifier.1.bias""")]
            UpperCamelCase_				:   int					    =							manually_copy_vissl_head(a_    ,   our_model.state_dict()    ,   a_	)
            our_model.load_state_dict(a_	)

      UpperCamelCase_				:   Optional[int]					    =							our_model(a_    ,   output_hidden_states=a_	)
      UpperCamelCase_				:   List[Any]					    =							(
          our_outputs.logits if isinstance(a_    ,   a_	) else our_outputs.last_hidden_state
      )

      UpperCamelCase_				:   Optional[int]					    =							from_model(a_	)
      UpperCamelCase_				:   List[str]					    =							from_output[-1] if type(a_	) is list else from_output

      # now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
      if "seer" in name and "in1k" in name:
            UpperCamelCase_				:   Optional[Any]					    =							our_outputs.hidden_states[-1]

      assert torch.allclose(a_    ,   a_	), "The model logits don't match the original one."

      if push_to_hub:
            our_model.push_to_hub(
                repo_path_or_name=save_directory / name    ,   commit_message="""Add model"""    ,   use_temp_dir=a_    ,   )

            UpperCamelCase_				:   Any					    =							224 if """seer""" not in name else 384
            # we can use the convnext one
            UpperCamelCase_				:   int					    =							AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""    ,   size=a_	)
            image_processor.push_to_hub(
                repo_path_or_name=save_directory / name    ,   commit_message="""Add image processor"""    ,   use_temp_dir=a_    ,   )

            print(f"Pushed {name}"	)



def 							snake_case					(     a_							: Path    ,   a_							: str = None    ,   a_							: bool = True	)       ->				Optional[int]:





      """simple docstring"""

      UpperCamelCase_				:   Union[str, Any]					    =							"""imagenet-1k-id2label.json"""
      UpperCamelCase_				:   List[Any]					    =							1_000
      UpperCamelCase_				:   Dict					    =							(1, num_labels)

      UpperCamelCase_				:   int					    =							"""huggingface/label-files"""
      UpperCamelCase_				:   Tuple					    =							num_labels
      UpperCamelCase_				:   List[str]					    =							json.load(open(cached_download(hf_hub_url(a_    ,   a_    ,   repo_type="""dataset"""	)	)    ,   """r"""	)	)
      UpperCamelCase_				:   int					    =							{int(a_	): v for k, v in idalabel.items()}

      UpperCamelCase_				:   Tuple					    =							idalabel
      UpperCamelCase_				:   List[str]					    =							{v: k for k, v in idalabel.items()}

      UpperCamelCase_				:   Union[str, Any]					    =							partial(a_    ,   num_labels=a_    ,   idalabel=a_    ,   labelaid=a_	)

      UpperCamelCase_				:   Optional[Any]					    =							{
          """regnet-x-002""": ImageNetPreTrainedConfig(
              depths=[1, 1, 4, 7]    ,   hidden_sizes=[24, 56, 152, 368]    ,   groups_width=8    ,   layer_type="""x"""	),
          """regnet-x-004""": ImageNetPreTrainedConfig(
              depths=[1, 2, 7, 12]    ,   hidden_sizes=[32, 64, 160, 384]    ,   groups_width=16    ,   layer_type="""x"""	),
          """regnet-x-006""": ImageNetPreTrainedConfig(
              depths=[1, 3, 5, 7]    ,   hidden_sizes=[48, 96, 240, 528]    ,   groups_width=24    ,   layer_type="""x"""	),
          """regnet-x-008""": ImageNetPreTrainedConfig(
              depths=[1, 3, 7, 5]    ,   hidden_sizes=[64, 128, 288, 672]    ,   groups_width=16    ,   layer_type="""x"""	),
          """regnet-x-016""": ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 2]    ,   hidden_sizes=[72, 168, 408, 912]    ,   groups_width=24    ,   layer_type="""x"""	),
          """regnet-x-032""": ImageNetPreTrainedConfig(
              depths=[2, 6, 15, 2]    ,   hidden_sizes=[96, 192, 432, 1_008]    ,   groups_width=48    ,   layer_type="""x"""	),
          """regnet-x-040""": ImageNetPreTrainedConfig(
              depths=[2, 5, 14, 2]    ,   hidden_sizes=[80, 240, 560, 1_360]    ,   groups_width=40    ,   layer_type="""x"""	),
          """regnet-x-064""": ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 1]    ,   hidden_sizes=[168, 392, 784, 1_624]    ,   groups_width=56    ,   layer_type="""x"""	),
          """regnet-x-080""": ImageNetPreTrainedConfig(
              depths=[2, 5, 15, 1]    ,   hidden_sizes=[80, 240, 720, 1_920]    ,   groups_width=120    ,   layer_type="""x"""	),
          """regnet-x-120""": ImageNetPreTrainedConfig(
              depths=[2, 5, 11, 1]    ,   hidden_sizes=[224, 448, 896, 2_240]    ,   groups_width=112    ,   layer_type="""x"""	),
          """regnet-x-160""": ImageNetPreTrainedConfig(
              depths=[2, 6, 13, 1]    ,   hidden_sizes=[256, 512, 896, 2_048]    ,   groups_width=128    ,   layer_type="""x"""	),
          """regnet-x-320""": ImageNetPreTrainedConfig(
              depths=[2, 7, 13, 1]    ,   hidden_sizes=[336, 672, 1_344, 2_520]    ,   groups_width=168    ,   layer_type="""x"""	),
          # y variant
          """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]    ,   hidden_sizes=[24, 56, 152, 368]    ,   groups_width=8	),
          """regnet-y-004""": ImageNetPreTrainedConfig(
              depths=[1, 3, 6, 6]    ,   hidden_sizes=[48, 104, 208, 440]    ,   groups_width=8	),
          """regnet-y-006""": ImageNetPreTrainedConfig(
              depths=[1, 3, 7, 4]    ,   hidden_sizes=[48, 112, 256, 608]    ,   groups_width=16	),
          """regnet-y-008""": ImageNetPreTrainedConfig(
              depths=[1, 3, 8, 2]    ,   hidden_sizes=[64, 128, 320, 768]    ,   groups_width=16	),
          """regnet-y-016""": ImageNetPreTrainedConfig(
              depths=[2, 6, 17, 2]    ,   hidden_sizes=[48, 120, 336, 888]    ,   groups_width=24	),
          """regnet-y-032""": ImageNetPreTrainedConfig(
              depths=[2, 5, 13, 1]    ,   hidden_sizes=[72, 216, 576, 1_512]    ,   groups_width=24	),
          """regnet-y-040""": ImageNetPreTrainedConfig(
              depths=[2, 6, 12, 2]    ,   hidden_sizes=[128, 192, 512, 1_088]    ,   groups_width=64	),
          """regnet-y-064""": ImageNetPreTrainedConfig(
              depths=[2, 7, 14, 2]    ,   hidden_sizes=[144, 288, 576, 1_296]    ,   groups_width=72	),
          """regnet-y-080""": ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 1]    ,   hidden_sizes=[168, 448, 896, 2_016]    ,   groups_width=56	),
          """regnet-y-120""": ImageNetPreTrainedConfig(
              depths=[2, 5, 11, 1]    ,   hidden_sizes=[224, 448, 896, 2_240]    ,   groups_width=112	),
          """regnet-y-160""": ImageNetPreTrainedConfig(
              depths=[2, 4, 11, 1]    ,   hidden_sizes=[224, 448, 1_232, 3_024]    ,   groups_width=112	),
          """regnet-y-320""": ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]    ,   hidden_sizes=[232, 696, 1_392, 3_712]    ,   groups_width=232	),
          # models created by SEER -> https://arxiv.org/abs/2202.08360
          """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 12, 1]    ,   hidden_sizes=[232, 696, 1_392, 3_712]    ,   groups_width=232	),
          """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 12, 1]    ,   hidden_sizes=[328, 984, 1_968, 4_920]    ,   groups_width=328	),
          """regnet-y-1280-seer""": RegNetConfig(
              depths=[2, 7, 17, 1]    ,   hidden_sizes=[528, 1_056, 2_904, 7_392]    ,   groups_width=264	),
          """regnet-y-2560-seer""": RegNetConfig(
              depths=[3, 7, 16, 1]    ,   hidden_sizes=[640, 1_696, 2_544, 5_088]    ,   groups_width=640	),
          """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]    ,   hidden_sizes=[2_020, 4_040, 11_110, 28_280]    ,   groups_width=1_010	),
          # finetuned on imagenet
          """regnet-y-320-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]    ,   hidden_sizes=[232, 696, 1_392, 3_712]    ,   groups_width=232	),
          """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]    ,   hidden_sizes=[328, 984, 1_968, 4_920]    ,   groups_width=328	),
          """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]    ,   hidden_sizes=[528, 1_056, 2_904, 7_392]    ,   groups_width=264	),
          """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[3, 7, 16, 1]    ,   hidden_sizes=[640, 1_696, 2_544, 5_088]    ,   groups_width=640	),
          """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]    ,   hidden_sizes=[2_020, 4_040, 11_110, 28_280]    ,   groups_width=1_010	),
      }

      UpperCamelCase_				:   str					    =							NameToOurModelFuncMap()
      UpperCamelCase_				:   List[Any]					    =							NameToFromModelFuncMap()
      # add seer weights logic

      def load_using_classy_vision(a_							: str    ,   a_							: Callable[[], nn.Module]	) -> Tuple[nn.Module, Dict]:
            UpperCamelCase_				:   List[str]					    =							torch.hub.load_state_dict_from_url(a_    ,   model_dir=str(a_	)    ,   map_location="""cpu"""	)
            UpperCamelCase_				:   Dict					    =							model_func()
            # check if we have a head, if yes add it
            UpperCamelCase_				:   str					    =							files["""classy_state_dict"""]["""base_model"""]["""model"""]
            UpperCamelCase_				:   Any					    =							model_state_dict["""trunk"""]
            model.load_state_dict(a_	)
            return model.eval(), model_state_dict["heads"]

      # pretrained
      UpperCamelCase_				:   Tuple					    =							partial(
          a_    ,   """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"""    ,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)    ,   )

      UpperCamelCase_				:   List[Any]					    =							partial(
          a_    ,   """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"""    ,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)    ,   )

      UpperCamelCase_				:   List[str]					    =							partial(
          a_    ,   """https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"""    ,   lambda: FakeRegNetVisslWrapper(RegNetYaaagf()	)    ,   )

      UpperCamelCase_				:   Dict					    =							partial(
          a_    ,   """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""    ,   lambda: FakeRegNetVisslWrapper(
              RegNet(RegNetParams(depth=27    ,   group_width=1_010    ,   w_a=1_744    ,   w_a=620.83    ,   w_m=2.52	)	)	)    ,   )

      # IN1K finetuned
      UpperCamelCase_				:   Union[str, Any]					    =							partial(
          a_    ,   """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"""    ,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)    ,   )

      UpperCamelCase_				:   Tuple					    =							partial(
          a_    ,   """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"""    ,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)    ,   )

      UpperCamelCase_				:   Optional[Any]					    =							partial(
          a_    ,   """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"""    ,   lambda: FakeRegNetVisslWrapper(RegNetYaaagf()	)    ,   )

      UpperCamelCase_				:   List[Any]					    =							partial(
          a_    ,   """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""    ,   lambda: FakeRegNetVisslWrapper(
              RegNet(RegNetParams(depth=27    ,   group_width=1_010    ,   w_a=1_744    ,   w_a=620.83    ,   w_m=2.52	)	)	)    ,   )

      if model_name:
            convert_weight_and_push(
                a_    ,   names_to_from_model_map[model_name]    ,   names_to_ours_model_map[model_name]    ,   names_to_config[model_name]    ,   a_    ,   a_    ,   )
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(
                      a_    ,   names_to_from_model_map[model_name]    ,   names_to_ours_model_map[model_name]    ,   a_    ,   a_    ,   a_    ,   )
      return config, expected_shape


if __name__ == "__main__":
      UpperCamelCase							=argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--model_name",
          default=None,
          type=str,
          help=(
              "The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
              " currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
          ),
      )
      parser.add_argument(
          "--pytorch_dump_folder_path",
          default=None,
          type=Path,
          required=True,
          help="Path to the output PyTorch model directory.",
      )
      parser.add_argument(
          "--push_to_hub",
          default=True,
          type=bool,
          required=False,
          help="If True, push model and image processor to the hub.",
      )

      UpperCamelCase							=parser.parse_args()

      UpperCamelCase							=args.pytorch_dump_folder_path
      pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
      convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)













'''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     A    (      datasets.Metric						):






 """simple docstring"""





 def   _UpperCAmelCase       (							self ):
       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   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase = 1			,     __lowerCAmelCase = 4			,     ):
       return {
           "google_bleu": gleu_score.corpus_gleu(
               list_of_references=__lowerCAmelCase			,     hypotheses=__lowerCAmelCase			,     min_len=__lowerCAmelCase			,     max_len=__lowerCAmelCase )
       }






