





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()
__lowercase       :Optional[int]       =  logging.get_logger()



@dataclass
class    _a     :






							"""simple docstring"""

							snake_case_							 =						42
							snake_case_							 =						field(default_factory=__A							)
							snake_case_							 =						field(default_factory=__A							)

							def      A_  (      self				:     Any							,	a				:     Optional[int]							,	a				:     Tensor							,	a				:     Tensor    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__     :				Any   =					len(list(m.modules()    )    ) == 1 or isinstance(a							,	nn.Convad    ) or isinstance(a							,	nn.BatchNormad    )
													if has_not_submodules:
																			self.traced.append(a    )

							def __call__(      self				:     Tuple							,	a				:     Tensor    )	->int:

													for m in self.module.modules():
																			self.handles.append(m.register_forward_hook(self._forward_hook    )    )
													self.module(a    )
													[x.remove() for x in self.handles]
													return self






							@property
							def      A_  (      self				:     Dict    )	->Optional[Any]:
													return list(filter(lambda a    : len(list(x.state_dict().keys()    )    ) > 0							,	self.traced    )    )







@dataclass
class    _a     :






							"""simple docstring"""

							snake_case_							 =						42
							snake_case_							 =						42
							snake_case_							 =						1
							snake_case_							 =						field(default_factory=__A							)
							snake_case_							 =						field(default_factory=__A							)
							snake_case_							 =						True

							def __call__(      self				:     str							,	a				:     Tensor    )	->List[Any]:
													SCREAMING_SNAKE_CASE__     :				str   =					Tracker(self.dest    )(a    ).parametrized
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					Tracker(self.src    )(a    ).parametrized

													SCREAMING_SNAKE_CASE__     :				Any   =					list(filter(lambda a    : type(a    ) not in self.src_skip							,	a    )    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					list(filter(lambda a    : type(a    ) not in self.dest_skip							,	a    )    )

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

													for dest_m, src_m in zip(a							,	a    ):
																			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				:     Optional[Any]							,	a				:     nn.Module    )	->str:
													super().__init__()

													SCREAMING_SNAKE_CASE__     :				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}"""
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					len(a    ) + 1
																			feature_blocks.append((f"""res{block_index}""", v)    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					nn.ModuleDict(a    )






							def      A_  (      self				:     Tuple							,	a				:     Tensor    )	->List[Any]:
													return get_trunk_forward_outputs(
													    a							,	out_feat_keys=a							,	feature_blocks=self._feature_blocks							,	)







class    _a     (				__A							):






							"""simple docstring"""

							def      A_  (      self				:     Union[str, Any]							,	a				:     str    )	->str:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					x.split("-"    )
													return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]    )






							def __getitem__(      self				:     Dict							,	a				:     str    )	->Callable[[], Tuple[nn.Module, Dict]]:

													if x not in self:
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.convert_name_to_timm(a    )
																			SCREAMING_SNAKE_CASE__     :				int   =					partial(lambda: (timm.create_model(a							,	pretrained=a    ).eval(), None)    )

													else:
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					super().__getitem__(a    )

													return val







class    _a     (				__A							):






							"""simple docstring"""

							def __getitem__(      self				:     List[str]							,	a				:     str    )	->Callable[[], nn.Module]:

													if "seer" in x and "in1k" not in x:
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					RegNetModel
													else:
																			SCREAMING_SNAKE_CASE__     :				Any   =					RegNetForImageClassification
													return val







def       UpperCAmelCase							(     _lowerCamelCase      :  int			,   _lowerCamelCase      :  List[Any]			,   _lowerCamelCase      :  List[Tuple[str, str]]     ):





						'''simple docstring'''


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







def       UpperCAmelCase							(     _lowerCamelCase      :  str			,   _lowerCamelCase      :  Callable[[], nn.Module]			,   _lowerCamelCase      :  Callable[[], nn.Module]			,   _lowerCamelCase      :  RegNetConfig			,   _lowerCamelCase      :  Path			,   _lowerCamelCase      :  bool = True			,   ):





						'''simple docstring'''


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

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

						SCREAMING_SNAKE_CASE__     :				int   =					our_model(__A			,   output_hidden_states=__A     )
						SCREAMING_SNAKE_CASE__     :				Dict   =					(
						    our_outputs.logits if isinstance(__A			,   __A     ) else our_outputs.last_hidden_state
						)

						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					from_model(__A     )
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					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:
												SCREAMING_SNAKE_CASE__     :				Union[str, 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			,   )

												SCREAMING_SNAKE_CASE__     :				Optional[int]   =					224 if '''seer''' not in name else 384
												# we can use the convnext one
												SCREAMING_SNAKE_CASE__     :				Tuple   =					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       UpperCAmelCase							(     _lowerCamelCase      :  Path			,   _lowerCamelCase      :  str = None			,   _lowerCamelCase      :  bool = True     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				str   =					'''imagenet-1k-id2label.json'''
						SCREAMING_SNAKE_CASE__     :				int   =					1_000
						SCREAMING_SNAKE_CASE__     :				str   =					(1, num_labels)

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

						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					idalabel
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					{v: k for k, v in idalabel.items()}

						SCREAMING_SNAKE_CASE__     :				Any   =					partial(__A			,   num_labels=__A			,   idalabel=__A			,   labelaid=__A     )

						SCREAMING_SNAKE_CASE__     :				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     ),
						}

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

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

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

						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					partial(
						    __A			,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"			,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )			,   )

						SCREAMING_SNAKE_CASE__     :				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()     )			,   )

						SCREAMING_SNAKE_CASE__     :				str   =					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=6_2_0.8_3			,   w_m=2.5_2     )     )     )			,   )

						# IN1K finetuned
						SCREAMING_SNAKE_CASE__     :				Dict   =					partial(
						    __A			,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"			,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )			,   )

						SCREAMING_SNAKE_CASE__     :				Any   =					partial(
						    __A			,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"			,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )			,   )

						SCREAMING_SNAKE_CASE__     :				Dict   =					partial(
						    __A			,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"			,   lambda: FakeRegNetVisslWrapper(RegNetYaaagf()     )			,   )

						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					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=6_2_0.8_3			,   w_m=2.5_2     )     )     )			,   )

						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__":
							__lowercase       :Optional[int]       =  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.",
							)

							__lowercase       :int       =  parser.parse_args()

							__lowercase       :Any       =  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)






from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


__lowercase       :Tuple       =  "\\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"

__lowercase       :str       =  "\\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"

__lowercase       :List[Any]       =  "\\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      A_  (      self				:     List[Any]    )	->MetricInfo:
													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      A_  (      self				:     str							,	a				:     List[List[List[str]]]							,	a				:     List[List[str]]							,	a				:     int = 1							,	a				:     int = 4							,	)	->Dict[str, float]:
													return {
													    "google_bleu": gleu_score.corpus_gleu(
													        list_of_references=a							,	hypotheses=a							,	min_len=a							,	max_len=a    )
													}