



import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


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



def 	__a					(   lowerCAmelCase_       : str    )				->			Optional[int]:






						'''simple docstring'''



						UpperCAmelCase_=       SwinConfig.from_pretrained(
						    """microsoft/swin-tiny-patch4-window7-224"""      ,out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]    )
						UpperCAmelCase_=       MaskFormerConfig(backbone_config=lowerCAmelCase_    )

						UpperCAmelCase_=       """huggingface/label-files"""
						if "ade20k-full" in model_name:
												# this should be ok
												UpperCAmelCase_=       8_47
												UpperCAmelCase_=       """maskformer-ade20k-full-id2label.json"""
						elif "ade" in model_name:
												# this should be ok
												UpperCAmelCase_=       1_50
												UpperCAmelCase_=       """ade20k-id2label.json"""
						elif "coco-stuff" in model_name:
												# this should be ok
												UpperCAmelCase_=       1_71
												UpperCAmelCase_=       """maskformer-coco-stuff-id2label.json"""
						elif "coco" in model_name:
												# TODO
												UpperCAmelCase_=       1_33
												UpperCAmelCase_=       """coco-panoptic-id2label.json"""
						elif "cityscapes" in model_name:
												# this should be ok
												UpperCAmelCase_=       19
												UpperCAmelCase_=       """cityscapes-id2label.json"""
						elif "vistas" in model_name:
												# this should be ok
												UpperCAmelCase_=       65
												UpperCAmelCase_=       """mapillary-vistas-id2label.json"""

						UpperCAmelCase_=       json.load(open(hf_hub_download(lowerCAmelCase_      ,lowerCAmelCase_      ,repo_type="""dataset"""    )      ,"""r"""    )    )
						UpperCAmelCase_=       {int(lowerCAmelCase_    ): v for k, v in idalabel.items()}

						return config



def 	__a					(   lowerCAmelCase_       : str    )				->			Tuple:






						'''simple docstring'''



						UpperCAmelCase_=       []
						# stem
						# fmt: off
						rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight""")    )
						rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias""")    )
						rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight""")    )
						rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.model.embeddings.norm.bias""")    )
						# stages
						for i in range(len(config.backbone_config.depths    )    ):
												for j in range(config.backbone_config.depths[i]    ):
																		rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")    )
																		rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")    )
																		rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")    )
																		rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")    )
																		rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")    )
																		rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")    )
																		rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")    )
																		rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")    )
																		rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")    )
																		rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")    )
																		rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")    )
																		rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")    )

												if i < 3:
																		rename_keys.append((F"""backbone.layers.{i}.downsample.reduction.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")    )
																		rename_keys.append((F"""backbone.layers.{i}.downsample.norm.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")    )
																		rename_keys.append((F"""backbone.layers.{i}.downsample.norm.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")    )
												rename_keys.append((F"""backbone.norm{i}.weight""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")    )
												rename_keys.append((F"""backbone.norm{i}.bias""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")    )

						# FPN
						rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight""")    )
						rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight""")    )
						rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias""")    )
						for source_index, target_index in zip(range(3      ,0      ,-1    )      ,range(0      ,3    )    ):
												rename_keys.append((F"""sem_seg_head.adapter_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")    )
												rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")    )
												rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")    )
												rename_keys.append((F"""sem_seg_head.layer_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")    )
												rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")    )
												rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")    )
						rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight""")    )
						rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias""")    )

						# Transformer decoder
						for idx in range(config.decoder_config.decoder_layers    ):
												# self-attention out projection
												rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")    )
												rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")    )
												# cross-attention out projection
												rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")    )
												rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")    )
												# MLP 1
												rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")    )
												rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")    )
												# MLP 2
												rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")    )
												rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")    )
												# layernorm 1 (self-attention layernorm)
												rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")    )
												rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")    )
												# layernorm 2 (cross-attention layernorm)
												rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")    )
												rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")    )
												# layernorm 3 (final layernorm)
												rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")    )
												rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")    )

						rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight""")    )
						rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias""")    )

						# heads on top
						rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight""")    )

						rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight""")    )
						rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias""")    )

						rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight""")    )
						rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias""")    )

						for i in range(3    ):
												rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", F"""mask_embedder.{i}.0.weight""")    )
												rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", F"""mask_embedder.{i}.0.bias""")    )
						# fmt: on

						return rename_keys



def 	__a					(   lowerCAmelCase_       : Union[str, Any]      ,lowerCAmelCase_       : Any      ,lowerCAmelCase_       : int    )				->			List[str]:






						'''simple docstring'''



						UpperCAmelCase_=       dct.pop(lowerCAmelCase_    )
						UpperCAmelCase_=       val



def 	__a					(   lowerCAmelCase_       : int      ,lowerCAmelCase_       : Union[str, Any]    )				->			Optional[int]:






						'''simple docstring'''



						UpperCAmelCase_=       [int(backbone_config.embed_dim * 2**i    ) for i in range(len(backbone_config.depths    )    )]
						for i in range(len(backbone_config.depths    )    ):
												UpperCAmelCase_=       num_features[i]
												for j in range(backbone_config.depths[i]    ):
																		# fmt: off
																		# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
																		UpperCAmelCase_=       state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""    )
																		UpperCAmelCase_=       state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""    )
																		# next, add query, keys and values (in that order) to the state dict
																		UpperCAmelCase_=       in_proj_weight[:dim, :]
																		UpperCAmelCase_=       in_proj_bias[: dim]
																		UpperCAmelCase_=       in_proj_weight[
																		    dim : dim * 2, :
																		]
																		UpperCAmelCase_=       in_proj_bias[
																		    dim : dim * 2
																		]
																		UpperCAmelCase_=       in_proj_weight[
																		    -dim :, :
																		]
																		UpperCAmelCase_=       in_proj_bias[-dim :]
																		# fmt: on



def 	__a					(   lowerCAmelCase_       : Tuple      ,lowerCAmelCase_       : int    )				->			Optional[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       config.decoder_config.hidden_size
						for idx in range(config.decoder_config.decoder_layers    ):
												# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
												UpperCAmelCase_=       state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""    )
												UpperCAmelCase_=       state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""    )
												# next, add query, keys and values (in that order) to the state dict
												UpperCAmelCase_=       in_proj_weight[: hidden_size, :]
												UpperCAmelCase_=       in_proj_bias[:config.hidden_size]
												UpperCAmelCase_=       in_proj_weight[hidden_size : hidden_size * 2, :]
												UpperCAmelCase_=       in_proj_bias[hidden_size : hidden_size * 2]
												UpperCAmelCase_=       in_proj_weight[-hidden_size :, :]
												UpperCAmelCase_=       in_proj_bias[-hidden_size :]
												# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
												UpperCAmelCase_=       state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""    )
												UpperCAmelCase_=       state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""    )
												# next, add query, keys and values (in that order) to the state dict
												UpperCAmelCase_=       in_proj_weight[: hidden_size, :]
												UpperCAmelCase_=       in_proj_bias[:config.hidden_size]
												UpperCAmelCase_=       in_proj_weight[hidden_size : hidden_size * 2, :]
												UpperCAmelCase_=       in_proj_bias[hidden_size : hidden_size * 2]
												UpperCAmelCase_=       in_proj_weight[-hidden_size :, :]
												UpperCAmelCase_=       in_proj_bias[-hidden_size :]
						# fmt: on



def 	__a					(   )				->			torch.Tensor:






						'''simple docstring'''



						UpperCAmelCase_=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
						UpperCAmelCase_=       Image.open(requests.get(lowerCAmelCase_      ,stream=lowerCAmelCase_    ).raw    )
						return im



@torch.no_grad()
def 	__a					(   lowerCAmelCase_       : str      ,lowerCAmelCase_       : str      ,lowerCAmelCase_       : str      ,lowerCAmelCase_       : bool = False    )				->			int:






						'''simple docstring'''



						UpperCAmelCase_=       get_maskformer_config(lowerCAmelCase_    )

						# load original state_dict
						with open(lowerCAmelCase_      ,"""rb"""    ) as f:
												UpperCAmelCase_=       pickle.load(lowerCAmelCase_    )
						UpperCAmelCase_=       data["""model"""]

						# for name, param in state_dict.items():
						#     print(name, param.shape)

						# rename keys
						UpperCAmelCase_=       create_rename_keys(lowerCAmelCase_    )
						for src, dest in rename_keys:
												rename_key(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )
						read_in_swin_q_k_v(lowerCAmelCase_      ,config.backbone_config    )
						read_in_decoder_q_k_v(lowerCAmelCase_      ,lowerCAmelCase_    )

						# update to torch tensors
						for key, value in state_dict.items():
												UpperCAmelCase_=       torch.from_numpy(lowerCAmelCase_    )

						# load 🤗 model
						UpperCAmelCase_=       MaskFormerForInstanceSegmentation(lowerCAmelCase_    )
						model.eval()

						for name, param in model.named_parameters():
												print(lowerCAmelCase_      ,param.shape    )

						UpperCAmelCase_,       UpperCAmelCase_=       model.load_state_dict(lowerCAmelCase_      ,strict=lowerCAmelCase_    )
						assert missing_keys == [
						    "model.pixel_level_module.encoder.model.layernorm.weight",
						    "model.pixel_level_module.encoder.model.layernorm.bias",
						]
						assert len(lowerCAmelCase_    ) == 0, F"""Unexpected keys: {unexpected_keys}"""

						# verify results
						UpperCAmelCase_=       prepare_img()
						if "vistas" in model_name:
												UpperCAmelCase_=       65
						elif "cityscapes" in model_name:
												UpperCAmelCase_=       6_55_35
						else:
												UpperCAmelCase_=       2_55
						UpperCAmelCase_=       True if """ade""" in model_name else False
						UpperCAmelCase_=       MaskFormerImageProcessor(ignore_index=lowerCAmelCase_      ,reduce_labels=lowerCAmelCase_    )

						UpperCAmelCase_=       image_processor(lowerCAmelCase_      ,return_tensors="""pt"""    )

						UpperCAmelCase_=       model(**lowerCAmelCase_    )

						print("""Logits:"""      ,outputs.class_queries_logits[0, :3, :3]    )

						if model_name == "maskformer-swin-tiny-ade":
												UpperCAmelCase_=       torch.tensor(
												    [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]]    )
						assert torch.allclose(outputs.class_queries_logits[0, :3, :3]      ,lowerCAmelCase_      ,atol=1E-4    )
						print("""Looks ok!"""    )

						if pytorch_dump_folder_path is not None:
												print(F"""Saving model and image processor to {pytorch_dump_folder_path}"""    )
												Path(lowerCAmelCase_    ).mkdir(exist_ok=lowerCAmelCase_    )
												model.save_pretrained(lowerCAmelCase_    )
												image_processor.save_pretrained(lowerCAmelCase_    )

						if push_to_hub:
												print("""Pushing model and image processor to the hub..."""    )
												model.push_to_hub(F"""nielsr/{model_name}"""    )
												image_processor.push_to_hub(F"""nielsr/{model_name}"""    )


if __name__ == "__main__":
	__A       =						argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--model_name''',
	    default='''maskformer-swin-tiny-ade''',
	    type=str,
	    help=('''Name of the MaskFormer model you\'d like to convert''',),
	)
	parser.add_argument(
	    '''--checkpoint_path''',
	    default='''/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl''',
	    type=str,
	    help='''Path to the original state dict (.pth file).''',
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
	)
	parser.add_argument(
	    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
	)

	__A       =						parser.parse_args()
	convert_maskformer_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)









from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


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

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

__A       =						'''\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.

Args:
    predictions (list of str): list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references (list of list of str): list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.

Returns:
    \'google_bleu\': google_bleu score

Examples:
    Example 1:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results["google_bleu"], 2))
        0.44

    Example 2:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results["google_bleu"], 2))
        0.61

    Example 3:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
        >>> print(round(results["google_bleu"], 2))
        0.53

    Example 4:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
        >>> print(round(results["google_bleu"], 2))
        0.4
'''






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






	"""simple docstring"""


	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, 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   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						List[List[List[str]]]						,  __UpperCAmelCase     :						List[List[str]]						,  __UpperCAmelCase     :						int = 1						,  __UpperCAmelCase     :						int = 4						,  )      ->			Dict[str, float]:
							return {
							    "google_bleu": gleu_score.corpus_gleu(
							        list_of_references=__UpperCAmelCase						,  hypotheses=__UpperCAmelCase						,  min_len=__UpperCAmelCase						,  max_len=__UpperCAmelCase					)
							}




