


from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


lowerCAmelCase			=				'\\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'

lowerCAmelCase			=				'\\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'

lowerCAmelCase			=				'\\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     ):

							def  lowerCamelCase_					(				self:      Tuple				)     ->  MetricInfo:





									"""simple docstring"""







									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  lowerCamelCase_					(				self:      str       ,		UpperCamelCase_:      List[List[List[str]]]       ,		UpperCamelCase_:      List[List[str]]       ,		UpperCamelCase_:      int = 1       ,		UpperCamelCase_:      int = 4       ,		)     ->  Dict[str, float]:





									"""simple docstring"""







									return {
									    "google_bleu": gleu_score.corpus_gleu(
									        list_of_references=UpperCamelCase_       ,		hypotheses=UpperCamelCase_       ,		min_len=UpperCamelCase_       ,		max_len=UpperCamelCase_				)
									}





'''simple docstring'''

import argparse
import json
from pathlib import Path

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

from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


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

def 					__UpperCamelCase				(						lowercase__    :	str			):






     '''simple docstring'''





     __lowercase      		=YolosConfig()

     # size of the architecture
     if "yolos_ti" in yolos_name:
          __lowercase      		=1_92
          __lowercase      		=7_68
          __lowercase      		=12
          __lowercase      		=3
          __lowercase      		=[8_00, 13_33]
          __lowercase      		=False
     elif yolos_name == "yolos_s_dWr":
          __lowercase      		=3_30
          __lowercase      		=14
          __lowercase      		=6
          __lowercase      		=13_20
     elif "yolos_s" in yolos_name:
          __lowercase      		=3_84
          __lowercase      		=15_36
          __lowercase      		=12
          __lowercase      		=6
     elif "yolos_b" in yolos_name:
          __lowercase      		=[8_00, 13_44]

     __lowercase      		=91
     __lowercase      		='huggingface/label-files'
     __lowercase      		='coco-detection-id2label.json'
     __lowercase      		=json.load(open(hf_hub_download(lowercase__,       lowercase__,       repo_type='dataset'			),       'r'			)			)
     __lowercase      		={int(lowercase__			): v for k, v in idalabel.items()}
     __lowercase      		=idalabel
     __lowercase      		={v: k for k, v in idalabel.items()}

     return config

def 					__UpperCamelCase				(						lowercase__    :	dict,       lowercase__    :	YolosConfig,       lowercase__    :	bool = False			):






     '''simple docstring'''





     for i in range(config.num_hidden_layers			):
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          __lowercase      		=state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''			)
          __lowercase      		=state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''			)
          # next, add query, keys and values (in that order) to the state dict
          __lowercase      		=in_proj_weight[: config.hidden_size, :]
          __lowercase      		=in_proj_bias[: config.hidden_size]
          __lowercase      		=in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          __lowercase      		=in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          __lowercase      		=in_proj_weight[-config.hidden_size :, :]
          __lowercase      		=in_proj_bias[-config.hidden_size :]

def 					__UpperCamelCase				(						lowercase__    :	str			):






     '''simple docstring'''





     if "backbone" in name:
          __lowercase      		=name.replace('backbone',       'vit'			)
     if "cls_token" in name:
          __lowercase      		=name.replace('cls_token',       'embeddings.cls_token'			)
     if "det_token" in name:
          __lowercase      		=name.replace('det_token',       'embeddings.detection_tokens'			)
     if "mid_pos_embed" in name:
          __lowercase      		=name.replace('mid_pos_embed',       'encoder.mid_position_embeddings'			)
     if "pos_embed" in name:
          __lowercase      		=name.replace('pos_embed',       'embeddings.position_embeddings'			)
     if "patch_embed.proj" in name:
          __lowercase      		=name.replace('patch_embed.proj',       'embeddings.patch_embeddings.projection'			)
     if "blocks" in name:
          __lowercase      		=name.replace('blocks',       'encoder.layer'			)
     if "attn.proj" in name:
          __lowercase      		=name.replace('attn.proj',       'attention.output.dense'			)
     if "attn" in name:
          __lowercase      		=name.replace('attn',       'attention.self'			)
     if "norm1" in name:
          __lowercase      		=name.replace('norm1',       'layernorm_before'			)
     if "norm2" in name:
          __lowercase      		=name.replace('norm2',       'layernorm_after'			)
     if "mlp.fc1" in name:
          __lowercase      		=name.replace('mlp.fc1',       'intermediate.dense'			)
     if "mlp.fc2" in name:
          __lowercase      		=name.replace('mlp.fc2',       'output.dense'			)
     if "class_embed" in name:
          __lowercase      		=name.replace('class_embed',       'class_labels_classifier'			)
     if "bbox_embed" in name:
          __lowercase      		=name.replace('bbox_embed',       'bbox_predictor'			)
     if "vit.norm" in name:
          __lowercase      		=name.replace('vit.norm',       'vit.layernorm'			)

     return name

def 					__UpperCamelCase				(						lowercase__    :	dict,       lowercase__    :	YolosForObjectDetection			):






     '''simple docstring'''





     for key in orig_state_dict.copy().keys():
          __lowercase      		=orig_state_dict.pop(lowercase__			)

          if "qkv" in key:
               __lowercase      		=key.split('.'			)
               __lowercase      		=int(key_split[2]			)
               __lowercase      		=model.vit.encoder.layer[layer_num].attention.attention.all_head_size
               if "weight" in key:
                    __lowercase      		=val[:dim, :]
                    __lowercase      		=val[
                        dim : dim * 2, :
                    ]
                    __lowercase      		=val[-dim:, :]
               else:
                    __lowercase      		=val[:dim]
                    __lowercase      		=val[dim : dim * 2]
                    __lowercase      		=val[-dim:]
          else:
               __lowercase      		=val

     return orig_state_dict

def 					__UpperCamelCase				(						):






     '''simple docstring'''





     __lowercase      		='http://images.cocodataset.org/val2017/000000039769.jpg'
     __lowercase      		=Image.open(requests.get(lowercase__,       stream=lowercase__			).raw			)
     return im

@torch.no_grad()
def 					__UpperCamelCase				(						lowercase__    :	str,       lowercase__    :	str,       lowercase__    :	str,       lowercase__    :	bool = False			):






     '''simple docstring'''





     __lowercase      		=get_yolos_config(lowercase__			)

     # load original state_dict
     __lowercase      		=torch.load(lowercase__,       map_location='cpu'			)['model']

     # load 🤗 model
     __lowercase      		=YolosForObjectDetection(lowercase__			)
     model.eval()
     __lowercase      		=convert_state_dict(lowercase__,       lowercase__			)
     model.load_state_dict(lowercase__			)

     # Check outputs on an image, prepared by YolosImageProcessor
     __lowercase      		=8_00 if yolos_name != 'yolos_ti' else 5_12
     __lowercase      		=YolosImageProcessor(format='coco_detection',       size=lowercase__			)
     __lowercase      		=image_processor(images=prepare_img(),       return_tensors='pt'			)
     __lowercase      		=model(**lowercase__			)
     __lowercase		,  __lowercase      		=outputs.logits, outputs.pred_boxes

     __lowercase		,  __lowercase      		=None, None
     if yolos_name == "yolos_ti":
          __lowercase      		=torch.tensor(
              [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]			)
          __lowercase      		=torch.tensor(
              [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]			)
     elif yolos_name == "yolos_s_200_pre":
          __lowercase      		=torch.tensor(
              [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]			)
          __lowercase      		=torch.tensor(
              [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]			)
     elif yolos_name == "yolos_s_300_pre":
          __lowercase      		=torch.tensor(
              [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]			)
          __lowercase      		=torch.tensor(
              [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]			)
     elif yolos_name == "yolos_s_dWr":
          __lowercase      		=torch.tensor(
              [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]			)
          __lowercase      		=torch.tensor(
              [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]			)
     elif yolos_name == "yolos_base":
          __lowercase      		=torch.tensor(
              [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]			)
          __lowercase      		=torch.tensor(
              [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]			)
     else:
          raise ValueError(F'''Unknown yolos_name: {yolos_name}'''			)

     assert torch.allclose(logits[0, :3, :3],       lowercase__,       atol=1E-4			)
     assert torch.allclose(pred_boxes[0, :3, :3],       lowercase__,       atol=1E-4			)

     Path(lowercase__			).mkdir(exist_ok=lowercase__			)
     print(F'''Saving model {yolos_name} to {pytorch_dump_folder_path}'''			)
     model.save_pretrained(lowercase__			)
     print(F'''Saving image processor to {pytorch_dump_folder_path}'''			)
     image_processor.save_pretrained(lowercase__			)

     if push_to_hub:
          __lowercase      		={
              'yolos_ti': 'yolos-tiny',
              'yolos_s_200_pre': 'yolos-small',
              'yolos_s_300_pre': 'yolos-small-300',
              'yolos_s_dWr': 'yolos-small-dwr',
              'yolos_base': 'yolos-base',
          }

          print('Pushing to the hub...'			)
          __lowercase      		=model_mapping[yolos_name]
          image_processor.push_to_hub(lowercase__,       organization='hustvl'			)
          model.push_to_hub(lowercase__,       organization='hustvl'			)


if __name__ == "__main__":
       UpperCAmelCase					 =	argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--yolos_name''',
           default='''yolos_s_200_pre''',
           type=str,
           help=(
               '''Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','''
               ''' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'''
           ),
       )
       parser.add_argument(
           '''--checkpoint_path''', default=None, 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.'''
       )

       UpperCAmelCase					 =	parser.parse_args()
       convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)


