


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()
SCREAMING_SNAKE_CASE :   List[Any]							      =				logging.get_logger(__name__)



def        UpperCamelCase_(   lowerCamelCase_				)					->		YolosConfig:
				_lowercase :							str   	=	YolosConfig()

				# size of the architecture
				if "yolos_ti" in yolos_name:
								_lowercase :							Union[str, Any]   	=	192
								_lowercase :							Dict   	=	768
								_lowercase :							str   	=	12
								_lowercase :							List[Any]   	=	3
								_lowercase :							List[Any]   	=	[800, 1333]
								_lowercase :							Union[str, Any]   	=	False
				elif yolos_name == "yolos_s_dWr":
								_lowercase :							Optional[int]   	=	330
								_lowercase :							int   	=	14
								_lowercase :							int   	=	6
								_lowercase :							Dict   	=	1320
				elif "yolos_s" in yolos_name:
								_lowercase :							Tuple   	=	384
								_lowercase :							List[Any]   	=	1536
								_lowercase :							Dict   	=	12
								_lowercase :							str   	=	6
				elif "yolos_b" in yolos_name:
								_lowercase :							int   	=	[800, 1344]

				_lowercase :							Union[str, Any]   	=	91
				_lowercase :							Dict   	=	"huggingface/label-files"
				_lowercase :							int   	=	"coco-detection-id2label.json"
				_lowercase :							Optional[int]   	=	json.load(open(hf_hub_download(lowercase_							,				lowercase_							,				repo_type='dataset'				)							,				'r'				)				)
				_lowercase :							Tuple   	=	{int(lowercase_				): v for k, v in idalabel.items()}
				_lowercase :							Optional[Any]   	=	idalabel
				_lowercase :							Any   	=	{v: k for k, v in idalabel.items()}

				return config



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_ = False				)					->		Optional[Any]:

				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 :							List[str]   	=	state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''				)
								_lowercase :							Optional[int]   	=	state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''				)
								# next, add query, keys and values (in that order) to the state dict
								_lowercase :							Union[str, Any]   	=	in_proj_weight[: config.hidden_size, :]
								_lowercase :							List[Any]   	=	in_proj_bias[: config.hidden_size]
								_lowercase :							Optional[int]   	=	in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								_lowercase :							int   	=	in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								_lowercase :							Dict   	=	in_proj_weight[-config.hidden_size :, :]
								_lowercase :							List[str]   	=	in_proj_bias[-config.hidden_size :]



def        UpperCamelCase_(   lowerCamelCase_				)					->		str:

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

				return name



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		dict:

				for key in orig_state_dict.copy().keys():
								_lowercase :							Any   	=	orig_state_dict.pop(lowercase_				)

								if "qkv" in key:
												_lowercase :							Dict   	=	key.split('.'				)
												_lowercase :							Optional[int]   	=	int(key_split[2]				)
												_lowercase :							List[Any]   	=	model.vit.encoder.layer[layer_num].attention.attention.all_head_size
												if "weight" in key:
																_lowercase :							Any   	=	val[:dim, :]
																_lowercase :							Tuple   	=	val[
																    dim : dim * 2, :
																]
																_lowercase :							str   	=	val[-dim:, :]
												else:
																_lowercase :							str   	=	val[:dim]
																_lowercase :							Optional[Any]   	=	val[dim : dim * 2]
																_lowercase :							List[Any]   	=	val[-dim:]
								else:
												_lowercase :							List[str]   	=	val

				return orig_state_dict



def        UpperCamelCase_(   )					->		torch.Tensor:
				_lowercase :							List[str]   	=	"http://images.cocodataset.org/val2017/000000039769.jpg"
				_lowercase :							Optional[int]   	=	Image.open(requests.get(lowercase_							,				stream=lowercase_				).raw				)
				return im



@torch.no_grad()
def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_ = False				)					->		List[str]:
				_lowercase :							Dict   	=	get_yolos_config(lowercase_				)

				# load original state_dict
				_lowercase :							Tuple   	=	torch.load(lowercase_							,				map_location='cpu'				)["model"]

				# load 🤗 model
				_lowercase :							Dict   	=	YolosForObjectDetection(lowercase_				)
				model.eval()
				_lowercase :							List[str]   	=	convert_state_dict(lowercase_							,				lowercase_				)
				model.load_state_dict(lowercase_				)

				# Check outputs on an image, prepared by YolosImageProcessor
				_lowercase :							List[str]   	=	800 if yolos_name != "yolos_ti" else 512
				_lowercase :							Any   	=	YolosImageProcessor(format='coco_detection'							,				size=lowercase_				)
				_lowercase :							Union[str, Any]   	=	image_processor(images=prepare_img()							,				return_tensors='pt'				)
				_lowercase :							Dict   	=	model(**lowercase_				)
				_lowercase :							Dict   	=	outputs.logits, outputs.pred_boxes

				_lowercase :							int   	=	None, None
				if yolos_name == "yolos_ti":
								_lowercase :							List[Any]   	=	torch.tensor(
								    [[-39.50_22, -11.98_20, -17.68_88], [-29.95_74, -9.97_69, -17.76_91], [-42.32_81, -20.72_00, -30.62_94]]				)
								_lowercase :							str   	=	torch.tensor(
								    [[0.40_21, 0.08_36, 0.79_79], [0.01_84, 0.26_09, 0.03_64], [0.17_81, 0.20_04, 0.20_95]]				)
				elif yolos_name == "yolos_s_200_pre":
								_lowercase :							Union[str, Any]   	=	torch.tensor(
								    [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]				)
								_lowercase :							Optional[Any]   	=	torch.tensor(
								    [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]				)
				elif yolos_name == "yolos_s_300_pre":
								_lowercase :							Any   	=	torch.tensor(
								    [[-36.22_20, -14.43_85, -23.54_57], [-35.69_70, -14.75_83, -21.39_35], [-31.59_39, -13.60_42, -16.80_49]]				)
								_lowercase :							int   	=	torch.tensor(
								    [[0.76_14, 0.23_16, 0.47_28], [0.71_68, 0.44_95, 0.38_55], [0.49_96, 0.14_66, 0.99_96]]				)
				elif yolos_name == "yolos_s_dWr":
								_lowercase :							int   	=	torch.tensor(
								    [[-42.86_68, -24.10_49, -41.16_90], [-34.74_56, -14.12_74, -24.91_94], [-33.78_98, -12.19_46, -25.64_95]]				)
								_lowercase :							Optional[Any]   	=	torch.tensor(
								    [[0.55_87, 0.27_73, 0.06_05], [0.50_04, 0.30_14, 0.99_94], [0.49_99, 0.15_48, 0.99_94]]				)
				elif yolos_name == "yolos_base":
								_lowercase :							Dict   	=	torch.tensor(
								    [[-40.60_64, -24.30_84, -32.64_47], [-55.19_90, -30.77_19, -35.58_77], [-51.43_11, -33.35_07, -35.64_62]]				)
								_lowercase :							Optional[Any]   	=	torch.tensor(
								    [[0.55_55, 0.27_94, 0.06_55], [0.90_49, 0.26_64, 0.18_94], [0.91_83, 0.19_84, 0.16_35]]				)
				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 :							str   	=	{
								    "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 :							List[str]   	=	model_mapping[yolos_name]
								image_processor.push_to_hub(lowercase_							,				organization='hustvl'				)
								model.push_to_hub(lowercase_							,				organization='hustvl'				)


if __name__ == "__main__":
	SCREAMING_SNAKE_CASE :   Any							      =				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."
	)

	SCREAMING_SNAKE_CASE :   Optional[int]							      =				parser.parse_args()
	convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
SCREAMING_SNAKE_CASE :   Optional[Any]							      =				logging.getLogger()



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		Optional[Any]:
    _lowercase :							List[Any]   	=	'\n'.join(lowerCamelCase_				)
    Path(lowerCamelCase_				).open('w'				).writelines(lowerCamelCase_				)


SCREAMING_SNAKE_CASE :   str							      =				"patrickvonplaten/t5-tiny-random"
SCREAMING_SNAKE_CASE :   List[Any]							      =				"sshleifer/bart-tiny-random"
SCREAMING_SNAKE_CASE :   int							      =				"sshleifer/tiny-mbart"

SCREAMING_SNAKE_CASE :   Optional[int]							      =				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






class _lowerCamelCase(		_a     ):
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     Dict:




           """simple docstring"""

           _lowercase :							int   	=	Path(self.get_auto_remove_tmp_dir()) / 'utest_input.source'
           _lowercase :							str   	=	input_file_name.parent / 'utest_output.txt'
           assert not output_file_name.exists()
           _lowercase :							str   	=	[' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
           _dump_articles(lowerCamelCase,   lowerCamelCase)

           _lowercase :							List[str]   	=	str(Path(self.get_auto_remove_tmp_dir()) / 'scores.json')
           _lowercase :							Optional[int]   	=	'translation_en_to_de' if model == T5_TINY else 'summarization'
           _lowercase :							Any   	=	F'''
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            '''.split()

           with patch.object(lowerCamelCase,   'argv',   lowerCamelCase):
               run_generate()
               assert Path(lowerCamelCase).exists()
               # os.remove(Path(output_file_name))
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           self.run_eval_tester(lowerCamelCase)
       @parameterized.expand([BART_TINY, MBART_TINY])
       @slow
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     int:




           """simple docstring"""

           self.run_eval_tester(lowerCamelCase)




       @parameterized.expand([T5_TINY, MBART_TINY])
       @slow
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     List[str]:




           """simple docstring"""

           _lowercase :							str   	=	Path(self.get_auto_remove_tmp_dir()) / 'utest_input.source'
           _lowercase :							Any   	=	input_file_name.parent / 'utest_output.txt'
           assert not output_file_name.exists()

           _lowercase :							List[str]   	=	{
               'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
               'de': [
                   'Maschinelles Lernen ist großartig, oder?',
                   'Ich esse gerne Bananen',
                   'Morgen ist wieder ein toller Tag!',
               ],
           }

           _lowercase :							Optional[Any]   	=	Path(self.get_auto_remove_tmp_dir())
           _lowercase :							Optional[Any]   	=	str(tmp_dir / 'scores.json')
           _lowercase :							str   	=	str(tmp_dir / 'val.target')
           _dump_articles(lowerCamelCase,   text['en'])
           _dump_articles(lowerCamelCase,   text['de'])
           _lowercase :							Tuple   	=	'translation_en_to_de' if model == T5_TINY else 'summarization'
           _lowercase :							Tuple   	=	F'''
            run_eval_search.py
            {model}
            {str(lowerCamelCase)}
            {str(lowerCamelCase)}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            '''.split()
           testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0'])

           with patch.object(lowerCamelCase,   'argv',   lowerCamelCase):
               with CaptureStdout() as cs:
                   run_search()
               _lowercase :							Dict   	=	[' num_beams | length_penalty', model, 'Best score args']
               _lowercase :							Optional[Any]   	=	['Info']
               if "translation" in task:
                   expected_strings.append('bleu')
               else:
                   expected_strings.extend(lowerCamelCase)
               for w in expected_strings:
                   assert w in cs.out
               for w in un_expected_strings:
                   assert w not in cs.out
               assert Path(lowerCamelCase).exists()
               os.remove(Path(lowerCamelCase))


