


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()
_lowercase       :      Tuple	=	logging.get_logger(__name__)





def   _lowerCAmelCase				(   UpperCamelCase__:     str						) ->   YolosConfig:





	"""simple docstring"""







	A								=				YolosConfig()

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

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

	return config





def   _lowerCAmelCase				(   UpperCamelCase__:     dict							,					UpperCamelCase__:     YolosConfig							,					UpperCamelCase__:     bool = False						) ->   Any:





	"""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)
		A								=				state_dict.pop(f'blocks.{i}.attn.qkv.weight'						)
		A								=				state_dict.pop(f'blocks.{i}.attn.qkv.bias'						)
		# next, add query, keys and values (in that order) to the state dict
		A								=				in_proj_weight[: config.hidden_size, :]
		A								=				in_proj_bias[: config.hidden_size]
		A								=				in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		A								=				in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		A								=				in_proj_weight[-config.hidden_size :, :]
		A								=				in_proj_bias[-config.hidden_size :]





def   _lowerCAmelCase				(   UpperCamelCase__:     str						) ->   str:





	"""simple docstring"""







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

	return name





def   _lowerCAmelCase				(   UpperCamelCase__:     dict							,					UpperCamelCase__:     YolosForObjectDetection						) ->   dict:





	"""simple docstring"""







	for key in orig_state_dict.copy().keys():
		A								=				orig_state_dict.pop(UpperCamelCase__						)

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

	return orig_state_dict





def   _lowerCAmelCase				(   ) ->   torch.Tensor:





	"""simple docstring"""







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





@torch.no_grad()
def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     str							,					UpperCamelCase__:     str							,					UpperCamelCase__:     bool = False						) ->   Optional[int]:





	"""simple docstring"""







	A								=				get_yolos_config(UpperCamelCase__						)

	# load original state_dict
	A								=				torch.load(UpperCamelCase__							,					map_location="""cpu"""						)["""model"""]

	# load 🤗 model
	A								=				YolosForObjectDetection(UpperCamelCase__						)
	model.eval()
	A								=				convert_state_dict(UpperCamelCase__							,					UpperCamelCase__						)
	model.load_state_dict(UpperCamelCase__						)

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

	A					,			A								=				None, None
	if yolos_name == "yolos_ti":
		A								=				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]]						)
		A								=				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":
		A								=				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]]						)
		A								=				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":
		A								=				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]]						)
		A								=				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":
		A								=				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]]						)
		A								=				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":
		A								=				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]]						)
		A								=				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]							,					UpperCamelCase__							,					atol=1e-4						)
	assert torch.allclose(pred_boxes[0, :3, :3]							,					UpperCamelCase__							,					atol=1e-4						)

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

	if push_to_hub:
		A								=				{
		    """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..."""						)
		A								=				model_mapping[yolos_name]
		image_processor.push_to_hub(UpperCamelCase__							,					organization="""hustvl"""						)
		model.push_to_hub(UpperCamelCase__							,					organization="""hustvl"""						)


if __name__ == "__main__":
				_lowercase       :      List[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."
				)

				_lowercase       :      str	=	parser.parse_args()
				convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD





def   _lowerCAmelCase				(   UpperCamelCase__:     Any						) ->   Tuple:





	"""simple docstring"""







	def wrapper(*UpperCamelCase__:     Union[str, Any]							,					**UpperCamelCase__:     List[str]						):
		A								=				timeit.default_timer()
		A								=				func(*UpperCamelCase__							,					**UpperCamelCase__						)
		A								=				timeit.default_timer() - starttime
		return delta

	A								=				func.__name__

	return wrapper





def   _lowerCAmelCase				(   UpperCamelCase__:     dict							,					UpperCamelCase__:     List[str]=1_00							,					UpperCamelCase__:     int=None						) ->   Any:





	"""simple docstring"""







	A								=				[]
	A								=				seq_shapes or {}
	for i in range(UpperCamelCase__						):
		A								=				{}
		for col_id, (k, v) in enumerate(features.items()						):
			if isinstance(UpperCamelCase__							,					_ArrayXD						):
				A								=				np.random.rand(*v.shape						).astype(v.dtype						)
			elif isinstance(UpperCamelCase__							,					datasets.Value						):
				if v.dtype == "string":
					A								=				"""The small grey turtle was surprisingly fast when challenged."""
				else:
					A								=				np.random.randint(10							,					size=1						).astype(v.dtype						).item()
			elif isinstance(UpperCamelCase__							,					datasets.Sequence						):
				while isinstance(UpperCamelCase__							,					datasets.Sequence						):
					A								=				v.feature
				A								=				seq_shapes[k]
				A								=				np.random.rand(*UpperCamelCase__						).astype(v.dtype						)
			A								=				data

		dummy_data.append((i, example)						)

	return dummy_data





def   _lowerCAmelCase				(   UpperCamelCase__:     int							,					UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     List[str]=1_00							,					UpperCamelCase__:     str=None						) ->   Optional[int]:





	"""simple docstring"""







	A								=				generate_examples(UpperCamelCase__							,					num_examples=UpperCamelCase__							,					seq_shapes=UpperCamelCase__						)

	with ArrowWriter(features=UpperCamelCase__							,					path=UpperCamelCase__						) as writer:
		for key, record in dummy_data:
			A								=				features.encode_example(UpperCamelCase__						)
			writer.write(UpperCamelCase__						)

		A					,			A								=				writer.finalize()

	if not num_final_examples == num_examples:
		raise ValueError(
		    f'Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'						)

	A								=				datasets.Dataset.from_file(filename=UpperCamelCase__							,					info=datasets.DatasetInfo(features=UpperCamelCase__						)						)

	return dataset



