"""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		):







		"""simple docstring"""
		_UpperCAmelCase						 =      YolosConfig()

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

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

		return config






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






def     __UpperCAmelCase					(			lowercase		):







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

		return name






def     __UpperCAmelCase					(			lowercase   ,lowercase		):







		"""simple docstring"""
		for key in orig_state_dict.copy().keys():
				_UpperCAmelCase						 =      orig_state_dict.pop(__UpperCamelCase		)

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

		return orig_state_dict






def     __UpperCAmelCase					(			):







		"""simple docstring"""
		_UpperCAmelCase						 =      """http://images.cocodataset.org/val2017/000000039769.jpg"""
		_UpperCAmelCase						 =      Image.open(requests.get(__UpperCamelCase   ,stream=__UpperCamelCase		).raw		)
		return im






@torch.no_grad()
def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase = False		):







		"""simple docstring"""
		_UpperCAmelCase						 =      get_yolos_config(__UpperCamelCase		)

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

		# load 🤗 model
		_UpperCAmelCase						 =      YolosForObjectDetection(__UpperCamelCase		)
		model.eval()
		_UpperCAmelCase						 =      convert_state_dict(__UpperCamelCase   ,__UpperCamelCase		)
		model.load_state_dict(__UpperCamelCase		)

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

		_UpperCAmelCase ,				_UpperCAmelCase						 =      None, None
		if yolos_name == "yolos_ti":
				_UpperCAmelCase						 =      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]]		)
				_UpperCAmelCase						 =      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":
				_UpperCAmelCase						 =      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]]		)
				_UpperCAmelCase						 =      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":
				_UpperCAmelCase						 =      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]]		)
				_UpperCAmelCase						 =      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":
				_UpperCAmelCase						 =      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]]		)
				_UpperCAmelCase						 =      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":
				_UpperCAmelCase						 =      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]]		)
				_UpperCAmelCase						 =      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:
				_UpperCAmelCase						 =      {
				    """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..."""		)
				_UpperCAmelCase						 =      model_mapping[yolos_name]
				image_processor.push_to_hub(__UpperCamelCase   ,organization="""hustvl"""		)
				model.push_to_hub(__UpperCamelCase   ,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)

"""simple docstring"""


import random






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase = False		):







  """simple docstring"""
  _UpperCAmelCase						 =      {i: [] for i in range(lowercase		)}

  # if probability is greater or equal than 1, then generate a complete graph
  if probability >= 1:
    return complete_graph(lowercase		)
  # if probability is lower or equal than 0, then return a graph without edges
  if probability <= 0:
    return graph

  # for each couple of nodes, add an edge from u to v
  # if the number randomly generated is greater than probability probability
  for i in range(lowercase		):
    for j in range(i + 1   ,lowercase		):
      if random.random() < probability:
        graph[i].append(lowercase		)
        if not directed:
          # if the graph is undirected, add an edge in from j to i, either
          graph[j].append(lowercase		)
  return graph






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return {
      i: [j for j in range(lowercase		) if i != j] for i in range(lowercase		)
  }


if __name__ == "__main__":
    import doctest

    doctest.testmod()
