



"""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__   :					Optional[Any]												=				logging.get_logger(__name__)

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     YolosConfig()

							# size of the architecture
							if "yolos_ti" in yolos_name:
														SCREAMING_SNAKE_CASE__		: Tuple     =     192
														SCREAMING_SNAKE_CASE__		: str     =     768
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     12
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     3
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     [800, 1_333]
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     False
							elif yolos_name == "yolos_s_dWr":
														SCREAMING_SNAKE_CASE__		: Any     =     330
														SCREAMING_SNAKE_CASE__		: List[str]     =     14
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     6
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     1_320
							elif "yolos_s" in yolos_name:
														SCREAMING_SNAKE_CASE__		: Tuple     =     384
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     1_536
														SCREAMING_SNAKE_CASE__		: List[str]     =     12
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     6
							elif "yolos_b" in yolos_name:
														SCREAMING_SNAKE_CASE__		: int     =     [800, 1_344]

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

							return config

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case = False		):
							for i in range(config.num_hidden_layers		):
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														SCREAMING_SNAKE_CASE__		: List[Any]     =     state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''		)
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''		)
														# next, add query, keys and values (in that order) to the state dict
														SCREAMING_SNAKE_CASE__		: int     =     in_proj_weight[: config.hidden_size, :]
														SCREAMING_SNAKE_CASE__		: Any     =     in_proj_bias[: config.hidden_size]
														SCREAMING_SNAKE_CASE__		: Tuple     =     in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														SCREAMING_SNAKE_CASE__		: List[Any]     =     in_proj_weight[-config.hidden_size :, :]
														SCREAMING_SNAKE_CASE__		: Tuple     =     in_proj_bias[-config.hidden_size :]

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

							return name

def lowercase_						(     _snake_case     ,_snake_case		):
							for key in orig_state_dict.copy().keys():
														SCREAMING_SNAKE_CASE__		: List[Any]     =     orig_state_dict.pop(__UpperCamelCase		)

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

							return orig_state_dict

def lowercase_						(     ):
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     Image.open(requests.get(__UpperCamelCase     ,stream=__UpperCamelCase		).raw		)
							return im

@torch.no_grad()
def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case     ,_snake_case = False		):
							SCREAMING_SNAKE_CASE__		: Any     =     get_yolos_config(__UpperCamelCase		)

							# load original state_dict
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     torch.load(__UpperCamelCase     ,map_location="""cpu"""		)["""model"""]

							# load 🤗 model
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     YolosForObjectDetection(__UpperCamelCase		)
							model.eval()
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     convert_state_dict(__UpperCamelCase     ,__UpperCamelCase		)
							model.load_state_dict(__UpperCamelCase		)

							# Check outputs on an image, prepared by YolosImageProcessor
							SCREAMING_SNAKE_CASE__		: str     =     800 if yolos_name != """yolos_ti""" else 512
							SCREAMING_SNAKE_CASE__		: Tuple     =     YolosImageProcessor(format="""coco_detection"""     ,size=__UpperCamelCase		)
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     image_processor(images=prepare_img()     ,return_tensors="""pt"""		)
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     model(**__UpperCamelCase		)
							SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Tuple     =     outputs.logits, outputs.pred_boxes

							SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Optional[int]     =     None, None
							if yolos_name == "yolos_ti":
														SCREAMING_SNAKE_CASE__		: Tuple     =     torch.tensor(
														    [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]		)
														SCREAMING_SNAKE_CASE__		: Tuple     =     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":
														SCREAMING_SNAKE_CASE__		: Tuple     =     torch.tensor(
														    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]		)
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     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":
														SCREAMING_SNAKE_CASE__		: int     =     torch.tensor(
														    [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]		)
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     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":
														SCREAMING_SNAKE_CASE__		: str     =     torch.tensor(
														    [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]		)
														SCREAMING_SNAKE_CASE__		: Dict     =     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":
														SCREAMING_SNAKE_CASE__		: List[str]     =     torch.tensor(
														    [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]		)
														SCREAMING_SNAKE_CASE__		: Dict     =     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]     ,__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:
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     {
														    """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..."""		)
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     model_mapping[yolos_name]
														image_processor.push_to_hub(__UpperCamelCase     ,organization="""hustvl"""		)
														model.push_to_hub(__UpperCamelCase     ,organization="""hustvl"""		)


if __name__ == "__main__":
					UpperCAmelCase__   :					Union[str, 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.'
					)

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






"""simple docstring"""




import math
import os
import sys

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: List[str]     =     """"""
							try:
														with open(_snake_case     ,"""rb"""		) as binary_file:
																					SCREAMING_SNAKE_CASE__		: str     =     binary_file.read()
														for dat in data:
																					SCREAMING_SNAKE_CASE__		: int     =     f'''{dat:08b}'''
																					result += curr_byte
														return result
							except OSError:
														print("""File not accessible"""		)
														sys.exit()

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case     ,_snake_case		):
							lexicon.pop(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     last_match_id

							if math.loga(_snake_case		).is_integer():
														for curr_key in lexicon:
																					SCREAMING_SNAKE_CASE__		: Optional[int]     =     """0""" + lexicon[curr_key]

							SCREAMING_SNAKE_CASE__		: Tuple     =     bin(_snake_case		)[2:]

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: List[str]     =     {"""0""": """0""", """1""": """1"""}
							SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: str     =     """""", """"""
							SCREAMING_SNAKE_CASE__		: int     =     len(_snake_case		)

							for i in range(len(_snake_case		)		):
														curr_string += data_bits[i]
														if curr_string not in lexicon:
																					continue

														SCREAMING_SNAKE_CASE__		: Dict     =     lexicon[curr_string]
														result += last_match_id
														add_key_to_lexicon(_snake_case     ,_snake_case     ,_snake_case     ,_snake_case		)
														index += 1
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     """"""

							while curr_string != "" and curr_string not in lexicon:
														curr_string += "0"

							if curr_string != "":
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     lexicon[curr_string]
														result += last_match_id

							return result

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: str     =     os.path.getsize(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Any     =     bin(_snake_case		)[2:]
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     len(_snake_case		)

							return "0" * (length_length - 1) + file_length_binary + compressed

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Tuple     =     8
							try:
														with open(_snake_case     ,"""wb"""		) as opened_file:
																					SCREAMING_SNAKE_CASE__		: Optional[int]     =     [
																					    to_write[i : i + byte_length]
																					    for i in range(0     ,len(_snake_case		)     ,_snake_case		)
																					]

																					if len(result_byte_array[-1]		) % byte_length == 0:
																												result_byte_array.append("""10000000"""		)
																					else:
																												result_byte_array[-1] += "1" + "0" * (
																												    byte_length - len(result_byte_array[-1]		) - 1
																												)

																					for elem in result_byte_array:
																												opened_file.write(int(_snake_case     ,2		).to_bytes(1     ,byteorder="""big"""		)		)
							except OSError:
														print("""File not accessible"""		)
														sys.exit()

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     read_file_binary(_snake_case		)
							SCREAMING_SNAKE_CASE__		: List[str]     =     compress_data(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     add_file_length(_snake_case     ,_snake_case		)
							write_file_binary(_snake_case     ,_snake_case		)


if __name__ == "__main__":
					compress(sys.argv[1], sys.argv[2])




