




import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
	import PIL.Image

	from .features import FeatureType


a__		     =     None
a__		     =     '''<''' if sys.byteorder == '''little''' else '''>'''
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
a__		     =     [
    np.dtype('''|b1'''),
    np.dtype('''|u1'''),
    np.dtype('''<u2'''),
    np.dtype('''>u2'''),
    np.dtype('''<i2'''),
    np.dtype('''>i2'''),
    np.dtype('''<u4'''),
    np.dtype('''>u4'''),
    np.dtype('''<i4'''),
    np.dtype('''>i4'''),
    np.dtype('''<f4'''),
    np.dtype('''>f4'''),
    np.dtype('''<f8'''),
    np.dtype('''>f8'''),
]


@dataclass
class    SCREAMING_SNAKE_CASE_				:



	"""simple docstring"""


	__magic_name__   :       bool      	=		True
	__magic_name__   :       Optional[str]      	=		None
	# Automatically constructed
	__magic_name__   :       ClassVar[str]      	=		"PIL.Image.Image"
	__magic_name__   :       ClassVar[Any]      	=		pa.struct({'bytes': pa.binary(), 'path': pa.string()}   )
	__magic_name__   :       str      	=		field(default='Image'	,  init=_UpperCamelCase	,  repr=_UpperCamelCase   )






	def __call__(					self		:						Tuple	)	->       Dict:







						"""simple docstring"""







						return self.pa_type






	def 					lowerCamelCase__     (					self		:						Tuple					,     lowerCAmelCase		:						Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]	)	->       dict:







						"""simple docstring"""







						if config.PIL_AVAILABLE:
											import PIL.Image
						else:
											raise ImportError("""To support encoding images, please install 'Pillow'."""	)

						if isinstance(lowerCAmelCase					,     lowerCAmelCase	):
											__UpperCamelCase					:	Optional[Any]             =      np.array(lowerCAmelCase	)

						if isinstance(lowerCAmelCase					,     lowerCAmelCase	):
											return {"path": value, "bytes": None}
						elif isinstance(lowerCAmelCase					,     lowerCAmelCase	):
											return {"path": None, "bytes": value}
						elif isinstance(lowerCAmelCase					,     np.ndarray	):
											# convert the image array to PNG/TIFF bytes
											return encode_np_array(lowerCAmelCase	)
						elif isinstance(lowerCAmelCase					,     PIL.Image.Image	):
											# convert the PIL image to bytes (default format is PNG/TIFF)
											return encode_pil_image(lowerCAmelCase	)
						elif value.get("""path"""	) is not None and os.path.isfile(value["""path"""]	):
											# we set "bytes": None to not duplicate the data if they're already available locally
											return {"bytes": None, "path": value.get("""path"""	)}
						elif value.get("""bytes"""	) is not None or value.get("""path"""	) is not None:
											# store the image bytes, and path is used to infer the image format using the file extension
											return {"bytes": value.get("""bytes"""	), "path": value.get("""path"""	)}
						else:
											raise ValueError(
											    F'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'''	)






	def 					lowerCamelCase__     (					self		:						List[str]					,     lowerCAmelCase		:						dict					,     lowerCAmelCase		:						Tuple=None	)	->       "PIL.Image.Image":







						"""simple docstring"""







						if not self.decode:
											raise RuntimeError("""Decoding is disabled for this feature. Please use Image(decode=True) instead."""	)

						if config.PIL_AVAILABLE:
											import PIL.Image
						else:
											raise ImportError("""To support decoding images, please install 'Pillow'."""	)

						if token_per_repo_id is None:
											__UpperCamelCase					:	List[str]             =      {}

						__UpperCamelCase      ,			__UpperCamelCase					:	Any             =      value["""path"""], value["""bytes"""]
						if bytes_ is None:
											if path is None:
																raise ValueError(F'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.'''	)
											else:
																if is_local_path(lowerCAmelCase	):
																					__UpperCamelCase					:	Dict             =      PIL.Image.open(lowerCAmelCase	)
																else:
																					__UpperCamelCase					:	Dict             =      path.split("""::"""	)[-1]
																					try:
																										__UpperCamelCase					:	Tuple             =      string_to_dict(lowerCAmelCase					,     config.HUB_DATASETS_URL	)["""repo_id"""]
																										__UpperCamelCase					:	List[str]             =      token_per_repo_id.get(lowerCAmelCase	)
																					except ValueError:
																										__UpperCamelCase					:	Union[str, Any]             =      None
																					with xopen(lowerCAmelCase					,     """rb"""					,     use_auth_token=lowerCAmelCase	) as f:
																										__UpperCamelCase					:	List[Any]             =      BytesIO(f.read()	)
																					__UpperCamelCase					:	int             =      PIL.Image.open(bytes_	)
						else:
											__UpperCamelCase					:	Optional[int]             =      PIL.Image.open(BytesIO(bytes_	)	)
						image.load()  # to avoid "Too many open files" errors
						return image






	def 					lowerCamelCase__     (					self		:						Optional[Any]	)	->       Union["FeatureType", Dict[str, "FeatureType"]]:







						"""simple docstring"""







						from .features import Value

						return (
						    self
						    if self.decode
						    else {
						        "bytes": Value("""binary"""	),
						        "path": Value("""string"""	),
						    }
						)






	def 					lowerCamelCase__     (					self		:						Optional[Any]					,     lowerCAmelCase		:						Union[pa.StringArray, pa.StructArray, pa.ListArray]	)	->       pa.StructArray:







						"""simple docstring"""







						if pa.types.is_string(storage.type	):
											__UpperCamelCase					:	Dict             =      pa.array([None] * len(lowerCAmelCase	)					,     type=pa.binary()	)
											__UpperCamelCase					:	List[Any]             =      pa.StructArray.from_arrays([bytes_array, storage]					,     ["""bytes""", """path"""]					,     mask=storage.is_null()	)
						elif pa.types.is_binary(storage.type	):
											__UpperCamelCase					:	int             =      pa.array([None] * len(lowerCAmelCase	)					,     type=pa.string()	)
											__UpperCamelCase					:	str             =      pa.StructArray.from_arrays([storage, path_array]					,     ["""bytes""", """path"""]					,     mask=storage.is_null()	)
						elif pa.types.is_struct(storage.type	):
											if storage.type.get_field_index("""bytes"""	) >= 0:
																__UpperCamelCase					:	Union[str, Any]             =      storage.field("""bytes"""	)
											else:
																__UpperCamelCase					:	str             =      pa.array([None] * len(lowerCAmelCase	)					,     type=pa.binary()	)
											if storage.type.get_field_index("""path"""	) >= 0:
																__UpperCamelCase					:	List[Any]             =      storage.field("""path"""	)
											else:
																__UpperCamelCase					:	List[Any]             =      pa.array([None] * len(lowerCAmelCase	)					,     type=pa.string()	)
											__UpperCamelCase					:	List[str]             =      pa.StructArray.from_arrays([bytes_array, path_array]					,     ["""bytes""", """path"""]					,     mask=storage.is_null()	)
						elif pa.types.is_list(storage.type	):
											__UpperCamelCase					:	Tuple             =      pa.array(
											    [encode_np_array(np.array(lowerCAmelCase	)	)["""bytes"""] if arr is not None else None for arr in storage.to_pylist()]					,     type=pa.binary()					,     )
											__UpperCamelCase					:	Dict             =      pa.array([None] * len(lowerCAmelCase	)					,     type=pa.string()	)
											__UpperCamelCase					:	Optional[int]             =      pa.StructArray.from_arrays(
											    [bytes_array, path_array]					,     ["""bytes""", """path"""]					,     mask=bytes_array.is_null()	)
						return array_cast(lowerCAmelCase					,     self.pa_type	)






	def 					lowerCamelCase__     (					self		:						Union[str, Any]					,     lowerCAmelCase		:						pa.StructArray	)	->       pa.StructArray:







						"""simple docstring"""







						@no_op_if_value_is_null
						def path_to_bytes(lowerCAmelCase		:						Union[str, Any]	):
											with xopen(lowerCAmelCase					,     """rb"""	) as f:
																__UpperCamelCase					:	List[Any]             =      f.read()
											return bytes_

						__UpperCamelCase					:	Any             =      pa.array(
						    [
						        (path_to_bytes(x["""path"""]	) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
						        for x in storage.to_pylist()
						    ]					,     type=pa.binary()					,     )
						__UpperCamelCase					:	Tuple             =      pa.array(
						    [os.path.basename(lowerCAmelCase	) if path is not None else None for path in storage.field("""path"""	).to_pylist()]					,     type=pa.string()					,     )
						__UpperCamelCase					:	Optional[int]             =      pa.StructArray.from_arrays([bytes_array, path_array]					,     ["""bytes""", """path"""]					,     mask=bytes_array.is_null()	)
						return array_cast(lowerCAmelCase					,     self.pa_type	)






def 				A__ ()     ->						List[str]:
					if config.PIL_AVAILABLE:
										import PIL.Image
					else:
										raise ImportError("""To support encoding images, please install 'Pillow'."""				)

					global _IMAGE_COMPRESSION_FORMATS
					if _IMAGE_COMPRESSION_FORMATS is None:
										PIL.Image.init()
										__UpperCamelCase					:	str             =      list(set(PIL.Image.OPEN.keys()				) & set(PIL.Image.SAVE.keys()				)				)
					return _IMAGE_COMPRESSION_FORMATS





def 				A__ (snake_case   :		"PIL.Image.Image"				)     ->						bytes:
					__UpperCamelCase					:	List[Any]             =      BytesIO()
					if image.format in list_image_compression_formats():
										__UpperCamelCase					:	List[Any]             =      image.format
					else:
										__UpperCamelCase					:	int             =      """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
					image.save(snake_case		,	format=snake_case				)
					return buffer.getvalue()





def 				A__ (snake_case   :		"PIL.Image.Image"				)     ->						dict:
					if hasattr(snake_case		,	"""filename"""				) and image.filename != "":
										return {"path": image.filename, "bytes": None}
					else:
										return {"path": None, "bytes": image_to_bytes(snake_case				)}





def 				A__ (snake_case   :		np.ndarray				)     ->						dict:
					if config.PIL_AVAILABLE:
										import PIL.Image
					else:
										raise ImportError("""To support encoding images, please install 'Pillow'."""				)

					__UpperCamelCase					:	Optional[int]             =      array.dtype
					__UpperCamelCase					:	List[Any]             =      dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
					__UpperCamelCase					:	Any             =      dtype.kind
					__UpperCamelCase					:	int             =      dtype.itemsize

					__UpperCamelCase					:	int             =      None

					# Multi-channel array case (only np.dtype("|u1") is allowed)
					if array.shape[2:]:
										__UpperCamelCase					:	Any             =      np.dtype("""|u1"""				)
										if dtype_kind not in ["u", "i"]:
															raise TypeError(
															    F'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.'''				)
										if dtype is not dest_dtype:
															warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''''				)
    # Exact match
					elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
										__UpperCamelCase					:	str             =      dtype
					else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
										while dtype_itemsize >= 1:
															__UpperCamelCase					:	List[str]             =      dtype_byteorder + dtype_kind + str(snake_case				)
															__UpperCamelCase					:	Tuple             =      np.dtype(snake_case				)
															if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
																				warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''''				)
																				break
															else:
																				dtype_itemsize //= 2

					if dest_dtype is None:
										raise TypeError(
										    F'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}'''				)
					__UpperCamelCase					:	Any             =      PIL.Image.fromarray(array.astype(snake_case				)				)
					return {"path": None, "bytes": image_to_bytes(snake_case				)}







def 				A__ (snake_case   :		Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]				)     ->						List[dict]:
					if config.PIL_AVAILABLE:
										import PIL.Image
					else:
										raise ImportError("""To support encoding images, please install 'Pillow'."""				)

					if objs:
										__UpperCamelCase      ,			__UpperCamelCase					:	List[str]             =      first_non_null_value(snake_case				)
										if isinstance(snake_case		,	snake_case				):
															return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
										if isinstance(snake_case		,	np.ndarray				):
															__UpperCamelCase					:	Tuple             =      no_op_if_value_is_null(snake_case				)
															return [obj_to_image_dict_func(snake_case				) for obj in objs]
										elif isinstance(snake_case		,	PIL.Image.Image				):
															__UpperCamelCase					:	Dict             =      no_op_if_value_is_null(snake_case				)
															return [obj_to_image_dict_func(snake_case				) for obj in objs]
										else:
															return objs
					else:
										return objs












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()
a__		     =     logging.get_logger(__name__)





def 				A__ (snake_case   :		str				)     ->						YolosConfig:
					__UpperCamelCase					:	Dict             =      YolosConfig()

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

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

					return config





def 				A__ (snake_case   :		dict		,	snake_case   :		YolosConfig		,	snake_case   :		bool = False				)     ->						List[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)
										__UpperCamelCase					:	Tuple             =      state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''				)
										__UpperCamelCase					:	str             =      state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''				)
										# next, add query, keys and values (in that order) to the state dict
										__UpperCamelCase					:	List[Any]             =      in_proj_weight[: config.hidden_size, :]
										__UpperCamelCase					:	Union[str, Any]             =      in_proj_bias[: config.hidden_size]
										__UpperCamelCase					:	Union[str, Any]             =      in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										__UpperCamelCase					:	Tuple             =      in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										__UpperCamelCase					:	Tuple             =      in_proj_weight[-config.hidden_size :, :]
										__UpperCamelCase					:	List[Any]             =      in_proj_bias[-config.hidden_size :]





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

					return name





def 				A__ (snake_case   :		dict		,	snake_case   :		YolosForObjectDetection				)     ->						dict:
					for key in orig_state_dict.copy().keys():
										__UpperCamelCase					:	Dict             =      orig_state_dict.pop(snake_case				)

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

					return orig_state_dict





def 				A__ ()     ->						torch.Tensor:
					__UpperCamelCase					:	Any             =      """http://images.cocodataset.org/val2017/000000039769.jpg"""
					__UpperCamelCase					:	Any             =      Image.open(requests.get(snake_case		,	stream=snake_case				).raw				)
					return im





@torch.no_grad()
def 				A__ (snake_case   :		str		,	snake_case   :		str		,	snake_case   :		str		,	snake_case   :		bool = False				)     ->						Optional[Any]:
					__UpperCamelCase					:	List[str]             =      get_yolos_config(snake_case				)

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

					# load 🤗 model
					__UpperCamelCase					:	Dict             =      YolosForObjectDetection(snake_case				)
					model.eval()
					__UpperCamelCase					:	int             =      convert_state_dict(snake_case		,	snake_case				)
					model.load_state_dict(snake_case				)

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

					__UpperCamelCase      ,			__UpperCamelCase					:	List[Any]             =      None, None
					if yolos_name == "yolos_ti":
										__UpperCamelCase					:	Dict             =      torch.tensor(
										    [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]				)
										__UpperCamelCase					:	Union[str, Any]             =      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":
										__UpperCamelCase					:	Optional[Any]             =      torch.tensor(
										    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]				)
										__UpperCamelCase					:	str             =      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":
										__UpperCamelCase					:	Union[str, Any]             =      torch.tensor(
										    [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]				)
										__UpperCamelCase					:	Tuple             =      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":
										__UpperCamelCase					:	str             =      torch.tensor(
										    [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]				)
										__UpperCamelCase					:	Optional[Any]             =      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":
										__UpperCamelCase					:	Optional[int]             =      torch.tensor(
										    [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]				)
										__UpperCamelCase					:	List[str]             =      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]		,	snake_case		,	atol=1e-4				)
					assert torch.allclose(pred_boxes[0, :3, :3]		,	snake_case		,	atol=1e-4				)

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

					if push_to_hub:
										__UpperCamelCase					:	Tuple             =      {
										    """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					:	int             =      model_mapping[yolos_name]
										image_processor.push_to_hub(snake_case		,	organization="""hustvl"""				)
										model.push_to_hub(snake_case		,	organization="""hustvl"""				)


if __name__ == "__main__":
	a__		     =     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.'''
	)

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






