






"""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()
A_    =							logging.get_logger(__name__)




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str      )		->YolosConfig:
						A__						:  Optional[int]		  =  YolosConfig()

						# size of the architecture
						if "yolos_ti" in yolos_name:
												A__						:  List[str]		  =  1_9_2
												A__						:  List[Any]		  =  7_6_8
												A__						:  List[Any]		  =  1_2
												A__						:  Tuple		  =  3
												A__						:  Optional[Any]		  =  [8_0_0, 1_3_3_3]
												A__						:  str		  =  False
						elif yolos_name == "yolos_s_dWr":
												A__						:  str		  =  3_3_0
												A__						:  str		  =  1_4
												A__						:  Tuple		  =  6
												A__						:  Any		  =  1_3_2_0
						elif "yolos_s" in yolos_name:
												A__						:  int		  =  3_8_4
												A__						:  str		  =  1_5_3_6
												A__						:  Optional[int]		  =  1_2
												A__						:  Optional[int]		  =  6
						elif "yolos_b" in yolos_name:
												A__						:  Union[str, Any]		  =  [8_0_0, 1_3_4_4]

						A__						:  Union[str, Any]		  =  9_1
						A__						:  List[str]		  =  """huggingface/label-files"""
						A__						:  List[str]		  =  """coco-detection-id2label.json"""
						A__						:  Any		  =  json.load(open(hf_hub_download(UpperCAmelCase__,						UpperCAmelCase__,						repo_type="""dataset"""      ),						"""r"""      )      )
						A__						:  Optional[Any]		  =  {int(UpperCAmelCase__      ): v for k, v in idalabel.items()}
						A__						:  Tuple		  =  idalabel
						A__						:  Optional[Any]		  =  {v: k for k, v in idalabel.items()}

						return config




def      _lowerCAmelCase		(							UpperCAmelCase__       :					dict,						UpperCAmelCase__       :					YolosConfig,						UpperCAmelCase__       :					bool = False      )		->Dict:
						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__						:  Union[str, Any]		  =  state_dict.pop(f'blocks.{i}.attn.qkv.weight'      )
												A__						:  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
												A__						:  List[Any]		  =  in_proj_weight[: config.hidden_size, :]
												A__						:  str		  =  in_proj_bias[: config.hidden_size]
												A__						:  Optional[Any]		  =  in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												A__						:  Tuple		  =  in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												A__						:  Union[str, Any]		  =  in_proj_weight[-config.hidden_size :, :]
												A__						:  str		  =  in_proj_bias[-config.hidden_size :]




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

						return name




def      _lowerCAmelCase		(							UpperCAmelCase__       :					dict,						UpperCAmelCase__       :					YolosForObjectDetection      )		->dict:
						for key in orig_state_dict.copy().keys():
												A__						:  List[Any]		  =  orig_state_dict.pop(UpperCAmelCase__      )

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

						return orig_state_dict




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




@torch.no_grad()
def      _lowerCAmelCase		(							UpperCAmelCase__       :					str,						UpperCAmelCase__       :					str,						UpperCAmelCase__       :					str,						UpperCAmelCase__       :					bool = False      )		->List[str]:
						A__						:  Union[str, Any]		  =  get_yolos_config(UpperCAmelCase__      )

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

						# load 🤗 model
						A__						:  Tuple		  =  YolosForObjectDetection(UpperCAmelCase__      )
						model.eval()
						A__						:  Optional[Any]		  =  convert_state_dict(UpperCAmelCase__,						UpperCAmelCase__      )
						model.load_state_dict(UpperCAmelCase__      )

						# Check outputs on an image, prepared by YolosImageProcessor
						A__						:  Dict		  =  8_0_0 if yolos_name != """yolos_ti""" else 5_1_2
						A__						:  Optional[Any]		  =  YolosImageProcessor(format="""coco_detection""",						size=UpperCAmelCase__      )
						A__						:  int		  =  image_processor(images=prepare_img(),						return_tensors="""pt"""      )
						A__						:  Tuple		  =  model(**UpperCAmelCase__      )
						A__  ,  A__						:  List[Any]		  =  outputs.logits, outputs.pred_boxes

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














"""simple docstring"""



import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):


							def __init__(			self      :						List[Any]			,    snake_case      :						NestedDataStructureLike[PathLike]			,    snake_case      :						Optional[NamedSplit] = None			,    snake_case      :						Optional[Features] = None			,    snake_case      :						str = None			,    snake_case      :						bool = False			,    snake_case      :						bool = False			,    snake_case      :						Optional[str] = None			,    snake_case      :						Optional[int] = None			,    **snake_case      :						Optional[int]			,    ):
													'''simple docstring'''





													super().__init__(
													    snake_case			,    split=snake_case			,    features=snake_case			,    cache_dir=snake_case			,    keep_in_memory=snake_case			,    streaming=snake_case			,    num_proc=snake_case			,    **snake_case			,    )
													A__						:  Optional[int]		  =  field
													A__						:  Tuple		  =  path_or_paths if isinstance(snake_case			,    snake_case							) else {self.split: path_or_paths}
													A__						:  List[Any]		  =  Json(
													    cache_dir=snake_case			,    data_files=snake_case			,    features=snake_case			,    field=snake_case			,    **snake_case			,    )


							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													if self.streaming:
																			A__						:  List[Any]		  =  self.builder.as_streaming_dataset(split=self.split							)
													# Build regular (map-style) dataset
													else:
																			A__						:  int		  =  None
																			A__						:  Dict		  =  None
																			A__						:  Tuple		  =  None
																			A__						:  Optional[Any]		  =  None

																			self.builder.download_and_prepare(
																			    download_config=snake_case			,    download_mode=snake_case			,    verification_mode=snake_case			,    base_path=snake_case			,    num_proc=self.num_proc			,    )
																			A__						:  str		  =  self.builder.as_dataset(
																			    split=self.split			,    verification_mode=snake_case			,    in_memory=self.keep_in_memory							)
													return dataset






class 							__SCREAMING_SNAKE_CASE						:


							def __init__(			self      :						Any			,    snake_case      :						Dataset			,    snake_case      :						Union[PathLike, BinaryIO]			,    snake_case      :						Optional[int] = None			,    snake_case      :						Optional[int] = None			,    **snake_case      :						Union[str, Any]			,    ):
													'''simple docstring'''





													if num_proc is not None and num_proc <= 0:
																			raise ValueError(F'num_proc {num_proc} must be an integer > 0.'							)

													A__						:  Union[str, Any]		  =  dataset
													A__						:  Any		  =  path_or_buf
													A__						:  str		  =  batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
													A__						:  str		  =  num_proc
													A__						:  Union[str, Any]		  =  """utf-8"""
													A__						:  Optional[int]		  =  to_json_kwargs


							def 						_UpperCamelCase       (			self      :						List[str]							):
													'''simple docstring'''





													A__						:  Any		  =  self.to_json_kwargs.pop("""path_or_buf"""			,    snake_case							)
													A__						:  List[Any]		  =  self.to_json_kwargs.pop("""orient"""			,    """records"""							)
													A__						:  List[str]		  =  self.to_json_kwargs.pop("""lines"""			,    True if orient == """records""" else False							)
													A__						:  str		  =  self.to_json_kwargs.pop("""index"""			,    False if orient in ["""split""", """table"""] else True							)
													A__						:  Optional[int]		  =  self.to_json_kwargs.pop("""compression"""			,    snake_case							)

													if compression not in [None, "infer", "gzip", "bz2", "xz"]:
																			raise NotImplementedError(F'`datasets` currently does not support {compression} compression'							)

													if isinstance(self.path_or_buf			,    (str, bytes, os.PathLike)							):
																			with fsspec.open(self.path_or_buf			,    """wb"""			,    compression=snake_case							) as buffer:
																									A__						:  Tuple		  =  self._write(file_obj=snake_case			,    orient=snake_case			,    lines=snake_case			,    index=snake_case			,    **self.to_json_kwargs							)
													else:
																			if compression:
																									raise NotImplementedError(
																									    F'The compression parameter is not supported when writing to a buffer, but compression={compression}'
																									    """ was passed. Please provide a local path instead."""							)
																			A__						:  List[Any]		  =  self._write(
																			    file_obj=self.path_or_buf			,    orient=snake_case			,    lines=snake_case			,    index=snake_case			,    **self.to_json_kwargs							)
													return written


							def 						_UpperCamelCase       (			self      :						List[Any]			,    snake_case      :						List[str]							):
													'''simple docstring'''





													A__  ,  A__  ,  A__  ,  A__  ,  A__						:  int		  =  args

													A__						:  Any		  =  query_table(
													    table=self.dataset.data			,    key=slice(snake_case			,    offset + self.batch_size							)			,    indices=self.dataset._indices			,    )
													A__						:  List[Any]		  =  batch.to_pandas().to_json(
													    path_or_buf=snake_case			,    orient=snake_case			,    lines=snake_case			,    index=snake_case			,    **snake_case							)
													if not json_str.endswith("""\n"""							):
																			json_str += "\n"
													return json_str.encode(self.encoding							)


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						BinaryIO			,    snake_case      :						List[Any]			,    snake_case      :						Tuple			,    snake_case      :						List[str]			,    **snake_case      :						str			,    ):
													'''simple docstring'''





													A__						:  List[Any]		  =  0

													if self.num_proc is None or self.num_proc == 1:
																			for offset in logging.tqdm(
																			    range(0			,    len(self.dataset							)			,    self.batch_size							)			,    unit="""ba"""			,    disable=not logging.is_progress_bar_enabled()			,    desc="""Creating json from Arrow format"""			,    ):
																									A__						:  List[str]		  =  self._batch_json((offset, orient, lines, index, to_json_kwargs)							)
																									written += file_obj.write(snake_case							)
													else:
																			A__  ,  A__						:  Optional[int]		  =  len(self.dataset							), self.batch_size
																			with multiprocessing.Pool(self.num_proc							) as pool:
																									for json_str in logging.tqdm(
																									    pool.imap(
																									        self._batch_json			,    [(offset, orient, lines, index, to_json_kwargs) for offset in range(0			,    snake_case			,    snake_case							)]			,    )			,    total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size			,    unit="""ba"""			,    disable=not logging.is_progress_bar_enabled()			,    desc="""Creating json from Arrow format"""			,    ):
																															written += file_obj.write(snake_case							)

													return written






