


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


def      lowercase			(				A_       )->      YolosConfig:
  '''simple docstring'''







  a						:		Any      							=			YolosConfig()

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

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

  return config


def      lowercase			(				A_  ,   A_  ,   A_ = False       )->      Tuple:
  '''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						:		Optional[int]      							=			state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''       )
    a						:		Any      							=			state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''       )
    # next, add query, keys and values (in that order) to the state dict
    a						:		Optional[Any]      							=			in_proj_weight[: config.hidden_size, :]
    a						:		Any      							=			in_proj_bias[: config.hidden_size]
    a						:		str      							=			in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    a						:		Optional[int]      							=			in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    a						:		Any      							=			in_proj_weight[-config.hidden_size :, :]
    a						:		str      							=			in_proj_bias[-config.hidden_size :]


def      lowercase			(				A_       )->      str:
  '''simple docstring'''







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

  return name


def      lowercase			(				A_  ,   A_       )->      dict:
  '''simple docstring'''







  for key in orig_state_dict.copy().keys():
    a						:		Optional[int]      							=			orig_state_dict.pop(A_       )

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

  return orig_state_dict


def      lowercase			(				)->      torch.Tensor:
  '''simple docstring'''







  a						:		List[str]      							=			"http://images.cocodataset.org/val2017/000000039769.jpg"
  a						:		str      							=			Image.open(requests.get(A_  ,   stream=A_       ).raw       )
  return im


@torch.no_grad()
def      lowercase			(				A_  ,   A_  ,   A_  ,   A_ = False       )->      Any:
  '''simple docstring'''







  a						:		str      							=			get_yolos_config(A_       )

  # load original state_dict
  a						:		List[str]      							=			torch.load(A_  ,   map_location="cpu"       )["model"]

  # load 🤗 model
  a						:		List[str]      							=			YolosForObjectDetection(A_       )
  model.eval()
  a						:		Tuple      							=			convert_state_dict(A_  ,   A_       )
  model.load_state_dict(A_       )

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

  a							,		a						:		Dict      							=			None, None
  if yolos_name == "yolos_ti":
    a						:		str      							=			torch.tensor(
        [[-3_9.5_0_2_2, -1_1.9_8_2_0, -1_7.6_8_8_8], [-2_9.9_5_7_4, -9.9_7_6_9, -1_7.7_6_9_1], [-4_2.3_2_8_1, -2_0.7_2_0_0, -3_0.6_2_9_4]]       )
    a						:		int      							=			torch.tensor(
        [[0.4_0_2_1, 0.0_8_3_6, 0.7_9_7_9], [0.0_1_8_4, 0.2_6_0_9, 0.0_3_6_4], [0.1_7_8_1, 0.2_0_0_4, 0.2_0_9_5]]       )
  elif yolos_name == "yolos_s_200_pre":
    a						:		Any      							=			torch.tensor(
        [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]]       )
    a						:		str      							=			torch.tensor(
        [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]]       )
  elif yolos_name == "yolos_s_300_pre":
    a						:		Optional[Any]      							=			torch.tensor(
        [[-3_6.2_2_2_0, -1_4.4_3_8_5, -2_3.5_4_5_7], [-3_5.6_9_7_0, -1_4.7_5_8_3, -2_1.3_9_3_5], [-3_1.5_9_3_9, -1_3.6_0_4_2, -1_6.8_0_4_9]]       )
    a						:		Optional[int]      							=			torch.tensor(
        [[0.7_6_1_4, 0.2_3_1_6, 0.4_7_2_8], [0.7_1_6_8, 0.4_4_9_5, 0.3_8_5_5], [0.4_9_9_6, 0.1_4_6_6, 0.9_9_9_6]]       )
  elif yolos_name == "yolos_s_dWr":
    a						:		List[str]      							=			torch.tensor(
        [[-4_2.8_6_6_8, -2_4.1_0_4_9, -4_1.1_6_9_0], [-3_4.7_4_5_6, -1_4.1_2_7_4, -2_4.9_1_9_4], [-3_3.7_8_9_8, -1_2.1_9_4_6, -2_5.6_4_9_5]]       )
    a						:		List[Any]      							=			torch.tensor(
        [[0.5_5_8_7, 0.2_7_7_3, 0.0_6_0_5], [0.5_0_0_4, 0.3_0_1_4, 0.9_9_9_4], [0.4_9_9_9, 0.1_5_4_8, 0.9_9_9_4]]       )
  elif yolos_name == "yolos_base":
    a						:		Any      							=			torch.tensor(
        [[-4_0.6_0_6_4, -2_4.3_0_8_4, -3_2.6_4_4_7], [-5_5.1_9_9_0, -3_0.7_7_1_9, -3_5.5_8_7_7], [-5_1.4_3_1_1, -3_3.3_5_0_7, -3_5.6_4_6_2]]       )
    a						:		Union[str, Any]      							=			torch.tensor(
        [[0.5_5_5_5, 0.2_7_9_4, 0.0_6_5_5], [0.9_0_4_9, 0.2_6_6_4, 0.1_8_9_4], [0.9_1_8_3, 0.1_9_8_4, 0.1_6_3_5]]       )
  else:
    raise ValueError(F'''Unknown yolos_name: {yolos_name}'''       )

  assert torch.allclose(logits[0, :3, :3]  ,   A_  ,   atol=1e-4       )
  assert torch.allclose(pred_boxes[0, :3, :3]  ,   A_  ,   atol=1e-4       )

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

  if push_to_hub:
    a						:		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..."       )
    a						:		Any      							=			model_mapping[yolos_name]
    image_processor.push_to_hub(A_  ,   organization="hustvl"       )
    model.push_to_hub(A_  ,   organization="hustvl"       )


if __name__ == "__main__":
 __lowercase    					=      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    					=      parser.parse_args()
 convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)










"""simple docstring"""







import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						_A  (  _a    ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase       :							str	=       LayoutLMTokenizer
    UpperCAmelCase       :							int	=       LayoutLMTokenizerFast
    UpperCAmelCase       :							Union[str, Any]	=       True
    UpperCAmelCase       :							Optional[Any]	=       True






    def 	__snake_case							(					self				:    Optional[int]):
      super().setUp()

      a						:		Tuple      							=			[
          "[UNK]",
          "[CLS]",
          "[SEP]",
          "want",
          "##want",
          "##ed",
          "wa",
          "un",
          "runn",
          "##ing",
          ",",
          "low",
          "lowest",
      ]
      a						:		Optional[Any]      							=			os.path.join(self.tmpdirname				, VOCAB_FILES_NAMES["vocab_file"])
      with open(self.vocab_file				, "w"				, encoding="utf-8") as vocab_writer:
        vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))






    def 	__snake_case							(					self				:    Optional[int]				, **__UpperCAmelCase				:    Tuple):
      return LayoutLMTokenizer.from_pretrained(self.tmpdirname				, **__UpperCAmelCase)






    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    str):
      a						:		Tuple      							=			"UNwant\u00E9d,running"
      a						:		Dict      							=			"unwanted, running"
      return input_text, output_text






    def 	__snake_case							(					self				:    Any):
      a						:		List[Any]      							=			self.tokenizer_class(self.vocab_file)

      a						:		str      							=			tokenizer.tokenize("UNwant\u00E9d,running")
      self.assertListEqual(__UpperCAmelCase				, ["un", "##want", "##ed", ",", "runn", "##ing"])
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase)				, [7, 4, 5, 10, 8, 9])






    def 	__snake_case							(					self				:    Dict):
      pass






