





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	:	List[Any]		     =       logging.get_logger(__name__)
def      _A       (		SCREAMING_SNAKE_CASE     :   str						):





  """simple docstring"""



  a__	:					Optional[Any]										=YolosConfig()

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

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

  return config
def      _A       (		SCREAMING_SNAKE_CASE     :   dict       ,       SCREAMING_SNAKE_CASE     :   YolosConfig       ,       SCREAMING_SNAKE_CASE     :   bool = 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)
    a__	:					str										=state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''						)
    a__	:					List[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[str]										=in_proj_weight[: config.hidden_size, :]
    a__	:					int										=in_proj_bias[: config.hidden_size]
    a__	:					Optional[Any]										=in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    a__	:					Any										=in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    a__	:					Any										=in_proj_weight[-config.hidden_size :, :]
    a__	:					Optional[int]										=in_proj_bias[-config.hidden_size :]
def      _A       (		SCREAMING_SNAKE_CASE     :   str						):





  """simple docstring"""



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

  return name
def      _A       (		SCREAMING_SNAKE_CASE     :   dict       ,       SCREAMING_SNAKE_CASE     :   YolosForObjectDetection						):





  """simple docstring"""



  for key in orig_state_dict.copy().keys():
    a__	:					int										=orig_state_dict.pop(SCREAMING_SNAKE_CASE						)

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

  return orig_state_dict
def      _A       (		):





  """simple docstring"""



  a__	:					str										="http://images.cocodataset.org/val2017/000000039769.jpg"
  a__	:					int										=Image.open(requests.get(SCREAMING_SNAKE_CASE       ,       stream=SCREAMING_SNAKE_CASE						).raw						)
  return im
@torch.no_grad()
def      _A       (		SCREAMING_SNAKE_CASE     :   str       ,       SCREAMING_SNAKE_CASE     :   str       ,       SCREAMING_SNAKE_CASE     :   str       ,       SCREAMING_SNAKE_CASE     :   bool = False						):





  """simple docstring"""



  a__	:					List[str]										=get_yolos_config(SCREAMING_SNAKE_CASE						)

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

  # load 🤗 model
  a__	:					Optional[Any]										=YolosForObjectDetection(SCREAMING_SNAKE_CASE						)
  model.eval()
  a__	:					Any										=convert_state_dict(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
  model.load_state_dict(SCREAMING_SNAKE_CASE						)

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

  a__					,							a__	:					List[Any]										=None, None
  if yolos_name == "yolos_ti":
    a__	:					Any										=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__	:					Union[str, Any]										=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__	:					str										=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__	:					Tuple										=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__	:					Dict										=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__	:					Union[str, Any]										=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__	:					Optional[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__	:					List[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__	:					Tuple										=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]       ,       SCREAMING_SNAKE_CASE       ,       atol=1e-4						)
  assert torch.allclose(pred_boxes[0, :3, :3]       ,       SCREAMING_SNAKE_CASE       ,       atol=1e-4						)

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

  if push_to_hub:
    a__	:					Dict										={
        "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__	:					int										=model_mapping[yolos_name]
    image_processor.push_to_hub(SCREAMING_SNAKE_CASE       ,       organization="hustvl"						)
    model.push_to_hub(SCREAMING_SNAKE_CASE       ,       organization="hustvl"						)


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













import numpy

# List of input, output pairs
UpperCAmelCase	:	str		     =       (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
UpperCAmelCase	:	Optional[int]		     =       (((515, 22, 13), 555), ((61, 35, 49), 150))
UpperCAmelCase	:	str		     =       [2, 4, 1, 5]
UpperCAmelCase	:	List[str]		     =       len(train_data)
UpperCAmelCase	:	Dict		     =       0.0_0_9
def      _A       (		SCREAMING_SNAKE_CASE     :   Tuple       ,       SCREAMING_SNAKE_CASE     :   Tuple="train"						):





  """simple docstring"""



  return calculate_hypothesis_value(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						) - output(
      SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
def      _A       (		SCREAMING_SNAKE_CASE     :   Tuple						):





  """simple docstring"""



  a__	:					Tuple										=0
  for i in range(len(SCREAMING_SNAKE_CASE						) - 1						):
    hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
  hyp_val += parameter_vector[0]
  return hyp_val
def      _A       (		SCREAMING_SNAKE_CASE     :   Dict       ,       SCREAMING_SNAKE_CASE     :   Dict						):





  """simple docstring"""



  if data_set == "train":
    return train_data[example_no][1]
  elif data_set == "test":
    return test_data[example_no][1]
  return None
def      _A       (		SCREAMING_SNAKE_CASE     :   Optional[Any]       ,       SCREAMING_SNAKE_CASE     :   Tuple						):





  """simple docstring"""



  if data_set == "train":
    return _hypothesis_value(train_data[example_no][0]						)
  elif data_set == "test":
    return _hypothesis_value(test_data[example_no][0]						)
  return None
def      _A       (		SCREAMING_SNAKE_CASE     :   Union[str, Any]       ,       SCREAMING_SNAKE_CASE     :   int=m						):





  """simple docstring"""



  a__	:					Any										=0
  for i in range(SCREAMING_SNAKE_CASE						):
    if index == -1:
      summation_value += _error(SCREAMING_SNAKE_CASE						)
    else:
      summation_value += _error(SCREAMING_SNAKE_CASE						) * train_data[i][0][index]
  return summation_value
def      _A       (		SCREAMING_SNAKE_CASE     :   int						):





  """simple docstring"""



  a__	:					Any										=summation_of_cost_derivative(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						) / m
  return cost_derivative_value
def      _A       (		):





  """simple docstring"""



  global parameter_vector
  # Tune these values to set a tolerance value for predicted output
  a__	:					Dict										=0.0_0_0_0_0_2
  a__	:					Union[str, Any]										=0
  a__	:					Any										=0
  while True:
    j += 1
    a__	:					Any										=[0, 0, 0, 0]
    for i in range(0       ,       len(SCREAMING_SNAKE_CASE						)						):
      a__	:					Tuple										=get_cost_derivative(i - 1						)
      a__	:					List[Any]										=(
          parameter_vector[i] - LEARNING_RATE * cost_derivative
      )
    if numpy.allclose(
        SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       atol=SCREAMING_SNAKE_CASE       ,       rtol=SCREAMING_SNAKE_CASE       ,       ):
      break
    a__	:					Optional[Any]										=temp_parameter_vector
  print(("Number of iterations:", j)						)
def      _A       (		):





  """simple docstring"""



  for i in range(len(SCREAMING_SNAKE_CASE						)						):
    print(("Actual output value:", output(SCREAMING_SNAKE_CASE       ,       "test"						))						)
    print(("Hypothesis output:", calculate_hypothesis_value(SCREAMING_SNAKE_CASE       ,       "test"						))						)


if __name__ == "__main__":
     run_gradient_descent()
     print("""\nTesting gradient descent for a linear hypothesis function.\n""")
     test_gradient_descent()






