



'''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()
lowerCAmelCase: Union[str, Any]							=						logging.get_logger(__name__)
def 				lowerCamelCase__	(						_A    ):
  a						: Tuple					       =					YolosConfig()

  # size of the architecture
  if "yolos_ti" in yolos_name:
    a						: Dict					       =					192
    a						: Optional[Any]					       =					768
    a						: Optional[int]					       =					12
    a						: Any					       =					3
    a						: List[Any]					       =					[800, 1333]
    a						: Tuple					       =					False
  elif yolos_name == "yolos_s_dWr":
    a						: Dict					       =					330
    a						: Any					       =					14
    a						: Tuple					       =					6
    a						: Any					       =					1320
  elif "yolos_s" in yolos_name:
    a						: Any					       =					384
    a						: int					       =					1536
    a						: List[Any]					       =					12
    a						: Tuple					       =					6
  elif "yolos_b" in yolos_name:
    a						: Tuple					       =					[800, 1344]

  a						: Dict					       =					91
  a						: Any					       =					'huggingface/label-files'
  a						: List[str]					       =					'coco-detection-id2label.json'
  a						: Union[str, Any]					       =					json.load(open(hf_hub_download(_A			,      _A			,      repo_type='dataset'    )			,      'r'    )    )
  a						: List[str]					       =					{int(_A    ): 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__	(						_A			,      _A			,      _A = 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)
    a						: List[str]					       =					state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""    )
    a						: int					       =					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						: Dict					       =					in_proj_bias[: config.hidden_size]
    a						: Any					       =					in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    a						: Any					       =					in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    a						: Optional[int]					       =					in_proj_weight[-config.hidden_size :, :]
    a						: str					       =					in_proj_bias[-config.hidden_size :]
def 				lowerCamelCase__	(						_A    ):
  if "backbone" in name:
    a						: Tuple					       =					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						: List[str]					       =					name.replace('det_token'			,      'embeddings.detection_tokens'    )
  if "mid_pos_embed" in name:
    a						: Dict					       =					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						: Optional[int]					       =					name.replace('patch_embed.proj'			,      'embeddings.patch_embeddings.projection'    )
  if "blocks" in name:
    a						: Dict					       =					name.replace('blocks'			,      'encoder.layer'    )
  if "attn.proj" in name:
    a						: Dict					       =					name.replace('attn.proj'			,      'attention.output.dense'    )
  if "attn" in name:
    a						: Any					       =					name.replace('attn'			,      'attention.self'    )
  if "norm1" in name:
    a						: Dict					       =					name.replace('norm1'			,      'layernorm_before'    )
  if "norm2" in name:
    a						: int					       =					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						: Dict					       =					name.replace('mlp.fc2'			,      'output.dense'    )
  if "class_embed" in name:
    a						: List[str]					       =					name.replace('class_embed'			,      'class_labels_classifier'    )
  if "bbox_embed" in name:
    a						: List[Any]					       =					name.replace('bbox_embed'			,      'bbox_predictor'    )
  if "vit.norm" in name:
    a						: List[str]					       =					name.replace('vit.norm'			,      'vit.layernorm'    )

  return name
def 				lowerCamelCase__	(						_A			,      _A    ):
  for key in orig_state_dict.copy().keys():
    a						: Dict					       =					orig_state_dict.pop(_A    )

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

  return orig_state_dict
def 				lowerCamelCase__	(						):
  a						: Union[str, Any]					       =					'http://images.cocodataset.org/val2017/000000039769.jpg'
  a						: List[Any]					       =					Image.open(requests.get(_A			,      stream=_A    ).raw    )
  return im
@torch.no_grad()
def 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A = False    ):
  a						: int					       =					get_yolos_config(_A    )

  # load original state_dict
  a						: Dict					       =					torch.load(_A			,      map_location='cpu'    )['model']

  # load 🤗 model
  a						: str					       =					YolosForObjectDetection(_A    )
  model.eval()
  a						: Any					       =					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						: str					       =					YolosImageProcessor(format='coco_detection'			,      size=_A    )
  a						: List[str]					       =					image_processor(images=prepare_img()			,      return_tensors='pt'    )
  a						: List[Any]					       =					model(**_A    )
  a       ,     a						: List[Any]					       =					outputs.logits, outputs.pred_boxes

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


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

     lowerCAmelCase: Dict							=						parser.parse_args()
     convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''






import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt

class 							a__:







    def __init__(				self			:  Optional[int]     ):
      a						: int					       =					''
      a						: List[str]					       =					''
      a						: int					       =					[]
      a						: Optional[Any]					       =					0
      a						: Optional[Any]					       =					2_56
      a						: int					       =					0
      a						: Optional[int]					       =					0
      a						: str					       =					0
      a						: int					       =					0







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  int     ):
      a						: Optional[Any]					       =					cva.imread(__snake_case						,      0     )
      a						: int					       =					copy.deepcopy(self.img     )
      a       ,     a       ,     a						: Optional[int]					       =					plt.hist(self.img.ravel()						,      2_56						,      [0, 2_56]						,      label='x'     )
      a						: str					       =					np.sum(__snake_case     )
      for i in range(len(__snake_case     )     ):
        a						: List[str]					       =					x[i] / self.k
        self.sk += prk
        a						: List[Any]					       =					(self.L - 1) * self.sk
        if self.rem != 0:
          a						: Union[str, Any]					       =					int(last % last     )
        a						: int					       =					int(last + 1 if self.rem >= 0.5 else last     )
        self.last_list.append(__snake_case     )
        a						: int					       =					int(np.ma.count(self.img     ) / self.img[1].size     )
        a						: Dict					       =					self.img[1].size
      for i in range(self.number_of_cols     ):
        for j in range(self.number_of_rows     ):
          a						: Tuple					       =					self.img[j][i]
          if num != self.last_list[num]:
            a						: Union[str, Any]					       =					self.last_list[num]
      cva.imwrite('output_data/output.jpg'						,      self.img     )







    def       lowercase_	(				self			:  Union[str, Any]     ):
      plt.hist(self.img.ravel()						,      2_56						,      [0, 2_56]     )







    def       lowercase_	(				self			:  Any     ):
      cva.imshow('Output-Image'						,      self.img     )
      cva.imshow('Input-Image'						,      self.original_image     )
      cva.waitKey(50_00     )
      cva.destroyAllWindows()


if __name__ == "__main__":
     lowerCAmelCase: Dict							=						os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
     lowerCAmelCase: Optional[Any]							=						ConstantStretch()
     stretcher.stretch(file_path)
     stretcher.plot_histogram()
     stretcher.show_image()