




"""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 		_snake_case	(   _snake_case				:   str				)       ->   YolosConfig:






      '''simple docstring'''


      _A     =  YolosConfig()

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

      _A     =  91
      _A     =  'huggingface/label-files'
      _A     =  'coco-detection-id2label.json'
      _A     =  json.load(open(hf_hub_download(_snake_case							,    _snake_case							,    repo_type='dataset'				)							,    'r'				)				)
      _A     =  {int(_snake_case				): v for k, v in idalabel.items()}
      _A     =  idalabel
      _A     =  {v: k for k, v in idalabel.items()}

      return config






def 		_snake_case	(   _snake_case				:   dict							,    _snake_case				:   YolosConfig							,    _snake_case				:   bool = False				)       ->   List[Any]:






      '''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     =  state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''				)
            _A     =  state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''				)
            # next, add query, keys and values (in that order) to the state dict
            _A     =  in_proj_weight[: config.hidden_size, :]
            _A     =  in_proj_bias[: config.hidden_size]
            _A     =  in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            _A     =  in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            _A     =  in_proj_weight[-config.hidden_size :, :]
            _A     =  in_proj_bias[-config.hidden_size :]






def 		_snake_case	(   _snake_case				:   str				)       ->   str:






      '''simple docstring'''


      if "backbone" in name:
            _A     =  name.replace('backbone'							,    'vit'				)
      if "cls_token" in name:
            _A     =  name.replace('cls_token'							,    'embeddings.cls_token'				)
      if "det_token" in name:
            _A     =  name.replace('det_token'							,    'embeddings.detection_tokens'				)
      if "mid_pos_embed" in name:
            _A     =  name.replace('mid_pos_embed'							,    'encoder.mid_position_embeddings'				)
      if "pos_embed" in name:
            _A     =  name.replace('pos_embed'							,    'embeddings.position_embeddings'				)
      if "patch_embed.proj" in name:
            _A     =  name.replace('patch_embed.proj'							,    'embeddings.patch_embeddings.projection'				)
      if "blocks" in name:
            _A     =  name.replace('blocks'							,    'encoder.layer'				)
      if "attn.proj" in name:
            _A     =  name.replace('attn.proj'							,    'attention.output.dense'				)
      if "attn" in name:
            _A     =  name.replace('attn'							,    'attention.self'				)
      if "norm1" in name:
            _A     =  name.replace('norm1'							,    'layernorm_before'				)
      if "norm2" in name:
            _A     =  name.replace('norm2'							,    'layernorm_after'				)
      if "mlp.fc1" in name:
            _A     =  name.replace('mlp.fc1'							,    'intermediate.dense'				)
      if "mlp.fc2" in name:
            _A     =  name.replace('mlp.fc2'							,    'output.dense'				)
      if "class_embed" in name:
            _A     =  name.replace('class_embed'							,    'class_labels_classifier'				)
      if "bbox_embed" in name:
            _A     =  name.replace('bbox_embed'							,    'bbox_predictor'				)
      if "vit.norm" in name:
            _A     =  name.replace('vit.norm'							,    'vit.layernorm'				)

      return name






def 		_snake_case	(   _snake_case				:   dict							,    _snake_case				:   YolosForObjectDetection				)       ->   dict:






      '''simple docstring'''


      for key in orig_state_dict.copy().keys():
            _A     =  orig_state_dict.pop(_snake_case				)

            if "qkv" in key:
                  _A     =  key.split('.'				)
                  _A     =  int(key_split[2]				)
                  _A     =  model.vit.encoder.layer[layer_num].attention.attention.all_head_size
                  if "weight" in key:
                        _A     =  val[:dim, :]
                        _A     =  val[
                            dim : dim * 2, :
                        ]
                        _A     =  val[-dim:, :]
                  else:
                        _A     =  val[:dim]
                        _A     =  val[dim : dim * 2]
                        _A     =  val[-dim:]
            else:
                  _A     =  val

      return orig_state_dict






def 		_snake_case	(   )       ->   torch.Tensor:






      '''simple docstring'''


      _A     =  'http://images.cocodataset.org/val2017/000000039769.jpg'
      _A     =  Image.open(requests.get(_snake_case							,    stream=_snake_case				).raw				)
      return im






@torch.no_grad()
def 		_snake_case	(   _snake_case				:   str							,    _snake_case				:   str							,    _snake_case				:   str							,    _snake_case				:   bool = False				)       ->   Union[str, Any]:






      '''simple docstring'''


      _A     =  get_yolos_config(_snake_case				)

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

      # load 🤗 model
      _A     =  YolosForObjectDetection(_snake_case				)
      model.eval()
      _A     =  convert_state_dict(_snake_case							,    _snake_case				)
      model.load_state_dict(_snake_case				)

      # Check outputs on an image, prepared by YolosImageProcessor
      _A     =  8_00 if yolos_name != 'yolos_ti' else 5_12
      _A     =  YolosImageProcessor(format='coco_detection'							,    size=_snake_case				)
      _A     =  image_processor(images=prepare_img()							,    return_tensors='pt'				)
      _A     =  model(**_snake_case				)
      _A  ,       _A     =  outputs.logits, outputs.pred_boxes

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







"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a							      =     logging.get_logger(__name__)

a							      =     {
    '''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	int     =      '''gpt_bigcode'''
 UpperCAmelCase       :	str     =      ['''past_key_values''']
 UpperCAmelCase       :	Dict     =      {
     '''hidden_size''': '''n_embd''',
     '''max_position_embeddings''': '''n_positions''',
     '''num_attention_heads''': '''n_head''',
     '''num_hidden_layers''': '''n_layer''',
 }

 def __init__(					self			:			Tuple       ,				_UpperCAmelCase			:			Dict=50_257       ,				_UpperCAmelCase			:			List[Any]=1_024       ,				_UpperCAmelCase			:			Any=768       ,				_UpperCAmelCase			:			int=12       ,				_UpperCAmelCase			:			Any=12       ,				_UpperCAmelCase			:			Tuple=None       ,				_UpperCAmelCase			:			str="gelu_pytorch_tanh"       ,				_UpperCAmelCase			:			str=0.1       ,				_UpperCAmelCase			:			Optional[Any]=0.1       ,				_UpperCAmelCase			:			Tuple=0.1       ,				_UpperCAmelCase			:			List[Any]=1E-5       ,				_UpperCAmelCase			:			List[Any]=0.02       ,				_UpperCAmelCase			:			Optional[Any]=True       ,				_UpperCAmelCase			:			Dict=True       ,				_UpperCAmelCase			:			List[Any]=50_256       ,				_UpperCAmelCase			:			Dict=50_256       ,				_UpperCAmelCase			:			int=True       ,				_UpperCAmelCase			:			Union[str, Any]=True       ,				_UpperCAmelCase			:			Any=True       ,				**_UpperCAmelCase			:			Any       ,				):
       _A     =  vocab_size
       _A     =  n_positions
       _A     =  n_embd
       _A     =  n_layer
       _A     =  n_head
       _A     =  n_inner
       _A     =  activation_function
       _A     =  resid_pdrop
       _A     =  embd_pdrop
       _A     =  attn_pdrop
       _A     =  layer_norm_epsilon
       _A     =  initializer_range
       _A     =  scale_attn_weights
       _A     =  use_cache
       _A     =  attention_softmax_in_fpaa
       _A     =  scale_attention_softmax_in_fpaa
       _A     =  multi_query

       _A     =  bos_token_id
       _A     =  eos_token_id

       super().__init__(bos_token_id=_UpperCAmelCase       ,				eos_token_id=_UpperCAmelCase       ,				**_UpperCAmelCase						)

