

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_(_UpperCamelCase					)							->     YolosConfig:



  """simple docstring"""






  _snake_case						=							YolosConfig()

  # size of the architecture
  if "yolos_ti" in yolos_name:
    _snake_case						=							192
    _snake_case						=							768
    _snake_case						=							12
    _snake_case						=							3
    _snake_case						=							[800, 1_333]
    _snake_case						=							False
  elif yolos_name == "yolos_s_dWr":
    _snake_case						=							330
    _snake_case						=							14
    _snake_case						=							6
    _snake_case						=							1_320
  elif "yolos_s" in yolos_name:
    _snake_case						=							384
    _snake_case						=							1_536
    _snake_case						=							12
    _snake_case						=							6
  elif "yolos_b" in yolos_name:
    _snake_case						=							[800, 1_344]

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

  return config



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase = False					)							->     Optional[int]:



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



def 				snake_case_(_UpperCamelCase					)							->     str:



  """simple docstring"""






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

  return name



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     dict:



  """simple docstring"""






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

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

  return orig_state_dict



def 				snake_case_()							->     torch.Tensor:



  """simple docstring"""






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



@torch.no_grad()
def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase = False					)							->     str:



  """simple docstring"""






  _snake_case						=							get_yolos_config(_UpperCamelCase					)

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

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

  # Check outputs on an image, prepared by YolosImageProcessor
  _snake_case						=							800 if yolos_name != '''yolos_ti''' else 512
  _snake_case						=							YolosImageProcessor(format='''coco_detection'''			,	size=_UpperCamelCase					)
  _snake_case						=							image_processor(images=prepare_img()			,	return_tensors='''pt'''					)
  _snake_case						=							model(**_UpperCamelCase					)
  _snake_case,					_snake_case						=							outputs.logits, outputs.pred_boxes

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




from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


__A      		=     logging.get_logger(__name__)

__A      		=     {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__A      		=     {
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

__A      		=     {'''allegro/herbert-base-cased''': 5_14}
__A      		=     {}



class   lowercase_   (  __lowercase					):
      UpperCamelCase_      :		Any										=    VOCAB_FILES_NAMES
      UpperCamelCase_      :		Optional[int]										=    PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_      :		List[str]										=    PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase_      :		Dict										=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_      :		Optional[Any]										=    HerbertTokenizer



      def __init__(      self				:    Tuple      , A__				:    str=None      , A__				:    Optional[Any]=None      , A__				:    Union[str, Any]=None      , A__				:    Optional[int]="<s>"      , A__				:    Optional[int]="<unk>"      , A__				:    str="<pad>"      , A__				:    List[Any]="<mask>"      , A__				:    Dict="</s>"      , **A__				:    Optional[int]      , )   ->					Optional[int]:
        super().__init__(
            A__      , A__      , tokenizer_file=A__      , cls_token=A__      , unk_token=A__      , pad_token=A__      , mask_token=A__      , sep_token=A__      , **A__      , )



      def 						UpperCamelCase_		(      self				:    str      , A__				:    List[int]      , A__				:    Optional[List[int]] = None		)   ->					List[int]:
        _snake_case						=							[self.cls_token_id]
        _snake_case						=							[self.sep_token_id]
        if token_ids_a is None:
          return cls + token_ids_a + sep

        return cls + token_ids_a + sep + token_ids_a + sep



      def 						UpperCamelCase_		(      self				:    Tuple      , A__				:    List[int]      , A__				:    Optional[List[int]] = None      , A__				:    bool = False		)   ->					List[int]:
        if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=A__      , token_ids_a=A__      , already_has_special_tokens=A__		)

        if token_ids_a is None:
          return [1] + ([0] * len(A__		)) + [1]
        return [1] + ([0] * len(A__		)) + [1] + ([0] * len(A__		)) + [1]



      def 						UpperCamelCase_		(      self				:    Any      , A__				:    List[int]      , A__				:    Optional[List[int]] = None		)   ->					List[int]:
        _snake_case						=							[self.sep_token_id]
        _snake_case						=							[self.cls_token_id]

        if token_ids_a is None:
          return len(cls + token_ids_a + sep		) * [0]
        return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]







      def 						UpperCamelCase_		(      self				:    Union[str, Any]      , A__				:    str      , A__				:    Optional[str] = None		)   ->					Tuple[str]:
        _snake_case						=							self._tokenizer.model.save(A__      , name=A__		)
        return tuple(A__		)

