



"""simple docstring"""







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case	:				Optional[Any]  					=			logging.get_logger(__name__)



def   a_	(			):
     A__				=   DetaConfig(
         num_queries=900		,							encoder_ffn_dim=2048		,							decoder_ffn_dim=2048		,							num_feature_levels=5		,							assign_first_stage=__a		,							with_box_refine=__a		,							two_stage=__a		,							)

     # set labels
     A__				=   91
     A__				=   '''huggingface/label-files'''
     A__				=   '''coco-detection-id2label.json'''
     A__				=   json.load(open(cached_download(hf_hub_url(__a		,							__a		,							repo_type='''dataset'''      )      )		,							'''r'''      )      )
     A__				=   {int(__a      ): v for k, v in idalabel.items()}
     A__				=   idalabel
     A__				=   {v: k for k, v in idalabel.items()}

     return config



def   a_	(			__a      ):
     A__				=   []

     # stem
     # fmt: off
     rename_keys.append(('''backbone.0.body.conv1.weight''', '''model.backbone.model.embedder.embedder.convolution.weight''')      )
     rename_keys.append(('''backbone.0.body.bn1.weight''', '''model.backbone.model.embedder.embedder.normalization.weight''')      )
     rename_keys.append(('''backbone.0.body.bn1.bias''', '''model.backbone.model.embedder.embedder.normalization.bias''')      )
     rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''model.backbone.model.embedder.embedder.normalization.running_mean''')      )
     rename_keys.append(('''backbone.0.body.bn1.running_var''', '''model.backbone.model.embedder.embedder.normalization.running_var''')      )
     # stages
     for stage_idx in range(len(config.backbone_config.depths      )      ):
          for layer_idx in range(config.backbone_config.depths[stage_idx]      ):
               # shortcut
               if layer_idx == 0:
                    rename_keys.append(
                        (
                            f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
                            f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
                        )      )
                    rename_keys.append(
                        (
                            f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
                            f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
                        )      )
                    rename_keys.append(
                        (
                            f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
                            f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
                        )      )
                    rename_keys.append(
                        (
                            f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
                            f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
                        )      )
                    rename_keys.append(
                        (
                            f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
                            f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
                        )      )
               # 3 convs
               for i in range(3      ):
                    rename_keys.append(
                        (
                            f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
                            f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
                        )      )
                    rename_keys.append(
                        (
                            f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
                            f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
                        )      )
                    rename_keys.append(
                        (
                            f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
                            f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
                        )      )
                    rename_keys.append(
                        (
                            f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
                            f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
                        )      )
                    rename_keys.append(
                        (
                            f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
                            f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
                        )      )
    # transformer encoder
     for i in range(config.encoder_layers      ):
          rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''')      )
          rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''')      )
          rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', f'''model.encoder.layers.{i}.self_attn.attention_weights.weight''')      )
          rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', f'''model.encoder.layers.{i}.self_attn.attention_weights.bias''')      )
          rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', f'''model.encoder.layers.{i}.self_attn.value_proj.weight''')      )
          rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', f'''model.encoder.layers.{i}.self_attn.value_proj.bias''')      )
          rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', f'''model.encoder.layers.{i}.self_attn.output_proj.weight''')      )
          rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', f'''model.encoder.layers.{i}.self_attn.output_proj.bias''')      )
          rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.weight''', f'''model.encoder.layers.{i}.self_attn_layer_norm.weight''')      )
          rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''model.encoder.layers.{i}.self_attn_layer_norm.bias''')      )
          rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''model.encoder.layers.{i}.fc1.weight''')      )
          rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''model.encoder.layers.{i}.fc1.bias''')      )
          rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''model.encoder.layers.{i}.fc2.weight''')      )
          rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''model.encoder.layers.{i}.fc2.bias''')      )
          rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''model.encoder.layers.{i}.final_layer_norm.weight''')      )
          rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''model.encoder.layers.{i}.final_layer_norm.bias''')      )

     # transformer decoder
     for i in range(config.decoder_layers      ):
          rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''')      )
          rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''')      )
          rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''')      )
          rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''')      )
          rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''')      )
          rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''')      )
          rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''')      )
          rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''')      )
          rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.weight''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''')      )
          rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''')      )
          rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''model.decoder.layers.{i}.self_attn.out_proj.weight''')      )
          rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''model.decoder.layers.{i}.self_attn.out_proj.bias''')      )
          rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.weight''', f'''model.decoder.layers.{i}.self_attn_layer_norm.weight''')      )
          rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.bias''', f'''model.decoder.layers.{i}.self_attn_layer_norm.bias''')      )
          rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''model.decoder.layers.{i}.fc1.weight''')      )
          rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''model.decoder.layers.{i}.fc1.bias''')      )
          rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''model.decoder.layers.{i}.fc2.weight''')      )
          rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''model.decoder.layers.{i}.fc2.bias''')      )
          rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''model.decoder.layers.{i}.final_layer_norm.weight''')      )
          rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''model.decoder.layers.{i}.final_layer_norm.bias''')      )

     # fmt: on

     return rename_keys



def   a_	(			__a		,							__a		,							__a      ):
     A__				=   dct.pop(__a      )
     A__				=   val



def   a_	(			__a		,							__a      ):
     # transformer decoder self-attention layers
     A__				=   config.d_model
     for i in range(config.decoder_layers      ):
          # read in weights + bias of input projection layer of self-attention
          A__				=   state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''      )
          A__				=   state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_bias'''      )
          # next, add query, keys and values (in that order) to the state dict
          A__				=   in_proj_weight[:hidden_size, :]
          A__				=   in_proj_bias[:hidden_size]
          A__				=   in_proj_weight[
              hidden_size : hidden_size * 2, :
          ]
          A__				=   in_proj_bias[hidden_size : hidden_size * 2]
          A__				=   in_proj_weight[-hidden_size:, :]
          A__				=   in_proj_bias[-hidden_size:]



def   a_	(			):
     A__				=   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     A__				=   Image.open(requests.get(__a		,							stream=__a      ).raw      )

     return im



@torch.no_grad()
def   a_	(			__a		,							__a		,							__a      ):
     A__				=   get_deta_config()

     # load original state dict
     if model_name == "deta-resnet-50":
          A__				=   '''adet_checkpoint0011.pth'''
     elif model_name == "deta-resnet-50-24-epochs":
          A__				=   '''adet_2x_checkpoint0023.pth'''
     else:
          raise ValueError(f'''Model name {model_name} not supported'''      )
     A__				=   hf_hub_download(repo_id='''nielsr/deta-checkpoints'''		,							filename=__a      )
     A__				=   torch.load(__a		,							map_location='''cpu'''      )['''model''']

     # rename keys
     A__				=   create_rename_keys(__a      )
     for src, dest in rename_keys:
          rename_key(__a		,							__a		,							__a      )
     read_in_decoder_q_k_v(__a		,							__a      )

     # fix some prefixes
     for key in state_dict.copy().keys():
          if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
               A__				=   state_dict.pop(__a      )
               A__				=   val
          if "input_proj" in key:
               A__				=   state_dict.pop(__a      )
               A__				=   val
          if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
               A__				=   state_dict.pop(__a      )
               A__				=   val

    # finally, create HuggingFace model and load state dict
     A__				=   DetaForObjectDetection(__a      )
     model.load_state_dict(__a      )
     model.eval()

     A__				=   '''cuda''' if torch.cuda.is_available() else '''cpu'''
     model.to(__a      )

     # load image processor
     A__				=   DetaImageProcessor(format='''coco_detection'''      )

     # verify our conversion on image
     A__				=   prepare_img()
     A__				=   processor(images=__a		,							return_tensors='''pt'''      )
     A__				=   encoding['''pixel_values''']
     A__				=   model(pixel_values.to(__a      )      )

     # verify logits
     if model_name == "deta-resnet-50":
          A__				=   torch.tensor(
              [[-7.39_78, -2.54_06, -4.16_68], [-8.26_84, -3.99_33, -3.80_96], [-7.05_15, -3.79_73, -5.85_16]]      )
          A__				=   torch.tensor([[0.50_43, 0.49_73, 0.99_98], [0.25_42, 0.54_89, 0.47_48], [0.54_90, 0.27_65, 0.05_70]]      )
     elif model_name == "deta-resnet-50-24-epochs":
          A__				=   torch.tensor(
              [[-7.16_88, -2.48_57, -4.86_69], [-7.86_30, -3.81_54, -4.26_74], [-7.27_30, -4.18_65, -5.53_23]]      )
          A__				=   torch.tensor([[0.50_21, 0.49_71, 0.99_94], [0.25_46, 0.54_86, 0.47_31], [0.16_86, 0.19_86, 0.21_42]]      )

     assert torch.allclose(outputs.logits[0, :3, :3]		,							expected_logits.to(__a      )		,							atol=1e-4      )
     assert torch.allclose(outputs.pred_boxes[0, :3, :3]		,							expected_boxes.to(__a      )		,							atol=1e-4      )
     print('''Everything ok!'''      )

     if pytorch_dump_folder_path:
          # Save model and processor
          logger.info(f'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''      )
          Path(__a      ).mkdir(exist_ok=__a      )
          model.save_pretrained(__a      )
          processor.save_pretrained(__a      )

     # Push to hub
     if push_to_hub:
          print('''Pushing model and processor to hub...'''      )
          model.push_to_hub(f'''jozhang97/{model_name}'''      )
          processor.push_to_hub(f'''jozhang97/{model_name}'''      )


if __name__ == "__main__":
 __snake_case	:				Optional[Any]  					=			argparse.ArgumentParser()

 parser.add_argument(
     '--model_name',
     type=str,
     default='deta-resnet-50',
     choices=['deta-resnet-50', 'deta-resnet-50-24-epochs'],
     help='Name of the model you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path',
     default=None,
     type=str,
     help='Path to the folder to output PyTorch model.',
 )
 parser.add_argument(
     '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
 )
 __snake_case	:				Optional[Any]  					=			parser.parse_args()
 convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








"""simple docstring"""







from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

__snake_case	:				Union[str, Any]  					=			{
    name: (getattr(transformers, name), getattr(transformers, name + 'Fast')) for name in SLOW_TO_FAST_CONVERTERS
}

__snake_case	:				List[Any]  					=			datasets.load_dataset('xnli', split='test+validation')

__snake_case	:				int  					=			0
__snake_case	:				int  					=			0
__snake_case	:				int  					=			0
__snake_case	:				Optional[int]  					=			0



def   a_	(			__a		,							__a		,							__a		,							__a      ):
     if spm_diff == list(reversed(__a      )      ):
          # AAA -> AA+A vs A+AA case.
          return True
     elif len(__a      ) == len(__a      ) and fast.decode(__a      ) == fast.decode(__a      ):
          # Second order OK
          # Barrich -> Barr + ich vs Bar + rich
          return True
     A__				=   slow.encode(slow.decode(__a      )      )
     A__				=   fast.encode(fast.decode(__a      )      )
     if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
          # Type 3 error.
          # Snehagatha ->
          #       Sne, h, aga, th, a
          #       Sne, ha, gat, ha
          # Encoding the wrong with sp does not even recover what spm gave us
          # It fits tokenizer however...
          return True
     return False



def   a_	(			__a		,							__a		,							__a      ):
     A__				=   fast.encode_plus(__a      )[0]
     A__				=   enc.offsets
     A__	,     A__				=   offsets[idx], offsets[idx - 1]
     if curr is not None and line[curr[0] : curr[1]] == "\u200f":
          return True
     if prev is not None and line[prev[0] : prev[1]] == "\u200f":
          return True



def   a_	(			__a		,							__a		,							__a		,							__a		,							__a      ):
     # Encoding can be the same with same result AAA -> A + AA vs AA + A
     # We can check that we use at least exactly the same number of tokens.
     for i, (spm_id, tok_id) in enumerate(zip(__a		,							__a      )      ):
          if spm_id != tok_id:
               break
     A__				=   i
     for i, (spm_id, tok_id) in enumerate(zip(reversed(__a      )		,							reversed(__a      )      )      ):
          if spm_id != tok_id:
               break
     A__				=   len(__a      ) - i

     A__				=   spm_ids[first:last]
     A__				=   tok_ids[first:last]

     if check_diff(__a		,							__a		,							__a		,							__a      ):
          return True

     if check_LTR_mark(__a		,							__a		,							__a      ):
          return True

     if last - first > 5:
          # We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
          A__				=   Counter(spm_ids[first:last]      )
          A__				=   Counter(tok_ids[first:last]      )

          A__				=   {spm_ for (spm_, si) in spms.items() if toks.get(spm_		,							0      ) == si}
          A__				=   3
          for i in range(last - first - min_width      ):
               if all(spm_ids[first + i + j] in removable_tokens for j in range(__a      )      ):
                    A__				=   [
                        k
                        for k in range(last - first - min_width      )
                        if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
                    ]
                    for j in possible_matches:
                         if check_diff(spm_ids[first : first + i]		,							tok_ids[first : first + j]		,							__a		,							__a      ) and check_details(
                             __a		,							spm_ids[first + i : last]		,							tok_ids[first + j : last]		,							__a		,							__a		,							):
                              return True

     print(f'''Spm: {[fast.decode([spm_ids[i]]      ) for i in range(__a		,							__a      )]}'''      )
     try:
          print(f'''Tok: {[fast.decode([tok_ids[i]]      ) for i in range(__a		,							__a      )]}'''      )
     except Exception:
          pass

     A__				=   fast.decode(spm_ids[:first]      )
     A__				=   fast.decode(spm_ids[last:]      )
     A__				=   fast.decode(spm_ids[first:last]      )
     print()
     print(__a      )
     return False



def   a_	(			__a		,							__a		,							__a      ):
     global perfect
     global imperfect
     global wrong
     global total

     A__				=   slow.encode(__a      )
     A__				=   fast.encode(__a      )

     A__				=   False
     total += 1

     if slow_ids != fast_ids:
          if check_details(__a		,							__a		,							__a		,							__a		,							__a      ):
               A__				=   True
               imperfect += 1
          else:
               wrong += 1
     else:
          perfect += 1

     if total % 1_0000 == 0:
          print(f'''({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})'''      )

     if skip_assert:
          return

     assert (
         slow_ids == fast_ids
     ), f'''line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(__a      )}\n{fast.tokenize(__a      )}'''



def   a_	(			__a		,							__a      ):
     global batch_total
     for i in range(len(__a      )      ):
          # premise, all languages
          for text in dataset[i]["premise"].values():
               test_string(__a		,							__a		,							__a      )

          # hypothesis, all languages
          for text in dataset[i]["hypothesis"]["translation"]:
               test_string(__a		,							__a		,							__a      )


if __name__ == "__main__":
 for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
  __snake_case	:				int  					=			list(slow_class.max_model_input_sizes.keys())
  for checkpoint in checkpoint_names:
   __snake_case	:				Union[str, Any]  					=			0
   __snake_case	:				Optional[Any]  					=			0
   __snake_case	:				List[Any]  					=			0
   __snake_case	:				List[str]  					=			0

   print(f'========================== Checking {name}: {checkpoint} ==========================')
   __snake_case	:				Union[str, Any]  					=			slow_class.from_pretrained(checkpoint, force_download=True)
   __snake_case	:				Optional[Any]  					=			fast_class.from_pretrained(checkpoint, force_download=True)
   test_tokenizer(slow, fast)
   print(f'Accuracy {perfect * 100 / total:.2f}')



