'''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()
_UpperCamelCase				:		Any       			=		logging.get_logger(__name__)



def 						__snake_case (       ):
 __UpperCAmelCase         =					DetaConfig(
     num_queries=900      ,  encoder_ffn_dim=2048      ,  decoder_ffn_dim=2048      ,  num_feature_levels=5      ,  assign_first_stage=lowerCAmelCase      ,  with_box_refine=lowerCAmelCase      ,  two_stage=lowerCAmelCase      ,  )

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

 return config



def 						__snake_case (       lowerCAmelCase       :     Union[str, Any]			):
 __UpperCAmelCase         =					[]

 # 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 						__snake_case (       lowerCAmelCase       :     Any      ,  lowerCAmelCase       :     Any      ,  lowerCAmelCase       :     str			):
 __UpperCAmelCase         =					dct.pop(lowerCAmelCase			)
 __UpperCAmelCase         =					val



def 						__snake_case (       lowerCAmelCase       :     str      ,  lowerCAmelCase       :     str			):
 # transformer decoder self-attention layers
 __UpperCAmelCase         =					config.d_model
 for i in range(config.decoder_layers			):
  # read in weights + bias of input projection layer of self-attention
  __UpperCAmelCase         =					state_dict.pop(F"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""			)
  __UpperCAmelCase         =					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
  __UpperCAmelCase         =					in_proj_weight[:hidden_size, :]
  __UpperCAmelCase         =					in_proj_bias[:hidden_size]
  __UpperCAmelCase         =					in_proj_weight[
      hidden_size : hidden_size * 2, :
  ]
  __UpperCAmelCase         =					in_proj_bias[hidden_size : hidden_size * 2]
  __UpperCAmelCase         =					in_proj_weight[-hidden_size:, :]
  __UpperCAmelCase         =					in_proj_bias[-hidden_size:]



def 						__snake_case (       ):
 __UpperCAmelCase         =					'http://images.cocodataset.org/val2017/000000039769.jpg'
 __UpperCAmelCase         =					Image.open(requests.get(lowerCAmelCase      ,  stream=lowerCAmelCase			).raw			)

 return im



@torch.no_grad()
def 						__snake_case (       lowerCAmelCase       :     Optional[int]      ,  lowerCAmelCase       :     Dict      ,  lowerCAmelCase       :     Tuple			):
 __UpperCAmelCase         =					get_deta_config()

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

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

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

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

 __UpperCAmelCase         =					'cuda' if torch.cuda.is_available() else 'cpu'
 model.to(lowerCAmelCase			)

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

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

 # verify logits
 if model_name == "deta-resnet-50":
  __UpperCAmelCase         =					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]]			)
  __UpperCAmelCase         =					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":
  __UpperCAmelCase         =					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]]			)
  __UpperCAmelCase         =					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(lowerCAmelCase			)      ,  atol=1E-4			)
 assert torch.allclose(outputs.pred_boxes[0, :3, :3]      ,  expected_boxes.to(lowerCAmelCase			)      ,  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(lowerCAmelCase			).mkdir(exist_ok=lowerCAmelCase			)
  model.save_pretrained(lowerCAmelCase			)
  processor.save_pretrained(lowerCAmelCase			)

 # 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__":
     _UpperCamelCase				:		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.'
     )
     _UpperCamelCase				:		str       			=		parser.parse_args()
     convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase				:		Dict       			=		{
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Optional[int]       			=		['MarianTokenizer']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Dict       			=		[
         'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MarianForCausalLM',
         'MarianModel',
         'MarianMTModel',
         'MarianPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Dict       			=		['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Any       			=		['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
     from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_marian import MarianTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_marian import (
              MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
              MarianForCausalLM,
              MarianModel,
              MarianMTModel,
              MarianPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
     import sys

     _UpperCamelCase				:		List[Any]       			=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



