






import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase				: Dict						    =	logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCAmelCase				: Union[str, Any]						    =	[]
for i in range(6):
	# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
	rename_keys.append(
	    (F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
	)
	rename_keys.append(
	    (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
	)
	rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
	rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
	rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
	rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
	rename_keys.append(
	    (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
	)
	rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
	rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
	rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
	# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
	)
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
	)
	rename_keys.append(
	    (
	        F'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
	        F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
	    )
	)
	rename_keys.append(
	    (
	        F'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
	        F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
	    )
	)
	rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
	rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
	rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
	rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
	)
	rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
	)
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
	)
	rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
	rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))

	# q, k, v projections in self/cross-attention in decoder for conditional DETR
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', F'''decoder.layers.{i}.sa_qcontent_proj.weight''')
	)
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', F'''decoder.layers.{i}.sa_kcontent_proj.weight''')
	)
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', F'''decoder.layers.{i}.sa_qpos_proj.weight''')
	)
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', F'''decoder.layers.{i}.sa_kpos_proj.weight''')
	)
	rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.weight''', F'''decoder.layers.{i}.sa_v_proj.weight'''))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', F'''decoder.layers.{i}.ca_qcontent_proj.weight''')
	)
	# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', F'''decoder.layers.{i}.ca_kcontent_proj.weight''')
	)
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', F'''decoder.layers.{i}.ca_kpos_proj.weight''')
	)
	rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.weight''', F'''decoder.layers.{i}.ca_v_proj.weight'''))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', F'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
	)

	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', F'''decoder.layers.{i}.sa_qcontent_proj.bias''')
	)
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', F'''decoder.layers.{i}.sa_kcontent_proj.bias''')
	)
	rename_keys.append((F'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', F'''decoder.layers.{i}.sa_qpos_proj.bias'''))
	rename_keys.append((F'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', F'''decoder.layers.{i}.sa_kpos_proj.bias'''))
	rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.bias''', F'''decoder.layers.{i}.sa_v_proj.bias'''))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', F'''decoder.layers.{i}.ca_qcontent_proj.bias''')
	)
	# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', F'''decoder.layers.{i}.ca_kcontent_proj.bias''')
	)
	rename_keys.append((F'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', F'''decoder.layers.{i}.ca_kpos_proj.bias'''))
	rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.bias''', F'''decoder.layers.{i}.ca_v_proj.bias'''))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', F'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
	)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ("input_proj.weight", "input_projection.weight"),
        ("input_proj.bias", "input_projection.bias"),
        ("query_embed.weight", "query_position_embeddings.weight"),
        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
        ("class_embed.weight", "class_labels_classifier.weight"),
        ("class_embed.bias", "class_labels_classifier.bias"),
        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
        ("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"),
        ("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"),
        ("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"),
        ("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"),
        ("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"),
        ("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"),
        ("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"),
        ("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"),
        ("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"),
        ("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"),
    ]
)





def        lowercase_			(     __snake_case  :				Dict						,						__snake_case  :				List[str]						,						__snake_case  :				Dict   )  ->			Dict:





				'''simple docstring'''


				snake_case__   :Any       = state_dict.pop(_UpperCamelCase   )
				snake_case__   :Dict       = val





def        lowercase_			(     __snake_case  :				Optional[Any]   )  ->			Any:





				'''simple docstring'''


				snake_case__   :str       = OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												snake_case__   :str       = key.replace("backbone.0.body"						,						"backbone.conv_encoder.model"   )
												snake_case__   :Optional[Any]       = value
								else:
												snake_case__   :List[str]       = value

				return new_state_dict





def        lowercase_			(     __snake_case  :				Dict						,						__snake_case  :				int=False   )  ->			Optional[Any]:





				'''simple docstring'''


				snake_case__   :Dict       = ""
				if is_panoptic:
								snake_case__   :Optional[Any]       = "conditional_detr."

				# first: transformer encoder
				for i in range(6   ):
								# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
								snake_case__   :str       = state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'   )
								snake_case__   :Any       = state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'   )
								# next, add query, keys and values (in that order) to the state dict
								snake_case__   :List[Any]       = in_proj_weight[:2_56, :]
								snake_case__   :Optional[Any]       = in_proj_bias[:2_56]
								snake_case__   :str       = in_proj_weight[2_56:5_12, :]
								snake_case__   :Dict       = in_proj_bias[2_56:5_12]
								snake_case__   :List[Any]       = in_proj_weight[-2_56:, :]
								snake_case__   :List[Any]       = in_proj_bias[-2_56:]





def        lowercase_			(     )  ->			Optional[int]:





				'''simple docstring'''


				snake_case__   :Tuple       = "http://images.cocodataset.org/val2017/000000039769.jpg"
				snake_case__   :str       = Image.open(requests.get(_UpperCamelCase						,						stream=_UpperCamelCase   ).raw   )

				return im





@torch.no_grad()
def        lowercase_			(     __snake_case  :				Optional[Any]						,						__snake_case  :				str   )  ->			Any:





				'''simple docstring'''


				snake_case__   :Union[str, Any]       = ConditionalDetrConfig()
				# set backbone and dilation attributes
				if "resnet101" in model_name:
								snake_case__   :Optional[Any]       = "resnet101"
				if "dc5" in model_name:
								snake_case__   :str       = True
				snake_case__   :Union[str, Any]       = "panoptic" in model_name
				if is_panoptic:
								snake_case__   :str       = 2_50
				else:
								snake_case__   :Tuple       = 91
								snake_case__   :List[Any]       = "huggingface/label-files"
								snake_case__   :Any       = "coco-detection-id2label.json"
								snake_case__   :str       = json.load(open(hf_hub_download(_UpperCamelCase						,						_UpperCamelCase						,						repo_type="dataset"   )						,						"r"   )   )
								snake_case__   :Optional[int]       = {int(_UpperCamelCase   ): v for k, v in idalabel.items()}
								snake_case__   :Dict       = idalabel
								snake_case__   :Optional[Any]       = {v: k for k, v in idalabel.items()}

				# load image processor
				snake_case__   :Optional[Any]       = "coco_panoptic" if is_panoptic else "coco_detection"
				snake_case__   :Optional[Any]       = ConditionalDetrImageProcessor(format=_UpperCamelCase   )

				# prepare image
				snake_case__   :List[str]       = prepare_img()
				snake_case__   :Optional[int]       = image_processor(images=_UpperCamelCase						,						return_tensors="pt"   )
				snake_case__   :List[str]       = encoding["pixel_values"]

				logger.info(F'Converting model {model_name}...'   )

				# load original model from torch hub
				snake_case__   :List[Any]       = torch.hub.load("DeppMeng/ConditionalDETR"						,						_UpperCamelCase						,						pretrained=_UpperCamelCase   ).eval()
				snake_case__   :Union[str, Any]       = conditional_detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												snake_case__   :str       = "conditional_detr." + src
								rename_key(_UpperCamelCase						,						_UpperCamelCase						,						_UpperCamelCase   )
				snake_case__   :Dict       = rename_backbone_keys(_UpperCamelCase   )
				# query, key and value matrices need special treatment
				read_in_q_k_v(_UpperCamelCase						,						is_panoptic=_UpperCamelCase   )
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				snake_case__   :List[str]       = "conditional_detr.model." if is_panoptic else "model."
				for key in state_dict.copy().keys():
								if is_panoptic:
												if (
												    key.startswith("conditional_detr"   )
												    and not key.startswith("class_labels_classifier"   )
												    and not key.startswith("bbox_predictor"   )
												):
																snake_case__   :str       = state_dict.pop(_UpperCamelCase   )
																snake_case__   :List[Any]       = val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																snake_case__   :Any       = state_dict.pop(_UpperCamelCase   )
																snake_case__   :str       = val
												elif key.startswith("bbox_attention"   ) or key.startswith("mask_head"   ):
																continue
												else:
																snake_case__   :Any       = state_dict.pop(_UpperCamelCase   )
																snake_case__   :Tuple       = val
								else:
												if not key.startswith("class_labels_classifier"   ) and not key.startswith("bbox_predictor"   ):
																snake_case__   :List[Any]       = state_dict.pop(_UpperCamelCase   )
																snake_case__   :int       = val
    # finally, create HuggingFace model and load state dict
				snake_case__   :Dict       = ConditionalDetrForSegmentation(_UpperCamelCase   ) if is_panoptic else ConditionalDetrForObjectDetection(_UpperCamelCase   )
				model.load_state_dict(_UpperCamelCase   )
				model.eval()
				model.push_to_hub(repo_id=_UpperCamelCase						,						organization="DepuMeng"						,						commit_message="Add model"   )
				# verify our conversion
				snake_case__   :List[Any]       = conditional_detr(_UpperCamelCase   )
				snake_case__   :Union[str, Any]       = model(_UpperCamelCase   )
				assert torch.allclose(outputs.logits						,						original_outputs["pred_logits"]						,						atol=1e-4   )
				assert torch.allclose(outputs.pred_boxes						,						original_outputs["pred_boxes"]						,						atol=1e-4   )
				if is_panoptic:
								assert torch.allclose(outputs.pred_masks						,						original_outputs["pred_masks"]						,						atol=1e-4   )

				# Save model and image processor
				logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'   )
				Path(_UpperCamelCase   ).mkdir(exist_ok=_UpperCamelCase   )
				model.save_pretrained(_UpperCamelCase   )
				image_processor.save_pretrained(_UpperCamelCase   )


if __name__ == "__main__":
	__UpperCAmelCase				: List[str]						    =	argparse.ArgumentParser()

	parser.add_argument(
	    "--model_name",
	    default="conditional_detr_resnet50",
	    type=str,
	    help="Name of the CONDITIONAL_DETR 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."
	)
	__UpperCAmelCase				: Optional[Any]						    =	parser.parse_args()
	convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)





'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase_		:     Optional[Any]          =		{
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   pass

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_		:     Union[str, Any]          =		[
       '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RoCBertForCausalLM''',
       '''RoCBertForMaskedLM''',
       '''RoCBertForMultipleChoice''',
       '''RoCBertForPreTraining''',
       '''RoCBertForQuestionAnswering''',
       '''RoCBertForSequenceClassification''',
       '''RoCBertForTokenClassification''',
       '''RoCBertLayer''',
       '''RoCBertModel''',
       '''RoCBertPreTrainedModel''',
       '''load_tf_weights_in_roc_bert''',
   ]

if TYPE_CHECKING:
   from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
   from .tokenization_roc_bert import RoCBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      raise OptionalDependencyNotAvailable()

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roc_bert import (
          ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          RoCBertForCausalLM,
          RoCBertForMaskedLM,
          RoCBertForMultipleChoice,
          RoCBertForPreTraining,
          RoCBertForQuestionAnswering,
          RoCBertForSequenceClassification,
          RoCBertForTokenClassification,
          RoCBertLayer,
          RoCBertModel,
          RoCBertPreTrainedModel,
          load_tf_weights_in_roc_bert,
      )


else:
   import sys

   UpperCamelCase_		:     int          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


