
import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


__lowerCamelCase							= {
    '''vit_b32''': {
        '''embed_dim''': 5_12,
        '''image_resolution''': 7_68,
        '''context_length''': 16,
        '''vocab_size''': 4_94_08,
        '''vision_layers''': 12,
        '''vision_width''': 7_68,
        '''vision_patch_size''': 32,
        '''transformer_width''': 5_12,
        '''transformer_heads''': 8,
        '''transformer_layers''': 12,
    },
    '''vit_b16''': {
        '''embed_dim''': 5_12,
        '''image_resolution''': 7_68,
        '''context_length''': 16,
        '''vocab_size''': 4_94_08,
        '''vision_layers''': 12,
        '''vision_width''': 7_68,
        '''vision_patch_size''': 16,
        '''transformer_width''': 5_12,
        '''transformer_heads''': 8,
        '''transformer_layers''': 12,
    },
    '''vit_l14''': {
        '''embed_dim''': 7_68,
        '''image_resolution''': 8_40,
        '''context_length''': 16,
        '''vocab_size''': 4_94_08,
        '''vision_layers''': 24,
        '''vision_width''': 10_24,
        '''vision_patch_size''': 14,
        '''transformer_width''': 7_68,
        '''transformer_heads''': 12,
        '''transformer_layers''': 12,
    },
}


def     UpperCamelCase      (					__lowerCamelCase  :				Dict       ,      __lowerCamelCase  :				str=""       ,      __lowerCamelCase  :				int="/"					):
							snake_case :     Dict           =   []

							for k, v in params.items():
														snake_case :     Dict           =   parent_key + sep + k if parent_key else k

														if isinstance(snake_case__       ,      collections.MutableMapping					):
																					items.extend(flatten_nested_dict(snake_case__       ,      snake_case__       ,      sep=snake_case__					).items()					)
														else:
																					items.append((new_key, v)					)
							return dict(snake_case__					)


def     UpperCamelCase      (					__lowerCamelCase  :				Optional[int]					):
							return jax.tree_util.tree_map(lambda __lowerCamelCase					: x.astype(jnp.floataa					) if x.dtype == jnp.bfloataa else x       ,      snake_case__					)


def     UpperCamelCase      (					__lowerCamelCase  :				List[Any]       ,      __lowerCamelCase  :				Dict					):
							snake_case       ,      snake_case       ,      snake_case :     List[Any]           =   pt_attn_layer.in_proj_weight.chunk(3       ,      dim=0					)
							snake_case       ,      snake_case       ,      snake_case :     Dict           =   pt_attn_layer.in_proj_bias.chunk(3       ,      dim=0					)

							snake_case :     Dict           =   pt_attn_layer.out_proj.weight
							snake_case :     Optional[int]           =   pt_attn_layer.out_proj.bias

							snake_case :     Union[str, Any]           =   q_proj
							snake_case :     Tuple           =   q_proj_bias

							snake_case :     str           =   k_proj
							snake_case :     List[str]           =   k_proj_bias

							snake_case :     Union[str, Any]           =   v_proj
							snake_case :     Union[str, Any]           =   v_proj_bias

							snake_case :     List[str]           =   out_proj_weights
							snake_case :     List[str]           =   out_proj_bias


def     UpperCamelCase      (					__lowerCamelCase  :				int       ,      __lowerCamelCase  :				str					):
							copy_linear(hf_mlp.fca       ,      pt_mlp.c_fc					)
							copy_linear(hf_mlp.fca       ,      pt_mlp.c_proj					)


def     UpperCamelCase      (					__lowerCamelCase  :				Union[str, Any]       ,      __lowerCamelCase  :				Tuple					):
							snake_case :     Dict           =   pt_linear.weight
							snake_case :     Dict           =   pt_linear.bias


def     UpperCamelCase      (					__lowerCamelCase  :				Optional[Any]       ,      __lowerCamelCase  :				Tuple					):
							# copy layer norms
							copy_linear(hf_layer.layer_norma       ,      pt_layer.ln_a					)
							copy_linear(hf_layer.layer_norma       ,      pt_layer.ln_a					)

							# copy MLP
							copy_mlp(hf_layer.mlp       ,      pt_layer.mlp					)

							# copy attn
							copy_attn_layer(hf_layer.self_attn       ,      pt_layer.attn					)


def     UpperCamelCase      (					__lowerCamelCase  :				str       ,      __lowerCamelCase  :				Any					):
							for hf_layer, pt_layer in zip(snake_case__       ,      snake_case__					):
														copy_layer(snake_case__       ,      snake_case__					)


def     UpperCamelCase      (					__lowerCamelCase  :				Optional[int]       ,      __lowerCamelCase  :				List[Any]					):
							# copy  embeds
							snake_case :     Optional[int]           =   pt_model.token_embedding.weight
							snake_case :     Any           =   pt_model.positional_embedding

							# copy layer norm
							copy_linear(hf_encoder.final_layer_norm       ,      pt_model.ln_final					)

							# copy hidden layers
							copy_layers(hf_encoder.encoder.layers       ,      pt_model.transformer.resblocks					)


def     UpperCamelCase      (					__lowerCamelCase  :				int       ,      __lowerCamelCase  :				List[str]					):
							# copy projection
							snake_case :     Tuple           =   pt_model.text_projection.data.T

							# copy text encoder
							copy_encoder(hf_model.text_model       ,      snake_case__					)


def     UpperCamelCase      (					__lowerCamelCase  :				Dict       ,      __lowerCamelCase  :				List[Any]					):
							# copy projection
							snake_case :     List[Any]           =   pt_model.visual.proj.data.T

							# copy layer norms
							copy_linear(hf_model.vision_model.pre_layernorm       ,      pt_model.visual.ln_pre					)
							copy_linear(hf_model.vision_model.post_layernorm       ,      pt_model.visual.ln_post					)

							# copy embeds
							snake_case :     int           =   pt_model.visual.conva.weight.data
							snake_case :     Any           =   pt_model.visual.class_embedding
							snake_case :     Optional[Any]           =   pt_model.visual.positional_embedding.data

							# copy encoder
							copy_layers(hf_model.vision_model.encoder.layers       ,      pt_model.visual.transformer.resblocks					)


def     UpperCamelCase      (					__lowerCamelCase  :				List[Any]       ,      __lowerCamelCase  :				Any					):
							snake_case :     str           =   flatten_nested_dict(flax_params["backbone"]["merged_class_token"]					)

							snake_case :     List[Any]           =   torch.from_numpy(flax_class_token_params["scale"]					)
							snake_case :     List[str]           =   torch.from_numpy(flax_class_token_params["bias"]					)
							snake_case :     Any           =   nn.Parameter(snake_case__					)
							snake_case :     str           =   nn.Parameter(snake_case__					)


def     UpperCamelCase      (					__lowerCamelCase  :				List[str]       ,      __lowerCamelCase  :				Optional[int]					):
							snake_case :     Union[str, Any]           =   hf_model.state_dict()
							snake_case :     Union[str, Any]           =   {}

							# Rename class prediction head flax params to pytorch HF
							snake_case :     Optional[int]           =   flatten_nested_dict(flax_params["class_head"]					)

							for flax_key, v in flax_class_params.items():
														snake_case :     List[Any]           =   flax_key.replace("/"       ,      "."					)
														snake_case :     Union[str, Any]           =   torch_key.replace(".kernel"       ,      ".weight"					)
														snake_case :     Dict           =   torch_key.replace("Dense_0"       ,      "dense0"					)
														snake_case :     List[str]           =   "class_head." + torch_key

														if "weight" in torch_key and v.ndim == 2:
																					snake_case :     List[str]           =   v.T

														snake_case :     Tuple           =   nn.Parameter(torch.from_numpy(snake_case__					)					)

							# Rename box prediction box flax params to pytorch HF
							snake_case :     Dict           =   flatten_nested_dict(flax_params["obj_box_head"]					)

							for flax_key, v in flax_box_params.items():
														snake_case :     Dict           =   flax_key.replace("/"       ,      "."					)
														snake_case :     str           =   torch_key.replace(".kernel"       ,      ".weight"					)
														snake_case :     Optional[int]           =   torch_key.replace("_"       ,      ""					).lower()
														snake_case :     Union[str, Any]           =   "box_head." + torch_key

														if "weight" in torch_key and v.ndim == 2:
																					snake_case :     Optional[Any]           =   v.T

														snake_case :     Optional[Any]           =   nn.Parameter(torch.from_numpy(snake_case__					)					)

							# Copy flax params to PyTorch params
							for name, param in new_params.items():
														if name in pt_params.keys():
																					pt_params[name].copy_(snake_case__					)


def     UpperCamelCase      (					__lowerCamelCase  :				Optional[int]       ,      __lowerCamelCase  :				int					):
							for k, v in flax_attn_params.items():
														if k.startswith("transformer"					):
																					snake_case :     Union[str, Any]           =   k.replace("transformer.resblocks"       ,      "text_model.encoder.layers"					)
														else:
																					snake_case :     Any           =   k.replace("visual.transformer.resblocks"       ,      "vision_model.encoder.layers"					)

														snake_case :     List[str]           =   torch_key.replace("attn"       ,      "self_attn"					)
														snake_case :     List[str]           =   torch_key.replace("key"       ,      "k_proj"					)
														snake_case :     Dict           =   torch_key.replace("value"       ,      "v_proj"					)
														snake_case :     List[str]           =   torch_key.replace("query"       ,      "q_proj"					)
														snake_case :     Any           =   torch_key.replace("out"       ,      "out_proj"					)

														if "bias" in torch_key and v.ndim == 2:
																					snake_case :     Dict           =   v.shape[0] * v.shape[1]
																					snake_case :     List[str]           =   v.reshape(snake_case__					)

														if "weight" in torch_key and "out" in torch_key:
																					snake_case :     Tuple           =   (v.shape[0] * v.shape[1], v.shape[2])
																					snake_case :     int           =   v.reshape(snake_case__					).T

														if "weight" in torch_key and "out" not in torch_key:
																					snake_case :     Tuple           =   (v.shape[0], v.shape[1] * v.shape[2])
																					snake_case :     Tuple           =   v.reshape(snake_case__					).T

														# Copy flax CLIP attn params to HF PyTorch params
														snake_case :     List[str]           =   torch.from_numpy(snake_case__					)
														hf_backbone.state_dict()[torch_key].copy_(snake_case__					)


def     UpperCamelCase      (					__lowerCamelCase  :				Any					):
							snake_case :     Tuple           =   {}
							snake_case :     Any           =   []

							for k, v in params.items():
														if "attn." in k:
																					snake_case :     Optional[int]           =   k[: k.rindex("attn."					) + 5]
																					if base in processed_attn_layers:
																												continue

																					processed_attn_layers.append(snake_case__					)
																					snake_case :     int           =   params[base + "out.weight"].shape[-1]
																					snake_case :     Optional[Any]           =   params[base + "out.weight"].reshape(snake_case__       ,      snake_case__					).T
																					snake_case :     int           =   params[base + "out.bias"]
														else:
																					snake_case :     int           =   v
							return new_params


def     UpperCamelCase      (					__lowerCamelCase  :				Tuple       ,      __lowerCamelCase  :				Union[str, Any]					):
							snake_case :     str           =   CLIP(**snake_case__					)
							torch_model.eval()
							snake_case :     Union[str, Any]           =   torch_model.state_dict()

							snake_case :     Any           =   flatten_nested_dict(flax_params["backbone"]["clip"]					)
							snake_case :     Tuple           =   {}

							for flax_key, v in flax_clip_params.items():
														snake_case :     str           =   flax_key.replace("/"       ,      "."					)
														snake_case :     Union[str, Any]           =   torch_key.replace("text.token_embedding.embedding"       ,      "token_embedding.kernel"					)

														if (
														    torch_key.startswith("text.transformer"					)
														    or torch_key.startswith("text.text_projection"					)
														    or torch_key.startswith("text.ln_final"					)
														    or torch_key.startswith("text.positional_embedding"					)
														):
																					snake_case :     Any           =   torch_key[5:]

														snake_case :     Optional[Any]           =   torch_key.replace("text_projection.kernel"       ,      "text_projection"					)
														snake_case :     Any           =   torch_key.replace("visual.proj.kernel"       ,      "visual.proj"					)
														snake_case :     Optional[int]           =   torch_key.replace(".scale"       ,      ".weight"					)
														snake_case :     Optional[int]           =   torch_key.replace(".kernel"       ,      ".weight"					)

														if "conv" in torch_key or "downsample.0.weight" in torch_key:
																					snake_case :     List[Any]           =   v.transpose(3       ,      2       ,      0       ,      1					)

														elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
																					# Fully connected layers are transposed, embeddings are not
																					snake_case :     int           =   v.T

														snake_case :     List[str]           =   v

							snake_case :     str           =   _convert_attn_layers(snake_case__					)
							new_torch_params.update(snake_case__					)
							snake_case :     List[str]           =   {}

							# Copy flax CLIP backbone params to PyTorch params
							for name, param in new_torch_params.items():
														if name in torch_clip_params.keys():
																					snake_case :     Any           =   torch.from_numpy(new_torch_params[name]					)
																					torch_clip_params[name].copy_(snake_case__					)
														else:
																					snake_case :     Any           =   param

							return torch_clip_params, torch_model, attn_params


@torch.no_grad()
def     UpperCamelCase      (					__lowerCamelCase  :				Dict       ,      __lowerCamelCase  :				List[Any]       ,      __lowerCamelCase  :				Dict       ,      __lowerCamelCase  :				Dict       ,      __lowerCamelCase  :				Tuple=None					):
							snake_case :     Union[str, Any]           =   Repository(snake_case__       ,      clone_from=f"""google/{pytorch_dump_folder_path}"""					)
							repo.git_pull()

							if config_path is not None:
														snake_case :     Union[str, Any]           =   OwlViTConfig.from_pretrained(snake_case__					)
							else:
														snake_case :     Any           =   OwlViTConfig()

							snake_case :     Any           =   OwlViTModel(snake_case__					).eval()
							snake_case :     List[Any]           =   OwlViTForObjectDetection(snake_case__					).eval()

							copy_text_model_and_projection(snake_case__       ,      snake_case__					)
							copy_vision_model_and_projection(snake_case__       ,      snake_case__					)
							snake_case :     Union[str, Any]           =   pt_backbone.logit_scale
							copy_flax_attn_params(snake_case__       ,      snake_case__					)

							snake_case :     Dict           =   hf_backbone
							copy_class_merge_token(snake_case__       ,      snake_case__					)
							copy_class_box_heads(snake_case__       ,      snake_case__					)

							# Save HF model
							hf_model.save_pretrained(repo.local_dir					)

							# Initialize image processor
							snake_case :     Dict           =   OwlViTImageProcessor(
							    size=config.vision_config.image_size       ,      crop_size=config.vision_config.image_size					)
							# Initialize tokenizer
							snake_case :     Optional[Any]           =   CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"       ,      pad_token="!"       ,      model_max_length=16					)

							# Initialize processor
							snake_case :     Union[str, Any]           =   OwlViTProcessor(image_processor=snake_case__       ,      tokenizer=snake_case__					)
							image_processor.save_pretrained(repo.local_dir					)
							processor.save_pretrained(repo.local_dir					)

							repo.git_add()
							repo.git_commit("Upload model and processor"					)
							repo.git_push()


if __name__ == "__main__":
			__lowerCamelCase							= argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--owlvit_version""",
			    default=None,
			    type=str,
			    required=True,
			    help="""OWL-ViT model name [clip_b16, clip_b32, clip_l14].""",
			)
			parser.add_argument(
			    """--owlvit_checkpoint""", default=None, type=str, required=True, help="""Path to flax model checkpoint."""
			)
			parser.add_argument("""--hf_config""", default=None, type=str, required=True, help="""Path to HF model config.""")
			parser.add_argument(
			    """--pytorch_dump_folder_path""", default="""hf_model""", type=str, help="""Path to the output PyTorch model."""
			)
			__lowerCamelCase							= parser.parse_args()

			# Initialize PyToch clip model
			__lowerCamelCase							= args.owlvit_version
			if model_name == "clip_b16":
						__lowerCamelCase							= CONFIGS['''vit_b16''']
			elif model_name == "clip_b32":
						__lowerCamelCase							= CONFIGS['''vit_b32''']
			elif model_name == "clip_l14":
						__lowerCamelCase							= CONFIGS['''vit_l14''']

			# Load from checkpoint and convert params to float-32
			__lowerCamelCase							= checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['''optimizer''']['''target''']
			__lowerCamelCase							= jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
			del variables

			# Convert CLIP backbone
			__lowerCamelCase							= convert_clip_backbone(flax_params, torch_config)

			convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)







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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase							= logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCamelCase							= []
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}.multihead_attn.out_proj.weight',
           F'decoder.layers.{i}.encoder_attn.out_proj.weight',
       )
   )
   rename_keys.append(
       (
           F'transformer.decoder.layers.{i}.multihead_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'))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
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"""),
    ]
)


def     UpperCamelCase      (					__lowerCamelCase  :				Dict       ,      __lowerCamelCase  :				List[str]       ,      __lowerCamelCase  :				Any					):
       snake_case :     int           =   state_dict.pop(__lowerCamelCase					)
       snake_case :     int           =   val


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

       return new_state_dict


def     UpperCamelCase      (					__lowerCamelCase  :				List[str]       ,      __lowerCamelCase  :				Dict=False					):
       snake_case :     List[str]           =   ""
       if is_panoptic:
              snake_case :     Dict           =   "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 :     Optional[Any]           =   state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""					)
              snake_case :     int           =   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 :     Optional[Any]           =   in_proj_weight[:256, :]
              snake_case :     List[str]           =   in_proj_bias[:256]
              snake_case :     Tuple           =   in_proj_weight[256:512, :]
              snake_case :     Dict           =   in_proj_bias[256:512]
              snake_case :     Optional[int]           =   in_proj_weight[-256:, :]
              snake_case :     Optional[int]           =   in_proj_bias[-256:]
       # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
       for i in range(6					):
              # read in weights + bias of input projection layer of self-attention
              snake_case :     int           =   state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""					)
              snake_case :     List[str]           =   state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""					)
              # next, add query, keys and values (in that order) to the state dict
              snake_case :     Dict           =   in_proj_weight[:256, :]
              snake_case :     Optional[int]           =   in_proj_bias[:256]
              snake_case :     Union[str, Any]           =   in_proj_weight[256:512, :]
              snake_case :     Optional[Any]           =   in_proj_bias[256:512]
              snake_case :     Dict           =   in_proj_weight[-256:, :]
              snake_case :     int           =   in_proj_bias[-256:]
              # read in weights + bias of input projection layer of cross-attention
              snake_case :     Dict           =   state_dict.pop(
                  f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""					)
              snake_case :     Dict           =   state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""					)
              # next, add query, keys and values (in that order) of cross-attention to the state dict
              snake_case :     Any           =   in_proj_weight_cross_attn[:256, :]
              snake_case :     Dict           =   in_proj_bias_cross_attn[:256]
              snake_case :     Any           =   in_proj_weight_cross_attn[256:512, :]
              snake_case :     Optional[int]           =   in_proj_bias_cross_attn[256:512]
              snake_case :     List[Any]           =   in_proj_weight_cross_attn[-256:, :]
              snake_case :     Optional[Any]           =   in_proj_bias_cross_attn[-256:]


def     UpperCamelCase      (					):
       snake_case :     Optional[Any]           =   "http://images.cocodataset.org/val2017/000000039769.jpg"
       snake_case :     List[str]           =   Image.open(requests.get(__lowerCamelCase       ,      stream=__lowerCamelCase					).raw					)

       return im


@torch.no_grad()
def     UpperCamelCase      (					__lowerCamelCase  :				Any       ,      __lowerCamelCase  :				List[Any]					):
       snake_case :     Tuple           =   DetrConfig()
       # set backbone and dilation attributes
       if "resnet101" in model_name:
              snake_case :     Optional[Any]           =   "resnet101"
       if "dc5" in model_name:
              snake_case :     int           =   True
       snake_case :     List[str]           =   "panoptic" in model_name
       if is_panoptic:
              snake_case :     Dict           =   250
       else:
              snake_case :     int           =   91
              snake_case :     List[Any]           =   "huggingface/label-files"
              snake_case :     Dict           =   "coco-detection-id2label.json"
              snake_case :     Dict           =   json.load(open(hf_hub_download(__lowerCamelCase       ,      __lowerCamelCase       ,      repo_type="dataset"					)       ,      "r"					)					)
              snake_case :     Union[str, Any]           =   {int(__lowerCamelCase					): v for k, v in idalabel.items()}
              snake_case :     int           =   idalabel
              snake_case :     str           =   {v: k for k, v in idalabel.items()}

       # load image processor
       snake_case :     Tuple           =   "coco_panoptic" if is_panoptic else "coco_detection"
       snake_case :     str           =   DetrImageProcessor(format=__lowerCamelCase					)

       # prepare image
       snake_case :     Optional[int]           =   prepare_img()
       snake_case :     Optional[Any]           =   image_processor(images=__lowerCamelCase       ,      return_tensors="pt"					)
       snake_case :     Optional[int]           =   encoding["pixel_values"]

       logger.info(f"""Converting model {model_name}..."""					)

       # load original model from torch hub
       snake_case :     Dict           =   torch.hub.load("facebookresearch/detr"       ,      __lowerCamelCase       ,      pretrained=__lowerCamelCase					).eval()
       snake_case :     Any           =   detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     snake_case :     List[str]           =   "detr." + src
              rename_key(__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					)
       snake_case :     str           =   rename_backbone_keys(__lowerCamelCase					)
       # query, key and value matrices need special treatment
       read_in_q_k_v(__lowerCamelCase       ,      is_panoptic=__lowerCamelCase					)
       # 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[Any]           =   "detr.model." if is_panoptic else "model."
       for key in state_dict.copy().keys():
              if is_panoptic:
                     if (
                         key.startswith("detr"					)
                         and not key.startswith("class_labels_classifier"					)
                         and not key.startswith("bbox_predictor"					)
                     ):
                            snake_case :     Tuple           =   state_dict.pop(__lowerCamelCase					)
                            snake_case :     Any           =   val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            snake_case :     Dict           =   state_dict.pop(__lowerCamelCase					)
                            snake_case :     Optional[Any]           =   val
                     elif key.startswith("bbox_attention"					) or key.startswith("mask_head"					):
                            continue
                     else:
                            snake_case :     Union[str, Any]           =   state_dict.pop(__lowerCamelCase					)
                            snake_case :     int           =   val
              else:
                     if not key.startswith("class_labels_classifier"					) and not key.startswith("bbox_predictor"					):
                            snake_case :     int           =   state_dict.pop(__lowerCamelCase					)
                            snake_case :     Optional[int]           =   val
    # finally, create HuggingFace model and load state dict
       snake_case :     Union[str, Any]           =   DetrForSegmentation(__lowerCamelCase					) if is_panoptic else DetrForObjectDetection(__lowerCamelCase					)
       model.load_state_dict(__lowerCamelCase					)
       model.eval()
       # verify our conversion
       snake_case :     List[Any]           =   detr(__lowerCamelCase					)
       snake_case :     Tuple           =   model(__lowerCamelCase					)
       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(__lowerCamelCase					).mkdir(exist_ok=__lowerCamelCase					)
       model.save_pretrained(__lowerCamelCase					)
       image_processor.save_pretrained(__lowerCamelCase					)


if __name__ == "__main__":
   __lowerCamelCase							= argparse.ArgumentParser()

   parser.add_argument(
       """--model_name""", default="""detr_resnet50""", type=str, help="""Name of the 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."""
   )
   __lowerCamelCase							= parser.parse_args()
   convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)





