



import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase    :     Any													=	'''https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'''
		__UpperCAmelCase    :     Dict													=	Image.open(requests.get(_UpperCAmelCase ,   stream=_UpperCAmelCase						).raw						).convert('''RGB'''						)

		return image

def        a						(      _UpperCAmelCase							:					Any						):







		'''simple docstring'''





		__UpperCAmelCase    :     str													=	[]
		# fmt: off

		# vision encoder
		rename_keys.append(('''visual_encoder.cls_token''', '''vision_model.embeddings.class_embedding''')						)
		rename_keys.append(('''visual_encoder.pos_embed''', '''vision_model.embeddings.position_embedding''')						)
		rename_keys.append(('''visual_encoder.patch_embed.proj.weight''', '''vision_model.embeddings.patch_embedding.weight''')						)
		rename_keys.append(('''visual_encoder.patch_embed.proj.bias''', '''vision_model.embeddings.patch_embedding.bias''')						)
		rename_keys.append(('''ln_vision.weight''', '''vision_model.post_layernorm.weight''')						)
		rename_keys.append(('''ln_vision.bias''', '''vision_model.post_layernorm.bias''')						)

		for i in range(config.vision_config.num_hidden_layers						):
				rename_keys.append((f'visual_encoder.blocks.{i}.norm1.weight', f'vision_model.encoder.layers.{i}.layer_norm1.weight')						)
				rename_keys.append((f'visual_encoder.blocks.{i}.norm1.bias', f'vision_model.encoder.layers.{i}.layer_norm1.bias')						)
				rename_keys.append((f'visual_encoder.blocks.{i}.norm2.weight', f'vision_model.encoder.layers.{i}.layer_norm2.weight')						)
				rename_keys.append((f'visual_encoder.blocks.{i}.norm2.bias', f'vision_model.encoder.layers.{i}.layer_norm2.bias')						)
				rename_keys.append((f'visual_encoder.blocks.{i}.attn.qkv.weight', f'vision_model.encoder.layers.{i}.self_attn.qkv.weight')						)
				rename_keys.append((f'visual_encoder.blocks.{i}.attn.proj.weight', f'vision_model.encoder.layers.{i}.self_attn.projection.weight',)						)
				rename_keys.append((f'visual_encoder.blocks.{i}.attn.proj.bias', f'vision_model.encoder.layers.{i}.self_attn.projection.bias')						)
				rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc1.weight', f'vision_model.encoder.layers.{i}.mlp.fc1.weight')						)
				rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc1.bias', f'vision_model.encoder.layers.{i}.mlp.fc1.bias')						)
				rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc2.weight', f'vision_model.encoder.layers.{i}.mlp.fc2.weight')						)
				rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc2.bias', f'vision_model.encoder.layers.{i}.mlp.fc2.bias')						)

		# QFormer
		rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.weight''', '''qformer.embeddings.layernorm.weight''')						)
		rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.bias''', '''qformer.embeddings.layernorm.bias''')						)

		# fmt: on
		return rename_keys

def        a						(      _UpperCAmelCase							:					List[Any] ,   _UpperCAmelCase							:					Union[str, Any] ,   _UpperCAmelCase							:					int						):







		'''simple docstring'''





		__UpperCAmelCase    :     str													=	dct.pop(_UpperCAmelCase						)
		__UpperCAmelCase    :     Any													=	val

def        a						(      _UpperCAmelCase							:					Any ,   _UpperCAmelCase							:					Optional[Any]						):







		'''simple docstring'''





		for i in range(config.vision_config.num_hidden_layers						):
				# read in original q and v biases
				__UpperCAmelCase    :     Optional[int]													=	state_dict.pop(f'visual_encoder.blocks.{i}.attn.q_bias'						)
				__UpperCAmelCase    :     List[str]													=	state_dict.pop(f'visual_encoder.blocks.{i}.attn.v_bias'						)

				# next, set bias in the state dict
				__UpperCAmelCase    :     List[str]													=	torch.cat((q_bias, torch.zeros_like(_UpperCAmelCase ,   requires_grad=_UpperCAmelCase						), v_bias)						)
				__UpperCAmelCase    :     Optional[Any]													=	qkv_bias

def        a						(      _UpperCAmelCase							:					List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	3_64 if '''coco''' in model_name else 2_24
		__UpperCAmelCase    :     Tuple													=	InstructBlipVisionConfig(image_size=_UpperCAmelCase						).to_dict()

		# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
		# seems like flan-T5 models don't have bos_token_id properly set?
		if "t5-xl" in model_name:
				__UpperCAmelCase    :     List[str]													=	TaConfig.from_pretrained('''google/flan-t5-xl''' ,   dense_act_fn='''gelu''' ,   bos_token_id=1						).to_dict()
		elif "t5-xxl" in model_name:
				__UpperCAmelCase    :     Any													=	TaConfig.from_pretrained('''google/flan-t5-xxl''' ,   dense_act_fn='''gelu''' ,   bos_token_id=1						).to_dict()
		elif "vicuna-7b" in model_name:
				__UpperCAmelCase    :     Union[str, Any]													=	LlamaConfig.from_pretrained('''decapoda-research/llama-7b-hf''' ,   vocab_size=3_20_01						).to_dict()
		elif "vicuna-13b" in model_name:
				__UpperCAmelCase    :     List[Any]													=	LlamaConfig.from_pretrained('''decapoda-research/llama-13b-hf''' ,   vocab_size=3_20_01						).to_dict()
		else:
				raise ValueError('''Model name not supported'''						)

		# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
		__UpperCAmelCase    :     Dict													=	InstructBlipQFormerConfig(vocab_size=3_05_23						).to_dict()
		__UpperCAmelCase    :     Union[str, Any]													=	InstructBlipConfig(vision_config=_UpperCAmelCase ,   text_config=_UpperCAmelCase ,   qformer_config=_UpperCAmelCase						)

		return config, image_size

@torch.no_grad()
def        a						(      _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					Union[str, Any]=None ,   _UpperCAmelCase							:					Optional[int]=False						):







		'''simple docstring'''





		__UpperCAmelCase    :     Tuple													=	AutoTokenizer.from_pretrained('''bert-base-uncased''' ,   truncation_side='''left'''						)
		qformer_tokenizer.add_special_tokens({'''bos_token''': '''[DEC]'''}						)

		if "t5" in model_name:
				__UpperCAmelCase    :     Optional[int]													=	TaTokenizerFast.from_pretrained('''google/flan-t5-xl''' ,   truncation_side='''left'''						)
		elif "vicuna" in model_name:
				# the following was used in the original implementation:
				# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
				# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
				# tokenizer.add_special_tokens({"bos_token": "</s>"})
				# tokenizer.add_special_tokens({"eos_token": "</s>"})
				# tokenizer.add_special_tokens({"unk_token": "</s>"})
				__UpperCAmelCase    :     int													=	LlamaTokenizerFast.from_pretrained(
				    '''huggyllama/llama-7b''' ,   truncation_side='''left''' ,   bos_token='''</s>''' ,   unk_token='''</s>'''						)
				tokenizer.add_special_tokens({'''pad_token''': '''[PAD]'''}						)

		__UpperCAmelCase							,					__UpperCAmelCase    :     Dict													=	get_blipa_config(_UpperCAmelCase						)
		__UpperCAmelCase    :     Optional[Any]													=	InstructBlipForConditionalGeneration(_UpperCAmelCase						).eval()

		__UpperCAmelCase    :     int													=	{
		    '''instructblip-vicuna-7b''': ('''blip2_vicuna_instruct''', '''vicuna7b'''),
		    '''instructblip-vicuna-13b''': ('''blip2_vicuna_instruct''', '''vicuna13b'''),
		    '''instructblip-flan-t5-xl''': ('''blip2_t5_instruct''', '''flant5xl'''),
		    '''instructblip-flan-t5-xxl''': ('''blip2_t5_instruct''', '''flant5xxl'''),
		}

		__UpperCAmelCase							,					__UpperCAmelCase    :     Optional[int]													=	model_name_to_original[model_name]

		# load original model
		print('''Loading original model...'''						)
		__UpperCAmelCase    :     int													=	'''cuda:1''' if torch.cuda.is_available() else '''cpu'''
		__UpperCAmelCase    :     Any													=	'''cuda:2''' if torch.cuda.is_available() else '''cpu'''
		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     Any													=	load_model_and_preprocess(
		    name=_UpperCAmelCase ,   model_type=_UpperCAmelCase ,   is_eval=_UpperCAmelCase ,   device=_UpperCAmelCase						)
		original_model.eval()
		print('''Done!'''						)

		# update state dict keys
		__UpperCAmelCase    :     int													=	original_model.state_dict()
		__UpperCAmelCase    :     Optional[Any]													=	create_rename_keys(_UpperCAmelCase						)
		for src, dest in rename_keys:
				rename_key(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)

		# some keys can be renamed efficiently
		for key, val in state_dict.copy().items():
				__UpperCAmelCase    :     List[str]													=	state_dict.pop(_UpperCAmelCase						)
				if key.startswith('''Qformer.bert'''						):
						__UpperCAmelCase    :     Dict													=	key.replace('''Qformer.bert''' ,   '''qformer'''						)
				if "attention.self" in key:
						__UpperCAmelCase    :     Dict													=	key.replace('''self''' ,   '''attention'''						)
				if "llm_proj" in key:
						__UpperCAmelCase    :     str													=	key.replace('''llm_proj''' ,   '''language_projection'''						)
				if "t5_proj" in key:
						__UpperCAmelCase    :     Tuple													=	key.replace('''t5_proj''' ,   '''language_projection'''						)
				if key.startswith('''llm_model'''						):
						__UpperCAmelCase    :     int													=	key.replace('''llm_model''' ,   '''language_model'''						)
				if key.startswith('''t5'''						):
						__UpperCAmelCase    :     Tuple													=	key.replace('''t5''' ,   '''language'''						)
				__UpperCAmelCase    :     List[str]													=	val

		# read in qv biases
		read_in_q_v_bias(_UpperCAmelCase ,   _UpperCAmelCase						)

		# note: weights get loaded in torch.float32 by default
		hf_model.load_state_dict(_UpperCAmelCase ,   strict=_UpperCAmelCase						)

		__UpperCAmelCase    :     Union[str, Any]													=	load_demo_image()
		__UpperCAmelCase    :     Dict													=	'''What is unusual about this image?'''

		# create processor
		__UpperCAmelCase    :     int													=	BlipImageProcessor(
		    size={'''height''': image_size, '''width''': image_size} ,   image_mean=_UpperCAmelCase ,   image_std=_UpperCAmelCase						)
		__UpperCAmelCase    :     Optional[int]													=	InstructBlipProcessor(
		    image_processor=_UpperCAmelCase ,   tokenizer=_UpperCAmelCase ,   qformer_tokenizer=_UpperCAmelCase ,   )
		__UpperCAmelCase    :     Optional[Any]													=	processor(images=_UpperCAmelCase ,   text=_UpperCAmelCase ,   return_tensors='''pt'''						).to(_UpperCAmelCase						)

		# make sure processor creates exact same pixel values
		__UpperCAmelCase    :     int													=	vis_processors['''eval'''](_UpperCAmelCase						).unsqueeze(0						).to(_UpperCAmelCase						)
		__UpperCAmelCase    :     Tuple													=	inputs.pixel_values
		assert torch.allclose(original_pixel_values.to(pixel_values.device						) ,   _UpperCAmelCase						)

		original_model.to(_UpperCAmelCase						)
		hf_model.to(_UpperCAmelCase						)
		with torch.no_grad():
				if "vicuna" in model_name:
						__UpperCAmelCase    :     Optional[int]													=	original_model({'''image''': original_pixel_values, '''text_input''': [prompt]}						).logits
						__UpperCAmelCase    :     str													=	hf_model(**_UpperCAmelCase						).logits
				else:
						__UpperCAmelCase    :     List[str]													=	original_model(
						    {'''image''': original_pixel_values, '''text_input''': [prompt], '''text_output''': ['''\n''']}						).logits
						__UpperCAmelCase    :     int													=	tokenizer('''\n''' ,   return_tensors='''pt'''						).input_ids.to(_UpperCAmelCase						)
						__UpperCAmelCase    :     Dict													=	label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id ,   -1_00						)
						__UpperCAmelCase    :     List[str]													=	hf_model(**_UpperCAmelCase ,   labels=_UpperCAmelCase						).logits

		print('''First values of original logits:''' ,   original_logits[0, :3, :3]						)
		print('''First values of HF logits:''' ,   logits[0, :3, :3]						)

		# assert values
		assert original_logits.shape == logits.shape
		__UpperCAmelCase    :     int													=	1e-4 if '''vicuna''' in model_name else 1e-5
		assert torch.allclose(original_logits.to(logits.device						) ,   _UpperCAmelCase ,   atol=_UpperCAmelCase						)
		print('''Looks ok!'''						)

		print('''Generating with original model...'''						)
		__UpperCAmelCase    :     str													=	original_model.generate({'''image''': original_pixel_values, '''prompt''': prompt} ,   num_beams=5						)

		# important: we need to cast the weights of the HF model to the appropriate type
		print('''Generating with HF model...'''						)
		__UpperCAmelCase    :     str													=	hf_model.generate(
		    **_UpperCAmelCase ,   do_sample=_UpperCAmelCase ,   num_beams=5 ,   max_length=2_56 ,   min_length=1 ,   top_p=0.9 ,   repetition_penalty=1.5 ,   length_penalty=1.0 ,   temperature=1 ,   )
		if "vicuna" in model_name:
				# convert output id 0 to 2 (eos_token_id)
				# TODO add this in the generate method?
				__UpperCAmelCase    :     List[Any]													=	2
		print('''Original generation:''' ,   _UpperCAmelCase						)
		__UpperCAmelCase    :     Dict													=	processor.batch_decode(_UpperCAmelCase ,   skip_special_tokens=_UpperCAmelCase						)
		__UpperCAmelCase    :     Optional[Any]													=	[text.strip() for text in output_text]
		print('''HF generation:''' ,   _UpperCAmelCase						)

		if pytorch_dump_folder_path is not None:
				processor.save_pretrained(_UpperCAmelCase						)
				hf_model.save_pretrained(_UpperCAmelCase						)

		if push_to_hub:
				processor.push_to_hub(f'Salesforce/{model_name}'						)
				hf_model.push_to_hub(f'Salesforce/{model_name}'						)


if __name__ == "__main__":
				__A   			=argparse.ArgumentParser()
				__A   			=[
				    "instructblip-vicuna-7b",
				    "instructblip-vicuna-13b",
				    "instructblip-flan-t5-xl",
				    "instructblip-flan-t5-xxl",
				]
				parser.add_argument(
				    "--model_name",
				    default="instructblip-flan-t5-xl",
				    choices=choices,
				    type=str,
				    help="Path to hf config.json of model to convert",
				)
				parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
				parser.add_argument(
				    "--push_to_hub",
				    action="store_true",
				    help="Whether to push the model and processor to the hub after converting",
				)

				__A   			=parser.parse_args()

				convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










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, SwinConfig
from transformers.utils import logging


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

def        a						(      _UpperCAmelCase							:					int						):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[Any]													=	SwinConfig(
		    embed_dim=1_92 ,   depths=(2, 2, 18, 2) ,   num_heads=(6, 12, 24, 48) ,   window_size=12 ,   out_features=['''stage2''', '''stage3''', '''stage4'''] ,   )

		__UpperCAmelCase    :     Optional[int]													=	DetaConfig(
		    backbone_config=_UpperCAmelCase ,   num_queries=9_00 ,   encoder_ffn_dim=20_48 ,   decoder_ffn_dim=20_48 ,   num_feature_levels=5 ,   assign_first_stage=_UpperCAmelCase ,   with_box_refine=_UpperCAmelCase ,   two_stage=_UpperCAmelCase ,   )

		# set labels
		__UpperCAmelCase    :     Optional[int]													=	'''huggingface/label-files'''
		if "o365" in model_name:
				__UpperCAmelCase    :     Tuple													=	3_66
				__UpperCAmelCase    :     List[str]													=	'''object365-id2label.json'''
		else:
				__UpperCAmelCase    :     Any													=	91
				__UpperCAmelCase    :     int													=	'''coco-detection-id2label.json'''

		__UpperCAmelCase    :     Optional[int]													=	num_labels
		__UpperCAmelCase    :     List[str]													=	json.load(open(cached_download(hf_hub_url(_UpperCAmelCase ,   _UpperCAmelCase ,   repo_type='''dataset'''						)						) ,   '''r'''						)						)
		__UpperCAmelCase    :     str													=	{int(_UpperCAmelCase						): v for k, v in idalabel.items()}
		__UpperCAmelCase    :     Optional[int]													=	idalabel
		__UpperCAmelCase    :     Tuple													=	{v: k for k, v in idalabel.items()}

		return config

def        a						(      _UpperCAmelCase							:					Dict						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[str]													=	[]

		# stem
		# fmt: off
		rename_keys.append(('''backbone.0.body.patch_embed.proj.weight''', '''model.backbone.model.embeddings.patch_embeddings.projection.weight''')						)
		rename_keys.append(('''backbone.0.body.patch_embed.proj.bias''', '''model.backbone.model.embeddings.patch_embeddings.projection.bias''')						)
		rename_keys.append(('''backbone.0.body.patch_embed.norm.weight''', '''model.backbone.model.embeddings.norm.weight''')						)
		rename_keys.append(('''backbone.0.body.patch_embed.norm.bias''', '''model.backbone.model.embeddings.norm.bias''')						)
		# stages
		for i in range(len(config.backbone_config.depths						)						):
				for j in range(config.backbone_config.depths[i]						):
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm1.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')						)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm1.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')						)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')						)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')						)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')						)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')						)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm2.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')						)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm2.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')						)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')						)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')						)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')						)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')						)

				if i < 3:
						rename_keys.append((f'backbone.0.body.layers.{i}.downsample.reduction.weight', f'model.backbone.model.encoder.layers.{i}.downsample.reduction.weight')						)
						rename_keys.append((f'backbone.0.body.layers.{i}.downsample.norm.weight', f'model.backbone.model.encoder.layers.{i}.downsample.norm.weight')						)
						rename_keys.append((f'backbone.0.body.layers.{i}.downsample.norm.bias', f'model.backbone.model.encoder.layers.{i}.downsample.norm.bias')						)

		rename_keys.append(('''backbone.0.body.norm1.weight''', '''model.backbone.model.hidden_states_norms.stage2.weight''')						)
		rename_keys.append(('''backbone.0.body.norm1.bias''', '''model.backbone.model.hidden_states_norms.stage2.bias''')						)
		rename_keys.append(('''backbone.0.body.norm2.weight''', '''model.backbone.model.hidden_states_norms.stage3.weight''')						)
		rename_keys.append(('''backbone.0.body.norm2.bias''', '''model.backbone.model.hidden_states_norms.stage3.bias''')						)
		rename_keys.append(('''backbone.0.body.norm3.weight''', '''model.backbone.model.hidden_states_norms.stage4.weight''')						)
		rename_keys.append(('''backbone.0.body.norm3.bias''', '''model.backbone.model.hidden_states_norms.stage4.bias''')						)

		# 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						(      _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					Union[str, Any]						):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[int]													=	dct.pop(_UpperCAmelCase						)
		__UpperCAmelCase    :     List[Any]													=	val

def        a						(      _UpperCAmelCase							:					Dict ,   _UpperCAmelCase							:					List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[str]													=	[int(backbone_config.embed_dim * 2**i						) for i in range(len(backbone_config.depths						)						)]
		for i in range(len(backbone_config.depths						)						):
				__UpperCAmelCase    :     str													=	num_features[i]
				for j in range(backbone_config.depths[i]						):
						# fmt: off
						# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
						__UpperCAmelCase    :     List[str]													=	state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'						)
						__UpperCAmelCase    :     List[Any]													=	state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'						)
						# next, add query, keys and values (in that order) to the state dict
						__UpperCAmelCase    :     Dict													=	in_proj_weight[:dim, :]
						__UpperCAmelCase    :     List[str]													=	in_proj_bias[: dim]
						__UpperCAmelCase    :     str													=	in_proj_weight[
						    dim : dim * 2, :
						]
						__UpperCAmelCase    :     Any													=	in_proj_bias[
						    dim : dim * 2
						]
						__UpperCAmelCase    :     Tuple													=	in_proj_weight[
						    -dim :, :
						]
						__UpperCAmelCase    :     int													=	in_proj_bias[-dim :]
						# fmt: on

def        a						(      _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					Any						):







		'''simple docstring'''





		__UpperCAmelCase    :     int													=	config.d_model
		for i in range(config.decoder_layers						):
				# read in weights + bias of input projection layer of self-attention
				__UpperCAmelCase    :     List[str]													=	state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_weight'						)
				__UpperCAmelCase    :     Tuple													=	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    :     Union[str, Any]													=	in_proj_weight[:hidden_size, :]
				__UpperCAmelCase    :     List[Any]													=	in_proj_bias[:hidden_size]
				__UpperCAmelCase    :     int													=	in_proj_weight[
				    hidden_size : hidden_size * 2, :
				]
				__UpperCAmelCase    :     str													=	in_proj_bias[hidden_size : hidden_size * 2]
				__UpperCAmelCase    :     Tuple													=	in_proj_weight[-hidden_size:, :]
				__UpperCAmelCase    :     Optional[Any]													=	in_proj_bias[-hidden_size:]

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		__UpperCAmelCase    :     int													=	Image.open(requests.get(_UpperCAmelCase ,   stream=_UpperCAmelCase						).raw						)

		return im

@torch.no_grad()
def        a						(      _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					List[Any] ,   _UpperCAmelCase							:					List[str]						):







		'''simple docstring'''





		__UpperCAmelCase    :     Tuple													=	get_deta_config(_UpperCAmelCase						)

		# load original state dict
		if model_name == "deta-swin-large":
				__UpperCAmelCase    :     Dict													=	hf_hub_download(repo_id='''nielsr/deta-checkpoints''' ,   filename='''adet_swin_ft.pth'''						)
		elif model_name == "deta-swin-large-o365":
				__UpperCAmelCase    :     Any													=	hf_hub_download(repo_id='''jozhang97/deta-swin-l-o365''' ,   filename='''deta_swin_pt_o365.pth'''						)
		else:
				raise ValueError(f'Model name {model_name} not supported'						)

		__UpperCAmelCase    :     str													=	torch.load(_UpperCAmelCase ,   map_location='''cpu'''						)['''model''']

		# original state dict
		for name, param in state_dict.items():
				print(_UpperCAmelCase ,   param.shape						)

		# rename keys
		__UpperCAmelCase    :     int													=	create_rename_keys(_UpperCAmelCase						)
		for src, dest in rename_keys:
				rename_key(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)
		read_in_swin_q_k_v(_UpperCAmelCase ,   config.backbone_config						)
		read_in_decoder_q_k_v(_UpperCAmelCase ,   _UpperCAmelCase						)

		# 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    :     Optional[Any]													=	state_dict.pop(_UpperCAmelCase						)
						__UpperCAmelCase    :     Union[str, Any]													=	val
				if "input_proj" in key:
						__UpperCAmelCase    :     Union[str, Any]													=	state_dict.pop(_UpperCAmelCase						)
						__UpperCAmelCase    :     List[str]													=	val
				if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
						__UpperCAmelCase    :     Union[str, Any]													=	state_dict.pop(_UpperCAmelCase						)
						__UpperCAmelCase    :     Union[str, Any]													=	val

    # finally, create HuggingFace model and load state dict
		__UpperCAmelCase    :     Union[str, Any]													=	DetaForObjectDetection(_UpperCAmelCase						)
		model.load_state_dict(_UpperCAmelCase						)
		model.eval()

		__UpperCAmelCase    :     Optional[int]													=	'''cuda''' if torch.cuda.is_available() else '''cpu'''
		model.to(_UpperCAmelCase						)

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

		# verify our conversion on image
		__UpperCAmelCase    :     str													=	prepare_img()
		__UpperCAmelCase    :     Optional[int]													=	processor(images=_UpperCAmelCase ,   return_tensors='''pt'''						)
		__UpperCAmelCase    :     List[Any]													=	encoding['''pixel_values''']
		__UpperCAmelCase    :     List[str]													=	model(pixel_values.to(_UpperCAmelCase						)						)

		# verify logits
		print('''Logits:''' ,   outputs.logits[0, :3, :3]						)
		print('''Boxes:''' ,   outputs.pred_boxes[0, :3, :3]						)
		if model_name == "deta-swin-large":
				__UpperCAmelCase    :     str													=	torch.tensor(
				    [[-7.63_08, -2.84_85, -5.37_37], [-7.20_37, -4.55_05, -4.80_27], [-7.29_43, -4.26_11, -4.66_17]]						)
				__UpperCAmelCase    :     Union[str, Any]													=	torch.tensor([[0.49_87, 0.49_69, 0.99_99], [0.25_49, 0.54_98, 0.48_05], [0.54_98, 0.27_57, 0.05_69]]						)
		elif model_name == "deta-swin-large-o365":
				__UpperCAmelCase    :     Optional[Any]													=	torch.tensor(
				    [[-8.01_22, -3.57_20, -4.97_17], [-8.15_47, -3.68_86, -4.63_89], [-7.66_10, -3.61_94, -5.01_34]]						)
				__UpperCAmelCase    :     str													=	torch.tensor([[0.25_23, 0.55_49, 0.48_81], [0.77_15, 0.41_49, 0.46_01], [0.55_03, 0.27_53, 0.05_75]]						)
		assert torch.allclose(outputs.logits[0, :3, :3] ,   expected_logits.to(_UpperCAmelCase						) ,   atol=1e-4						)
		assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,   expected_boxes.to(_UpperCAmelCase						) ,   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(_UpperCAmelCase						).mkdir(exist_ok=_UpperCAmelCase						)
				model.save_pretrained(_UpperCAmelCase						)
				processor.save_pretrained(_UpperCAmelCase						)

		# 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__":
				__A   			=argparse.ArgumentParser()

				parser.add_argument(
				    "--model_name",
				    type=str,
				    default="deta-swin-large",
				    choices=["deta-swin-large", "deta-swin-large-o365"],
				    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."
				)
				__A   			=parser.parse_args()
				convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





