

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 			UpperCAmelCase__				(			):





  '''simple docstring'''







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

  return image




def 			UpperCAmelCase__				(			_A      : int							):





  '''simple docstring'''







  a__  =[]
  # 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 			UpperCAmelCase__				(			_A      : Dict	,      _A      : Tuple	,      _A      : int							):





  '''simple docstring'''







  a__  =dct.pop(_A							)
  a__  =val




def 			UpperCAmelCase__				(			_A      : List[str]	,      _A      : List[str]							):





  '''simple docstring'''







  for i in range(config.vision_config.num_hidden_layers							):
    # read in original q and v biases
    a__  =state_dict.pop(F"""visual_encoder.blocks.{i}.attn.q_bias"""							)
    a__  =state_dict.pop(F"""visual_encoder.blocks.{i}.attn.v_bias"""							)

    # next, set bias in the state dict
    a__  =torch.cat((q_bias, torch.zeros_like(_A	,      requires_grad=_A							), v_bias)							)
    a__  =qkv_bias




def 			UpperCAmelCase__				(			_A      : Optional[Any]							):





  '''simple docstring'''







  a__  =3_64 if '''coco''' in model_name else 2_24
  a__  =InstructBlipVisionConfig(image_size=_A							).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:
    a__  =TaConfig.from_pretrained('''google/flan-t5-xl'''	,      dense_act_fn='''gelu'''	,      bos_token_id=1							).to_dict()
  elif "t5-xxl" in model_name:
    a__  =TaConfig.from_pretrained('''google/flan-t5-xxl'''	,      dense_act_fn='''gelu'''	,      bos_token_id=1							).to_dict()
  elif "vicuna-7b" in model_name:
    a__  =LlamaConfig.from_pretrained('''decapoda-research/llama-7b-hf'''	,      vocab_size=3_20_01							).to_dict()
  elif "vicuna-13b" in model_name:
    a__  =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
  a__  =InstructBlipQFormerConfig(vocab_size=3_05_23							).to_dict()
  a__  =InstructBlipConfig(vision_config=_A	,      text_config=_A	,      qformer_config=_A							)

  return config, image_size




@torch.no_grad()
def 			UpperCAmelCase__				(			_A      : List[Any]	,      _A      : List[str]=None	,      _A      : Tuple=False							):





  '''simple docstring'''







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

  if "t5" in model_name:
    a__  =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>"})
    a__  =LlamaTokenizerFast.from_pretrained(
        '''huggyllama/llama-7b'''	,      truncation_side='''left'''	,      bos_token='''</s>'''	,      unk_token='''</s>'''							)
    tokenizer.add_special_tokens({'''pad_token''': '''[PAD]'''}							)

  a__,      a__  =get_blipa_config(_A							)
  a__  =InstructBlipForConditionalGeneration(_A							).eval()

  a__  ={
      '''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'''),
  }

  a__,      a__  =model_name_to_original[model_name]

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

  # update state dict keys
  a__  =original_model.state_dict()
  a__  =create_rename_keys(_A							)
  for src, dest in rename_keys:
    rename_key(_A	,      _A	,      _A							)

  # some keys can be renamed efficiently
  for key, val in state_dict.copy().items():
    a__  =state_dict.pop(_A							)
    if key.startswith('''Qformer.bert'''							):
      a__  =key.replace('''Qformer.bert'''	,      '''qformer'''							)
    if "attention.self" in key:
      a__  =key.replace('''self'''	,      '''attention'''							)
    if "llm_proj" in key:
      a__  =key.replace('''llm_proj'''	,      '''language_projection'''							)
    if "t5_proj" in key:
      a__  =key.replace('''t5_proj'''	,      '''language_projection'''							)
    if key.startswith('''llm_model'''							):
      a__  =key.replace('''llm_model'''	,      '''language_model'''							)
    if key.startswith('''t5'''							):
      a__  =key.replace('''t5'''	,      '''language'''							)
    a__  =val

  # read in qv biases
  read_in_q_v_bias(_A	,      _A							)

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

  a__  =load_demo_image()
  a__  ='''What is unusual about this image?'''

  # create processor
  a__  =BlipImageProcessor(
      size={'''height''': image_size, '''width''': image_size}	,      image_mean=_A	,      image_std=_A							)
  a__  =InstructBlipProcessor(
      image_processor=_A	,      tokenizer=_A	,      qformer_tokenizer=_A	,      )
  a__  =processor(images=_A	,      text=_A	,      return_tensors='''pt'''							).to(_A							)

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

  original_model.to(_A							)
  hf_model.to(_A							)
  with torch.no_grad():
    if "vicuna" in model_name:
      a__  =original_model({'''image''': original_pixel_values, '''text_input''': [prompt]}							).logits
      a__  =hf_model(**_A							).logits
    else:
      a__  =original_model(
          {'''image''': original_pixel_values, '''text_input''': [prompt], '''text_output''': ['''\n''']}							).logits
      a__  =tokenizer('''\n'''	,      return_tensors='''pt'''							).input_ids.to(_A							)
      a__  =label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id	,      -1_00							)
      a__  =hf_model(**_A	,      labels=_A							).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
  a__  =1E-4 if '''vicuna''' in model_name else 1E-5
  assert torch.allclose(original_logits.to(logits.device							)	,      _A	,      atol=_A							)
  print('''Looks ok!'''							)

  print('''Generating with original model...'''							)
  a__  =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...'''							)
  a__  =hf_model.generate(
      **_A	,      do_sample=_A	,      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?
    a__  =2
  print('''Original generation:'''	,      _A							)
  a__  =processor.batch_decode(_A	,      skip_special_tokens=_A							)
  a__  =[text.strip() for text in output_text]
  print('''HF generation:'''	,      _A							)

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

  if push_to_hub:
    processor.push_to_hub(F"""Salesforce/{model_name}"""							)
    hf_model.push_to_hub(F"""Salesforce/{model_name}"""							)


if __name__ == "__main__":
       lowerCamelCase           =					argparse.ArgumentParser()
       lowerCamelCase           =					[
           '''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''',
       )

       lowerCamelCase           =					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 hf_hub_download
from PIL import Image

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


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




def 			UpperCAmelCase__				(			_A      : List[Any]							):





  '''simple docstring'''







  if "resnet-50" in model_name:
    a__  =ResNetConfig.from_pretrained('''microsoft/resnet-50'''							)
  elif "resnet-101" in model_name:
    a__  =ResNetConfig.from_pretrained('''microsoft/resnet-101'''							)
  else:
    raise ValueError('''Model name should include either resnet50 or resnet101'''							)

  a__  =DetrConfig(use_timm_backbone=_A	,      backbone_config=_A							)

  # set label attributes
  a__  ='''panoptic''' in model_name
  if is_panoptic:
    a__  =2_50
  else:
    a__  =91
    a__  ='''huggingface/label-files'''
    a__  ='''coco-detection-id2label.json'''
    a__  =json.load(open(hf_hub_download(_A	,      _A	,      repo_type='''dataset'''							)	,      '''r'''							)							)
    a__  ={int(_A							): v for k, v in idalabel.items()}
    a__  =idalabel
    a__  ={v: k for k, v in idalabel.items()}

  return config, is_panoptic




def 			UpperCAmelCase__				(			_A      : Optional[int]							):





  '''simple docstring'''







  a__  =[]

  # stem
  # fmt: off
  rename_keys.append(('''backbone.0.body.conv1.weight''', '''backbone.conv_encoder.model.embedder.embedder.convolution.weight''')							)
  rename_keys.append(('''backbone.0.body.bn1.weight''', '''backbone.conv_encoder.model.embedder.embedder.normalization.weight''')							)
  rename_keys.append(('''backbone.0.body.bn1.bias''', '''backbone.conv_encoder.model.embedder.embedder.normalization.bias''')							)
  rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_mean''')							)
  rename_keys.append(('''backbone.0.body.bn1.running_var''', '''backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.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"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
            )							)
    # fmt: on

  for i in range(config.encoder_layers							):
    # 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'''),
      ]							)

  return rename_keys




def 			UpperCAmelCase__				(			_A      : str	,      _A      : Union[str, Any]	,      _A      : int							):





  '''simple docstring'''







  a__  =state_dict.pop(_A							)
  a__  =val




def 			UpperCAmelCase__				(			_A      : Dict	,      _A      : str=False							):





  '''simple docstring'''







  a__  =''''''
  if is_panoptic:
    a__  ='''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)
    a__  =state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""							)
    a__  =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
    a__  =in_proj_weight[:2_56, :]
    a__  =in_proj_bias[:2_56]
    a__  =in_proj_weight[2_56:5_12, :]
    a__  =in_proj_bias[2_56:5_12]
    a__  =in_proj_weight[-2_56:, :]
    a__  =in_proj_bias[-2_56:]
  # 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
    a__  =state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""							)
    a__  =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
    a__  =in_proj_weight[:2_56, :]
    a__  =in_proj_bias[:2_56]
    a__  =in_proj_weight[2_56:5_12, :]
    a__  =in_proj_bias[2_56:5_12]
    a__  =in_proj_weight[-2_56:, :]
    a__  =in_proj_bias[-2_56:]
    # read in weights + bias of input projection layer of cross-attention
    a__  =state_dict.pop(
        F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""							)
    a__  =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
    a__  =in_proj_weight_cross_attn[:2_56, :]
    a__  =in_proj_bias_cross_attn[:2_56]
    a__  =in_proj_weight_cross_attn[2_56:5_12, :]
    a__  =in_proj_bias_cross_attn[2_56:5_12]
    a__  =in_proj_weight_cross_attn[-2_56:, :]
    a__  =in_proj_bias_cross_attn[-2_56:]




def 			UpperCAmelCase__				(			):





  '''simple docstring'''







  a__  ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
  a__  =Image.open(requests.get(_A	,      stream=_A							).raw							)

  return im




@torch.no_grad()
def 			UpperCAmelCase__				(			_A      : Dict	,      _A      : Optional[Any]=None	,      _A      : List[str]=False							):





  '''simple docstring'''







  a__,      a__  =get_detr_config(_A							)

  # load original model from torch hub
  a__  ={
      '''detr-resnet-50''': '''detr_resnet50''',
      '''detr-resnet-101''': '''detr_resnet101''',
  }
  logger.info(F"""Converting model {model_name}..."""							)
  a__  =torch.hub.load('''facebookresearch/detr'''	,      model_name_to_original_name[model_name]	,      pretrained=_A							).eval()
  a__  =detr.state_dict()
  # rename keys
  for src, dest in create_rename_keys(_A							):
    if is_panoptic:
      a__  ='''detr.''' + src
    rename_key(_A	,      _A	,      _A							)
  # query, key and value matrices need special treatment
  read_in_q_k_v(_A	,      is_panoptic=_A							)
  # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  a__  ='''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'''							)
      ):
        a__  =state_dict.pop(_A							)
        a__  =val
      elif "class_labels_classifier" in key or "bbox_predictor" in key:
        a__  =state_dict.pop(_A							)
        a__  =val
      elif key.startswith('''bbox_attention'''							) or key.startswith('''mask_head'''							):
        continue
      else:
        a__  =state_dict.pop(_A							)
        a__  =val
    else:
      if not key.startswith('''class_labels_classifier'''							) and not key.startswith('''bbox_predictor'''							):
        a__  =state_dict.pop(_A							)
        a__  =val

    # finally, create HuggingFace model and load state dict
  a__  =DetrForSegmentation(_A							) if is_panoptic else DetrForObjectDetection(_A							)
  model.load_state_dict(_A							)
  model.eval()

  # verify our conversion on an image
  a__  ='''coco_panoptic''' if is_panoptic else '''coco_detection'''
  a__  =DetrImageProcessor(format=_A							)

  a__  =processor(images=prepare_img()	,      return_tensors='''pt'''							)
  a__  =encoding['''pixel_values''']

  a__  =detr(_A							)
  a__  =model(_A							)

  assert torch.allclose(outputs.logits	,      original_outputs['''pred_logits''']	,      atol=1E-3							)
  assert torch.allclose(outputs.pred_boxes	,      original_outputs['''pred_boxes''']	,      atol=1E-3							)
  if is_panoptic:
    assert torch.allclose(outputs.pred_masks	,      original_outputs['''pred_masks''']	,      atol=1E-4							)
  print('''Looks ok!'''							)

  if pytorch_dump_folder_path is not None:
    # Save model and image processor
    logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""							)
    Path(_A							).mkdir(exist_ok=_A							)
    model.save_pretrained(_A							)
    processor.save_pretrained(_A							)

  if push_to_hub:
    # Upload model and image processor to the hub
    logger.info('''Uploading PyTorch model and image processor to the hub...'''							)
    model.push_to_hub(F"""nielsr/{model_name}"""							)
    processor.push_to_hub(F"""nielsr/{model_name}"""							)


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

       parser.add_argument(
           '''--model_name''',
           default='''detr-resnet-50''',
           type=str,
           choices=['''detr-resnet-50''', '''detr-resnet-101'''],
           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.'''
       )
       parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the model to the hub or not.''')
       lowerCamelCase           =					parser.parse_args()
       convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
