'''simple docstring'''

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_     :						Union[str, Any]    							=logging.get_logger(__name__)

A_     :						str    							={
    '''Salesforce/blip-vqa-base''': '''https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json''',
    '''Salesforce/blip-vqa-capfit-large''': (
        '''https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json'''
    ),
    '''Salesforce/blip-image-captioning-base''': (
        '''https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json'''
    ),
    '''Salesforce/blip-image-captioning-large''': (
        '''https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json'''
    ),
    '''Salesforce/blip-itm-base-coco''': '''https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json''',
    '''Salesforce/blip-itm-large-coco''': '''https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json''',
    '''Salesforce/blip-itm-base-flikr''': '''https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json''',
    '''Salesforce/blip-itm-large-flikr''': (
        '''https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json'''
    ),
}


class     __UpperCAmelCase       ( __a		):
  __A :				str											= 'blip_text_model'





  def __init__(	self    ,     _lowerCamelCase=3_0524    ,     _lowerCamelCase=768    ,     _lowerCamelCase=768    ,     _lowerCamelCase=3072    ,     _lowerCamelCase=768    ,     _lowerCamelCase=12    ,     _lowerCamelCase=8    ,     _lowerCamelCase=512    ,     _lowerCamelCase="gelu"    ,     _lowerCamelCase=1E-12    ,     _lowerCamelCase=0.0    ,     _lowerCamelCase=0.0    ,     _lowerCamelCase=0.02    ,     _lowerCamelCase=3_0522    ,     _lowerCamelCase=2    ,     _lowerCamelCase=0    ,     _lowerCamelCase=102    ,     _lowerCamelCase=True    ,     _lowerCamelCase=True    ,     **_lowerCamelCase    ,     ):
     super().__init__(
         pad_token_id=_lowerCamelCase    ,     bos_token_id=_lowerCamelCase    ,     eos_token_id=_lowerCamelCase    ,     sep_token_id=_lowerCamelCase    ,     **_lowerCamelCase    ,     )

     lowerCAmelCase_			=      vocab_size
     lowerCAmelCase_			=      hidden_size
     lowerCAmelCase_			=      encoder_hidden_size
     lowerCAmelCase_			=      intermediate_size
     lowerCAmelCase_			=      projection_dim
     lowerCAmelCase_			=      hidden_dropout_prob
     lowerCAmelCase_			=      num_hidden_layers
     lowerCAmelCase_			=      num_attention_heads
     lowerCAmelCase_			=      max_position_embeddings
     lowerCAmelCase_			=      layer_norm_eps
     lowerCAmelCase_			=      hidden_act
     lowerCAmelCase_			=      initializer_range
     lowerCAmelCase_			=      attention_probs_dropout_prob
     lowerCAmelCase_			=      is_decoder
     lowerCAmelCase_			=      use_cache







  @classmethod
  def 						UpperCAmelCase_	(	cls    ,     _lowerCamelCase    ,     **_lowerCamelCase ):
     cls._set_token_in_kwargs(_lowerCamelCase )

     lowerCAmelCase_	,lowerCAmelCase_			=      cls.get_config_dict(_lowerCamelCase    ,     **_lowerCamelCase )

     # get the text config dict if we are loading from BlipConfig
     if config_dict.get('''model_type''' ) == "blip":
        lowerCAmelCase_			=      config_dict['''text_config''']

     if "model_type" in config_dict and hasattr(cls    ,     '''model_type''' ) and config_dict["model_type"] != cls.model_type:
        logger.warning(
            F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
            F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )

     return cls.from_dict(_lowerCamelCase    ,     **_lowerCamelCase )






class     __UpperCAmelCase       ( __a		):
  __A :				Optional[int]											= 'blip_vision_model'





  def __init__(	self    ,     _lowerCamelCase=768    ,     _lowerCamelCase=3072    ,     _lowerCamelCase=512    ,     _lowerCamelCase=12    ,     _lowerCamelCase=12    ,     _lowerCamelCase=384    ,     _lowerCamelCase=16    ,     _lowerCamelCase="gelu"    ,     _lowerCamelCase=1E-5    ,     _lowerCamelCase=0.0    ,     _lowerCamelCase=1E-10    ,     **_lowerCamelCase    ,     ):
     super().__init__(**_lowerCamelCase )

     lowerCAmelCase_			=      hidden_size
     lowerCAmelCase_			=      intermediate_size
     lowerCAmelCase_			=      projection_dim
     lowerCAmelCase_			=      num_hidden_layers
     lowerCAmelCase_			=      num_attention_heads
     lowerCAmelCase_			=      patch_size
     lowerCAmelCase_			=      image_size
     lowerCAmelCase_			=      initializer_range
     lowerCAmelCase_			=      attention_dropout
     lowerCAmelCase_			=      layer_norm_eps
     lowerCAmelCase_			=      hidden_act







  @classmethod
  def 						UpperCAmelCase_	(	cls    ,     _lowerCamelCase    ,     **_lowerCamelCase ):
     cls._set_token_in_kwargs(_lowerCamelCase )

     lowerCAmelCase_	,lowerCAmelCase_			=      cls.get_config_dict(_lowerCamelCase    ,     **_lowerCamelCase )

     # get the vision config dict if we are loading from BlipConfig
     if config_dict.get('''model_type''' ) == "blip":
        lowerCAmelCase_			=      config_dict['''vision_config''']

     if "model_type" in config_dict and hasattr(cls    ,     '''model_type''' ) and config_dict["model_type"] != cls.model_type:
        logger.warning(
            F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
            F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )

     return cls.from_dict(_lowerCamelCase    ,     **_lowerCamelCase )






class     __UpperCAmelCase       ( __a		):
  __A :				str											= 'blip'
  __A :				Any											= True





  def __init__(	self    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None    ,     _lowerCamelCase=512    ,     _lowerCamelCase=2.65_92    ,     _lowerCamelCase=256    ,     **_lowerCamelCase    ,     ):
     super().__init__(**_lowerCamelCase )

     if text_config is None:
        lowerCAmelCase_			=      {}
        logger.info('''`text_config` is `None`. Initializing the `BlipTextConfig` with default values.''' )

     if vision_config is None:
        lowerCAmelCase_			=      {}
        logger.info('''`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.''' )

     lowerCAmelCase_			=      BlipTextConfig(**_lowerCamelCase )
     lowerCAmelCase_			=      BlipVisionConfig(**_lowerCamelCase )

     lowerCAmelCase_			=      self.vision_config.hidden_size

     lowerCAmelCase_			=      projection_dim
     lowerCAmelCase_			=      logit_scale_init_value
     lowerCAmelCase_			=      1.0
     lowerCAmelCase_			=      0.02
     lowerCAmelCase_			=      image_text_hidden_size





  @classmethod
  def 						UpperCAmelCase_	(	cls    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     **_lowerCamelCase ):
     return cls(text_config=text_config.to_dict()    ,     vision_config=vision_config.to_dict()    ,     **_lowerCamelCase )







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      copy.deepcopy(self.__dict__ )
     lowerCAmelCase_			=      self.text_config.to_dict()
     lowerCAmelCase_			=      self.vision_config.to_dict()
     lowerCAmelCase_			=      self.__class__.model_type
     return output






'''simple docstring'''

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()
A_     :						Dict    							=logging.get_logger(__name__)



def snake_case_      (					__snake_case  :							Any)			->					Tuple:
   # initialize config
   if "resnet-50" in model_name:
      lowerCAmelCase_			=      ResNetConfig.from_pretrained('''microsoft/resnet-50''')
   elif "resnet-101" in model_name:
      lowerCAmelCase_			=      ResNetConfig.from_pretrained('''microsoft/resnet-101''')
   else:
      raise ValueError('''Model name should include either resnet50 or resnet101''')

   lowerCAmelCase_			=      DetrConfig(use_timm_backbone=__snake_case	,   backbone_config=__snake_case)

   # set label attributes
   lowerCAmelCase_			=      '''panoptic''' in model_name
   if is_panoptic:
      lowerCAmelCase_			=      250
   else:
      lowerCAmelCase_			=      91
      lowerCAmelCase_			=      '''huggingface/label-files'''
      lowerCAmelCase_			=      '''coco-detection-id2label.json'''
      lowerCAmelCase_			=      json.load(open(hf_hub_download(__snake_case	,   __snake_case	,   repo_type='''dataset''')	,   '''r'''))
      lowerCAmelCase_			=      {int(__snake_case): v for k, v in idalabel.items()}
      lowerCAmelCase_			=      idalabel
      lowerCAmelCase_			=      {v: k for k, v in idalabel.items()}

   return config, is_panoptic



def snake_case_      (					__snake_case  :							Optional[Any])			->					Tuple:
   # here we list all keys to be renamed (original name on the left, our name on the right)
   lowerCAmelCase_			=      []

   # 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 snake_case_      (					__snake_case  :							List[str]	,   __snake_case  :							Tuple	,   __snake_case  :							Optional[Any])			->					List[str]:
   lowerCAmelCase_			=      state_dict.pop(__snake_case)
   lowerCAmelCase_			=      val



def snake_case_      (					__snake_case  :							List[Any]	,   __snake_case  :							Optional[int]=False)			->					str:
   lowerCAmelCase_			=      ''''''
   if is_panoptic:
      lowerCAmelCase_			=      '''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)
      lowerCAmelCase_			=      state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''')
      lowerCAmelCase_			=      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
      lowerCAmelCase_			=      in_proj_weight[:256, :]
      lowerCAmelCase_			=      in_proj_bias[:256]
      lowerCAmelCase_			=      in_proj_weight[256:512, :]
      lowerCAmelCase_			=      in_proj_bias[256:512]
      lowerCAmelCase_			=      in_proj_weight[-256:, :]
      lowerCAmelCase_			=      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
      lowerCAmelCase_			=      state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''')
      lowerCAmelCase_			=      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
      lowerCAmelCase_			=      in_proj_weight[:256, :]
      lowerCAmelCase_			=      in_proj_bias[:256]
      lowerCAmelCase_			=      in_proj_weight[256:512, :]
      lowerCAmelCase_			=      in_proj_bias[256:512]
      lowerCAmelCase_			=      in_proj_weight[-256:, :]
      lowerCAmelCase_			=      in_proj_bias[-256:]
      # read in weights + bias of input projection layer of cross-attention
      lowerCAmelCase_			=      state_dict.pop(
          F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''')
      lowerCAmelCase_			=      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
      lowerCAmelCase_			=      in_proj_weight_cross_attn[:256, :]
      lowerCAmelCase_			=      in_proj_bias_cross_attn[:256]
      lowerCAmelCase_			=      in_proj_weight_cross_attn[256:512, :]
      lowerCAmelCase_			=      in_proj_bias_cross_attn[256:512]
      lowerCAmelCase_			=      in_proj_weight_cross_attn[-256:, :]
      lowerCAmelCase_			=      in_proj_bias_cross_attn[-256:]



def snake_case_      (					)			->					str:
   lowerCAmelCase_			=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   lowerCAmelCase_			=      Image.open(requests.get(__snake_case	,   stream=__snake_case).raw)

   return im



@torch.no_grad()
def snake_case_      (					__snake_case  :							Union[str, Any]	,   __snake_case  :							List[Any]=None	,   __snake_case  :							Any=False)			->					Optional[Any]:
   lowerCAmelCase_	,lowerCAmelCase_			=      get_detr_config(__snake_case)

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

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

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

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

   lowerCAmelCase_			=      detr(__snake_case)
   lowerCAmelCase_			=      model(__snake_case)

   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(__snake_case).mkdir(exist_ok=__snake_case)
      model.save_pretrained(__snake_case)
      processor.save_pretrained(__snake_case)

   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__":
 A_     :						Any    							=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.''')
 A_     :						Optional[int]    							=parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





