


"""simple docstring"""







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

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

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


logging.set_verbosity_info()
lowerCamelCase_     :     Optional[int]    		=						logging.get_logger(__name__)

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

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
        ("""class_embed.weight""", """class_labels_classifier.weight"""),
        ("""class_embed.bias""", """class_labels_classifier.bias"""),
        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
    ]
)
def _A		(		lowercase ,					lowercase ,					lowercase	):







    """simple docstring"""
    a					=state_dict.pop(lowercase	)
    a					=val
def _A		(		lowercase	):







    """simple docstring"""
    a					=OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            a					=key.replace('''backbone.0.body''' ,					'''backbone.conv_encoder.model'''	)
            a					=value
        else:
            a					=value

    return new_state_dict
def _A		(		lowercase ,					lowercase=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 _A		(		):







    """simple docstring"""
    a					='''http://images.cocodataset.org/val2017/000000039769.jpg'''
    a					=Image.open(requests.get(lowercase ,					stream=lowercase	).raw	)

    return im
@torch.no_grad()
def _A		(		lowercase ,					lowercase	):







    """simple docstring"""
    a					=DetrConfig()
    # set backbone and dilation attributes
    if "resnet101" in model_name:
        a					='''resnet101'''
    if "dc5" in model_name:
        a					=True
    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(lowercase ,					lowercase ,					repo_type='''dataset'''	) ,					'''r'''	)	)
        a					={int(lowercase	): v for k, v in idalabel.items()}
        a					=idalabel
        a					={v: k for k, v in idalabel.items()}

    # load image processor
    a					='''coco_panoptic''' if is_panoptic else '''coco_detection'''
    a					=DetrImageProcessor(format=lowercase	)

    # prepare image
    a					=prepare_img()
    a					=image_processor(images=lowercase ,					return_tensors='''pt'''	)
    a					=encoding['''pixel_values''']

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

    # load original model from torch hub
    a					=torch.hub.load('''facebookresearch/detr''' ,					lowercase ,					pretrained=lowercase	).eval()
    a					=detr.state_dict()
    # rename keys
    for src, dest in rename_keys:
        if is_panoptic:
            a					='''detr.''' + src
        rename_key(lowercase ,					lowercase ,					lowercase	)
    a					=rename_backbone_keys(lowercase	)
    # query, key and value matrices need special treatment
    read_in_q_k_v(lowercase ,					is_panoptic=lowercase	)
    # 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(lowercase	)
                a					=val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                a					=state_dict.pop(lowercase	)
                a					=val
            elif key.startswith('''bbox_attention'''	) or key.startswith('''mask_head'''	):
                continue
            else:
                a					=state_dict.pop(lowercase	)
                a					=val
        else:
            if not key.startswith('''class_labels_classifier'''	) and not key.startswith('''bbox_predictor'''	):
                a					=state_dict.pop(lowercase	)
                a					=val
    # finally, create HuggingFace model and load state dict
    a					=DetrForSegmentation(lowercase	) if is_panoptic else DetrForObjectDetection(lowercase	)
    model.load_state_dict(lowercase	)
    model.eval()
    # verify our conversion
    a					=detr(lowercase	)
    a					=model(lowercase	)
    assert torch.allclose(outputs.logits ,					original_outputs['''pred_logits'''] ,					atol=1E-4	)
    assert torch.allclose(outputs.pred_boxes ,					original_outputs['''pred_boxes'''] ,					atol=1E-4	)
    if is_panoptic:
        assert torch.allclose(outputs.pred_masks ,					original_outputs['''pred_masks'''] ,					atol=1E-4	)

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


if __name__ == "__main__":
       lowerCamelCase_     :     Tuple    		=						argparse.ArgumentParser()

       parser.add_argument(
           """--model_name""", default="""detr_resnet50""", type=str, help="""Name of the DETR model you'd like to convert."""
       )
       parser.add_argument(
           """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
       )
       lowerCamelCase_     :     Any    		=						parser.parse_args()
       convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)



"""simple docstring"""







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


lowerCamelCase_     :     int    		=						logging.get_logger(__name__)

lowerCamelCase_     :     Optional[Any]    		=						{
    """dandelin/vilt-b32-mlm""": """https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"""
}






class    __A       ( _SCREAMING_SNAKE_CASE    ):






 """simple docstring"""




 __lowerCAmelCase				      =						"vilt"


 def __init__(	self				,					__A=3_0522				,					__A=2				,					__A=2				,					__A=40				,					__A=768				,					__A=12				,					__A=12				,					__A=3072				,					__A="gelu"				,					__A=0.0				,					__A=0.0				,					__A=0.02				,					__A=1E-1_2				,					__A=384				,					__A=32				,					__A=3				,					__A=True				,					__A=-1				,					__A=False				,					__A=-1				,					**__A				,					)      ->  Dict:
     super().__init__(tie_word_embeddings=__A				,					**__A		)

     a					=vocab_size
     a					=type_vocab_size
     a					=modality_type_vocab_size
     a					=max_position_embeddings

     a					=hidden_size
     a					=num_hidden_layers
     a					=num_attention_heads
     a					=intermediate_size
     a					=hidden_act
     a					=hidden_dropout_prob
     a					=attention_probs_dropout_prob
     a					=initializer_range
     a					=layer_norm_eps

     a					=image_size
     a					=patch_size
     a					=num_channels
     a					=qkv_bias
     a					=max_image_length
     a					=num_images