

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()
UpperCamelCase__  :      int			=    logging.get_logger(__name__)


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		str       ):





    """simple docstring"""


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

    SCREAMING_SNAKE_CASE_									=						DetrConfig(use_timm_backbone=_SCREAMING_SNAKE_CASE ,   backbone_config=_SCREAMING_SNAKE_CASE       )

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

    return config, is_panoptic


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[]

    # 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       (  _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		Dict ,   _SCREAMING_SNAKE_CASE					:		List[Any]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						state_dict.pop(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						val


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Tuple ,   _SCREAMING_SNAKE_CASE					:		int=False       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						''
    if is_panoptic:
        SCREAMING_SNAKE_CASE_									=						'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)
        SCREAMING_SNAKE_CASE_									=						state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""       )
        SCREAMING_SNAKE_CASE_									=						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
        SCREAMING_SNAKE_CASE_									=						in_proj_weight[:256, :]
        SCREAMING_SNAKE_CASE_									=						in_proj_bias[:256]
        SCREAMING_SNAKE_CASE_									=						in_proj_weight[256:512, :]
        SCREAMING_SNAKE_CASE_									=						in_proj_bias[256:512]
        SCREAMING_SNAKE_CASE_									=						in_proj_weight[-256:, :]
        SCREAMING_SNAKE_CASE_									=						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
        SCREAMING_SNAKE_CASE_									=						state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""       )
        SCREAMING_SNAKE_CASE_									=						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
        SCREAMING_SNAKE_CASE_									=						in_proj_weight[:256, :]
        SCREAMING_SNAKE_CASE_									=						in_proj_bias[:256]
        SCREAMING_SNAKE_CASE_									=						in_proj_weight[256:512, :]
        SCREAMING_SNAKE_CASE_									=						in_proj_bias[256:512]
        SCREAMING_SNAKE_CASE_									=						in_proj_weight[-256:, :]
        SCREAMING_SNAKE_CASE_									=						in_proj_bias[-256:]
        # read in weights + bias of input projection layer of cross-attention
        SCREAMING_SNAKE_CASE_									=						state_dict.pop(
            f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""       )
        SCREAMING_SNAKE_CASE_									=						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
        SCREAMING_SNAKE_CASE_									=						in_proj_weight_cross_attn[:256, :]
        SCREAMING_SNAKE_CASE_									=						in_proj_bias_cross_attn[:256]
        SCREAMING_SNAKE_CASE_									=						in_proj_weight_cross_attn[256:512, :]
        SCREAMING_SNAKE_CASE_									=						in_proj_bias_cross_attn[256:512]
        SCREAMING_SNAKE_CASE_									=						in_proj_weight_cross_attn[-256:, :]
        SCREAMING_SNAKE_CASE_									=						in_proj_bias_cross_attn[-256:]


def 					_UpperCAmelCase       (  ):





    """simple docstring"""


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

    return im


@torch.no_grad()
def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		List[Any]=None ,   _SCREAMING_SNAKE_CASE					:		Any=False       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						get_detr_config(_SCREAMING_SNAKE_CASE       )

    # load original model from torch hub
    SCREAMING_SNAKE_CASE_									=						{
        'detr-resnet-50': 'detr_resnet50',
        'detr-resnet-101': 'detr_resnet101',
    }
    logger.info(f"""Converting model {model_name}..."""       )
    SCREAMING_SNAKE_CASE_									=						torch.hub.load('facebookresearch/detr' ,   model_name_to_original_name[model_name] ,   pretrained=_SCREAMING_SNAKE_CASE       ).eval()
    SCREAMING_SNAKE_CASE_									=						detr.state_dict()
    # rename keys
    for src, dest in create_rename_keys(_SCREAMING_SNAKE_CASE       ):
        if is_panoptic:
            SCREAMING_SNAKE_CASE_									=						'detr.' + src
        rename_key(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
    # query, key and value matrices need special treatment
    read_in_q_k_v(_SCREAMING_SNAKE_CASE ,   is_panoptic=_SCREAMING_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
    SCREAMING_SNAKE_CASE_									=						'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'       )
            ):
                SCREAMING_SNAKE_CASE_									=						state_dict.pop(_SCREAMING_SNAKE_CASE       )
                SCREAMING_SNAKE_CASE_									=						val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                SCREAMING_SNAKE_CASE_									=						state_dict.pop(_SCREAMING_SNAKE_CASE       )
                SCREAMING_SNAKE_CASE_									=						val
            elif key.startswith('bbox_attention'       ) or key.startswith('mask_head'       ):
                continue
            else:
                SCREAMING_SNAKE_CASE_									=						state_dict.pop(_SCREAMING_SNAKE_CASE       )
                SCREAMING_SNAKE_CASE_									=						val
        else:
            if not key.startswith('class_labels_classifier'       ) and not key.startswith('bbox_predictor'       ):
                SCREAMING_SNAKE_CASE_									=						state_dict.pop(_SCREAMING_SNAKE_CASE       )
                SCREAMING_SNAKE_CASE_									=						val

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

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

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

    SCREAMING_SNAKE_CASE_									=						detr(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						model(_SCREAMING_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(_SCREAMING_SNAKE_CASE       ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE       )
        model.save_pretrained(_SCREAMING_SNAKE_CASE       )
        processor.save_pretrained(_SCREAMING_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__":
       UpperCamelCase__  :      Dict			=    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.")
       UpperCamelCase__  :      str			=    parser.parse_args()
       convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





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


UpperCamelCase__  :      str			=    logging.get_logger(__name__)

UpperCamelCase__  :      Optional[int]			=    {
    "facebook/dpr-ctx_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-single-nq-base": (
        "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-ctx_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-multiset-base": (
        "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"
    ),
}



class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	Optional[int]						 =  'dpr'

  def __init__(					self						,		_A=30522						,		_A=768						,		_A=12						,		_A=12						,		_A=3072						,		_A="gelu"						,		_A=0.1						,		_A=0.1						,		_A=512						,		_A=2						,		_A=0.0_2						,		_A=1E-12						,		_A=0						,		_A="absolute"						,		_A = 0						,		**_A						,		):
      super().__init__(pad_token_id=_A						,		**_A)

      SCREAMING_SNAKE_CASE_									=						vocab_size
      SCREAMING_SNAKE_CASE_									=						hidden_size
      SCREAMING_SNAKE_CASE_									=						num_hidden_layers
      SCREAMING_SNAKE_CASE_									=						num_attention_heads
      SCREAMING_SNAKE_CASE_									=						hidden_act
      SCREAMING_SNAKE_CASE_									=						intermediate_size
      SCREAMING_SNAKE_CASE_									=						hidden_dropout_prob
      SCREAMING_SNAKE_CASE_									=						attention_probs_dropout_prob
      SCREAMING_SNAKE_CASE_									=						max_position_embeddings
      SCREAMING_SNAKE_CASE_									=						type_vocab_size
      SCREAMING_SNAKE_CASE_									=						initializer_range
      SCREAMING_SNAKE_CASE_									=						layer_norm_eps
      SCREAMING_SNAKE_CASE_									=						projection_dim
      SCREAMING_SNAKE_CASE_									=						position_embedding_type


