import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


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

def   a_					()->				Tuple:
    snake_case:						Dict         =   DetaConfig(
        num_queries=900			,	encoder_ffn_dim=2048			,	decoder_ffn_dim=2048			,	num_feature_levels=5			,	assign_first_stage=_lowerCAmelCase			,	with_box_refine=_lowerCAmelCase			,	two_stage=_lowerCAmelCase			,	)

    # set labels
    snake_case:						str         =   91
    snake_case:						Dict         =   """huggingface/label-files"""
    snake_case:						Dict         =   """coco-detection-id2label.json"""
    snake_case:						Tuple         =   json.load(open(cached_download(hf_hub_url(_lowerCAmelCase			,	_lowerCAmelCase			,	repo_type="""dataset"""       )       )			,	"""r"""       )       )
    snake_case:						Optional[int]         =   {int(_lowerCAmelCase       ): v for k, v in idalabel.items()}
    snake_case:						Union[str, Any]         =   idalabel
    snake_case:						Optional[int]         =   {v: k for k, v in idalabel.items()}

    return config

def   a_					(_lowerCAmelCase  :   Any       )->				str:
    snake_case:						int         =   []

    # stem
    # fmt: off
    rename_keys.append(("""backbone.0.body.conv1.weight""", """model.backbone.model.embedder.embedder.convolution.weight""")       )
    rename_keys.append(("""backbone.0.body.bn1.weight""", """model.backbone.model.embedder.embedder.normalization.weight""")       )
    rename_keys.append(("""backbone.0.body.bn1.bias""", """model.backbone.model.embedder.embedder.normalization.bias""")       )
    rename_keys.append(("""backbone.0.body.bn1.running_mean""", """model.backbone.model.embedder.embedder.normalization.running_mean""")       )
    rename_keys.append(("""backbone.0.body.bn1.running_var""", """model.backbone.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"model.backbone.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"model.backbone.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"model.backbone.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"model.backbone.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"model.backbone.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"model.backbone.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"model.backbone.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"model.backbone.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"model.backbone.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"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
                    )       )
    # transformer encoder
    for i in range(config.encoder_layers       ):
        rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight", F"model.encoder.layers.{i}.self_attn.sampling_offsets.weight")       )
        rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias", F"model.encoder.layers.{i}.self_attn.sampling_offsets.bias")       )
        rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.attention_weights.weight", F"model.encoder.layers.{i}.self_attn.attention_weights.weight")       )
        rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.attention_weights.bias", F"model.encoder.layers.{i}.self_attn.attention_weights.bias")       )
        rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.value_proj.weight", F"model.encoder.layers.{i}.self_attn.value_proj.weight")       )
        rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.value_proj.bias", F"model.encoder.layers.{i}.self_attn.value_proj.bias")       )
        rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.output_proj.weight", F"model.encoder.layers.{i}.self_attn.output_proj.weight")       )
        rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.output_proj.bias", F"model.encoder.layers.{i}.self_attn.output_proj.bias")       )
        rename_keys.append((F"transformer.encoder.layers.{i}.norm1.weight", F"model.encoder.layers.{i}.self_attn_layer_norm.weight")       )
        rename_keys.append((F"transformer.encoder.layers.{i}.norm1.bias", F"model.encoder.layers.{i}.self_attn_layer_norm.bias")       )
        rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"model.encoder.layers.{i}.fc1.weight")       )
        rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"model.encoder.layers.{i}.fc1.bias")       )
        rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"model.encoder.layers.{i}.fc2.weight")       )
        rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"model.encoder.layers.{i}.fc2.bias")       )
        rename_keys.append((F"transformer.encoder.layers.{i}.norm2.weight", F"model.encoder.layers.{i}.final_layer_norm.weight")       )
        rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"model.encoder.layers.{i}.final_layer_norm.bias")       )

    # transformer decoder
    for i in range(config.decoder_layers       ):
        rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight", F"model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight")       )
        rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias", F"model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias")       )
        rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.attention_weights.weight", F"model.decoder.layers.{i}.encoder_attn.attention_weights.weight")       )
        rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.attention_weights.bias", F"model.decoder.layers.{i}.encoder_attn.attention_weights.bias")       )
        rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.value_proj.weight", F"model.decoder.layers.{i}.encoder_attn.value_proj.weight")       )
        rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.value_proj.bias", F"model.decoder.layers.{i}.encoder_attn.value_proj.bias")       )
        rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.output_proj.weight", F"model.decoder.layers.{i}.encoder_attn.output_proj.weight")       )
        rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.output_proj.bias", F"model.decoder.layers.{i}.encoder_attn.output_proj.bias")       )
        rename_keys.append((F"transformer.decoder.layers.{i}.norm1.weight", F"model.decoder.layers.{i}.encoder_attn_layer_norm.weight")       )
        rename_keys.append((F"transformer.decoder.layers.{i}.norm1.bias", F"model.decoder.layers.{i}.encoder_attn_layer_norm.bias")       )
        rename_keys.append((F"transformer.decoder.layers.{i}.self_attn.out_proj.weight", F"model.decoder.layers.{i}.self_attn.out_proj.weight")       )
        rename_keys.append((F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"model.decoder.layers.{i}.self_attn.out_proj.bias")       )
        rename_keys.append((F"transformer.decoder.layers.{i}.norm2.weight", F"model.decoder.layers.{i}.self_attn_layer_norm.weight")       )
        rename_keys.append((F"transformer.decoder.layers.{i}.norm2.bias", F"model.decoder.layers.{i}.self_attn_layer_norm.bias")       )
        rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"model.decoder.layers.{i}.fc1.weight")       )
        rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"model.decoder.layers.{i}.fc1.bias")       )
        rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"model.decoder.layers.{i}.fc2.weight")       )
        rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"model.decoder.layers.{i}.fc2.bias")       )
        rename_keys.append((F"transformer.decoder.layers.{i}.norm3.weight", F"model.decoder.layers.{i}.final_layer_norm.weight")       )
        rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"model.decoder.layers.{i}.final_layer_norm.bias")       )

    # fmt: on

    return rename_keys

def   a_					(_lowerCAmelCase  :   int			,	_lowerCAmelCase  :   Tuple			,	_lowerCAmelCase  :   int       )->				Union[str, Any]:
    snake_case:						Optional[Any]         =   dct.pop(_lowerCAmelCase       )
    snake_case:						Optional[int]         =   val

def   a_					(_lowerCAmelCase  :   List[str]			,	_lowerCAmelCase  :   List[Any]       )->				Optional[Any]:
    # transformer decoder self-attention layers
    snake_case:						List[Any]         =   config.d_model
    for i in range(config.decoder_layers       ):
        # read in weights + bias of input projection layer of self-attention
        snake_case:						int         =   state_dict.pop(F"transformer.decoder.layers.{i}.self_attn.in_proj_weight"       )
        snake_case:						Optional[int]         =   state_dict.pop(F"transformer.decoder.layers.{i}.self_attn.in_proj_bias"       )
        # next, add query, keys and values (in that order) to the state dict
        snake_case:						Tuple         =   in_proj_weight[:hidden_size, :]
        snake_case:						Union[str, Any]         =   in_proj_bias[:hidden_size]
        snake_case:						Union[str, Any]         =   in_proj_weight[
            hidden_size : hidden_size * 2, :
        ]
        snake_case:						Any         =   in_proj_bias[hidden_size : hidden_size * 2]
        snake_case:						List[Any]         =   in_proj_weight[-hidden_size:, :]
        snake_case:						Optional[Any]         =   in_proj_bias[-hidden_size:]

def   a_					()->				List[str]:
    snake_case:						Union[str, Any]         =   """http://images.cocodataset.org/val2017/000000039769.jpg"""
    snake_case:						Any         =   Image.open(requests.get(_lowerCAmelCase			,	stream=_lowerCAmelCase       ).raw       )

    return im

@torch.no_grad()
def   a_					(_lowerCAmelCase  :   List[Any]			,	_lowerCAmelCase  :   Optional[Any]			,	_lowerCAmelCase  :   List[Any]       )->				Tuple:
    snake_case:						int         =   get_deta_config()

    # load original state dict
    if model_name == "deta-resnet-50":
        snake_case:						Optional[int]         =   """adet_checkpoint0011.pth"""
    elif model_name == "deta-resnet-50-24-epochs":
        snake_case:						Tuple         =   """adet_2x_checkpoint0023.pth"""
    else:
        raise ValueError(F"Model name {model_name} not supported"       )
    snake_case:						Optional[Any]         =   hf_hub_download(repo_id="""nielsr/deta-checkpoints"""			,	filename=_lowerCAmelCase       )
    snake_case:						List[Any]         =   torch.load(_lowerCAmelCase			,	map_location="""cpu"""       )["""model"""]

    # rename keys
    snake_case:						Any         =   create_rename_keys(_lowerCAmelCase       )
    for src, dest in rename_keys:
        rename_key(_lowerCAmelCase			,	_lowerCAmelCase			,	_lowerCAmelCase       )
    read_in_decoder_q_k_v(_lowerCAmelCase			,	_lowerCAmelCase       )

    # fix some prefixes
    for key in state_dict.copy().keys():
        if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
            snake_case:						Any         =   state_dict.pop(_lowerCAmelCase       )
            snake_case:						str         =   val
        if "input_proj" in key:
            snake_case:						List[Any]         =   state_dict.pop(_lowerCAmelCase       )
            snake_case:						Optional[Any]         =   val
        if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
            snake_case:						Tuple         =   state_dict.pop(_lowerCAmelCase       )
            snake_case:						Any         =   val

    # finally, create HuggingFace model and load state dict
    snake_case:						Dict         =   DetaForObjectDetection(_lowerCAmelCase       )
    model.load_state_dict(_lowerCAmelCase       )
    model.eval()

    snake_case:						int         =   """cuda""" if torch.cuda.is_available() else """cpu"""
    model.to(_lowerCAmelCase       )

    # load image processor
    snake_case:						Dict         =   DetaImageProcessor(format="""coco_detection"""       )

    # verify our conversion on image
    snake_case:						Dict         =   prepare_img()
    snake_case:						Optional[int]         =   processor(images=_lowerCAmelCase			,	return_tensors="""pt"""       )
    snake_case:						Tuple         =   encoding["""pixel_values"""]
    snake_case:						Union[str, Any]         =   model(pixel_values.to(_lowerCAmelCase       )       )

    # verify logits
    if model_name == "deta-resnet-50":
        snake_case:						Union[str, Any]         =   torch.tensor(
            [[-7.3_978, -2.5_406, -4.1_668], [-8.2_684, -3.9_933, -3.8_096], [-7.0_515, -3.7_973, -5.8_516]]       )
        snake_case:						Tuple         =   torch.tensor([[0.5_043, 0.4_973, 0.9_998], [0.2_542, 0.5_489, 0.4_748], [0.5_490, 0.2_765, 0.0_570]]       )
    elif model_name == "deta-resnet-50-24-epochs":
        snake_case:						str         =   torch.tensor(
            [[-7.1_688, -2.4_857, -4.8_669], [-7.8_630, -3.8_154, -4.2_674], [-7.2_730, -4.1_865, -5.5_323]]       )
        snake_case:						List[str]         =   torch.tensor([[0.5_021, 0.4_971, 0.9_994], [0.2_546, 0.5_486, 0.4_731], [0.1_686, 0.1_986, 0.2_142]]       )

    assert torch.allclose(outputs.logits[0, :3, :3]			,	expected_logits.to(_lowerCAmelCase       )			,	atol=1e-4       )
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]			,	expected_boxes.to(_lowerCAmelCase       )			,	atol=1e-4       )
    print("""Everything ok!"""       )

    if pytorch_dump_folder_path:
        # Save model and processor
        logger.info(F"Saving PyTorch model and processor to {pytorch_dump_folder_path}..."       )
        Path(_lowerCAmelCase       ).mkdir(exist_ok=_lowerCAmelCase       )
        model.save_pretrained(_lowerCAmelCase       )
        processor.save_pretrained(_lowerCAmelCase       )

    # Push to hub
    if push_to_hub:
        print("""Pushing model and processor to hub..."""       )
        model.push_to_hub(F"jozhang97/{model_name}"       )
        processor.push_to_hub(F"jozhang97/{model_name}"       )


if __name__ == "__main__":
    __lowerCAmelCase    :  Union[str, Any]										= argparse.ArgumentParser()

    parser.add_argument(
        '--model_name',
        type=str,
        default='deta-resnet-50',
        choices=['deta-resnet-50', 'deta-resnet-50-24-epochs'],
        help='Name of the model you\'d like to convert.',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path',
        default=None,
        type=str,
        help='Path to the folder to output PyTorch model.',
    )
    parser.add_argument(
        '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
    )
    __lowerCAmelCase    :  List[str]										= parser.parse_args()
    convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



import warnings

from ...utils import logging
from .image_processing_conditional_detr import ConditionalDetrImageProcessor


__lowerCAmelCase    :  Tuple										= logging.get_logger(__name__)
class 				lowerCamelCase			(							__snake_case					):






      def __init__(      self  ,		*__lowerCamelCase  ,		**__lowerCamelCase						)     ->				None:


          '''simple docstring'''







          warnings.warn(
              """The class ConditionalDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
              """ Please use ConditionalDetrImageProcessor instead."""  ,		__lowerCamelCase  ,		)
          super().__init__(*__lowerCamelCase  ,		**__lowerCamelCase						)


