




import flax.linen as nn
import jax
import jax.numpy as jnp



class       a					(       nn.Module      ):

     '''simple docstring'''





     lowerCAmelCase    :	int
     lowerCAmelCase    :	jnp.dtype       		=  jnp.floataa
     def   lowerCamelCase_       (     self       :      str    ):
         UpperCAmelCase_										=					nn.Conv(
             self.out_channels ,   kernel_size=(3, 3) ,   strides=(1, 1) ,   padding=((1, 1), (1, 1)) ,   dtype=self.dtype ,   )




     def __call__(     self       :      Union[str, Any] ,   __snake_case       :      int    ):
         UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_										=					hidden_states.shape
         UpperCAmelCase_										=					jax.image.resize(
             __snake_case ,   shape=(batch, height * 2, width * 2, channels) ,   method='''nearest''' ,   )
         UpperCAmelCase_										=					self.conv(__snake_case    )
         return hidden_states



class       a					(       nn.Module      ):

     '''simple docstring'''





     lowerCAmelCase    :	int
     lowerCAmelCase    :	jnp.dtype       		=  jnp.floataa
     def   lowerCamelCase_       (     self       :      int    ):
         UpperCAmelCase_										=					nn.Conv(
             self.out_channels ,   kernel_size=(3, 3) ,   strides=(2, 2) ,   padding=((1, 1), (1, 1)) ,   dtype=self.dtype ,   )




     def __call__(     self       :      List[str] ,   __snake_case       :      Union[str, Any]    ):
         # pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
         # hidden_states = jnp.pad(hidden_states, pad_width=pad)
         UpperCAmelCase_										=					self.conv(__snake_case    )
         return hidden_states



class       a					(       nn.Module      ):

     '''simple docstring'''





     lowerCAmelCase    :	int
     lowerCAmelCase    :	int       		=  None
     lowerCAmelCase    :	float       		=  0.0
     lowerCAmelCase    :	bool       		=  None
     lowerCAmelCase    :	jnp.dtype       		=  jnp.floataa
     def   lowerCamelCase_       (     self       :      List[str]    ):
         UpperCAmelCase_										=					self.in_channels if self.out_channels is None else self.out_channels

         UpperCAmelCase_										=					nn.GroupNorm(num_groups=32 ,   epsilon=1E-5    )
         UpperCAmelCase_										=					nn.Conv(
             __snake_case ,   kernel_size=(3, 3) ,   strides=(1, 1) ,   padding=((1, 1), (1, 1)) ,   dtype=self.dtype ,   )

         UpperCAmelCase_										=					nn.Dense(__snake_case ,   dtype=self.dtype    )

         UpperCAmelCase_										=					nn.GroupNorm(num_groups=32 ,   epsilon=1E-5    )
         UpperCAmelCase_										=					nn.Dropout(self.dropout_prob    )
         UpperCAmelCase_										=					nn.Conv(
             __snake_case ,   kernel_size=(3, 3) ,   strides=(1, 1) ,   padding=((1, 1), (1, 1)) ,   dtype=self.dtype ,   )

         UpperCAmelCase_										=					self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

         UpperCAmelCase_										=					None
         if use_nin_shortcut:
             UpperCAmelCase_										=					nn.Conv(
                 __snake_case ,   kernel_size=(1, 1) ,   strides=(1, 1) ,   padding='''VALID''' ,   dtype=self.dtype ,   )




     def __call__(     self       :      Tuple ,   __snake_case       :      Tuple ,   __snake_case       :      Optional[int] ,   __snake_case       :      Any=True    ):
         UpperCAmelCase_										=					hidden_states
         UpperCAmelCase_										=					self.norma(__snake_case    )
         UpperCAmelCase_										=					nn.swish(__snake_case    )
         UpperCAmelCase_										=					self.conva(__snake_case    )

         UpperCAmelCase_										=					self.time_emb_proj(nn.swish(__snake_case    )    )
         UpperCAmelCase_										=					jnp.expand_dims(jnp.expand_dims(__snake_case ,   1    ) ,   1    )
         UpperCAmelCase_										=					hidden_states + temb

         UpperCAmelCase_										=					self.norma(__snake_case    )
         UpperCAmelCase_										=					nn.swish(__snake_case    )
         UpperCAmelCase_										=					self.dropout(__snake_case ,   __snake_case    )
         UpperCAmelCase_										=					self.conva(__snake_case    )

         if self.conv_shortcut is not None:
             UpperCAmelCase_										=					self.conv_shortcut(__snake_case    )

         return hidden_states + residual











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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Tuple				)  ->							Dict:
    # initialize config
    if "resnet-50" in model_name:
        UpperCAmelCase_										=					ResNetConfig.from_pretrained('''microsoft/resnet-50'''				)
    elif "resnet-101" in model_name:
        UpperCAmelCase_										=					ResNetConfig.from_pretrained('''microsoft/resnet-101'''				)
    else:
        raise ValueError('''Model name should include either resnet50 or resnet101'''				)

    UpperCAmelCase_										=					DetrConfig(use_timm_backbone=__UpperCamelCase	,					backbone_config=__UpperCamelCase				)

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

    return config, is_panoptic




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						List[str]				)  ->							Union[str, Any]:
    # here we list all keys to be renamed (original name on the left, our name on the right)
    UpperCAmelCase_										=					[]

    # 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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[Any]	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						Optional[int]				)  ->							Union[str, Any]:
    UpperCAmelCase_										=					state_dict.pop(__UpperCamelCase				)
    UpperCAmelCase_										=					val




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




def  SCREAMING_SNAKE_CASE     (	)  ->							int:
    UpperCAmelCase_										=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCAmelCase_										=					Image.open(requests.get(__UpperCamelCase	,					stream=__UpperCamelCase				).raw				)

    return im




@torch.no_grad()
def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Tuple	,					__UpperCamelCase      :						Any=None	,					__UpperCamelCase      :						Optional[Any]=False				)  ->							Optional[Any]:
    UpperCAmelCase_			,							UpperCAmelCase_										=					get_detr_config(__UpperCamelCase				)

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

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

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

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

    UpperCAmelCase_										=					detr(__UpperCamelCase				)
    UpperCAmelCase_										=					model(__UpperCamelCase				)

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

    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)





