
"""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 (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_            =    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
SCREAMING_SNAKE_CASE_            =    []
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}.cross_attn.out_proj.weight',
           F'decoder.layers.{i}.encoder_attn.out_proj.weight',
       )
   )
   rename_keys.append(
       (
           F'transformer.decoder.layers.{i}.cross_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'))

   # q, k, v projections in self/cross-attention in decoder for conditional DETR
   rename_keys.append(
       (F'transformer.decoder.layers.{i}.sa_qcontent_proj.weight', F'decoder.layers.{i}.sa_qcontent_proj.weight')
   )
   rename_keys.append(
       (F'transformer.decoder.layers.{i}.sa_kcontent_proj.weight', F'decoder.layers.{i}.sa_kcontent_proj.weight')
   )
   rename_keys.append(
       (F'transformer.decoder.layers.{i}.sa_qpos_proj.weight', F'decoder.layers.{i}.sa_qpos_proj.weight')
   )
   rename_keys.append(
       (F'transformer.decoder.layers.{i}.sa_kpos_proj.weight', F'decoder.layers.{i}.sa_kpos_proj.weight')
   )
   rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.weight', F'decoder.layers.{i}.sa_v_proj.weight'))
   rename_keys.append(
       (F'transformer.decoder.layers.{i}.ca_qcontent_proj.weight', F'decoder.layers.{i}.ca_qcontent_proj.weight')
   )
   # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
   rename_keys.append(
       (F'transformer.decoder.layers.{i}.ca_kcontent_proj.weight', F'decoder.layers.{i}.ca_kcontent_proj.weight')
   )
   rename_keys.append(
       (F'transformer.decoder.layers.{i}.ca_kpos_proj.weight', F'decoder.layers.{i}.ca_kpos_proj.weight')
   )
   rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.weight', F'decoder.layers.{i}.ca_v_proj.weight'))
   rename_keys.append(
       (F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight', F'decoder.layers.{i}.ca_qpos_sine_proj.weight')
   )

   rename_keys.append(
       (F'transformer.decoder.layers.{i}.sa_qcontent_proj.bias', F'decoder.layers.{i}.sa_qcontent_proj.bias')
   )
   rename_keys.append(
       (F'transformer.decoder.layers.{i}.sa_kcontent_proj.bias', F'decoder.layers.{i}.sa_kcontent_proj.bias')
   )
   rename_keys.append((F'transformer.decoder.layers.{i}.sa_qpos_proj.bias', F'decoder.layers.{i}.sa_qpos_proj.bias'))
   rename_keys.append((F'transformer.decoder.layers.{i}.sa_kpos_proj.bias', F'decoder.layers.{i}.sa_kpos_proj.bias'))
   rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.bias', F'decoder.layers.{i}.sa_v_proj.bias'))
   rename_keys.append(
       (F'transformer.decoder.layers.{i}.ca_qcontent_proj.bias', F'decoder.layers.{i}.ca_qcontent_proj.bias')
   )
   # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
   rename_keys.append(
       (F'transformer.decoder.layers.{i}.ca_kcontent_proj.bias', F'decoder.layers.{i}.ca_kcontent_proj.bias')
   )
   rename_keys.append((F'transformer.decoder.layers.{i}.ca_kpos_proj.bias', F'decoder.layers.{i}.ca_kpos_proj.bias'))
   rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.bias', F'decoder.layers.{i}.ca_v_proj.bias'))
   rename_keys.append(
       (F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias', F'decoder.layers.{i}.ca_qpos_sine_proj.bias')
   )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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'''),
        ('''transformer.decoder.ref_point_head.layers.0.weight''', '''decoder.ref_point_head.layers.0.weight'''),
        ('''transformer.decoder.ref_point_head.layers.0.bias''', '''decoder.ref_point_head.layers.0.bias'''),
        ('''transformer.decoder.ref_point_head.layers.1.weight''', '''decoder.ref_point_head.layers.1.weight'''),
        ('''transformer.decoder.ref_point_head.layers.1.bias''', '''decoder.ref_point_head.layers.1.bias'''),
        ('''transformer.decoder.query_scale.layers.0.weight''', '''decoder.query_scale.layers.0.weight'''),
        ('''transformer.decoder.query_scale.layers.0.bias''', '''decoder.query_scale.layers.0.bias'''),
        ('''transformer.decoder.query_scale.layers.1.weight''', '''decoder.query_scale.layers.1.weight'''),
        ('''transformer.decoder.query_scale.layers.1.bias''', '''decoder.query_scale.layers.1.bias'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.weight''', '''decoder.layers.0.ca_qpos_proj.weight'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.bias''', '''decoder.layers.0.ca_qpos_proj.bias'''),
    ]
)




def      lowercase__    (       lowerCAmelCase					:       List[Any]			,					lowerCAmelCase					:       Any			,					lowerCAmelCase					:       int			)			->   Optional[Any]:






    """simple docstring"""




    UpperCAmelCase          =       state_dict.pop(lowerCAmelCase			)
    UpperCAmelCase          =       val




def      lowercase__    (       lowerCAmelCase					:       Union[str, Any]			)			->   Any:






    """simple docstring"""




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

    return new_state_dict




def      lowercase__    (       lowerCAmelCase					:       List[str]			,					lowerCAmelCase					:       Dict=False			)			->   Tuple:






    """simple docstring"""




    UpperCAmelCase          =       ''
    if is_panoptic:
        UpperCAmelCase          =       'conditional_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:]




def      lowercase__    (       )			->   List[str]:






    """simple docstring"""




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

    return im




@torch.no_grad()
def      lowercase__    (       lowerCAmelCase					:       Union[str, Any]			,					lowerCAmelCase					:       str			)			->   List[Any]:






    """simple docstring"""




    UpperCAmelCase          =       ConditionalDetrConfig()
    # set backbone and dilation attributes
    if "resnet101" in model_name:
        UpperCAmelCase          =       'resnet101'
    if "dc5" in model_name:
        UpperCAmelCase          =       True
    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(lowerCAmelCase			,					lowerCAmelCase			,					repo_type='dataset'			)			,					'r'			)			)
        UpperCAmelCase          =       {int(lowerCAmelCase			): v for k, v in idalabel.items()}
        UpperCAmelCase          =       idalabel
        UpperCAmelCase          =       {v: k for k, v in idalabel.items()}

    # load image processor
    UpperCAmelCase          =       'coco_panoptic' if is_panoptic else 'coco_detection'
    UpperCAmelCase          =       ConditionalDetrImageProcessor(format=lowerCAmelCase			)

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

    logger.info(F"Converting model {model_name}..."			)

    # load original model from torch hub
    UpperCAmelCase          =       torch.hub.load('DeppMeng/ConditionalDETR'			,					lowerCAmelCase			,					pretrained=lowerCAmelCase			).eval()
    UpperCAmelCase          =       conditional_detr.state_dict()
    # rename keys
    for src, dest in rename_keys:
        if is_panoptic:
            UpperCAmelCase          =       'conditional_detr.' + src
        rename_key(lowerCAmelCase			,					lowerCAmelCase			,					lowerCAmelCase			)
    UpperCAmelCase          =       rename_backbone_keys(lowerCAmelCase			)
    # query, key and value matrices need special treatment
    read_in_q_k_v(lowerCAmelCase			,					is_panoptic=lowerCAmelCase			)
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    UpperCAmelCase          =       'conditional_detr.model.' if is_panoptic else 'model.'
    for key in state_dict.copy().keys():
        if is_panoptic:
            if (
                key.startswith('conditional_detr'			)
                and not key.startswith('class_labels_classifier'			)
                and not key.startswith('bbox_predictor'			)
            ):
                UpperCAmelCase          =       state_dict.pop(lowerCAmelCase			)
                UpperCAmelCase          =       val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                UpperCAmelCase          =       state_dict.pop(lowerCAmelCase			)
                UpperCAmelCase          =       val
            elif key.startswith('bbox_attention'			) or key.startswith('mask_head'			):
                continue
            else:
                UpperCAmelCase          =       state_dict.pop(lowerCAmelCase			)
                UpperCAmelCase          =       val
        else:
            if not key.startswith('class_labels_classifier'			) and not key.startswith('bbox_predictor'			):
                UpperCAmelCase          =       state_dict.pop(lowerCAmelCase			)
                UpperCAmelCase          =       val
    # finally, create HuggingFace model and load state dict
    UpperCAmelCase          =       ConditionalDetrForSegmentation(lowerCAmelCase			) if is_panoptic else ConditionalDetrForObjectDetection(lowerCAmelCase			)
    model.load_state_dict(lowerCAmelCase			)
    model.eval()
    model.push_to_hub(repo_id=lowerCAmelCase			,					organization='DepuMeng'			,					commit_message='Add model'			)
    # verify our conversion
    UpperCAmelCase          =       conditional_detr(lowerCAmelCase			)
    UpperCAmelCase          =       model(lowerCAmelCase			)
    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(lowerCAmelCase			).mkdir(exist_ok=lowerCAmelCase			)
    model.save_pretrained(lowerCAmelCase			)
    image_processor.save_pretrained(lowerCAmelCase			)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_            =    argparse.ArgumentParser()

   parser.add_argument(
       '''--model_name''',
       default='''conditional_detr_resnet50''',
       type=str,
       help='''Name of the CONDITIONAL_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.'''
   )
   SCREAMING_SNAKE_CASE_            =    parser.parse_args()
   convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)





"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_            =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            =    {
    '''kssteven/ibert-roberta-base''': '''https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large''': '''https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large-mnli''': (
        '''https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'''
    ),
}
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				Tuple				   =						"ibert"




      def __init__(				self ,							lowercase_=3_0_5_2_2 ,							lowercase_=7_6_8 ,							lowercase_=1_2 ,							lowercase_=1_2 ,							lowercase_=3_0_7_2 ,							lowercase_="gelu" ,							lowercase_=0.1 ,							lowercase_=0.1 ,							lowercase_=5_1_2 ,							lowercase_=2 ,							lowercase_=0.0_2 ,							lowercase_=1E-12 ,							lowercase_=1 ,							lowercase_=0 ,							lowercase_=2 ,							lowercase_="absolute" ,							lowercase_=False ,							lowercase_="none" ,							**lowercase_ ,							)    ->       Optional[int]:
          super().__init__(pad_token_id=lowercase_ ,							bos_token_id=lowercase_ ,							eos_token_id=lowercase_ ,							**lowercase_				)

          UpperCAmelCase          =       vocab_size
          UpperCAmelCase          =       hidden_size
          UpperCAmelCase          =       num_hidden_layers
          UpperCAmelCase          =       num_attention_heads
          UpperCAmelCase          =       hidden_act
          UpperCAmelCase          =       intermediate_size
          UpperCAmelCase          =       hidden_dropout_prob
          UpperCAmelCase          =       attention_probs_dropout_prob
          UpperCAmelCase          =       max_position_embeddings
          UpperCAmelCase          =       type_vocab_size
          UpperCAmelCase          =       initializer_range
          UpperCAmelCase          =       layer_norm_eps
          UpperCAmelCase          =       position_embedding_type
          UpperCAmelCase          =       quant_mode
          UpperCAmelCase          =       force_dequant

class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):




      @property
      def      a_   (				self				)    ->       Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
              UpperCAmelCase          =       {0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
              UpperCAmelCase          =       {0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ]				)



