

'''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()
UpperCAmelCase       =     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase       =     []
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  __UpperCamelCase							(		lowercase__  :     Union[str, Any],    lowercase__  :     Dict,    lowercase__  :     Any       ):






     '''simple docstring'''





     __lowercase							=state_dict.pop(lowercase__       )
     __lowercase							=val

def  __UpperCamelCase							(		lowercase__  :     int       ):






     '''simple docstring'''





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

     return new_state_dict

def  __UpperCamelCase							(		lowercase__  :     Any,    lowercase__  :     Dict=False       ):






     '''simple docstring'''





     __lowercase							=''
     if is_panoptic:
          __lowercase							='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)
          __lowercase							=state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''       )
          __lowercase							=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
          __lowercase							=in_proj_weight[:2_56, :]
          __lowercase							=in_proj_bias[:2_56]
          __lowercase							=in_proj_weight[2_56:5_12, :]
          __lowercase							=in_proj_bias[2_56:5_12]
          __lowercase							=in_proj_weight[-2_56:, :]
          __lowercase							=in_proj_bias[-2_56:]

def  __UpperCamelCase							(		):






     '''simple docstring'''





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

     return im

@torch.no_grad()
def  __UpperCamelCase							(		lowercase__  :     List[Any],    lowercase__  :     Tuple       ):






     '''simple docstring'''





     __lowercase							=ConditionalDetrConfig()
     # set backbone and dilation attributes
     if "resnet101" in model_name:
          __lowercase							='resnet101'
     if "dc5" in model_name:
          __lowercase							=True
     __lowercase							='panoptic' in model_name
     if is_panoptic:
          __lowercase							=2_50
     else:
          __lowercase							=91
          __lowercase							='huggingface/label-files'
          __lowercase							='coco-detection-id2label.json'
          __lowercase							=json.load(open(hf_hub_download(lowercase__,    lowercase__,    repo_type='dataset'       ),    'r'       )       )
          __lowercase							={int(lowercase__       ): v for k, v in idalabel.items()}
          __lowercase							=idalabel
          __lowercase							={v: k for k, v in idalabel.items()}

     # load image processor
     __lowercase							='coco_panoptic' if is_panoptic else 'coco_detection'
     __lowercase							=ConditionalDetrImageProcessor(format=lowercase__       )

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

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

     # load original model from torch hub
     __lowercase							=torch.hub.load('DeppMeng/ConditionalDETR',    lowercase__,    pretrained=lowercase__       ).eval()
     __lowercase							=conditional_detr.state_dict()
     # rename keys
     for src, dest in rename_keys:
          if is_panoptic:
               __lowercase							='conditional_detr.' + src
          rename_key(lowercase__,    lowercase__,    lowercase__       )
     __lowercase							=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
     __lowercase							='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'       )
               ):
                    __lowercase							=state_dict.pop(lowercase__       )
                    __lowercase							=val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    __lowercase							=state_dict.pop(lowercase__       )
                    __lowercase							=val
               elif key.startswith('bbox_attention'       ) or key.startswith('mask_head'       ):
                    continue
               else:
                    __lowercase							=state_dict.pop(lowercase__       )
                    __lowercase							=val
          else:
               if not key.startswith('class_labels_classifier'       ) and not key.startswith('bbox_predictor'       ):
                    __lowercase							=state_dict.pop(lowercase__       )
                    __lowercase							=val
    # finally, create HuggingFace model and load state dict
     __lowercase							=ConditionalDetrForSegmentation(lowercase__       ) if is_panoptic else ConditionalDetrForObjectDetection(lowercase__       )
     model.load_state_dict(lowercase__       )
     model.eval()
     model.push_to_hub(repo_id=lowercase__,    organization='DepuMeng',    commit_message='Add model'       )
     # verify our conversion
     __lowercase							=conditional_detr(lowercase__       )
     __lowercase							=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__":
       UpperCAmelCase       =     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.'''
       )
       UpperCAmelCase       =     parser.parse_args()
       convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)






'''simple docstring'''

import unittest

from transformers import DonutProcessor


lowerCamelCase							:   Tuple  						=	'naver-clova-ix/donut-base'
class   __lowerCAmelCase						(unittest.TestCase     ):





      '''simple docstring'''






      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             lowercase__      			= DonutProcessor.from_pretrained(UpperCamelCase )






      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







             lowercase__      			= {
                 '''name''': '''John Doe''',
                 '''age''': '''99''',
                 '''city''': '''Atlanta''',
                 '''state''': '''GA''',
                 '''zip''': '''30301''',
                 '''phone''': '''123-4567''',
                 '''nicknames''': [{'''nickname''': '''Johnny'''}, {'''nickname''': '''JD'''}],
             }

             lowercase__      			= (
                 '''<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>'''
                 '''<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>'''
                 '''<s_nicknames><s_nickname>Johnny</s_nickname>'''
                 '''<sep/><s_nickname>JD</s_nickname></s_nicknames>'''
             )
             lowercase__      			= self.processor.tokenajson(UpperCamelCase )

             self.assertDictEqual(UpperCamelCase   ,						UpperCamelCase )




