'''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()
_A			:      Optional[Any]					      =				logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_A			:      Optional[Any]					      =				[]
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_						(					snake_case_       :       List[Any]						,		snake_case_       :       Optional[int]						,		snake_case_       :       Any      )					->     Dict:






 '''simple docstring'''

 __lowerCAmelCase											=						state_dict.pop(_SCREAMING_SNAKE_CASE      )
 __lowerCAmelCase											=						val







def      UpperCamelCase_						(					snake_case_       :       Optional[int]      )					->     Optional[int]:






 '''simple docstring'''

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

 return new_state_dict







def      UpperCamelCase_						(					snake_case_       :       str						,		snake_case_       :       Union[str, Any]=False      )					->     List[str]:






 '''simple docstring'''

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







def      UpperCamelCase_						(					)					->     List[str]:






 '''simple docstring'''

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

 return im







@torch.no_grad()
def      UpperCamelCase_						(					snake_case_       :       Dict						,		snake_case_       :       Tuple      )					->     Dict:






 '''simple docstring'''

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

 # load image processor
 __lowerCAmelCase											=						"coco_panoptic" if is_panoptic else "coco_detection"
 __lowerCAmelCase											=						ConditionalDetrImageProcessor(format=_SCREAMING_SNAKE_CASE      )

 # prepare image
 __lowerCAmelCase											=						prepare_img()
 __lowerCAmelCase											=						image_processor(images=_SCREAMING_SNAKE_CASE						,		return_tensors="""pt"""      )
 __lowerCAmelCase											=						encoding["pixel_values"]

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

 # load original model from torch hub
 __lowerCAmelCase											=						torch.hub.load("""DeppMeng/ConditionalDETR"""						,		_SCREAMING_SNAKE_CASE						,		pretrained=_SCREAMING_SNAKE_CASE      ).eval()
 __lowerCAmelCase											=						conditional_detr.state_dict()
 # rename keys
 for src, dest in rename_keys:
  if is_panoptic:
   __lowerCAmelCase											=						"conditional_detr." + src
  rename_key(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE      )
 __lowerCAmelCase											=						rename_backbone_keys(_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
 __lowerCAmelCase											=						"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"""      )
   ):
    __lowerCAmelCase											=						state_dict.pop(_SCREAMING_SNAKE_CASE      )
    __lowerCAmelCase											=						val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    __lowerCAmelCase											=						state_dict.pop(_SCREAMING_SNAKE_CASE      )
    __lowerCAmelCase											=						val
   elif key.startswith("""bbox_attention"""      ) or key.startswith("""mask_head"""      ):
    continue
   else:
    __lowerCAmelCase											=						state_dict.pop(_SCREAMING_SNAKE_CASE      )
    __lowerCAmelCase											=						val
  else:
   if not key.startswith("""class_labels_classifier"""      ) and not key.startswith("""bbox_predictor"""      ):
    __lowerCAmelCase											=						state_dict.pop(_SCREAMING_SNAKE_CASE      )
    __lowerCAmelCase											=						val
    # finally, create HuggingFace model and load state dict
 __lowerCAmelCase											=						ConditionalDetrForSegmentation(_SCREAMING_SNAKE_CASE      ) if is_panoptic else ConditionalDetrForObjectDetection(_SCREAMING_SNAKE_CASE      )
 model.load_state_dict(_SCREAMING_SNAKE_CASE      )
 model.eval()
 model.push_to_hub(repo_id=_SCREAMING_SNAKE_CASE						,		organization="""DepuMeng"""						,		commit_message="""Add model"""      )
 # verify our conversion
 __lowerCAmelCase											=						conditional_detr(_SCREAMING_SNAKE_CASE      )
 __lowerCAmelCase											=						model(_SCREAMING_SNAKE_CASE      )
 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(_SCREAMING_SNAKE_CASE      ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE      )
 model.save_pretrained(_SCREAMING_SNAKE_CASE      )
 image_processor.save_pretrained(_SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
 _A			:      List[Any]					      =				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.'''
 )
 _A			:      Optional[Any]					      =				parser.parse_args()
 convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)












import argparse
import os
import re


SCREAMING_SNAKE_CASE__       :      List[Any]  =   'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE__       :      Optional[int]  =   re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
SCREAMING_SNAKE_CASE__       :      Tuple  =   re.compile(r'\s*\(\s*"(\S[^"]+)"')

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = False    )	-> int:
       with open(_SCREAMING_SNAKE_CASE ,"r" ,encoding="utf-8"    ) as f:
              lowerCamelCase   :      List[Any]			 =					f.read()

       lowerCamelCase   :      str			 =					content.split("\n"    )
       lowerCamelCase   :      int			 =					[]
       lowerCamelCase   :      List[Any]			 =					0
       while line_idx < len(_SCREAMING_SNAKE_CASE    ):
              if _re_intro_mapping.search(lines[line_idx]    ) is not None:
                     lowerCamelCase   :      Optional[int]			 =					len(re.search(r"^(\s*)\S" ,lines[line_idx]    ).groups()[0]    ) + 8
                     # Start of a new mapping!
                     while not lines[line_idx].startswith(" " * indent + "("    ):
                            new_lines.append(lines[line_idx]    )
                            line_idx += 1

                     lowerCamelCase   :      Optional[int]			 =					[]
                     while lines[line_idx].strip() != "]":
                            # Blocks either fit in one line or not
                            if lines[line_idx].strip() == "(":
                                   lowerCamelCase   :      List[str]			 =					line_idx
                                   while not lines[line_idx].startswith(" " * indent + ")"    ):
                                          line_idx += 1
                                   blocks.append("\n".join(lines[start_idx : line_idx + 1]    )    )
                            else:
                                   blocks.append(lines[line_idx]    )
                            line_idx += 1

                     # Sort blocks by their identifiers
                     lowerCamelCase   :      Union[str, Any]			 =					sorted(_SCREAMING_SNAKE_CASE ,key=lambda _SCREAMING_SNAKE_CASE    : _re_identifier.search(_SCREAMING_SNAKE_CASE    ).groups()[0]    )
                     new_lines += blocks
              else:
                     new_lines.append(lines[line_idx]    )
                     line_idx += 1

       if overwrite:
              with open(_SCREAMING_SNAKE_CASE ,"w" ,encoding="utf-8"    ) as f:
                     f.write("\n".join(_SCREAMING_SNAKE_CASE    )    )
       elif "\n".join(_SCREAMING_SNAKE_CASE    ) != content:
              return True

def      A      (      _SCREAMING_SNAKE_CASE = False    )	-> List[str]:
       lowerCamelCase   :      str			 =					[os.path.join(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    ) for f in os.listdir(_SCREAMING_SNAKE_CASE    ) if f.endswith(".py"    )]
       lowerCamelCase   :      Union[str, Any]			 =					[sort_auto_mapping(_SCREAMING_SNAKE_CASE ,overwrite=_SCREAMING_SNAKE_CASE    ) for fname in fnames]

       if not overwrite and any(_SCREAMING_SNAKE_CASE    ):
              lowerCamelCase   :      str			 =					[f for f, d in zip(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    ) if d]
              raise ValueError(
                  f'''The following files have auto mappings that need sorting: {", ".join(_SCREAMING_SNAKE_CASE    )}. Run `make style` to fix'''
                  " this."    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      List[str]  =   argparse.ArgumentParser()
       parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
       SCREAMING_SNAKE_CASE__       :      List[str]  =   parser.parse_args()

       sort_all_auto_mappings(not args.check_only)






