



import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple

import numpy as np

from . import residue_constants


a__    =	Mapping[str, np.ndarray]
a__    =	Mapping[str, Any]  # Is a nested dict.
a__    =	0.01







@dataclasses.dataclass(frozen=SCREAMING_SNAKE_CASE_  )
class 		snake_case						:



   '''simple docstring'''






   snake_case_					: np.ndarray  # [num_res, num_atom_type, 3]

   # Amino-acid type for each residue represented as an integer between 0 and
   # 20, where 20 is 'X'.
   snake_case_					: np.ndarray  # [num_res]

   # Binary float mask to indicate presence of a particular atom. 1.0 if an atom
   # is present and 0.0 if not. This should be used for loss masking.
   snake_case_					: np.ndarray  # [num_res, num_atom_type]

   # Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
   snake_case_					: np.ndarray  # [num_res]

   # B-factors, or temperature factors, of each residue (in sq. angstroms units),
   # representing the displacement of the residue from its ground truth mean
   # value.
   snake_case_					: np.ndarray  # [num_res, num_atom_type]

   # Chain indices for multi-chain predictions
   snake_case_					: Optional[np.ndarray]          =		None

   # Optional remark about the protein. Included as a comment in output PDB
   # files
   snake_case_					: Optional[str]          =		None

   # Templates used to generate this protein (prediction-only)
   snake_case_					: Optional[Sequence[str]]          =		None

   # Chain corresponding to each parent
   snake_case_					: Optional[Sequence[int]]          =		None



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	str				)  ->     Protein:
 _snake_case    :  List[Any]					=					R"""(\[[A-Z]+\]\n)"""
 _snake_case    :  List[str]					=					[tag.strip() for tag in re.split(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				) if len(SCREAMING_SNAKE_CASE__				) > 0]
 _snake_case    :  Iterator[Tuple[str, List[str]]]					=					zip(tags[0::2]				,       [l.split("""\n"""				) for l in tags[1::2]]				)

 _snake_case    :  List[str]					=					["N", "CA", "C"]
 _snake_case    :  Any					=					None
 _snake_case    :  List[Any]					=					None
 _snake_case    :  Any					=					None
 for g in groups:
  if "[PRIMARY]" == g[0]:
   _snake_case    :  Optional[int]					=					g[1][0].strip()
   for i in range(len(SCREAMING_SNAKE_CASE__				)				):
    if seq[i] not in residue_constants.restypes:
     _snake_case    :  Tuple					=					"""X"""  # FIXME: strings are immutable
   _snake_case    :  Optional[int]					=					np.array(
       [residue_constants.restype_order.get(SCREAMING_SNAKE_CASE__				,       residue_constants.restype_num				) for res_symbol in seq]				)
  elif "[TERTIARY]" == g[0]:
   _snake_case    :  List[List[float]]					=					[]
   for axis in range(3				):
    tertiary.append(list(map(SCREAMING_SNAKE_CASE__				,       g[1][axis].split()				)				)				)
   _snake_case    :  Dict					=					np.array(SCREAMING_SNAKE_CASE__				)
   _snake_case    :  Optional[int]					=					np.zeros((len(tertiary[0]				) // 3, residue_constants.atom_type_num, 3)				).astype(np.floataa				)
   for i, atom in enumerate(SCREAMING_SNAKE_CASE__				):
    _snake_case    :  Any					=					np.transpose(tertiary_np[:, i::3]				)
   atom_positions *= PICO_TO_ANGSTROM
  elif "[MASK]" == g[0]:
   _snake_case    :  str					=					np.array(list(map({"""-""": 0, """+""": 1}.get				,       g[1][0].strip()				)				)				)
   _snake_case    :  Tuple					=					np.zeros(
       (
           len(SCREAMING_SNAKE_CASE__				),
           residue_constants.atom_type_num,
       )				).astype(np.floataa				)
   for i, atom in enumerate(SCREAMING_SNAKE_CASE__				):
    _snake_case    :  Tuple					=					1
   atom_mask *= mask[..., None]

 assert aatype is not None

 return Protein(
     atom_positions=SCREAMING_SNAKE_CASE__				,       atom_mask=SCREAMING_SNAKE_CASE__				,       aatype=SCREAMING_SNAKE_CASE__				,       residue_index=np.arange(len(SCREAMING_SNAKE_CASE__				)				)				,       b_factors=SCREAMING_SNAKE_CASE__				,       )



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Protein				,       SCREAMING_SNAKE_CASE__       :	int = 0				)  ->     List[str]:
 _snake_case    :  List[str]					=					[]

 _snake_case    :  Union[str, Any]					=					prot.remark
 if remark is not None:
  pdb_headers.append(F'''REMARK {remark}'''				)

 _snake_case    :  List[Any]					=					prot.parents
 _snake_case    :  Optional[int]					=					prot.parents_chain_index
 if parents is not None and parents_chain_index is not None:
  _snake_case    :  str					=					[p for i, p in zip(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				) if i == chain_id]

 if parents is None or len(SCREAMING_SNAKE_CASE__				) == 0:
  _snake_case    :  List[str]					=					["""N/A"""]

 pdb_headers.append(F'''PARENT {' '.join(SCREAMING_SNAKE_CASE__				)}'''				)

 return pdb_headers



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Protein				,       SCREAMING_SNAKE_CASE__       :	str				)  ->     str:
 _snake_case    :  List[str]					=					[]
 _snake_case    :  List[Any]					=					pdb_str.split("""\n"""				)

 _snake_case    :  List[str]					=					prot.remark
 if remark is not None:
  out_pdb_lines.append(F'''REMARK {remark}'''				)

 _snake_case    :  List[List[str]]
 if prot.parents is not None and len(prot.parents				) > 0:
  _snake_case    :  Tuple					=					[]
  if prot.parents_chain_index is not None:
   _snake_case    :  Dict[str, List[str]]					=					{}
   for p, i in zip(prot.parents				,       prot.parents_chain_index				):
    parent_dict.setdefault(str(SCREAMING_SNAKE_CASE__				)				,       []				)
    parent_dict[str(SCREAMING_SNAKE_CASE__				)].append(SCREAMING_SNAKE_CASE__				)

   _snake_case    :  str					=					max([int(SCREAMING_SNAKE_CASE__				) for chain_idx in parent_dict]				)
   for i in range(max_idx + 1				):
    _snake_case    :  Optional[Any]					=					parent_dict.get(str(SCREAMING_SNAKE_CASE__				)				,       ["""N/A"""]				)
    parents_per_chain.append(SCREAMING_SNAKE_CASE__				)
  else:
   parents_per_chain.append(list(prot.parents				)				)
 else:
  _snake_case    :  List[Any]					=					[["""N/A"""]]

 def make_parent_line(SCREAMING_SNAKE_CASE__       :	Sequence[str]				) -> str:
  return F'''PARENT {' '.join(SCREAMING_SNAKE_CASE__				)}'''

 out_pdb_lines.append(make_parent_line(parents_per_chain[0]				)				)

 _snake_case    :  Optional[int]					=					0
 for i, l in enumerate(SCREAMING_SNAKE_CASE__				):
  if "PARENT" not in l and "REMARK" not in l:
   out_pdb_lines.append(SCREAMING_SNAKE_CASE__				)
  if "TER" in l and "END" not in lines[i + 1]:
   chain_counter += 1
   if not chain_counter >= len(SCREAMING_SNAKE_CASE__				):
    _snake_case    :  Optional[Any]					=					parents_per_chain[chain_counter]
   else:
    _snake_case    :  Any					=					["""N/A"""]

   out_pdb_lines.append(make_parent_line(SCREAMING_SNAKE_CASE__				)				)

 return "\n".join(SCREAMING_SNAKE_CASE__				)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Protein				)  ->     str:
 _snake_case    :  Dict					=					residue_constants.restypes + ["""X"""]

 def res_atoa(SCREAMING_SNAKE_CASE__       :	int				) -> str:
  return residue_constants.restype_atoa.get(restypes[r]				,       """UNK"""				)

 _snake_case    :  str					=					residue_constants.atom_types

 _snake_case    :  List[str]					=					[]

 _snake_case    :  str					=					prot.atom_mask
 _snake_case    :  str					=					prot.aatype
 _snake_case    :  Optional[int]					=					prot.atom_positions
 _snake_case    :  str					=					prot.residue_index.astype(np.intaa				)
 _snake_case    :  List[Any]					=					prot.b_factors
 _snake_case    :  List[Any]					=					prot.chain_index

 if np.any(aatype > residue_constants.restype_num				):
  raise ValueError("""Invalid aatypes."""				)

 _snake_case    :  Optional[int]					=					get_pdb_headers(SCREAMING_SNAKE_CASE__				)
 if len(SCREAMING_SNAKE_CASE__				) > 0:
  pdb_lines.extend(SCREAMING_SNAKE_CASE__				)

 _snake_case    :  int					=					aatype.shape[0]
 _snake_case    :  List[Any]					=					1
 _snake_case    :  List[str]					=					0
 _snake_case    :  Dict					=					string.ascii_uppercase
 _snake_case    :  Any					=					None
 # Add all atom sites.
 for i in range(SCREAMING_SNAKE_CASE__				):
  _snake_case    :  Any					=					res_atoa(aatype[i]				)
  for atom_name, pos, mask, b_factor in zip(SCREAMING_SNAKE_CASE__				,       atom_positions[i]				,       atom_mask[i]				,       b_factors[i]				):
   if mask < 0.5:
    continue

   _snake_case    :  str					=					"""ATOM"""
   _snake_case    :  Optional[int]					=					atom_name if len(SCREAMING_SNAKE_CASE__				) == 4 else F''' {atom_name}'''
   _snake_case    :  Optional[Any]					=					""""""
   _snake_case    :  Dict					=					""""""
   _snake_case    :  Optional[Any]					=					1.0_0
   _snake_case    :  Dict					=					atom_name[0]  # Protein supports only C, N, O, S, this works.
   _snake_case    :  int					=					""""""

   _snake_case    :  Tuple					=					"""A"""
   if chain_index is not None:
    _snake_case    :  List[str]					=					chain_tags[chain_index[i]]

   # PDB is a columnar format, every space matters here!
   _snake_case    :  List[str]					=					(
       F'''{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}'''
       F'''{res_name_a:>3} {chain_tag:>1}'''
       F'''{residue_index[i]:>4}{insertion_code:>1}   '''
       F'''{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}'''
       F'''{occupancy:>6.2f}{b_factor:>6.2f}          '''
       F'''{element:>2}{charge:>2}'''
   )
   pdb_lines.append(SCREAMING_SNAKE_CASE__				)
   atom_index += 1

  _snake_case    :  Any					=					i == n - 1
  if chain_index is not None:
   if i != n - 1 and chain_index[i + 1] != prev_chain_index:
    _snake_case    :  str					=					True
    _snake_case    :  str					=					chain_index[i + 1]

  if should_terminate:
   # Close the chain.
   _snake_case    :  str					=					"""TER"""
   _snake_case    :  Dict					=					(
       F'''{chain_end:<6}{atom_index:>5}      {res_atoa(aatype[i]				):>3} {chain_tag:>1}{residue_index[i]:>4}'''
   )
   pdb_lines.append(SCREAMING_SNAKE_CASE__				)
   atom_index += 1

   if i != n - 1:
    # "prev" is a misnomer here. This happens at the beginning of
    # each new chain.
    pdb_lines.extend(get_pdb_headers(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)				)

 pdb_lines.append("""END"""				)
 pdb_lines.append(""""""				)
 return "\n".join(SCREAMING_SNAKE_CASE__				)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Protein				)  ->     np.ndarray:
 return residue_constants.STANDARD_ATOM_MASK[prot.aatype]



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	FeatureDict				,       SCREAMING_SNAKE_CASE__       :	ModelOutput				,       SCREAMING_SNAKE_CASE__       :	Optional[np.ndarray] = None				,       SCREAMING_SNAKE_CASE__       :	Optional[np.ndarray] = None				,       SCREAMING_SNAKE_CASE__       :	Optional[str] = None				,       SCREAMING_SNAKE_CASE__       :	Optional[Sequence[str]] = None				,       SCREAMING_SNAKE_CASE__       :	Optional[Sequence[int]] = None				,       )  ->     Protein:
 return Protein(
     aatype=features["""aatype"""]				,       atom_positions=result["""final_atom_positions"""]				,       atom_mask=result["""final_atom_mask"""]				,       residue_index=features["""residue_index"""] + 1				,       b_factors=b_factors if b_factors is not None else np.zeros_like(result["""final_atom_mask"""]				)				,       chain_index=SCREAMING_SNAKE_CASE__				,       remark=SCREAMING_SNAKE_CASE__				,       parents=SCREAMING_SNAKE_CASE__				,       parents_chain_index=SCREAMING_SNAKE_CASE__				,       )





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()
a__    =	logging.get_logger(__name__)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[Any]				)  ->     Optional[Any]:
 # initialize config
 if "resnet-50" in model_name:
  _snake_case    :  List[Any]					=					ResNetConfig.from_pretrained("""microsoft/resnet-50"""				)
 elif "resnet-101" in model_name:
  _snake_case    :  Any					=					ResNetConfig.from_pretrained("""microsoft/resnet-101"""				)
 else:
  raise ValueError("""Model name should include either resnet50 or resnet101"""				)

 _snake_case    :  Union[str, Any]					=					DetrConfig(use_timm_backbone=SCREAMING_SNAKE_CASE__				,       backbone_config=SCREAMING_SNAKE_CASE__				)

 # set label attributes
 _snake_case    :  List[str]					=					"""panoptic""" in model_name
 if is_panoptic:
  _snake_case    :  Optional[int]					=					250
 else:
  _snake_case    :  Optional[int]					=					91
  _snake_case    :  Optional[Any]					=					"""huggingface/label-files"""
  _snake_case    :  Optional[int]					=					"""coco-detection-id2label.json"""
  _snake_case    :  str					=					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       repo_type="""dataset"""				)				,       """r"""				)				)
  _snake_case    :  Dict					=					{int(SCREAMING_SNAKE_CASE__				): v for k, v in idalabel.items()}
  _snake_case    :  List[str]					=					idalabel
  _snake_case    :  str					=					{v: k for k, v in idalabel.items()}

 return config, is_panoptic



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Tuple				)  ->     Optional[int]:
 # here we list all keys to be renamed (original name on the left, our name on the right)
 _snake_case    :  Optional[Any]					=					[]

 # 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       lowercase       (   SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	str				)  ->     Optional[Any]:
 _snake_case    :  str					=					state_dict.pop(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  str					=					val



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	str=False				)  ->     Union[str, Any]:
 _snake_case    :  Optional[Any]					=					""""""
 if is_panoptic:
  _snake_case    :  Optional[Any]					=					"""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)
  _snake_case    :  Any					=					state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''				)
  _snake_case    :  List[str]					=					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
  _snake_case    :  Optional[Any]					=					in_proj_weight[:256, :]
  _snake_case    :  Any					=					in_proj_bias[:256]
  _snake_case    :  List[Any]					=					in_proj_weight[256:512, :]
  _snake_case    :  Optional[int]					=					in_proj_bias[256:512]
  _snake_case    :  int					=					in_proj_weight[-256:, :]
  _snake_case    :  Union[str, Any]					=					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
  _snake_case    :  Tuple					=					state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''				)
  _snake_case    :  Dict					=					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
  _snake_case    :  Optional[int]					=					in_proj_weight[:256, :]
  _snake_case    :  Optional[int]					=					in_proj_bias[:256]
  _snake_case    :  Any					=					in_proj_weight[256:512, :]
  _snake_case    :  int					=					in_proj_bias[256:512]
  _snake_case    :  int					=					in_proj_weight[-256:, :]
  _snake_case    :  Tuple					=					in_proj_bias[-256:]
  # read in weights + bias of input projection layer of cross-attention
  _snake_case    :  List[str]					=					state_dict.pop(
      F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''				)
  _snake_case    :  Optional[Any]					=					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
  _snake_case    :  int					=					in_proj_weight_cross_attn[:256, :]
  _snake_case    :  Tuple					=					in_proj_bias_cross_attn[:256]
  _snake_case    :  int					=					in_proj_weight_cross_attn[256:512, :]
  _snake_case    :  Tuple					=					in_proj_bias_cross_attn[256:512]
  _snake_case    :  Dict					=					in_proj_weight_cross_attn[-256:, :]
  _snake_case    :  Union[str, Any]					=					in_proj_bias_cross_attn[-256:]



def       lowercase       (   )  ->     Optional[Any]:
 _snake_case    :  Dict					=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 _snake_case    :  Dict					=					Image.open(requests.get(SCREAMING_SNAKE_CASE__				,       stream=SCREAMING_SNAKE_CASE__				).raw				)

 return im



@torch.no_grad()
def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Any				,       SCREAMING_SNAKE_CASE__       :	Dict=None				,       SCREAMING_SNAKE_CASE__       :	Any=False				)  ->     List[Any]:
 _snake_case	,    _snake_case    :  Union[str, Any]					=					get_detr_config(SCREAMING_SNAKE_CASE__				)

 # load original model from torch hub
 _snake_case    :  Dict					=					{
     """detr-resnet-50""": """detr_resnet50""",
     """detr-resnet-101""": """detr_resnet101""",
 }
 logger.info(F'''Converting model {model_name}...'''				)
 _snake_case    :  List[Any]					=					torch.hub.load("""facebookresearch/detr"""				,       model_name_to_original_name[model_name]				,       pretrained=SCREAMING_SNAKE_CASE__				).eval()
 _snake_case    :  Optional[int]					=					detr.state_dict()
 # rename keys
 for src, dest in create_rename_keys(SCREAMING_SNAKE_CASE__				):
  if is_panoptic:
   _snake_case    :  int					=					"""detr.""" + src
  rename_key(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       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
 _snake_case    :  List[Any]					=					"""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"""				)
   ):
    _snake_case    :  List[str]					=					state_dict.pop(SCREAMING_SNAKE_CASE__				)
    _snake_case    :  Optional[int]					=					val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    _snake_case    :  Tuple					=					state_dict.pop(SCREAMING_SNAKE_CASE__				)
    _snake_case    :  List[str]					=					val
   elif key.startswith("""bbox_attention"""				) or key.startswith("""mask_head"""				):
    continue
   else:
    _snake_case    :  Optional[int]					=					state_dict.pop(SCREAMING_SNAKE_CASE__				)
    _snake_case    :  Dict					=					val
  else:
   if not key.startswith("""class_labels_classifier"""				) and not key.startswith("""bbox_predictor"""				):
    _snake_case    :  Union[str, Any]					=					state_dict.pop(SCREAMING_SNAKE_CASE__				)
    _snake_case    :  Tuple					=					val

    # finally, create HuggingFace model and load state dict
 _snake_case    :  int					=					DetrForSegmentation(SCREAMING_SNAKE_CASE__				) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE__				)
 model.load_state_dict(SCREAMING_SNAKE_CASE__				)
 model.eval()

 # verify our conversion on an image
 _snake_case    :  List[Any]					=					"""coco_panoptic""" if is_panoptic else """coco_detection"""
 _snake_case    :  int					=					DetrImageProcessor(format=SCREAMING_SNAKE_CASE__				)

 _snake_case    :  int					=					processor(images=prepare_img()				,       return_tensors="""pt"""				)
 _snake_case    :  str					=					encoding["""pixel_values"""]

 _snake_case    :  Tuple					=					detr(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Optional[int]					=					model(SCREAMING_SNAKE_CASE__				)

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

 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__":
  a__    =	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.""")
  a__    =	parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
