





'''simple docstring'''





from __future__ import annotations

from random import random
from typing import Generic, TypeVar

UpperCAmelCase_  							=				TypeVar('KT')
UpperCAmelCase_  							=				TypeVar('VT')






class 					lowercase__ (			Generic[KT, VT]       ):

   '''simple docstring'''


   def __init__(					self,	__magic_name__ = "root",	__magic_name__ = None							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   key
       UpperCamelCase__							:       Dict      =   value
       UpperCamelCase__							:       list[Node[KT, VT]]      =   []




   def __repr__(					self							)				->   str:






       """simple docstring"""
       return f"Node({self.key}: {self.value})"




   @property
   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       return len(self.forward							)






class 					lowercase__ (			Generic[KT, VT]       ):

   '''simple docstring'''


   def __init__(					self,	__magic_name__ = 0.5,	__magic_name__ = 16							)				->   Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:       Node[KT, VT]      =   Node[KT, VT]()
       UpperCamelCase__							:       Optional[int]      =   0
       UpperCamelCase__							:       int      =   p
       UpperCamelCase__							:       Any      =   max_level




   def __str__(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       Optional[Any]      =   list(self							)

       if len(__magic_name__							) == 0:
           return f"SkipList(level={self.level})"

       UpperCamelCase__							:       List[Any]      =   max((len(str(__magic_name__							)							) for item in items),	default=4							)
       UpperCamelCase__							:       List[Any]      =   max(__magic_name__,	4							) + 4

       UpperCamelCase__							:       List[str]      =   self.head
       UpperCamelCase__							:       str      =   []

       UpperCamelCase__							:       Any      =   node.forward.copy()
       lines.append(f"[{node.key}]".ljust(__magic_name__,	'''-'''							) + '''* ''' * len(__magic_name__							)							)
       lines.append(''' ''' * label_size + '''| ''' * len(__magic_name__							)							)

       while len(node.forward							) != 0:
           UpperCamelCase__							:       Optional[int]      =   node.forward[0]

           lines.append(
               f"[{node.key}]".ljust(__magic_name__,	'''-'''							)
               + ''' '''.join(str(n.key							) if n.key == node.key else '''|''' for n in forwards							)							)
           lines.append(''' ''' * label_size + '''| ''' * len(__magic_name__							)							)
           UpperCamelCase__							:       Any      =   node.forward

       lines.append('''None'''.ljust(__magic_name__							) + '''* ''' * len(__magic_name__							)							)
       return f"SkipList(level={self.level})\n" + "\n".join(__magic_name__							)




   def __iter__(					self							)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__							:       str      =   self.head

       while len(node.forward							) != 0:
           yield node.forward[0].key
           UpperCamelCase__							:       Optional[Any]      =   node.forward[0]




   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   1
       while random() < self.p and level < self.max_level:
           level += 1

       return level




   def    UpperCamelCase__						(					self,	__magic_name__							)				->   tuple[Node[KT, VT] | None, list[Node[KT, VT]]]:






       """simple docstring"""
       UpperCamelCase__							:       Optional[Any]      =   []

       UpperCamelCase__							:       List[str]      =   self.head

       for i in reversed(range(self.level							)							):
           # i < node.level - When node level is lesser than `i` decrement `i`.
           # node.forward[i].key < key - Jumping to node with key value higher
           #                             or equal to searched key would result
           #                             in skipping searched key.
           while i < node.level and node.forward[i].key < key:
               UpperCamelCase__							:       str      =   node.forward[i]
           # Each leftmost node (relative to searched node) will potentially have to
           # be updated.
           update_vector.append(__magic_name__							)

       update_vector.reverse()  # Note that we were inserting values in reverse order.

       # len(node.forward) != 0 - If current node doesn't contain any further
       #                          references then searched key is not present.
       # node.forward[0].key == key - Next node key should be equal to search key
       #                              if key is present.
       if len(node.forward							) != 0 and node.forward[0].key == key:
           return node.forward[0], update_vector
       else:
           return None, update_vector




   def    UpperCamelCase__						(					self,	__magic_name__							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   self._locate_node(__magic_name__							)

       if node is not None:
           for i, update_node in enumerate(__magic_name__							):
               # Remove or replace all references to removed node.
               if update_node.level > i and update_node.forward[i].key == key:
                   if node.level > i:
                       UpperCamelCase__							:       Any      =   node.forward[i]
                   else:
                       UpperCamelCase__							:       str      =   update_node.forward[:i]




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   self._locate_node(__magic_name__							)
       if node is not None:
           UpperCamelCase__							:       int      =   value
       else:
           UpperCamelCase__							:       Tuple      =   self.random_level()

           if level > self.level:
               # After level increase we have to add additional nodes to head.
               for _ in range(self.level - 1,	__magic_name__							):
                   update_vector.append(self.head							)
               UpperCamelCase__							:       Any      =   level

           UpperCamelCase__							:       List[Any]      =   Node(__magic_name__,	__magic_name__							)

           for i, update_node in enumerate(update_vector[:level]							):
               # Change references to pass through new node.
               if update_node.level > i:
                   new_node.forward.append(update_node.forward[i]							)

               if update_node.level < i + 1:
                   update_node.forward.append(__magic_name__							)
               else:
                   UpperCamelCase__							:       Dict      =   new_node




   def    UpperCamelCase__						(					self,	__magic_name__							)				->   VT | None:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   self._locate_node(__magic_name__							)

       if node is not None:
           return node.value

       return None




def 	lowerCAmelCase_					(						)   ->			Optional[Any]:
    UpperCamelCase__							:       Optional[int]      =   SkipList()
    skip_list.insert('''Key1'''	,     3   )
    skip_list.insert('''Key2'''	,     12   )
    skip_list.insert('''Key3'''	,     41   )
    skip_list.insert('''Key4'''	,     -19   )

    UpperCamelCase__							:       Optional[Any]      =   skip_list.head
    UpperCamelCase__							:       Tuple      =   {}
    while node.level != 0:
        UpperCamelCase__							:       Optional[int]      =   node.forward[0]
        UpperCamelCase__							:       Optional[Any]      =   node.value

    assert len(__UpperCAmelCase   ) == 4
    assert all_values["Key1"] == 3
    assert all_values["Key2"] == 12
    assert all_values["Key3"] == 41
    assert all_values["Key4"] == -19




def 	lowerCAmelCase_					(						)   ->			Any:
    UpperCamelCase__							:       Union[str, Any]      =   SkipList()
    skip_list.insert('''Key1'''	,     10   )
    skip_list.insert('''Key1'''	,     12   )

    skip_list.insert('''Key5'''	,     7   )
    skip_list.insert('''Key7'''	,     10   )
    skip_list.insert('''Key10'''	,     5   )

    skip_list.insert('''Key7'''	,     7   )
    skip_list.insert('''Key5'''	,     5   )
    skip_list.insert('''Key10'''	,     10   )

    UpperCamelCase__							:       List[str]      =   skip_list.head
    UpperCamelCase__							:       Optional[Any]      =   {}
    while node.level != 0:
        UpperCamelCase__							:       List[str]      =   node.forward[0]
        UpperCamelCase__							:       Union[str, Any]      =   node.value

    if len(__UpperCAmelCase   ) != 4:
        print()
    assert len(__UpperCAmelCase   ) == 4
    assert all_values["Key1"] == 12
    assert all_values["Key7"] == 7
    assert all_values["Key5"] == 5
    assert all_values["Key10"] == 10




def 	lowerCAmelCase_					(						)   ->			Optional[int]:
    UpperCamelCase__							:       Dict      =   SkipList()
    assert skip_list.find('''Some key'''   ) is None




def 	lowerCAmelCase_					(						)   ->			Tuple:
    UpperCamelCase__							:       Optional[Any]      =   SkipList()

    skip_list.insert('''Key2'''	,     20   )
    assert skip_list.find('''Key2'''   ) == 20

    skip_list.insert('''Some Key'''	,     10   )
    skip_list.insert('''Key2'''	,     8   )
    skip_list.insert('''V'''	,     13   )

    assert skip_list.find('''Y'''   ) is None
    assert skip_list.find('''Key2'''   ) == 8
    assert skip_list.find('''Some Key'''   ) == 10
    assert skip_list.find('''V'''   ) == 13




def 	lowerCAmelCase_					(						)   ->			List[Any]:
    UpperCamelCase__							:       int      =   SkipList()
    skip_list.delete('''Some key'''   )

    assert len(skip_list.head.forward   ) == 0




def 	lowerCAmelCase_					(						)   ->			List[Any]:
    UpperCamelCase__							:       Optional[int]      =   SkipList()

    skip_list.insert('''Key1'''	,     12   )
    skip_list.insert('''V'''	,     13   )
    skip_list.insert('''X'''	,     14   )
    skip_list.insert('''Key2'''	,     15   )

    skip_list.delete('''V'''   )
    skip_list.delete('''Key2'''   )

    assert skip_list.find('''V'''   ) is None
    assert skip_list.find('''Key2'''   ) is None




def 	lowerCAmelCase_					(						)   ->			List[Any]:
    UpperCamelCase__							:       int      =   SkipList()

    skip_list.insert('''Key1'''	,     12   )
    skip_list.insert('''V'''	,     13   )
    skip_list.insert('''X'''	,     14   )
    skip_list.insert('''Key2'''	,     15   )

    skip_list.delete('''V'''   )
    assert skip_list.find('''V'''   ) is None
    assert skip_list.find('''X'''   ) == 14
    assert skip_list.find('''Key1'''   ) == 12
    assert skip_list.find('''Key2'''   ) == 15

    skip_list.delete('''X'''   )
    assert skip_list.find('''V'''   ) is None
    assert skip_list.find('''X'''   ) is None
    assert skip_list.find('''Key1'''   ) == 12
    assert skip_list.find('''Key2'''   ) == 15

    skip_list.delete('''Key1'''   )
    assert skip_list.find('''V'''   ) is None
    assert skip_list.find('''X'''   ) is None
    assert skip_list.find('''Key1'''   ) is None
    assert skip_list.find('''Key2'''   ) == 15

    skip_list.delete('''Key2'''   )
    assert skip_list.find('''V'''   ) is None
    assert skip_list.find('''X'''   ) is None
    assert skip_list.find('''Key1'''   ) is None
    assert skip_list.find('''Key2'''   ) is None




def 	lowerCAmelCase_					(						)   ->			Union[str, Any]:
    UpperCamelCase__							:       str      =   SkipList()

    skip_list.insert('''Key1'''	,     12   )
    skip_list.insert('''V'''	,     13   )
    skip_list.insert('''X'''	,     142   )
    skip_list.insert('''Key2'''	,     15   )

    skip_list.delete('''X'''   )

    def traverse_keys(__UpperCAmelCase:    int   ):
        yield node.key
        for forward_node in node.forward:
            yield from traverse_keys(__UpperCAmelCase   )

    assert len(set(traverse_keys(skip_list.head   )   )   ) == 4




def 	lowerCAmelCase_					(						)   ->			List[Any]:
    def is_sorted(__UpperCAmelCase:    Union[str, Any]   ):
        return all(next_item >= item for item, next_item in zip(__UpperCAmelCase	,     lst[1:]   )   )

    UpperCamelCase__							:       Any      =   SkipList()
    for i in range(10   ):
        skip_list.insert(__UpperCAmelCase	,     __UpperCAmelCase   )
    assert is_sorted(list(__UpperCAmelCase   )   )
    skip_list.delete(5   )
    skip_list.delete(8   )
    skip_list.delete(2   )
    assert is_sorted(list(__UpperCAmelCase   )   )
    skip_list.insert(-12	,     -12   )
    skip_list.insert(77	,     77   )
    assert is_sorted(list(__UpperCAmelCase   )   )




def 	lowerCAmelCase_					(						)   ->			Optional[Any]:
    for _ in range(100   ):
        # Repeat test 100 times due to the probabilistic nature of skip list
        # random values == random bugs
        test_insert()
        test_insert_overrides_existing_value()

        test_searching_empty_list_returns_none()
        test_search()

        test_deleting_item_from_empty_list_do_nothing()
        test_deleted_items_are_not_founded_by_find_method()
        test_delete_removes_only_given_key()
        test_delete_doesnt_leave_dead_nodes()

        test_iter_always_yields_sorted_values()




def 	lowerCAmelCase_					(						)   ->			int:
    UpperCamelCase__							:       Union[str, Any]      =   SkipList()
    skip_list.insert(2	,     '''2'''   )
    skip_list.insert(4	,     '''4'''   )
    skip_list.insert(6	,     '''4'''   )
    skip_list.insert(4	,     '''5'''   )
    skip_list.insert(8	,     '''4'''   )
    skip_list.insert(9	,     '''4'''   )

    skip_list.delete(4   )

    print(__UpperCAmelCase   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()








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 	lowerCAmelCase_					(						__UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    List[Any]	,     __UpperCAmelCase:    Dict   )   ->			Dict:
    UpperCamelCase__							:       List[str]      =   state_dict.pop(__UpperCAmelCase   )
    UpperCamelCase__							:       Union[str, Any]      =   val




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Dict   )   ->			List[str]:
    UpperCamelCase__							:       List[str]      =   OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            UpperCamelCase__							:       Optional[Any]      =   key.replace('''backbone.0.body'''	,     '''backbone.conv_encoder.model'''   )
            UpperCamelCase__							:       List[str]      =   value
        else:
            UpperCamelCase__							:       Optional[int]      =   value

    return new_state_dict




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[int]	,     __UpperCAmelCase:    Optional[int]=False   )   ->			Optional[int]:
    UpperCamelCase__							:       List[str]      =   ''''''
    if is_panoptic:
        UpperCamelCase__							:       List[Any]      =   '''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__							:       List[Any]      =   state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"   )
        UpperCamelCase__							:       int      =   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__							:       Any      =   in_proj_weight[:256, :]
        UpperCamelCase__							:       List[str]      =   in_proj_bias[:256]
        UpperCamelCase__							:       Optional[int]      =   in_proj_weight[256:512, :]
        UpperCamelCase__							:       List[str]      =   in_proj_bias[256:512]
        UpperCamelCase__							:       Tuple      =   in_proj_weight[-256:, :]
        UpperCamelCase__							:       List[Any]      =   in_proj_bias[-256:]




def 	lowerCAmelCase_					(						)   ->			int:
    UpperCamelCase__							:       Any      =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCamelCase__							:       Optional[int]      =   Image.open(requests.get(__UpperCAmelCase	,     stream=__UpperCAmelCase   ).raw   )

    return im




@torch.no_grad()
def 	lowerCAmelCase_					(						__UpperCAmelCase:    Any	,     __UpperCAmelCase:    Any   )   ->			Optional[int]:
    UpperCamelCase__							:       Tuple      =   ConditionalDetrConfig()
    # set backbone and dilation attributes
    if "resnet101" in model_name:
        UpperCamelCase__							:       Optional[Any]      =   '''resnet101'''
    if "dc5" in model_name:
        UpperCamelCase__							:       Dict      =   True
    UpperCamelCase__							:       Union[str, Any]      =   '''panoptic''' in model_name
    if is_panoptic:
        UpperCamelCase__							:       Any      =   250
    else:
        UpperCamelCase__							:       int      =   91
        UpperCamelCase__							:       List[str]      =   '''huggingface/label-files'''
        UpperCamelCase__							:       List[str]      =   '''coco-detection-id2label.json'''
        UpperCamelCase__							:       Tuple      =   json.load(open(hf_hub_download(__UpperCAmelCase	,     __UpperCAmelCase	,     repo_type='''dataset'''   )	,     '''r'''   )   )
        UpperCamelCase__							:       List[str]      =   {int(__UpperCAmelCase   ): v for k, v in idalabel.items()}
        UpperCamelCase__							:       Dict      =   idalabel
        UpperCamelCase__							:       Dict      =   {v: k for k, v in idalabel.items()}

    # load image processor
    UpperCamelCase__							:       int      =   '''coco_panoptic''' if is_panoptic else '''coco_detection'''
    UpperCamelCase__							:       str      =   ConditionalDetrImageProcessor(format=__UpperCAmelCase   )

    # prepare image
    UpperCamelCase__							:       Tuple      =   prepare_img()
    UpperCamelCase__							:       List[str]      =   image_processor(images=__UpperCAmelCase	,     return_tensors='''pt'''   )
    UpperCamelCase__							:       Union[str, Any]      =   encoding['''pixel_values''']

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

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


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)

