



"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()
class  lowercase__  (       unittest.TestCase ):







       '''simple docstring'''


       def 	lowercase__							(				self     :    List[str]       )					->		Any:


             '''simple docstring'''




             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()



       @property
       def 	lowercase__							(				self     :    int       )					->		str:


             '''simple docstring'''




             UpperCAmelCase_     					=				1
             UpperCAmelCase_     					=				3
             UpperCAmelCase_     					=				(32, 32)

             UpperCAmelCase_     					=				floats_tensor((batch_size, num_channels) + sizes						,							rng=random.Random(0       )       ).to(_UpperCAmelCase       )
             return image



       @property
       def 	lowercase__							(				self     :    int       )					->		Tuple:


             '''simple docstring'''




             torch.manual_seed(0       )
             UpperCAmelCase_     					=				UNetaDConditionModel(
                 block_out_channels=(32, 64)						,							layers_per_block=2						,							sample_size=32						,							in_channels=4						,							out_channels=4						,							down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")						,							up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")						,							cross_attention_dim=32						,							)
             return model



       @property
       def 	lowercase__							(				self     :    Dict       )					->		Any:


             '''simple docstring'''




             torch.manual_seed(0       )
             UpperCAmelCase_     					=				AutoencoderKL(
                 block_out_channels=[32, 64]						,							in_channels=3						,							out_channels=3						,							down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]						,							up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]						,							latent_channels=4						,							)
             return model



       @property
       def 	lowercase__							(				self     :    str       )					->		Optional[Any]:


             '''simple docstring'''




             torch.manual_seed(0       )
             UpperCAmelCase_     					=				RobertaSeriesConfig(
                 hidden_size=32						,							project_dim=32						,							intermediate_size=37						,							layer_norm_eps=1e-05						,							num_attention_heads=4						,							num_hidden_layers=5						,							pad_token_id=1						,							vocab_size=5006						,							)
             return RobertaSeriesModelWithTransformation(_UpperCAmelCase       )



       @property
       def 	lowercase__							(				self     :    Optional[Any]       )					->		Dict:


             '''simple docstring'''




             def extract(*_UpperCAmelCase     :    Union[str, Any]						,							**_UpperCAmelCase     :    Union[str, Any]       ):
                   class  lowercase__  :







                          '''simple docstring'''


                          def __init__(				self     :    Tuple       )					->		Optional[int]:


                                '''simple docstring'''




                                UpperCAmelCase_     					=				torch.ones([0]       )







                          def 	lowercase__							(				self     :    Dict						,							_UpperCAmelCase     :    Union[str, Any]       )					->		str:


                                '''simple docstring'''




                                self.pixel_values.to(_UpperCAmelCase       )
                                return self



                   return Out()

             return extract



       def 	lowercase__							(				self     :    List[str]       )					->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				"cpu"  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase_     					=				self.dummy_cond_unet
             UpperCAmelCase_     					=				PNDMScheduler(skip_prk_steps=_UpperCAmelCase       )
             UpperCAmelCase_     					=				self.dummy_vae
             UpperCAmelCase_     					=				self.dummy_text_encoder
             UpperCAmelCase_     					=				XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"       )
             UpperCAmelCase_     					=				77

             UpperCAmelCase_     					=				self.dummy_image.to(_UpperCAmelCase       )
             UpperCAmelCase_     					=				init_image / 2 + 0.5

             # make sure here that pndm scheduler skips prk
             UpperCAmelCase_     					=				AltDiffusionImgaImgPipeline(
                 unet=_UpperCAmelCase						,							scheduler=_UpperCAmelCase						,							vae=_UpperCAmelCase						,							text_encoder=_UpperCAmelCase						,							tokenizer=_UpperCAmelCase						,							safety_checker=_UpperCAmelCase						,							feature_extractor=self.dummy_extractor						,							)
             UpperCAmelCase_     					=				VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor						,							do_normalize=_UpperCAmelCase       )
             UpperCAmelCase_     					=				alt_pipe.to(_UpperCAmelCase       )
             alt_pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

             UpperCAmelCase_     					=				"A painting of a squirrel eating a burger"
             UpperCAmelCase_     					=				torch.Generator(device=_UpperCAmelCase       ).manual_seed(0       )
             UpperCAmelCase_     					=				alt_pipe(
                 [prompt]						,							generator=_UpperCAmelCase						,							guidance_scale=6.0						,							num_inference_steps=2						,							output_type="np"						,							image=_UpperCAmelCase						,							)

             UpperCAmelCase_     					=				output.images

             UpperCAmelCase_     					=				torch.Generator(device=_UpperCAmelCase       ).manual_seed(0       )
             UpperCAmelCase_     					=				alt_pipe(
                 [prompt]						,							generator=_UpperCAmelCase						,							guidance_scale=6.0						,							num_inference_steps=2						,							output_type="np"						,							image=_UpperCAmelCase						,							return_dict=_UpperCAmelCase						,							)[0]

             UpperCAmelCase_     					=				image[0, -3:, -3:, -1]
             UpperCAmelCase_     					=				image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             UpperCAmelCase_     					=				np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]       )

             assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5e-3
             assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 5e-3



       @unittest.skipIf(torch_device != "cuda"						,							"This test requires a GPU"       )
       def 	lowercase__							(				self     :    int       )					->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.dummy_cond_unet
             UpperCAmelCase_     					=				PNDMScheduler(skip_prk_steps=_UpperCAmelCase       )
             UpperCAmelCase_     					=				self.dummy_vae
             UpperCAmelCase_     					=				self.dummy_text_encoder
             UpperCAmelCase_     					=				XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"       )
             UpperCAmelCase_     					=				77

             UpperCAmelCase_     					=				self.dummy_image.to(_UpperCAmelCase       )

             # put models in fp16
             UpperCAmelCase_     					=				unet.half()
             UpperCAmelCase_     					=				vae.half()
             UpperCAmelCase_     					=				bert.half()

             # make sure here that pndm scheduler skips prk
             UpperCAmelCase_     					=				AltDiffusionImgaImgPipeline(
                 unet=_UpperCAmelCase						,							scheduler=_UpperCAmelCase						,							vae=_UpperCAmelCase						,							text_encoder=_UpperCAmelCase						,							tokenizer=_UpperCAmelCase						,							safety_checker=_UpperCAmelCase						,							feature_extractor=self.dummy_extractor						,							)
             UpperCAmelCase_     					=				VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor						,							do_normalize=_UpperCAmelCase       )
             UpperCAmelCase_     					=				alt_pipe.to(_UpperCAmelCase       )
             alt_pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

             UpperCAmelCase_     					=				"A painting of a squirrel eating a burger"
             UpperCAmelCase_     					=				torch.manual_seed(0       )
             UpperCAmelCase_     					=				alt_pipe(
                 [prompt]						,							generator=_UpperCAmelCase						,							num_inference_steps=2						,							output_type="np"						,							image=_UpperCAmelCase						,							).images

             assert image.shape == (1, 32, 32, 3)







       @unittest.skipIf(torch_device != "cuda"						,							"This test requires a GPU"       )
       def 	lowercase__							(				self     :    Tuple       )					->		Any:


             '''simple docstring'''




             UpperCAmelCase_     					=				load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/img2img/sketch-mountains-input.jpg"       )
             # resize to resolution that is divisible by 8 but not 16 or 32
             UpperCAmelCase_     					=				init_image.resize((760, 504)       )

             UpperCAmelCase_     					=				"BAAI/AltDiffusion"
             UpperCAmelCase_     					=				AltDiffusionImgaImgPipeline.from_pretrained(
                 _UpperCAmelCase						,							safety_checker=_UpperCAmelCase						,							)
             pipe.to(_UpperCAmelCase       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )
             pipe.enable_attention_slicing()

             UpperCAmelCase_     					=				"A fantasy landscape, trending on artstation"

             UpperCAmelCase_     					=				torch.manual_seed(0       )
             UpperCAmelCase_     					=				pipe(
                 prompt=_UpperCAmelCase						,							image=_UpperCAmelCase						,							strength=0.75						,							guidance_scale=7.5						,							generator=_UpperCAmelCase						,							output_type="np"						,							)
             UpperCAmelCase_     					=				output.images[0]

             UpperCAmelCase_     					=				image[255:258, 383:386, -1]

             assert image.shape == (504, 760, 3)
             UpperCAmelCase_     					=				np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]       )

             assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2



@slow
@require_torch_gpu
class  lowercase__  (       unittest.TestCase ):







       '''simple docstring'''


       def 	lowercase__							(				self     :    Optional[Any]       )					->		Optional[Any]:


             '''simple docstring'''




             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()







       def 	lowercase__							(				self     :    Dict       )					->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_     					=				load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/img2img/sketch-mountains-input.jpg"       )
             UpperCAmelCase_     					=				init_image.resize((768, 512)       )
             UpperCAmelCase_     					=				load_numpy(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"       )

             UpperCAmelCase_     					=				"BAAI/AltDiffusion"
             UpperCAmelCase_     					=				AltDiffusionImgaImgPipeline.from_pretrained(
                 _UpperCAmelCase						,							safety_checker=_UpperCAmelCase						,							)
             pipe.to(_UpperCAmelCase       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )
             pipe.enable_attention_slicing()

             UpperCAmelCase_     					=				"A fantasy landscape, trending on artstation"

             UpperCAmelCase_     					=				torch.manual_seed(0       )
             UpperCAmelCase_     					=				pipe(
                 prompt=_UpperCAmelCase						,							image=_UpperCAmelCase						,							strength=0.75						,							guidance_scale=7.5						,							generator=_UpperCAmelCase						,							output_type="np"						,							)
             UpperCAmelCase_     					=				output.images[0]

             assert image.shape == (512, 768, 3)
             # img2img is flaky across GPUs even in fp32, so using MAE here
             assert np.abs(expected_image - image       ).max() < 1e-2












import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

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


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

# here we list all keys to be renamed (original name on the left, our name on the right)
_SCREAMING_SNAKE_CASE   =  []
for i in range(6):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append(
        (f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', f'''encoder.layers.{i}.self_attn.out_proj.weight''')
    )
    rename_keys.append(
        (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')
    )
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
    rename_keys.append(
        (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')
    )
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
    # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''decoder.layers.{i}.self_attn.out_proj.weight''')
    )
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')
    )
    rename_keys.append(
        (
            f'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
            f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
        )
    )
    rename_keys.append(
        (
            f'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
            f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
        )
    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight'''))
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias'''))
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight'''))
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias'''))
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')
    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
    )
    rename_keys.append(
        (f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
    )
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight'''))
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias'''))

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

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

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ("input_proj.weight", "input_projection.weight"),
        ("input_proj.bias", "input_projection.bias"),
        ("query_embed.weight", "query_position_embeddings.weight"),
        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
        ("class_embed.weight", "class_labels_classifier.weight"),
        ("class_embed.bias", "class_labels_classifier.bias"),
        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
        ("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"),
        ("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"),
        ("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"),
        ("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"),
        ("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"),
        ("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"),
        ("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"),
        ("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"),
        ("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"),
        ("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"),
    ]
)





def        _snake_case   (_snake_case				:       Optional[int]      ,    _snake_case				:       Dict      ,    _snake_case				:       Dict)					->	str:
   _lowercase              =state_dict.pop(_snake_case)
   _lowercase              =val





def        _snake_case   (_snake_case				:       Any)					->	Union[str, Any]:
   _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        _snake_case   (_snake_case				:       Tuple      ,    _snake_case				:       Tuple=False)					->	str:
   _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[:256, :]
      _lowercase              =in_proj_bias[:256]
      _lowercase              =in_proj_weight[256:512, :]
      _lowercase              =in_proj_bias[256:512]
      _lowercase              =in_proj_weight[-256:, :]
      _lowercase              =in_proj_bias[-256:]





def        _snake_case   ()					->	List[Any]:
   _lowercase              ='http://images.cocodataset.org/val2017/000000039769.jpg'
   _lowercase              =Image.open(requests.get(_snake_case      ,    stream=_snake_case).raw)

   return im





@torch.no_grad()
def        _snake_case   (_snake_case				:       Any      ,    _snake_case				:       int)					->	Union[str, Any]:
   _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              =250
   else:
      _lowercase              =91
      _lowercase              ='huggingface/label-files'
      _lowercase              ='coco-detection-id2label.json'
      _lowercase              =json.load(open(hf_hub_download(_snake_case      ,    _snake_case      ,    repo_type='dataset')      ,    'r'))
      _lowercase              ={int(_snake_case): 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=_snake_case)

   # prepare image
   _lowercase              =prepare_img()
   _lowercase              =image_processor(images=_snake_case      ,    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'      ,    _snake_case      ,    pretrained=_snake_case).eval()
   _lowercase              =conditional_detr.state_dict()
   # rename keys
   for src, dest in rename_keys:
      if is_panoptic:
         _lowercase              ='conditional_detr.' + src
      rename_key(_snake_case      ,    _snake_case      ,    _snake_case)
   _lowercase              =rename_backbone_keys(_snake_case)
   # query, key and value matrices need special treatment
   read_in_q_k_v(_snake_case      ,    is_panoptic=_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
   _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(_snake_case)
            _lowercase              =val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            _lowercase              =state_dict.pop(_snake_case)
            _lowercase              =val
         elif key.startswith('bbox_attention') or key.startswith('mask_head'):
            continue
         else:
            _lowercase              =state_dict.pop(_snake_case)
            _lowercase              =val
      else:
         if not key.startswith('class_labels_classifier') and not key.startswith('bbox_predictor'):
            _lowercase              =state_dict.pop(_snake_case)
            _lowercase              =val
    # finally, create HuggingFace model and load state dict
   _lowercase              =ConditionalDetrForSegmentation(_snake_case) if is_panoptic else ConditionalDetrForObjectDetection(_snake_case)
   model.load_state_dict(_snake_case)
   model.eval()
   model.push_to_hub(repo_id=_snake_case      ,    organization='DepuMeng'      ,    commit_message='Add model')
   # verify our conversion
   _lowercase              =conditional_detr(_snake_case)
   _lowercase              =model(_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(_snake_case).mkdir(exist_ok=_snake_case)
   model.save_pretrained(_snake_case)
   image_processor.save_pretrained(_snake_case)


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

    parser.add_argument(
        "--model_name",
        default="conditional_detr_resnet50",
        type=str,
        help="Name of the CONDITIONAL_DETR model you'd like to convert.",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
    )
    _SCREAMING_SNAKE_CASE   =  parser.parse_args()
    convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)


