





from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


__lowercase  :   Union[str, Any]						      =logging.get_logger(__name__)  # pylint: disable=invalid-name

__lowercase  :   Dict						      ="\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")\n        >>> pipe_prior.to(\"cuda\")\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")\n        >>> pipe.to(\"cuda\")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save(\"cat.png\")\n        ```\n"




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__=8  ):






   '''simple docstring'''






   UpperCAmelCase_											=height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   UpperCAmelCase_											=width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor





class      A       (	_UpperCAmelCase						):



      def __init__(	self:						Optional[int]		,  _lowerCAmelCase:						UNetaDConditionModel		,  _lowerCAmelCase:						DDPMScheduler		,  _lowerCAmelCase:						VQModel		,  )						->    Tuple:
         '''simple docstring'''

         super().__init__()

         self.register_modules(
             unet=lowerCamelCase_		,  scheduler=lowerCamelCase_		,  movq=lowerCamelCase_		,  )
         UpperCAmelCase_											=2 ** (len(self.movq.config.block_out_channels       ) - 1)



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						Union[str, Any]		,  _lowerCAmelCase:						Optional[int]		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						Union[str, Any]		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						int       )						->    Optional[int]:
         '''simple docstring'''

         if latents is None:
            UpperCAmelCase_											=randn_tensor(lowerCamelCase_		,  generator=lowerCamelCase_		,  device=lowerCamelCase_		,  dtype=lowerCamelCase_       )
         else:
            if latents.shape != shape:
               raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'       )
            UpperCAmelCase_											=latents.to(lowerCamelCase_       )

         UpperCAmelCase_											=latents * scheduler.init_noise_sigma
         return latents



      def 	lowerCAmelCase__    (	self:						Optional[int]		,  _lowerCAmelCase:						Optional[Any]=0       )						->    List[Any]:
         '''simple docstring'''

         if is_accelerate_available():
            from accelerate import cpu_offload
         else:
            raise ImportError("Please install accelerate via `pip install accelerate`"       )

         UpperCAmelCase_											=torch.device(F'cuda:{gpu_id}'       )

         UpperCAmelCase_											=[
             self.unet,
             self.movq,
         ]
         for cpu_offloaded_model in models:
            if cpu_offloaded_model is not None:
               cpu_offload(lowerCamelCase_		,  lowerCamelCase_       )



      def 	lowerCAmelCase__    (	self:						Tuple		,  _lowerCAmelCase:						Union[str, Any]=0       )						->    Union[str, Any]:
         '''simple docstring'''

         if is_accelerate_available() and is_accelerate_version(">="		,  "0.17.0.dev0"       ):
            from accelerate import cpu_offload_with_hook
         else:
            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."       )

         UpperCAmelCase_											=torch.device(F'cuda:{gpu_id}'       )

         if self.device.type != "cpu":
            self.to("cpu"		,  silence_dtype_warnings=lowerCamelCase_       )
            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

         UpperCAmelCase_											=None
         for cpu_offloaded_model in [self.unet, self.movq]:
            UpperCAmelCase_											=cpu_offload_with_hook(lowerCamelCase_		,  lowerCamelCase_		,  prev_module_hook=lowerCamelCase_       )

         # We'll offload the last model manually.
         UpperCAmelCase_											=hook



      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    Dict:
         '''simple docstring'''

         if not hasattr(self.unet		,  "_hf_hook"       ):
            return self.device
         for module in self.unet.modules():
            if (
                hasattr(lowerCamelCase_		,  "_hf_hook"       )
                and hasattr(module._hf_hook		,  "execution_device"       )
                and module._hf_hook.execution_device is not None
            ):
               return torch.device(module._hf_hook.execution_device       )
         return self.device






      @torch.no_grad()
      @replace_example_docstring(lowerCamelCase_       )
      def __call__(	self:						Union[str, Any]		,  _lowerCAmelCase:						Union[torch.FloatTensor, List[torch.FloatTensor]]		,  _lowerCAmelCase:						Union[torch.FloatTensor, List[torch.FloatTensor]]		,  _lowerCAmelCase:						int = 512		,  _lowerCAmelCase:						int = 512		,  _lowerCAmelCase:						int = 100		,  _lowerCAmelCase:						float = 4.0		,  _lowerCAmelCase:						int = 1		,  _lowerCAmelCase:						Optional[Union[torch.Generator, List[torch.Generator]]] = None		,  _lowerCAmelCase:						Optional[torch.FloatTensor] = None		,  _lowerCAmelCase:						Optional[str] = "pil"		,  _lowerCAmelCase:						bool = True		,  )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											=self._execution_device

         UpperCAmelCase_											=guidance_scale > 1.0

         if isinstance(lowerCamelCase_		,  lowerCamelCase_       ):
            UpperCAmelCase_											=torch.cat(lowerCamelCase_		,  dim=0       )
         UpperCAmelCase_											=image_embeds.shape[0] * num_images_per_prompt
         if isinstance(lowerCamelCase_		,  lowerCamelCase_       ):
            UpperCAmelCase_											=torch.cat(lowerCamelCase_		,  dim=0       )

         if do_classifier_free_guidance:
            UpperCAmelCase_											=image_embeds.repeat_interleave(lowerCamelCase_		,  dim=0       )
            UpperCAmelCase_											=negative_image_embeds.repeat_interleave(lowerCamelCase_		,  dim=0       )

         UpperCAmelCase_											=torch.cat([negative_image_embeds, image_embeds]		,  dim=0       ).to(dtype=self.unet.dtype		,  device=lowerCamelCase_       )

         self.scheduler.set_timesteps(lowerCamelCase_		,  device=lowerCamelCase_       )
         UpperCAmelCase_											=self.scheduler.timesteps

         UpperCAmelCase_											=self.unet.config.in_channels

         UpperCAmelCase_											=downscale_height_and_width(lowerCamelCase_		,  lowerCamelCase_		,  self.movq_scale_factor       )

         # create initial latent
         UpperCAmelCase_											=self.prepare_latents(
             (batch_size, num_channels_latents, height, width)		,  image_embeds.dtype		,  lowerCamelCase_		,  lowerCamelCase_		,  lowerCamelCase_		,  self.scheduler		,  )

         for i, t in enumerate(self.progress_bar(lowerCamelCase_       )       ):
            # expand the latents if we are doing classifier free guidance
            UpperCAmelCase_											=torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

            UpperCAmelCase_											={'''image_embeds''': image_embeds}
            UpperCAmelCase_											=self.unet(
                sample=lowerCamelCase_		,  timestep=lowerCamelCase_		,  encoder_hidden_states=lowerCamelCase_		,  added_cond_kwargs=lowerCamelCase_		,  return_dict=lowerCamelCase_		,  )[0]

            if do_classifier_free_guidance:
               UpperCAmelCase_											=noise_pred.split(latents.shape[1]		,  dim=1       )
               UpperCAmelCase_											=noise_pred.chunk(2       )
               UpperCAmelCase_											=variance_pred.chunk(2       )
               UpperCAmelCase_											=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
               UpperCAmelCase_											=torch.cat([noise_pred, variance_pred_text]		,  dim=1       )

            if not (
                hasattr(self.scheduler.config		,  "variance_type"       )
                and self.scheduler.config.variance_type in ["learned", "learned_range"]
            ):
               UpperCAmelCase_											=noise_pred.split(latents.shape[1]		,  dim=1       )

            # compute the previous noisy sample x_t -> x_t-1
            UpperCAmelCase_											=self.scheduler.step(
                lowerCamelCase_		,  lowerCamelCase_		,  lowerCamelCase_		,  generator=lowerCamelCase_		,  )[0]
         # post-processing
         UpperCAmelCase_											=self.movq.decode(lowerCamelCase_		,  force_not_quantize=lowerCamelCase_       )['''sample''']

         if output_type not in ["pt", "np", "pil"]:
            raise ValueError(F'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'       )

         if output_type in ["np", "pil"]:
            UpperCAmelCase_											=image * 0.5 + 0.5
            UpperCAmelCase_											=image.clamp(0		,  1       )
            UpperCAmelCase_											=image.cpu().permute(0		,  2		,  3		,  1       ).float().numpy()

         if output_type == "pil":
            UpperCAmelCase_											=self.numpy_to_pil(lowerCamelCase_       )

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=lowerCamelCase_       )






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

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowercase  :   str						      =[]
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     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=state_dict.pop(lowercase__  )
   UpperCAmelCase_											=val




def     a__ (					lowercase__  ):






   '''simple docstring'''






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

   return new_state_dict




def     a__ (					lowercase__  ,    lowercase__=False  ):






   '''simple docstring'''






   UpperCAmelCase_											=""
   if is_panoptic:
      UpperCAmelCase_											="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_											=state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'  )
      UpperCAmelCase_											=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_											=in_proj_weight[:2_5_6, :]
      UpperCAmelCase_											=in_proj_bias[:2_5_6]
      UpperCAmelCase_											=in_proj_weight[2_5_6:5_1_2, :]
      UpperCAmelCase_											=in_proj_bias[2_5_6:5_1_2]
      UpperCAmelCase_											=in_proj_weight[-2_5_6:, :]
      UpperCAmelCase_											=in_proj_bias[-2_5_6:]




def     a__ (					):






   '''simple docstring'''






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

   return im




@torch.no_grad()
def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=ConditionalDetrConfig()
   # set backbone and dilation attributes
   if "resnet101" in model_name:
      UpperCAmelCase_											="resnet101"
   if "dc5" in model_name:
      UpperCAmelCase_											=True
   UpperCAmelCase_											="panoptic" in model_name
   if is_panoptic:
      UpperCAmelCase_											=2_5_0
   else:
      UpperCAmelCase_											=9_1
      UpperCAmelCase_											="huggingface/label-files"
      UpperCAmelCase_											="coco-detection-id2label.json"
      UpperCAmelCase_											=json.load(open(hf_hub_download(lowercase__  ,    lowercase__  ,    repo_type="dataset"  )  ,    "r"  )  )
      UpperCAmelCase_											={int(lowercase__  ): v for k, v in idalabel.items()}
      UpperCAmelCase_											=idalabel
      UpperCAmelCase_											={v: k for k, v in idalabel.items()}

   # load image processor
   UpperCAmelCase_											="coco_panoptic" if is_panoptic else "coco_detection"
   UpperCAmelCase_											=ConditionalDetrImageProcessor(format=lowercase__  )

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

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

   # load original model from torch hub
   UpperCAmelCase_											=torch.hub.load("DeppMeng/ConditionalDETR"  ,    lowercase__  ,    pretrained=lowercase__  ).eval()
   UpperCAmelCase_											=conditional_detr.state_dict()
   # rename keys
   for src, dest in rename_keys:
      if is_panoptic:
         UpperCAmelCase_											="conditional_detr." + src
      rename_key(lowercase__  ,    lowercase__  ,    lowercase__  )
   UpperCAmelCase_											=rename_backbone_keys(lowercase__  )
   # query, key and value matrices need special treatment
   read_in_q_k_v(lowercase__  ,    is_panoptic=lowercase__  )
   # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
   UpperCAmelCase_											="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_											=state_dict.pop(lowercase__  )
            UpperCAmelCase_											=val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            UpperCAmelCase_											=state_dict.pop(lowercase__  )
            UpperCAmelCase_											=val
         elif key.startswith("bbox_attention"  ) or key.startswith("mask_head"  ):
            continue
         else:
            UpperCAmelCase_											=state_dict.pop(lowercase__  )
            UpperCAmelCase_											=val
      else:
         if not key.startswith("class_labels_classifier"  ) and not key.startswith("bbox_predictor"  ):
            UpperCAmelCase_											=state_dict.pop(lowercase__  )
            UpperCAmelCase_											=val
    # finally, create HuggingFace model and load state dict
   UpperCAmelCase_											=ConditionalDetrForSegmentation(lowercase__  ) if is_panoptic else ConditionalDetrForObjectDetection(lowercase__  )
   model.load_state_dict(lowercase__  )
   model.eval()
   model.push_to_hub(repo_id=lowercase__  ,    organization="DepuMeng"  ,    commit_message="Add model"  )
   # verify our conversion
   UpperCAmelCase_											=conditional_detr(lowercase__  )
   UpperCAmelCase_											=model(lowercase__  )
   assert torch.allclose(outputs.logits  ,    original_outputs["pred_logits"]  ,    atol=1E-4  )
   assert torch.allclose(outputs.pred_boxes  ,    original_outputs["pred_boxes"]  ,    atol=1E-4  )
   if is_panoptic:
      assert torch.allclose(outputs.pred_masks  ,    original_outputs["pred_masks"]  ,    atol=1E-4  )

   # Save model and image processor
   logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'  )
   Path(lowercase__  ).mkdir(exist_ok=lowercase__  )
   model.save_pretrained(lowercase__  )
   image_processor.save_pretrained(lowercase__  )


if __name__ == "__main__":
     __lowercase  :   Dict						      =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."""
     )
     __lowercase  :   Any						      =parser.parse_args()
     convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)




