





from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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,
)


_UpperCAmelCase										=					logging.get_logger(__name__)  # pylint: disable=invalid-name

_UpperCAmelCase										=					'\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_=8	)  ->		Tuple:
       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




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_=512       ,		UpperCamelCase_=512	)  ->		Dict:
       UpperCamelCase_ 							=	pil_image.resize((w, h)       ,		resample=Image.BICUBIC       ,		reducing_gap=1	)
       UpperCamelCase_ 							=	np.array(pil_image.convert("RGB"	)	)
       UpperCamelCase_ 							=	arr.astype(np.floataa	) / 1_27.5 - 1
       UpperCamelCase_ 							=	np.transpose(UpperCamelCase_       ,		[2, 0, 1]	)
       UpperCamelCase_ 							=	torch.from_numpy(UpperCamelCase_	).unsqueeze(0	)
       return image







class 				_UpperCamelCase       (						lowerCAmelCase_  ):

       def __init__(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			UNetaDConditionModel       , _SCREAMING_SNAKE_CASE:			DDPMScheduler       , _SCREAMING_SNAKE_CASE:			VQModel       , )							->			Union[str, Any]:




              """simple docstring"""


              super().__init__()

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

       def lowercase			(		self:			Any       , _SCREAMING_SNAKE_CASE:			List[str]       , _SCREAMING_SNAKE_CASE:			Tuple       , _SCREAMING_SNAKE_CASE:			Any		)							->			List[str]:




              """simple docstring"""


              UpperCamelCase_ 							=	min(int(num_inference_steps * strength		)       , _SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	max(num_inference_steps - init_timestep       , 0		)
              UpperCamelCase_ 							=	self.scheduler.timesteps[t_start:]

              return timesteps, num_inference_steps - t_start

       def lowercase			(		self:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			str       , _SCREAMING_SNAKE_CASE:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Tuple       , _SCREAMING_SNAKE_CASE:			Optional[int]       , _SCREAMING_SNAKE_CASE:			List[Any]=None		)							->			str:




              """simple docstring"""


              if not isinstance(_SCREAMING_SNAKE_CASE       , (torch.Tensor, PIL.Image.Image, list)		):
                     raise ValueError(
                         f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_SCREAMING_SNAKE_CASE		)}'''		)

              UpperCamelCase_ 							=	image.to(device=_SCREAMING_SNAKE_CASE       , dtype=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	batch_size * num_images_per_prompt

              if image.shape[1] == 4:
                     UpperCamelCase_ 							=	image

              else:
                     if isinstance(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		) and len(_SCREAMING_SNAKE_CASE		) != batch_size:
                            raise ValueError(
                                f'''You have passed a list of generators of length {len(_SCREAMING_SNAKE_CASE		)}, but requested an effective batch'''
                                f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''		)

                     elif isinstance(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		):
                            UpperCamelCase_ 							=	[
                                self.movq.encode(image[i : i + 1]		).latent_dist.sample(generator[i]		) for i in range(_SCREAMING_SNAKE_CASE		)
                            ]
                            UpperCamelCase_ 							=	torch.cat(_SCREAMING_SNAKE_CASE       , dim=0		)
                     else:
                            UpperCamelCase_ 							=	self.movq.encode(_SCREAMING_SNAKE_CASE		).latent_dist.sample(_SCREAMING_SNAKE_CASE		)

                     UpperCamelCase_ 							=	self.movq.config.scaling_factor * init_latents

              UpperCamelCase_ 							=	torch.cat([init_latents]       , dim=0		)

              UpperCamelCase_ 							=	init_latents.shape
              UpperCamelCase_ 							=	randn_tensor(_SCREAMING_SNAKE_CASE       , generator=_SCREAMING_SNAKE_CASE       , device=_SCREAMING_SNAKE_CASE       , dtype=_SCREAMING_SNAKE_CASE		)

              # get latents
              UpperCamelCase_ 							=	self.scheduler.add_noise(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	init_latents

              return latents

       def lowercase			(		self:			List[str]       , _SCREAMING_SNAKE_CASE:			Tuple=0		)							->			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(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			Dict       , _SCREAMING_SNAKE_CASE:			Optional[Any]=0		)							->			Dict:




              """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=_SCREAMING_SNAKE_CASE		)
                     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_							,			UpperCamelCase_ 							=	cpu_offload_with_hook(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , prev_module_hook=_SCREAMING_SNAKE_CASE		)

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

       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def lowercase			(		self:			Tuple		)							->			Dict:




              """simple docstring"""


              if not hasattr(self.unet       , "_hf_hook"		):
                     return self.device
              for module in self.unet.modules():
                     if (
                         hasattr(_SCREAMING_SNAKE_CASE       , "_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(_SCREAMING_SNAKE_CASE		)
       def __call__(		self:			Dict       , _SCREAMING_SNAKE_CASE:			Union[torch.FloatTensor, List[torch.FloatTensor]]       , _SCREAMING_SNAKE_CASE:			Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]       , _SCREAMING_SNAKE_CASE:			Union[torch.FloatTensor, List[torch.FloatTensor]]       , _SCREAMING_SNAKE_CASE:			torch.FloatTensor       , _SCREAMING_SNAKE_CASE:			int = 512       , _SCREAMING_SNAKE_CASE:			int = 512       , _SCREAMING_SNAKE_CASE:			int = 100       , _SCREAMING_SNAKE_CASE:			float = 4.0       , _SCREAMING_SNAKE_CASE:			float = 0.3       , _SCREAMING_SNAKE_CASE:			int = 1       , _SCREAMING_SNAKE_CASE:			Optional[Union[torch.Generator, List[torch.Generator]]] = None       , _SCREAMING_SNAKE_CASE:			Optional[str] = "pil"       , _SCREAMING_SNAKE_CASE:			bool = True       , )							->			List[str]:




              """simple docstring"""


              UpperCamelCase_ 							=	self._execution_device

              UpperCamelCase_ 							=	guidance_scale > 1.0

              if isinstance(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		):
                     UpperCamelCase_ 							=	torch.cat(_SCREAMING_SNAKE_CASE       , dim=0		)
              if isinstance(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		):
                     UpperCamelCase_ 							=	torch.cat(_SCREAMING_SNAKE_CASE       , dim=0		)
              if isinstance(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		):
                     UpperCamelCase_ 							=	torch.cat(_SCREAMING_SNAKE_CASE       , dim=0		)

              UpperCamelCase_ 							=	image_embeds.shape[0]

              if do_classifier_free_guidance:
                     UpperCamelCase_ 							=	image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE       , dim=0		)
                     UpperCamelCase_ 							=	negative_image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE       , dim=0		)
                     UpperCamelCase_ 							=	hint.repeat_interleave(_SCREAMING_SNAKE_CASE       , dim=0		)

              UpperCamelCase_ 							=	torch.cat([negative_image_embeds, image_embeds]       , dim=0		).to(dtype=self.unet.dtype       , device=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	torch.cat([hint, hint]       , dim=0		).to(dtype=self.unet.dtype       , device=_SCREAMING_SNAKE_CASE		)

              if not isinstance(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		):
                     UpperCamelCase_ 							=	[image]
              if not all(isinstance(_SCREAMING_SNAKE_CASE       , (PIL.Image.Image, torch.Tensor)		) for i in image		):
                     raise ValueError(
                         f'''Input is in incorrect format: {[type(_SCREAMING_SNAKE_CASE		) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''		)

              UpperCamelCase_ 							=	torch.cat([prepare_image(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		) for i in image]       , dim=0		)
              UpperCamelCase_ 							=	image.to(dtype=image_embeds.dtype       , device=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	self.movq.encode(_SCREAMING_SNAKE_CASE		)["latents"]
              UpperCamelCase_ 							=	latents.repeat_interleave(_SCREAMING_SNAKE_CASE       , dim=0		)
              self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE       , device=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_							,			UpperCamelCase_ 							=	self.get_timesteps(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	timesteps[:1].repeat(batch_size * num_images_per_prompt		)
              UpperCamelCase_							,			UpperCamelCase_ 							=	downscale_height_and_width(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , self.movq_scale_factor		)
              UpperCamelCase_ 							=	self.prepare_latents(
                  _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , image_embeds.dtype       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)
              for i, t in enumerate(self.progress_bar(_SCREAMING_SNAKE_CASE		)		):
                     # 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, "hint": hint}
                     UpperCamelCase_ 							=	self.unet(
                         sample=_SCREAMING_SNAKE_CASE       , timestep=_SCREAMING_SNAKE_CASE       , encoder_hidden_states=_SCREAMING_SNAKE_CASE       , added_cond_kwargs=_SCREAMING_SNAKE_CASE       , return_dict=_SCREAMING_SNAKE_CASE       , )[0]

                     if do_classifier_free_guidance:
                            UpperCamelCase_							,			UpperCamelCase_ 							=	noise_pred.split(latents.shape[1]       , dim=1		)
                            UpperCamelCase_							,			UpperCamelCase_ 							=	noise_pred.chunk(2		)
                            UpperCamelCase_							,			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_							,			UpperCamelCase_ 							=	noise_pred.split(latents.shape[1]       , dim=1		)

                     # compute the previous noisy sample x_t -> x_t-1

                     UpperCamelCase_ 							=	self.scheduler.step(
                         _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , generator=_SCREAMING_SNAKE_CASE       , )[0]

              # post-processing
              UpperCamelCase_ 							=	self.movq.decode(_SCREAMING_SNAKE_CASE       , force_not_quantize=_SCREAMING_SNAKE_CASE		)["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(_SCREAMING_SNAKE_CASE		)

              if not return_dict:
                     return (image,)

              return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE		)


'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


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



def 				UpperCamelCase__      (      a__   ):


						'''simple docstring'''






						if "backbone.0.body" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('backbone.0.body'      ,   'backbone.conv_encoder.model'   )
						if "transformer" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('transformer.'      ,   ''   )
						if "norm1" in orig_key:
												if "encoder" in orig_key:
																		_lowerCAmelCase					    =orig_key.replace('norm1'      ,   'self_attn_layer_norm'   )
												else:
																		_lowerCAmelCase					    =orig_key.replace('norm1'      ,   'encoder_attn_layer_norm'   )
						if "norm2" in orig_key:
												if "encoder" in orig_key:
																		_lowerCAmelCase					    =orig_key.replace('norm2'      ,   'final_layer_norm'   )
												else:
																		_lowerCAmelCase					    =orig_key.replace('norm2'      ,   'self_attn_layer_norm'   )
						if "norm3" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('norm3'      ,   'final_layer_norm'   )
						if "linear1" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('linear1'      ,   'fc1'   )
						if "linear2" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('linear2'      ,   'fc2'   )
						if "query_embed" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('query_embed'      ,   'query_position_embeddings'   )
						if "cross_attn" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('cross_attn'      ,   'encoder_attn'   )

						return orig_key



def 				UpperCamelCase__      (      a__   ):


						'''simple docstring'''






						for i in range(6   ):
												# read in weights + bias of input projection layer of self-attention
												_lowerCAmelCase					    =state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_weight'''   )
												_lowerCAmelCase					    =state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_bias'''   )
												# next, add query, keys and values (in that order) to the state dict
												_lowerCAmelCase					    =in_proj_weight[:2_5_6, :]
												_lowerCAmelCase					    =in_proj_bias[:2_5_6]
												_lowerCAmelCase					    =in_proj_weight[2_5_6:5_1_2, :]
												_lowerCAmelCase					    =in_proj_bias[2_5_6:5_1_2]
												_lowerCAmelCase					    =in_proj_weight[-2_5_6:, :]
												_lowerCAmelCase					    =in_proj_bias[-2_5_6:]



def 				UpperCamelCase__      (      ):


						'''simple docstring'''






						_lowerCAmelCase					    ='http://images.cocodataset.org/val2017/000000039769.jpg'
						_lowerCAmelCase					    =Image.open(requests.get(a__      ,   stream=a__   ).raw   )

						return im



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


						'''simple docstring'''






						_lowerCAmelCase					    =DeformableDetrConfig()
						# set config attributes
						if single_scale:
												_lowerCAmelCase					    =1
						_lowerCAmelCase					    =dilation
						_lowerCAmelCase					    =with_box_refine
						_lowerCAmelCase					    =two_stage
						# set labels
						_lowerCAmelCase					    =9_1
						_lowerCAmelCase					    ='huggingface/label-files'
						_lowerCAmelCase					    ='coco-detection-id2label.json'
						_lowerCAmelCase					    =json.load(open(cached_download(hf_hub_url(a__      ,   a__      ,   repo_type='dataset'   )   )      ,   'r'   )   )
						_lowerCAmelCase					    ={int(a__   ): v for k, v in idalabel.items()}
						_lowerCAmelCase					    =idalabel
						_lowerCAmelCase					    ={v: k for k, v in idalabel.items()}

						# load image processor
						_lowerCAmelCase					    =DeformableDetrImageProcessor(format='coco_detection'   )

						# prepare image
						_lowerCAmelCase					    =prepare_img()
						_lowerCAmelCase					    =image_processor(images=a__      ,   return_tensors='pt'   )
						_lowerCAmelCase					    =encoding['pixel_values']

						logger.info('Converting model...'   )

						# load original state dict
						_lowerCAmelCase					    =torch.load(a__      ,   map_location='cpu'   )['model']
						# rename keys
						for key in state_dict.copy().keys():
												_lowerCAmelCase					    =state_dict.pop(a__   )
												_lowerCAmelCase					    =val
						# query, key and value matrices need special treatment
						read_in_q_k_v(a__   )
						# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
						_lowerCAmelCase					    ='model.'
						for key in state_dict.copy().keys():
												if not key.startswith('class_embed'   ) and not key.startswith('bbox_embed'   ):
																		_lowerCAmelCase					    =state_dict.pop(a__   )
																		_lowerCAmelCase					    =val
    # finally, create HuggingFace model and load state dict
						_lowerCAmelCase					    =DeformableDetrForObjectDetection(a__   )
						model.load_state_dict(a__   )
						model.eval()

						_lowerCAmelCase					    ='cuda' if torch.cuda.is_available() else 'cpu'
						model.to(a__   )
						# verify our conversion
						_lowerCAmelCase					    =model(pixel_values.to(a__   )   )

						_lowerCAmelCase					    =torch.tensor(
						    [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]]   )
						_lowerCAmelCase					    =torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]]   )

						if single_scale:
												_lowerCAmelCase					    =torch.tensor(
												    [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]]   )
												_lowerCAmelCase					    =torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]]   )

						if single_scale and dilation:
												_lowerCAmelCase					    =torch.tensor(
												    [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]]   )
												_lowerCAmelCase					    =torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]]   )

						if with_box_refine:
												_lowerCAmelCase					    =torch.tensor(
												    [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]]   )
												_lowerCAmelCase					    =torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]]   )

						if with_box_refine and two_stage:
												_lowerCAmelCase					    =torch.tensor(
												    [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]]   )
												_lowerCAmelCase					    =torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]]   )

						print('Logits:'      ,   outputs.logits[0, :3, :3]   )

						assert torch.allclose(outputs.logits[0, :3, :3]      ,   expected_logits.to(a__   )      ,   atol=1E-4   )
						assert torch.allclose(outputs.pred_boxes[0, :3, :3]      ,   expected_boxes.to(a__   )      ,   atol=1E-4   )

						print('Everything ok!'   )

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

						# Push to hub
						if push_to_hub:
												_lowerCAmelCase					    ='deformable-detr'
												model_name += "-single-scale" if single_scale else ""
												model_name += "-dc5" if dilation else ""
												model_name += "-with-box-refine" if with_box_refine else ""
												model_name += "-two-stage" if two_stage else ""
												print('Pushing model to hub...'   )
												model.push_to_hub(repo_path_or_name=a__      ,   organization='nielsr'      ,   commit_message='Add model'   )


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

							parser.add_argument(
							    '''--checkpoint_path''',
							    type=str,
							    default='''/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth''',
							    help='''Path to Pytorch checkpoint (.pth file) you\'d like to convert.''',
							)
							parser.add_argument('''--single_scale''', action='''store_true''', help='''Whether to set config.num_features_levels = 1.''')
							parser.add_argument('''--dilation''', action='''store_true''', help='''Whether to set config.dilation=True.''')
							parser.add_argument('''--with_box_refine''', action='''store_true''', help='''Whether to set config.with_box_refine=True.''')
							parser.add_argument('''--two_stage''', action='''store_true''', help='''Whether to set config.two_stage=True.''')
							parser.add_argument(
							    '''--pytorch_dump_folder_path''',
							    default=None,
							    type=str,
							    required=True,
							    help='''Path to the folder to output PyTorch model.''',
							)
							parser.add_argument(
							    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
							)
							lowercase_  		=			parser.parse_args()
							convert_deformable_detr_checkpoint(
							    args.checkpoint_path,
							    args.single_scale,
							    args.dilation,
							    args.with_box_refine,
							    args.two_stage,
							    args.pytorch_dump_folder_path,
							    args.push_to_hub,
							)





