




"""simple docstring"""

from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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


__lowerCamelCase								=						"\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = \"cuda\"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = \"openai/diffusers-cd_imagenet64_l2\"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample.png\")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample_penguin.png\")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_multistep_sample_penguin.png\")\n        ```\n"



class  _snake_case				(							__SCREAMING_SNAKE_CASE       ):

				'''simple docstring'''




				def __init__(							self      :  List[Any]					,							snake_case      :  UNetaDModel					,							snake_case      :  CMStochasticIterativeScheduler     ):
								super().__init__()

								self.register_modules(
								    unet=UpperCamelCase__					,							scheduler=UpperCamelCase__					,							)

								UpperCAmelCase_					:str      			=      None





				def 				snake_case_ (							self      :  Tuple					,							snake_case      :  str=0     ):







								if is_accelerate_available() and is_accelerate_version('''>='''					,							'''0.14.0'''     ):
												from accelerate import cpu_offload
								else:
												raise ImportError('''`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher'''     )

								UpperCAmelCase_					:Optional[Any]      			=      torch.device(f'cuda:{gpu_id}'     )

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

								for cpu_offloaded_model in [self.unet]:
												cpu_offload(UpperCamelCase__					,							UpperCamelCase__     )

								if self.safety_checker is not None:
												cpu_offload(self.safety_checker					,							execution_device=UpperCamelCase__					,							offload_buffers=UpperCamelCase__     )





				def 				snake_case_ (							self      :  Optional[int]					,							snake_case      :  int=0     ):







								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_					:Union[str, Any]      			=      torch.device(f'cuda:{gpu_id}'     )

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

								UpperCAmelCase_					:List[Any]      			=      None
								for cpu_offloaded_model in [self.unet]:
												UpperCAmelCase_					:Union[str, Any]      			=      cpu_offload_with_hook(UpperCamelCase__					,							UpperCamelCase__					,							prev_module_hook=UpperCamelCase__     )

								if self.safety_checker is not None:
												UpperCAmelCase_					:Optional[Any]      			=      cpu_offload_with_hook(self.safety_checker					,							UpperCamelCase__					,							prev_module_hook=UpperCamelCase__     )

								# We'll offload the last model manually.
								UpperCAmelCase_					:Optional[Any]      			=      hook





				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def 				snake_case_ (							self      :  int     ):







								if not hasattr(self.unet					,							'''_hf_hook'''     ):
												return self.device
								for module in self.unet.modules():
												if (
												    hasattr(UpperCamelCase__					,							'''_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





				def 				snake_case_ (							self      :  int					,							snake_case      :  Optional[Any]					,							snake_case      :  Dict					,							snake_case      :  int					,							snake_case      :  List[str]					,							snake_case      :  int					,							snake_case      :  Optional[int]					,							snake_case      :  Any					,							snake_case      :  Optional[int]=None     ):
								UpperCAmelCase_					:Union[str, Any]      			=      (batch_size, num_channels, height, width)
								if isinstance(UpperCamelCase__					,							UpperCamelCase__     ) and len(UpperCamelCase__     ) != batch_size:
												raise ValueError(
												    f'You have passed a list of generators of length {len(UpperCamelCase__     )}, but requested an effective batch'
												    f' size of {batch_size}. Make sure the batch size matches the length of the generators.'     )

								if latents is None:
												UpperCAmelCase_					:List[Any]      			=      randn_tensor(UpperCamelCase__					,							generator=UpperCamelCase__					,							device=UpperCamelCase__					,							dtype=UpperCamelCase__     )
								else:
												UpperCAmelCase_					:Tuple      			=      latents.to(device=UpperCamelCase__					,							dtype=UpperCamelCase__     )

								# scale the initial noise by the standard deviation required by the scheduler
								UpperCAmelCase_					:str      			=      latents * self.scheduler.init_noise_sigma
								return latents





				def 				snake_case_ (							self      :  Any					,							snake_case      :  torch.FloatTensor					,							snake_case      :  str = "pil"     ):







								if output_type not in ["pt", "np", "pil"]:
												raise ValueError(
												    f'output_type={output_type} is not supported. Make sure to choose one of [\'pt\', \'np\', or \'pil\']'     )

								# Equivalent to diffusers.VaeImageProcessor.denormalize
								UpperCAmelCase_					:Any      			=      (sample / 2 + 0.5).clamp(0					,							1     )
								if output_type == "pt":
												return sample

								# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
								UpperCAmelCase_					:Optional[int]      			=      sample.cpu().permute(0					,							2					,							3					,							1     ).numpy()
								if output_type == "np":
												return sample

								# Output_type must be 'pil'
								UpperCAmelCase_					:Tuple      			=      self.numpy_to_pil(UpperCamelCase__     )
								return sample





				def 				snake_case_ (							self      :  Tuple					,							snake_case      :  List[str]					,							snake_case      :  Dict					,							snake_case      :  List[str]=None     ):







								if self.unet.config.num_class_embeds is not None:
												if isinstance(UpperCamelCase__					,							UpperCamelCase__     ):
																UpperCAmelCase_					:str      			=      torch.tensor(UpperCamelCase__					,							dtype=torch.int     )
												elif isinstance(UpperCamelCase__					,							UpperCamelCase__     ):
																assert batch_size == 1, "Batch size must be 1 if classes is an int"
																UpperCAmelCase_					:Dict      			=      torch.tensor([class_labels]					,							dtype=torch.int     )
												elif class_labels is None:
																# Randomly generate batch_size class labels
																# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
																UpperCAmelCase_					:List[Any]      			=      torch.randint(0					,							self.unet.config.num_class_embeds					,							size=(batch_size,)     )
												UpperCAmelCase_					:Dict      			=      class_labels.to(UpperCamelCase__     )
								else:
												UpperCAmelCase_					:int      			=      None
								return class_labels





				def 				snake_case_ (							self      :  List[Any]					,							snake_case      :  int					,							snake_case      :  Any					,							snake_case      :  List[Any]					,							snake_case      :  List[Any]					,							snake_case      :  List[str]					,							snake_case      :  Union[str, Any]     ):







								if num_inference_steps is None and timesteps is None:
												raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''     )

								if num_inference_steps is not None and timesteps is not None:
												logger.warning(
												    f'Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;'
												    ''' `timesteps` will be used over `num_inference_steps`.'''     )

								if latents is not None:
												UpperCAmelCase_					:List[Any]      			=      (batch_size, 3, img_size, img_size)
												if latents.shape != expected_shape:
																raise ValueError(f'The shape of latents is {latents.shape} but is expected to be {expected_shape}.'     )

								if (callback_steps is None) or (
								    callback_steps is not None and (not isinstance(UpperCamelCase__					,							UpperCamelCase__     ) or callback_steps <= 0)
								):
												raise ValueError(
												    f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
												    f' {type(UpperCamelCase__     )}.'     )





				@torch.no_grad()
				@replace_example_docstring(UpperCamelCase__     )
				def __call__(							self      :  Optional[int]					,							snake_case      :  int = 1					,							snake_case      :  Optional[Union[torch.Tensor, List[int], int]] = None					,							snake_case      :  int = 1					,							snake_case      :  List[int] = None					,							snake_case      :  Optional[Union[torch.Generator, List[torch.Generator]]] = None					,							snake_case      :  Optional[torch.FloatTensor] = None					,							snake_case      :  Optional[str] = "pil"					,							snake_case      :  bool = True					,							snake_case      :  Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,							snake_case      :  int = 1					,							):
								UpperCAmelCase_					:Optional[int]      			=      self.unet.config.sample_size
								UpperCAmelCase_					:List[Any]      			=      self._execution_device

								# 1. Check inputs
								self.check_inputs(UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__     )

								# 2. Prepare image latents
								# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
								UpperCAmelCase_					:List[str]      			=      self.prepare_latents(
								    batch_size=UpperCamelCase__					,							num_channels=self.unet.config.in_channels					,							height=UpperCamelCase__					,							width=UpperCamelCase__					,							dtype=self.unet.dtype					,							device=UpperCamelCase__					,							generator=UpperCamelCase__					,							latents=UpperCamelCase__					,							)

								# 3. Handle class_labels for class-conditional models
								UpperCAmelCase_					:List[str]      			=      self.prepare_class_labels(UpperCamelCase__					,							UpperCamelCase__					,							class_labels=UpperCamelCase__     )

								# 4. Prepare timesteps
								if timesteps is not None:
												self.scheduler.set_timesteps(timesteps=UpperCamelCase__					,							device=UpperCamelCase__     )
												UpperCAmelCase_					:Union[str, Any]      			=      self.scheduler.timesteps
												UpperCAmelCase_					:Optional[int]      			=      len(UpperCamelCase__     )
								else:
												self.scheduler.set_timesteps(UpperCamelCase__     )
												UpperCAmelCase_					:List[str]      			=      self.scheduler.timesteps

								# 5. Denoising loop
								# Multistep sampling: implements Algorithm 1 in the paper
								with self.progress_bar(total=UpperCamelCase__     ) as progress_bar:
												for i, t in enumerate(UpperCamelCase__     ):
																UpperCAmelCase_					:Any      			=      self.scheduler.scale_model_input(UpperCamelCase__					,							UpperCamelCase__     )
																UpperCAmelCase_					:int      			=      self.unet(UpperCamelCase__					,							UpperCamelCase__					,							class_labels=UpperCamelCase__					,							return_dict=UpperCamelCase__     )[0]

																UpperCAmelCase_					:Dict      			=      self.scheduler.step(UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__					,							generator=UpperCamelCase__     )[0]

																# call the callback, if provided
																progress_bar.update()
																if callback is not None and i % callback_steps == 0:
																				callback(UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__     )

        # 6. Post-process image sample
								UpperCAmelCase_					:Any      			=      self.postprocess_image(UpperCamelCase__					,							output_type=UpperCamelCase__     )

								# Offload last model to CPU
								if hasattr(self					,							'''final_offload_hook'''     ) and self.final_offload_hook is not None:
												self.final_offload_hook.offload()

								if not return_dict:
												return (image,)

								return ImagePipelineOutput(images=UpperCamelCase__     )








"""simple docstring"""

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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCamelCase								=						[]
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}.multihead_attn.out_proj.weight''',
						        f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
						    )
						)
						rename_keys.append(
						    (
						        f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
						        f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
						    )
						)
						rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight'''))
						rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias'''))
						rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight'''))
						rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias'''))
						rename_keys.append(
						    (f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')
						)
						rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
						rename_keys.append(
						    (f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
						)
						rename_keys.append(
						    (f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
						)
						rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight'''))
						rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias'''))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ("input_proj.weight", "input_projection.weight"),
        ("input_proj.bias", "input_projection.bias"),
        ("query_embed.weight", "query_position_embeddings.weight"),
        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
        ("class_embed.weight", "class_labels_classifier.weight"),
        ("class_embed.bias", "class_labels_classifier.bias"),
        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
    ]
)



def 				a (						__snake_case     :				Optional[Any],    __snake_case     :				Any,    __snake_case     :				Tuple  ):



				'''simple docstring'''





				UpperCAmelCase_					:Optional[Any]      			=      state_dict.pop(__snake_case  )
				UpperCAmelCase_					:List[str]      			=      val



def 				a (						__snake_case     :				int  ):



				'''simple docstring'''





				UpperCAmelCase_					:int      			=      OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												UpperCAmelCase_					:List[Any]      			=      key.replace('''backbone.0.body''',    '''backbone.conv_encoder.model'''  )
												UpperCAmelCase_					:Union[str, Any]      			=      value
								else:
												UpperCAmelCase_					:Any      			=      value

				return new_state_dict



def 				a (						__snake_case     :				Tuple,    __snake_case     :				Optional[Any]=False  ):



				'''simple docstring'''





				UpperCAmelCase_					:Union[str, Any]      			=      ''''''
				if is_panoptic:
								UpperCAmelCase_					:Tuple      			=      '''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_					:Optional[Any]      			=      state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'  )
								UpperCAmelCase_					:Tuple      			=      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_					:List[str]      			=      in_proj_weight[:256, :]
								UpperCAmelCase_					:Union[str, Any]      			=      in_proj_bias[:256]
								UpperCAmelCase_					:List[Any]      			=      in_proj_weight[256:512, :]
								UpperCAmelCase_					:List[Any]      			=      in_proj_bias[256:512]
								UpperCAmelCase_					:str      			=      in_proj_weight[-256:, :]
								UpperCAmelCase_					:Optional[int]      			=      in_proj_bias[-256:]
				# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
				for i in range(6  ):
								# read in weights + bias of input projection layer of self-attention
								UpperCAmelCase_					:List[str]      			=      state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'  )
								UpperCAmelCase_					:str      			=      state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'  )
								# next, add query, keys and values (in that order) to the state dict
								UpperCAmelCase_					:Dict      			=      in_proj_weight[:256, :]
								UpperCAmelCase_					:Optional[int]      			=      in_proj_bias[:256]
								UpperCAmelCase_					:List[Any]      			=      in_proj_weight[256:512, :]
								UpperCAmelCase_					:List[str]      			=      in_proj_bias[256:512]
								UpperCAmelCase_					:int      			=      in_proj_weight[-256:, :]
								UpperCAmelCase_					:List[str]      			=      in_proj_bias[-256:]
								# read in weights + bias of input projection layer of cross-attention
								UpperCAmelCase_					:int      			=      state_dict.pop(
								    f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'  )
								UpperCAmelCase_					:Optional[int]      			=      state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'  )
								# next, add query, keys and values (in that order) of cross-attention to the state dict
								UpperCAmelCase_					:int      			=      in_proj_weight_cross_attn[:256, :]
								UpperCAmelCase_					:str      			=      in_proj_bias_cross_attn[:256]
								UpperCAmelCase_					:Tuple      			=      in_proj_weight_cross_attn[256:512, :]
								UpperCAmelCase_					:Any      			=      in_proj_bias_cross_attn[256:512]
								UpperCAmelCase_					:str      			=      in_proj_weight_cross_attn[-256:, :]
								UpperCAmelCase_					:List[Any]      			=      in_proj_bias_cross_attn[-256:]



def 				a (						):



				'''simple docstring'''





				UpperCAmelCase_					:Tuple      			=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				UpperCAmelCase_					:List[str]      			=      Image.open(requests.get(__snake_case,    stream=__snake_case  ).raw  )

				return im



@torch.no_grad()
def 				a (						__snake_case     :				Union[str, Any],    __snake_case     :				List[Any]  ):



				'''simple docstring'''





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

				# load image processor
				UpperCAmelCase_					:Dict      			=      '''coco_panoptic''' if is_panoptic else '''coco_detection'''
				UpperCAmelCase_					:Optional[Any]      			=      DetrImageProcessor(format=__snake_case  )

				# prepare image
				UpperCAmelCase_					:Dict      			=      prepare_img()
				UpperCAmelCase_					:Union[str, Any]      			=      image_processor(images=__snake_case,    return_tensors='''pt'''  )
				UpperCAmelCase_					:Optional[Any]      			=      encoding['''pixel_values''']

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

				# load original model from torch hub
				UpperCAmelCase_					:int      			=      torch.hub.load('''facebookresearch/detr''',    __snake_case,    pretrained=__snake_case  ).eval()
				UpperCAmelCase_					:List[Any]      			=      detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												UpperCAmelCase_					:Optional[int]      			=      '''detr.''' + src
								rename_key(__snake_case,    __snake_case,    __snake_case  )
				UpperCAmelCase_					:int      			=      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
				UpperCAmelCase_					:Tuple      			=      '''detr.model.''' if is_panoptic else '''model.'''
				for key in state_dict.copy().keys():
								if is_panoptic:
												if (
												    key.startswith('''detr'''  )
												    and not key.startswith('''class_labels_classifier'''  )
												    and not key.startswith('''bbox_predictor'''  )
												):
																UpperCAmelCase_					:List[str]      			=      state_dict.pop(__snake_case  )
																UpperCAmelCase_					:List[Any]      			=      val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																UpperCAmelCase_					:int      			=      state_dict.pop(__snake_case  )
																UpperCAmelCase_					:Union[str, Any]      			=      val
												elif key.startswith('''bbox_attention'''  ) or key.startswith('''mask_head'''  ):
																continue
												else:
																UpperCAmelCase_					:Tuple      			=      state_dict.pop(__snake_case  )
																UpperCAmelCase_					:Dict      			=      val
								else:
												if not key.startswith('''class_labels_classifier'''  ) and not key.startswith('''bbox_predictor'''  ):
																UpperCAmelCase_					:Any      			=      state_dict.pop(__snake_case  )
																UpperCAmelCase_					:Any      			=      val
    # finally, create HuggingFace model and load state dict
				UpperCAmelCase_					:Optional[int]      			=      DetrForSegmentation(__snake_case  ) if is_panoptic else DetrForObjectDetection(__snake_case  )
				model.load_state_dict(__snake_case  )
				model.eval()
				# verify our conversion
				UpperCAmelCase_					:List[Any]      			=      detr(__snake_case  )
				UpperCAmelCase_					:Optional[int]      			=      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__":
						__lowerCamelCase								=						argparse.ArgumentParser()

						parser.add_argument(
						    "--model_name", default="detr_resnet50", type=str, help="Name of the DETR model you'd like to convert."
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
						)
						__lowerCamelCase								=						parser.parse_args()
						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)


