




'''simple docstring'''




import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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

def 	__UpperCAmelCase       ( __magic_name__						)->    str:



			"""simple docstring"""



			warnings.warn(
			    "The preprocess method is deprecated and will be removed in a future version. Please"
			    " use VaeImageProcessor.preprocess instead"     ,__lowerCAmelCase     ,)
			if isinstance(__lowerCAmelCase     ,torch.Tensor						):
						return image
			elif isinstance(__lowerCAmelCase     ,PIL.Image.Image						):
						snake_case_				:					Union[str, Any]   =       [image]

			if isinstance(image[0]     ,PIL.Image.Image						):
						snake_case_				:					List[Any]   =       image[0].size
						snake_case_				:					str   =       (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

						snake_case_				:					int   =       [np.array(i.resize((w, h)     ,resample=PIL_INTERPOLATION["lanczos"]						)						)[None, :] for i in image]
						snake_case_				:					List[Any]   =       np.concatenate(__lowerCAmelCase     ,axis=0						)
						snake_case_				:					str   =       np.array(__lowerCAmelCase						).astype(np.floataa						) / 255.0
						snake_case_				:					Optional[Any]   =       image.transpose(0     ,3     ,1     ,2						)
						snake_case_				:					Optional[int]   =       2.0 * image - 1.0
						snake_case_				:					Union[str, Any]   =       torch.from_numpy(__lowerCAmelCase						)
			elif isinstance(image[0]     ,torch.Tensor						):
						snake_case_				:					Union[str, Any]   =       torch.cat(__lowerCAmelCase     ,dim=0						)
			return image

def 	__UpperCAmelCase       ( __magic_name__						)->    int:



			"""simple docstring"""



			if isinstance(__lowerCAmelCase     ,torch.Tensor						):
						return mask
			elif isinstance(__lowerCAmelCase     ,PIL.Image.Image						):
						snake_case_				:					Optional[int]   =       [mask]

			if isinstance(mask[0]     ,PIL.Image.Image						):
						snake_case_				:					str   =       mask[0].size
						snake_case_				:					str   =       (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
						snake_case_				:					Tuple   =       [np.array(m.convert("L"						).resize((w, h)     ,resample=PIL_INTERPOLATION["nearest"]						)						)[None, :] for m in mask]
						snake_case_				:					int   =       np.concatenate(__lowerCAmelCase     ,axis=0						)
						snake_case_				:					Optional[int]   =       mask.astype(np.floataa						) / 255.0
						snake_case_				:					Union[str, Any]   =       0
						snake_case_				:					List[Any]   =       1
						snake_case_				:					List[Any]   =       torch.from_numpy(__lowerCAmelCase						)
			elif isinstance(mask[0]     ,torch.Tensor						):
						snake_case_				:					Tuple   =       torch.cat(__lowerCAmelCase     ,dim=0						)
			return mask







class    A_	(UpperCAmelCase__							):
							"""simple docstring"""




							a__   		=	42
							a__   		=	42

							def __init__(		self			:List[str]       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:List[Any]				)				->  Optional[int]:


										'''simple docstring'''




										super().__init__()
										self.register_modules(unet=lowerCamelCase__       ,							scheduler=lowerCamelCase__				)






							@torch.no_grad()
							def __call__(		self			:Union[str, Any]       ,							lowerCAmelCase__			:Union[torch.Tensor, PIL.Image.Image]       ,							lowerCAmelCase__			:Union[torch.Tensor, PIL.Image.Image]       ,							lowerCAmelCase__			:int = 250       ,							lowerCAmelCase__			:float = 0.0       ,							lowerCAmelCase__			:int = 10       ,							lowerCAmelCase__			:int = 10       ,							lowerCAmelCase__			:Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,							lowerCAmelCase__			:Optional[str] = "pil"       ,							lowerCAmelCase__			:bool = True       ,							)				->  Union[ImagePipelineOutput, Tuple]:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       image

										snake_case_				:					Tuple   =       _preprocess_image(lowerCamelCase__				)
										snake_case_				:					List[Any]   =       original_image.to(device=self.device       ,							dtype=self.unet.dtype				)
										snake_case_				:					int   =       _preprocess_mask(lowerCamelCase__				)
										snake_case_				:					List[Any]   =       mask_image.to(device=self.device       ,							dtype=self.unet.dtype				)

										snake_case_				:					Union[str, Any]   =       original_image.shape[0]

										# sample gaussian noise to begin the loop
										if isinstance(lowerCamelCase__       ,							lowerCamelCase__				) and len(lowerCamelCase__				) != batch_size:
													raise ValueError(
													    F'''You have passed a list of generators of length {len(lowerCamelCase__				)}, but requested an effective batch'''
													    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''				)

										snake_case_				:					int   =       original_image.shape
										snake_case_				:					List[str]   =       randn_tensor(lowerCamelCase__       ,							generator=lowerCamelCase__       ,							device=self.device       ,							dtype=self.unet.dtype				)

										# set step values
										self.scheduler.set_timesteps(lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							self.device				)
										snake_case_				:					str   =       eta

										snake_case_				:					Optional[int]   =       self.scheduler.timesteps[0] + 1
										snake_case_				:					Optional[int]   =       generator[0] if isinstance(lowerCamelCase__       ,							lowerCamelCase__				) else generator
										for i, t in enumerate(self.progress_bar(self.scheduler.timesteps				)				):
													if t < t_last:
																# predict the noise residual
																snake_case_				:					Any   =       self.unet(lowerCamelCase__       ,							lowerCamelCase__				).sample
																# compute previous image: x_t -> x_t-1
																snake_case_				:					Optional[int]   =       self.scheduler.step(lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__				).prev_sample

													else:
																# compute the reverse: x_t-1 -> x_t
																snake_case_				:					Union[str, Any]   =       self.scheduler.undo_step(lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__				)
													snake_case_				:					Tuple   =       t

										snake_case_				:					Optional[Any]   =       (image / 2 + 0.5).clamp(0       ,							1				)
										snake_case_				:					List[Any]   =       image.cpu().permute(0       ,							2       ,							3       ,							1				).numpy()
										if output_type == "pil":
													snake_case_				:					Union[str, Any]   =       self.numpy_to_pil(lowerCamelCase__				)

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=lowerCamelCase__				)












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


logging.set_verbosity_info()
__lowerCamelCase   :					Union[str, Any]   							=					logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCamelCase   :					Optional[Any]   							=					[]
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 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    int:



			"""simple docstring"""



			snake_case_				:					Optional[Any]   =       state_dict.pop(__magic_name__						)
			snake_case_				:					Any   =       val

def 	__UpperCAmelCase       ( __magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					Any   =       OrderedDict()
			for key, value in state_dict.items():
						if "backbone.0.body" in key:
									snake_case_				:					Optional[Any]   =       key.replace("backbone.0.body"     ,"backbone.conv_encoder.model"						)
									snake_case_				:					int   =       value
						else:
									snake_case_				:					int   =       value

			return new_state_dict

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=False						)->    Optional[int]:



			"""simple docstring"""



			snake_case_				:					str   =       ""
			if is_panoptic:
						snake_case_				:					Dict   =       "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)
						snake_case_				:					Any   =       state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''						)
						snake_case_				:					Optional[int]   =       state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''						)
						# next, add query, keys and values (in that order) to the state dict
						snake_case_				:					Tuple   =       in_proj_weight[:256, :]
						snake_case_				:					List[Any]   =       in_proj_bias[:256]
						snake_case_				:					Optional[Any]   =       in_proj_weight[256:512, :]
						snake_case_				:					Optional[int]   =       in_proj_bias[256:512]
						snake_case_				:					Optional[int]   =       in_proj_weight[-256:, :]
						snake_case_				:					str   =       in_proj_bias[-256:]

def 	__UpperCAmelCase       ( )->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					Optional[int]   =       "http://images.cocodataset.org/val2017/000000039769.jpg"
			snake_case_				:					Optional[Any]   =       Image.open(requests.get(__magic_name__     ,stream=__magic_name__						).raw						)

			return im

@torch.no_grad()
def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    List[str]:



			"""simple docstring"""



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

			# load image processor
			snake_case_				:					Optional[int]   =       "coco_panoptic" if is_panoptic else "coco_detection"
			snake_case_				:					str   =       ConditionalDetrImageProcessor(format=__magic_name__						)

			# prepare image
			snake_case_				:					str   =       prepare_img()
			snake_case_				:					int   =       image_processor(images=__magic_name__     ,return_tensors="pt"						)
			snake_case_				:					Union[str, Any]   =       encoding["pixel_values"]

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

			# load original model from torch hub
			snake_case_				:					Union[str, Any]   =       torch.hub.load("DeppMeng/ConditionalDETR"     ,__magic_name__     ,pretrained=__magic_name__						).eval()
			snake_case_				:					Any   =       conditional_detr.state_dict()
			# rename keys
			for src, dest in rename_keys:
						if is_panoptic:
									snake_case_				:					Any   =       "conditional_detr." + src
						rename_key(__magic_name__     ,__magic_name__     ,__magic_name__						)
			snake_case_				:					Tuple   =       rename_backbone_keys(__magic_name__						)
			# query, key and value matrices need special treatment
			read_in_q_k_v(__magic_name__     ,is_panoptic=__magic_name__						)
			# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
			snake_case_				:					int   =       "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"						)
									):
												snake_case_				:					Any   =       state_dict.pop(__magic_name__						)
												snake_case_				:					Optional[int]   =       val
									elif "class_labels_classifier" in key or "bbox_predictor" in key:
												snake_case_				:					Tuple   =       state_dict.pop(__magic_name__						)
												snake_case_				:					Any   =       val
									elif key.startswith("bbox_attention"						) or key.startswith("mask_head"						):
												continue
									else:
												snake_case_				:					Union[str, Any]   =       state_dict.pop(__magic_name__						)
												snake_case_				:					List[Any]   =       val
						else:
									if not key.startswith("class_labels_classifier"						) and not key.startswith("bbox_predictor"						):
												snake_case_				:					Any   =       state_dict.pop(__magic_name__						)
												snake_case_				:					List[Any]   =       val
    # finally, create HuggingFace model and load state dict
			snake_case_				:					Optional[int]   =       ConditionalDetrForSegmentation(__magic_name__						) if is_panoptic else ConditionalDetrForObjectDetection(__magic_name__						)
			model.load_state_dict(__magic_name__						)
			model.eval()
			model.push_to_hub(repo_id=__magic_name__     ,organization="DepuMeng"     ,commit_message="Add model"						)
			# verify our conversion
			snake_case_				:					Dict   =       conditional_detr(__magic_name__						)
			snake_case_				:					Union[str, Any]   =       model(__magic_name__						)
			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(__magic_name__						).mkdir(exist_ok=__magic_name__						)
			model.save_pretrained(__magic_name__						)
			image_processor.save_pretrained(__magic_name__						)


if __name__ == "__main__":
		__lowerCamelCase   :					Tuple   							=					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.'''
		)
		__lowerCamelCase   :					int   							=					parser.parse_args()
		convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)




